Divergent effects of inversion on facilitation and interference in holistic face processing
Haiyang Jin
2025 Jul 05
| This document | Manuscript |
|---|---|
| Experiment 1 | Experiment 1a |
| Experiment 2 | Experiment 1b |
| Experiment 3 | Experiment 2 |
1 Preparations
Load libraries and general settings:
## load libraries
library(tidyverse)
library(lme4)
library(lmerTest)
library(optimx)
library(emmeans)
library(ggpubr)
library(here)
library(psychr) # remotes::install_github("haiyangjin/psychr")
library(afex)
library(brms)
library(bayestestR)
library(tidybayes)emm_options(lmer.df = "Satterthwaite") # as pre-registered
# two_colors <- c("#D55E00", "#56B4E9") # facilitation and interference
two_colors <- c("#D55E00", "#229CE2") # facilitation and interference
sig_color <- "red"
dir_lmm <- "lmm"
dir_fig <- "figure"
ylimit_cf_d <- c(-.5, 3.6)
ylimit_cf_fi_d <- c(-1.1, 1.1)
ylimit_cf_rt <- c(650, 1150)
ylimit_cf_fi_rt <- c(-100, 100)
ylimit_cf_fi_ie <- c(-150, 300)
# parallel cores
options(mc.cores = min(parallel::detectCores(), 10))
# APA theme for figures
theme_set(papaja::theme_apa(base_size = 13.5, base_family = "Helvetica", box = FALSE))
theme_update(strip.placement = "outside")1.1 Experiment 1
# Order of completing tasks/blocks
readRDS(file = here("data", "df_E1_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
pivot_wider(names_from = Order,
values_from = Orientation) %>%
arrange(SubjCode)# Order of completing tasks/blocks
readRDS(file = here("data", "df_E1_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
filter(Order == 1) %>%
group_by(Orientation) %>%
summarize(N = n(),
.groups = "drop")Number of trials whose RTs were below 200 ms (if not 0):
# check number of trials RT<200
readRDS(file = here("data", "df_E1_raw.rds")) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_below200 = sum(!isabove200),
.groups = "drop") %>%
filter(N_below200 != 0)Number of trials whose responses were NAs (if not 0):
# check number of NA in `isSame`
readRDS(file = here("data", "df_E1_raw.rds")) %>%
mutate(isSameNA = is.na(isSame)) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_NA = sum(isSameNA),
.groups = "drop") %>%
filter(N_NA != 0)df_E1_raw <- readRDS(file = here("data", "df_E1_raw.rds")) %>%
filter(isabove200, # remove trials whose RT was below 200ms
!is.na(isSame)) # remove trials where no responses were recorded
str(df_E1_raw)## 'data.frame': 25557 obs. of 12 variables:
## $ SubjCode : Factor w/ 40 levels "41","45","46",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 8 6 1 9 7 5 4 10 5 1 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation: Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cue : Factor w/ 2 levels "top","bot": 2 1 2 1 1 2 1 1 1 2 ...
## $ Congruency : Factor w/ 2 levels "con","inc": 1 2 2 1 1 2 1 2 1 2 ...
## $ Alignment : Factor w/ 2 levels "ali","mis": 2 2 1 2 2 1 1 1 1 1 ...
## $ CA : Factor w/ 2 levels "sam","dif": 1 2 1 1 2 1 2 2 2 2 ...
## $ isCorrect : num 0 0 1 1 0 0 0 1 1 0 ...
## $ isSame : int 0 1 1 1 1 0 1 0 0 1 ...
## $ RT : num 913 955 1382 1248 1296 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...# apply successive difference coding and add columns for LMM analysis (psychr)
df_E1_lmm <- df_E1_raw %>%
psychr::set_contr(c(Orientation, Cue, Congruency, Alignment, CA)) %>%
psychr::add_lmmcols(isSame ~ Orientation * Congruency * Alignment * CA + Cue) ## old_name
## Ori_C Orientation2-1
## Con_C Congruency2-1
## Ali_C Alignment2-1
## CA_C CA2-1
## Cue_C Cue2-1
## Ori_Con Orientation2-1:Congruency2-1
## Ori_Ali Orientation2-1:Alignment2-1
## Con_Ali Congruency2-1:Alignment2-1
## Ori_CA Orientation2-1:CA2-1
## Con_CA Congruency2-1:CA2-1
## Ali_CA Alignment2-1:CA2-1
## Ori_Con_Ali Orientation2-1:Congruency2-1:Alignment2-1
## Ori_Con_CA Orientation2-1:Congruency2-1:CA2-1
## Ori_Ali_CA Orientation2-1:Alignment2-1:CA2-1
## Con_Ali_CA Congruency2-1:Alignment2-1:CA2-1
## Ori_Con_Ali_CA Orientation2-1:Congruency2-1:Alignment2-1:CA2-1## [1] "Ori_C + Con_C + Ali_C + CA_C + Cue_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA"saveRDS(df_E1_lmm, file = here(dir_lmm, "df_E1_lmm.rds"))str(df_E1_lmm)## 'data.frame': 25557 obs. of 28 variables:
## $ SubjCode : Factor w/ 40 levels "41","45","46",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 8 6 1 9 7 5 4 10 5 1 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation : Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "upr" "inv"
## .. .. ..$ : chr "2-1"
## $ Cue : Factor w/ 2 levels "top","bot": 2 1 2 1 1 2 1 1 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "top" "bot"
## .. .. ..$ : chr "2-1"
## $ Congruency : Factor w/ 2 levels "con","inc": 1 2 2 1 1 2 1 2 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "con" "inc"
## .. .. ..$ : chr "2-1"
## $ Alignment : Factor w/ 2 levels "ali","mis": 2 2 1 2 2 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "ali" "mis"
## .. .. ..$ : chr "2-1"
## $ CA : Factor w/ 2 levels "sam","dif": 1 2 1 1 2 1 2 2 2 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "sam" "dif"
## .. .. ..$ : chr "2-1"
## $ isCorrect : num 0 0 1 1 0 0 0 1 1 0 ...
## $ isSame : int 0 1 1 1 1 0 1 0 0 1 ...
## $ RT : num 913 955 1382 1248 1296 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ Ori_C : num -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Con_C : num -0.5 0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 ...
## $ Ali_C : num 0.5 0.5 -0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ CA_C : num -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 0.5 0.5 ...
## $ Cue_C : num 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 -0.5 -0.5 0.5 ...
## $ Ori_Con : num 0.25 -0.25 -0.25 0.25 0.25 -0.25 0.25 -0.25 0.25 -0.25 ...
## $ Ori_Ali : num -0.25 -0.25 0.25 -0.25 -0.25 0.25 0.25 0.25 0.25 0.25 ...
## $ Con_Ali : num -0.25 0.25 -0.25 -0.25 -0.25 -0.25 0.25 -0.25 0.25 -0.25 ...
## $ Ori_CA : num 0.25 -0.25 0.25 0.25 -0.25 0.25 -0.25 -0.25 -0.25 -0.25 ...
## $ Con_CA : num 0.25 0.25 -0.25 0.25 -0.25 -0.25 -0.25 0.25 -0.25 0.25 ...
## $ Ali_CA : num -0.25 0.25 0.25 -0.25 0.25 0.25 -0.25 -0.25 -0.25 -0.25 ...
## $ Ori_Con_Ali : num 0.125 -0.125 0.125 0.125 0.125 0.125 -0.125 0.125 -0.125 0.125 ...
## $ Ori_Con_CA : num -0.125 -0.125 0.125 -0.125 0.125 0.125 0.125 -0.125 0.125 -0.125 ...
## $ Ori_Ali_CA : num 0.125 -0.125 -0.125 0.125 -0.125 -0.125 0.125 0.125 0.125 0.125 ...
## $ Con_Ali_CA : num 0.125 0.125 0.125 0.125 -0.125 0.125 0.125 -0.125 0.125 -0.125 ...
## $ Ori_Con_Ali_CA: num -0.0625 -0.0625 -0.0625 -0.0625 0.0625 -0.0625 -0.0625 0.0625 -0.0625 0.0625 ...1.2 Experiment 2
# Order of completing tasks/blocks
readRDS(file = here("data", "df_E2_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
pivot_wider(names_from = Order,
values_from = Orientation) %>%
arrange(SubjCode)# Order of completing tasks/blocks
readRDS(file = here("data", "df_E2_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
filter(Order == 1) %>%
group_by(Orientation) %>%
summarize(N = n(),
.groups = "drop")Number of trials whose RTs were below 200 ms (if not 0):
# check number of trials RT<200
readRDS(file = here("data", "df_E2_raw.rds")) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_below200 = sum(!isabove200),
.groups = "drop") %>%
filter(N_below200 != 0)Number of trials whose responses were NAs (if not 0):
# check number of NA in `isSame`
readRDS(file = here("data", "df_E2_raw.rds")) %>%
mutate(isSameNA = is.na(isSame)) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_NA = sum(isSameNA),
.groups = "drop") %>%
filter(N_NA != 0)df_E2_raw <- readRDS(file = here("data", "df_E2_raw.rds")) %>%
filter(isabove200,
!is.na(isSame)) # remove trials where no responses were recorded
str(df_E2_raw)## 'data.frame': 25593 obs. of 12 variables:
## $ SubjCode : Factor w/ 40 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 8 4 7 9 7 9 9 2 7 3 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation: Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cue : Factor w/ 2 levels "top","bot": 1 1 2 2 2 2 1 2 2 1 ...
## $ Congruency : Factor w/ 2 levels "con","inc": 2 2 1 2 2 1 2 2 1 1 ...
## $ Alignment : Factor w/ 2 levels "ali","mis": 1 2 1 2 2 2 1 2 2 2 ...
## $ CA : Factor w/ 2 levels "sam","dif": 1 2 2 1 1 2 1 1 1 1 ...
## $ isCorrect : num 1 0 1 1 1 1 1 1 1 1 ...
## $ isSame : int 1 1 0 1 1 0 1 1 1 1 ...
## $ RT : num 994 842 810 811 1277 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...# apply successive difference coding and add columns for LMM analysis (psychr)
df_E2_lmm <- df_E2_raw %>%
psychr::set_contr(c(Orientation, Cue, Congruency, Alignment, CA)) %>%
psychr::add_lmmcols(isSame ~ Orientation * Congruency * Alignment * CA + Cue) ## old_name
## Ori_C Orientation2-1
## Con_C Congruency2-1
## Ali_C Alignment2-1
## CA_C CA2-1
## Cue_C Cue2-1
## Ori_Con Orientation2-1:Congruency2-1
## Ori_Ali Orientation2-1:Alignment2-1
## Con_Ali Congruency2-1:Alignment2-1
## Ori_CA Orientation2-1:CA2-1
## Con_CA Congruency2-1:CA2-1
## Ali_CA Alignment2-1:CA2-1
## Ori_Con_Ali Orientation2-1:Congruency2-1:Alignment2-1
## Ori_Con_CA Orientation2-1:Congruency2-1:CA2-1
## Ori_Ali_CA Orientation2-1:Alignment2-1:CA2-1
## Con_Ali_CA Congruency2-1:Alignment2-1:CA2-1
## Ori_Con_Ali_CA Orientation2-1:Congruency2-1:Alignment2-1:CA2-1## [1] "Ori_C + Con_C + Ali_C + CA_C + Cue_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA"saveRDS(df_E2_lmm, file = here(dir_lmm, "df_E2_lmm.rds"))str(df_E2_lmm)## 'data.frame': 25593 obs. of 28 variables:
## $ SubjCode : Factor w/ 40 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 8 4 7 9 7 9 9 2 7 3 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation : Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "upr" "inv"
## .. .. ..$ : chr "2-1"
## $ Cue : Factor w/ 2 levels "top","bot": 1 1 2 2 2 2 1 2 2 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "top" "bot"
## .. .. ..$ : chr "2-1"
## $ Congruency : Factor w/ 2 levels "con","inc": 2 2 1 2 2 1 2 2 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "con" "inc"
## .. .. ..$ : chr "2-1"
## $ Alignment : Factor w/ 2 levels "ali","mis": 1 2 1 2 2 2 1 2 2 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "ali" "mis"
## .. .. ..$ : chr "2-1"
## $ CA : Factor w/ 2 levels "sam","dif": 1 2 2 1 1 2 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "sam" "dif"
## .. .. ..$ : chr "2-1"
## $ isCorrect : num 1 0 1 1 1 1 1 1 1 1 ...
## $ isSame : int 1 1 0 1 1 0 1 1 1 1 ...
## $ RT : num 994 842 810 811 1277 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ Ori_C : num -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Con_C : num 0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 0.5 -0.5 -0.5 ...
## $ Ali_C : num -0.5 0.5 -0.5 0.5 0.5 0.5 -0.5 0.5 0.5 0.5 ...
## $ CA_C : num -0.5 0.5 0.5 -0.5 -0.5 0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Cue_C : num -0.5 -0.5 0.5 0.5 0.5 0.5 -0.5 0.5 0.5 -0.5 ...
## $ Ori_Con : num -0.25 -0.25 0.25 -0.25 -0.25 0.25 -0.25 -0.25 0.25 0.25 ...
## $ Ori_Ali : num 0.25 -0.25 0.25 -0.25 -0.25 -0.25 0.25 -0.25 -0.25 -0.25 ...
## $ Con_Ali : num -0.25 0.25 0.25 0.25 0.25 -0.25 -0.25 0.25 -0.25 -0.25 ...
## $ Ori_CA : num 0.25 -0.25 -0.25 0.25 0.25 -0.25 0.25 0.25 0.25 0.25 ...
## $ Con_CA : num -0.25 0.25 -0.25 -0.25 -0.25 -0.25 -0.25 -0.25 0.25 0.25 ...
## $ Ali_CA : num 0.25 0.25 -0.25 -0.25 -0.25 0.25 0.25 -0.25 -0.25 -0.25 ...
## $ Ori_Con_Ali : num 0.125 -0.125 -0.125 -0.125 -0.125 0.125 0.125 -0.125 0.125 0.125 ...
## $ Ori_Con_CA : num 0.125 -0.125 0.125 0.125 0.125 0.125 0.125 0.125 -0.125 -0.125 ...
## $ Ori_Ali_CA : num -0.125 -0.125 0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 0.125 ...
## $ Con_Ali_CA : num 0.125 0.125 0.125 -0.125 -0.125 -0.125 0.125 -0.125 0.125 0.125 ...
## $ Ori_Con_Ali_CA: num -0.0625 -0.0625 -0.0625 0.0625 0.0625 0.0625 -0.0625 0.0625 -0.0625 -0.0625 ...1.3 Experiment 1 and 2
df_E12_lmm <- rbind(mutate(df_E1_lmm, VA="10"),
mutate(df_E2_lmm, VA="5")) %>%
mutate(VA = factor(VA, levels = c("5", "10"))) %>%
set_contr(c(Orientation, Cue, Congruency, Alignment, CA, VA))
saveRDS(df_E12_lmm, file = here(dir_lmm, "df_E12_lmm.rds"))
str(df_E12_lmm)## 'data.frame': 51150 obs. of 29 variables:
## $ SubjCode : Factor w/ 80 levels "41","45","46",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 8 6 1 9 7 5 4 10 5 1 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation : Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "upr" "inv"
## .. .. ..$ : chr "2-1"
## $ Cue : Factor w/ 2 levels "top","bot": 2 1 2 1 1 2 1 1 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "top" "bot"
## .. .. ..$ : chr "2-1"
## $ Congruency : Factor w/ 2 levels "con","inc": 1 2 2 1 1 2 1 2 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "con" "inc"
## .. .. ..$ : chr "2-1"
## $ Alignment : Factor w/ 2 levels "ali","mis": 2 2 1 2 2 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "ali" "mis"
## .. .. ..$ : chr "2-1"
## $ CA : Factor w/ 2 levels "sam","dif": 1 2 1 1 2 1 2 2 2 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "sam" "dif"
## .. .. ..$ : chr "2-1"
## $ isCorrect : num 0 0 1 1 0 0 0 1 1 0 ...
## $ isSame : int 0 1 1 1 1 0 1 0 0 1 ...
## $ RT : num 913 955 1382 1248 1296 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ Ori_C : num -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Con_C : num -0.5 0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 -0.5 0.5 ...
## $ Ali_C : num 0.5 0.5 -0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ CA_C : num -0.5 0.5 -0.5 -0.5 0.5 -0.5 0.5 0.5 0.5 0.5 ...
## $ Cue_C : num 0.5 -0.5 0.5 -0.5 -0.5 0.5 -0.5 -0.5 -0.5 0.5 ...
## $ Ori_Con : num 0.25 -0.25 -0.25 0.25 0.25 -0.25 0.25 -0.25 0.25 -0.25 ...
## $ Ori_Ali : num -0.25 -0.25 0.25 -0.25 -0.25 0.25 0.25 0.25 0.25 0.25 ...
## $ Con_Ali : num -0.25 0.25 -0.25 -0.25 -0.25 -0.25 0.25 -0.25 0.25 -0.25 ...
## $ Ori_CA : num 0.25 -0.25 0.25 0.25 -0.25 0.25 -0.25 -0.25 -0.25 -0.25 ...
## $ Con_CA : num 0.25 0.25 -0.25 0.25 -0.25 -0.25 -0.25 0.25 -0.25 0.25 ...
## $ Ali_CA : num -0.25 0.25 0.25 -0.25 0.25 0.25 -0.25 -0.25 -0.25 -0.25 ...
## $ Ori_Con_Ali : num 0.125 -0.125 0.125 0.125 0.125 0.125 -0.125 0.125 -0.125 0.125 ...
## $ Ori_Con_CA : num -0.125 -0.125 0.125 -0.125 0.125 0.125 0.125 -0.125 0.125 -0.125 ...
## $ Ori_Ali_CA : num 0.125 -0.125 -0.125 0.125 -0.125 -0.125 0.125 0.125 0.125 0.125 ...
## $ Con_Ali_CA : num 0.125 0.125 0.125 0.125 -0.125 0.125 0.125 -0.125 0.125 -0.125 ...
## $ Ori_Con_Ali_CA: num -0.0625 -0.0625 -0.0625 -0.0625 0.0625 -0.0625 -0.0625 0.0625 -0.0625 0.0625 ...
## $ VA : Factor w/ 2 levels "5","10": 2 2 2 2 2 2 2 2 2 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "5" "10"
## .. .. ..$ : chr "2-1"1.4 Experiment 3 (pre-registered experiment)
# Order of completing tasks/blocks
readRDS(file = here("data", "df_E3_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
pivot_wider(names_from = Order,
values_from = Orientation) %>%
arrange(SubjCode) # Order of completing tasks/blocks
readRDS(file = here("data", "df_E3_raw.rds")) %>%
select(SubjCode, Order, Orientation) %>%
distinct() %>%
filter(Order == 1) %>%
group_by(Orientation) %>%
summarize(N = n(),
.groups = "drop")Number of trials whose RTs were below 200 ms (if not 0):
# check number of trials RT<200
readRDS(file = here("data", "df_E3_raw.rds")) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_below200 = sum(!isabove200),
.groups = "drop") %>%
filter(N_below200 != 0)Number of trials whose responses were NAs (if not 0):
# check number of NA in `isSame`
readRDS(file = here("data", "df_E3_raw.rds")) %>%
mutate(isSameNA = is.na(isSame)) %>%
group_by(SubjCode) %>% # , Orientation, Cue, Congruency, Alignment, CA
summarize(N = n(),
N_NA = sum(isSameNA),
.groups = "drop") %>%
filter(N_NA != 0)df_E3_raw <- readRDS(file = here("data", "df_E3_raw.rds")) %>%
filter(isabove200, # remove trials whose RT was below 200ms
!is.na(isSame)) # remove trials where no responses were recorded
str(df_E3_raw)## 'data.frame': 76678 obs. of 12 variables:
## $ SubjCode : Factor w/ 120 levels "91","92","93",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 9 9 10 3 6 2 9 7 5 2 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation: Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cue : Factor w/ 2 levels "top","bot": 2 2 2 2 1 2 2 1 1 1 ...
## $ Congruency : Factor w/ 2 levels "con","inc": 1 1 2 1 2 2 2 1 1 2 ...
## $ Alignment : Factor w/ 2 levels "ali","mis": 2 2 1 2 2 1 2 1 1 2 ...
## $ CA : Factor w/ 2 levels "sam","dif": 2 1 2 2 2 1 1 1 1 1 ...
## $ isCorrect : num 0 1 1 1 1 1 1 0 1 1 ...
## $ isSame : int 1 1 0 0 0 1 1 0 1 1 ...
## $ RT : num 3404 1680 2222 1189 1655 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...# apply successive difference coding and add columns for LMM analysis (psychr)
df_E3_lmm <- df_E3_raw %>%
psychr::set_contr(c(Orientation, Cue, Congruency, Alignment, CA)) %>%
psychr::add_lmmcols(isSame ~ Orientation * Congruency * Alignment * CA + Cue) ## old_name
## Ori_C Orientation2-1
## Con_C Congruency2-1
## Ali_C Alignment2-1
## CA_C CA2-1
## Cue_C Cue2-1
## Ori_Con Orientation2-1:Congruency2-1
## Ori_Ali Orientation2-1:Alignment2-1
## Con_Ali Congruency2-1:Alignment2-1
## Ori_CA Orientation2-1:CA2-1
## Con_CA Congruency2-1:CA2-1
## Ali_CA Alignment2-1:CA2-1
## Ori_Con_Ali Orientation2-1:Congruency2-1:Alignment2-1
## Ori_Con_CA Orientation2-1:Congruency2-1:CA2-1
## Ori_Ali_CA Orientation2-1:Alignment2-1:CA2-1
## Con_Ali_CA Congruency2-1:Alignment2-1:CA2-1
## Ori_Con_Ali_CA Orientation2-1:Congruency2-1:Alignment2-1:CA2-1## [1] "Ori_C + Con_C + Ali_C + CA_C + Cue_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA"saveRDS(df_E3_lmm, file = here(dir_lmm, "df_E3_lmm.rds"))str(df_E3_lmm)## 'data.frame': 76678 obs. of 28 variables:
## $ SubjCode : Factor w/ 120 levels "91","92","93",..: 10 10 10 10 10 10 10 10 10 10 ...
## $ StimGroup : Factor w/ 10 levels "F1","F2","F3",..: 9 9 10 3 6 2 9 7 5 2 ...
## $ Order : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Orientation : Factor w/ 2 levels "upr","inv": 1 1 1 1 1 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "upr" "inv"
## .. .. ..$ : chr "2-1"
## $ Cue : Factor w/ 2 levels "top","bot": 2 2 2 2 1 2 2 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "top" "bot"
## .. .. ..$ : chr "2-1"
## $ Congruency : Factor w/ 2 levels "con","inc": 1 1 2 1 2 2 2 1 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "con" "inc"
## .. .. ..$ : chr "2-1"
## $ Alignment : Factor w/ 2 levels "ali","mis": 2 2 1 2 2 1 2 1 1 2 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "ali" "mis"
## .. .. ..$ : chr "2-1"
## $ CA : Factor w/ 2 levels "sam","dif": 2 1 2 2 2 1 1 1 1 1 ...
## ..- attr(*, "contrasts")= num [1:2, 1] -0.5 0.5
## .. ..- attr(*, "dimnames")=List of 2
## .. .. ..$ : chr [1:2] "sam" "dif"
## .. .. ..$ : chr "2-1"
## $ isCorrect : num 0 1 1 1 1 1 1 0 1 1 ...
## $ isSame : int 1 1 0 0 0 1 1 0 1 1 ...
## $ RT : num 3404 1680 2222 1189 1655 ...
## $ isabove200 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ Ori_C : num -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Con_C : num -0.5 -0.5 0.5 -0.5 0.5 0.5 0.5 -0.5 -0.5 0.5 ...
## $ Ali_C : num 0.5 0.5 -0.5 0.5 0.5 -0.5 0.5 -0.5 -0.5 0.5 ...
## $ CA_C : num 0.5 -0.5 0.5 0.5 0.5 -0.5 -0.5 -0.5 -0.5 -0.5 ...
## $ Cue_C : num 0.5 0.5 0.5 0.5 -0.5 0.5 0.5 -0.5 -0.5 -0.5 ...
## $ Ori_Con : num 0.25 0.25 -0.25 0.25 -0.25 -0.25 -0.25 0.25 0.25 -0.25 ...
## $ Ori_Ali : num -0.25 -0.25 0.25 -0.25 -0.25 0.25 -0.25 0.25 0.25 -0.25 ...
## $ Con_Ali : num -0.25 -0.25 -0.25 -0.25 0.25 -0.25 0.25 0.25 0.25 0.25 ...
## $ Ori_CA : num -0.25 0.25 -0.25 -0.25 -0.25 0.25 0.25 0.25 0.25 0.25 ...
## $ Con_CA : num -0.25 0.25 0.25 -0.25 0.25 -0.25 -0.25 0.25 0.25 -0.25 ...
## $ Ali_CA : num 0.25 -0.25 -0.25 0.25 0.25 0.25 -0.25 0.25 0.25 -0.25 ...
## $ Ori_Con_Ali : num 0.125 0.125 0.125 0.125 -0.125 0.125 -0.125 -0.125 -0.125 -0.125 ...
## $ Ori_Con_CA : num 0.125 -0.125 -0.125 0.125 -0.125 0.125 0.125 -0.125 -0.125 0.125 ...
## $ Ori_Ali_CA : num -0.125 0.125 0.125 -0.125 -0.125 -0.125 0.125 -0.125 -0.125 0.125 ...
## $ Con_Ali_CA : num -0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 -0.125 -0.125 -0.125 ...
## $ Ori_Con_Ali_CA: num 0.0625 -0.0625 0.0625 0.0625 -0.0625 -0.0625 0.0625 0.0625 0.0625 0.0625 ...2 Demographics
df_subjinfo <- read.csv(file.path("data", "2faces_inversion-demographics.csv")) %>%
group_by(Exp) %>%
summarize(N_total = n(),
N_female = sum(Sex=="female"),
N_male = sum(Sex=="male"),
Age_mean = mean(Age),
Age_SD = sd(Age),
Age_min = min(Age),
Age_max = max(Age))
df_subjinfo3 Open data
readRDS(file = here("data", "df_E1_raw.rds")) %>%
write_csv(file = "data_E1.csv")
readRDS(file = here("data", "df_E2_raw.rds")) %>%
write_csv(file = "data_E2.csv")
readRDS(file = here("data", "df_E3_raw.rds")) %>%
write_csv(file = "data_E3.csv")4 Experiment 1
Visual angle: 11.14°×14.24°
4.1 Behavioral choices (d’)
4.1.1 Fitting the generalized linear mixed-effects models
4.1.1.1 The maximal model
# file_E1_resp_max <- file.path(dir_lmm, "lmm_E1_resp_max.rds")
#
# # fit the max model
# if (!file.exists(file_E1_resp_max)) {
# glmm_E1_resp_max <- glmer(
# isSame ~ Orientation * Congruency * Alignment * CA + Cue +
# (Orientation * Congruency * Alignment * CA | SubjCode) +
# (Orientation * Congruency * Alignment * CA | StimGroup),
# data = df_E1_lmm,
# family = binomial(link = "probit"),
# control = glmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(glmm_E1_resp_max, file = file_E1_resp_max)
# } else {
# glmm_E1_resp_max <- readRDS(file_E1_resp_max)
# }
#
# print(summary(glmm_E1_resp_max), corr = FALSE)4.1.1.2 The zero-correlation-parameter model
file_E1_resp_zcp <- file.path(dir_lmm, "lmm_E1_resp_zcp.rds")
# fit the max model
if (!file.exists(file_E1_resp_zcp)) {
glmm_E1_resp_zcp <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || SubjCode) +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || StimGroup),
data = df_E1_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E1_resp_zcp, file = file_E1_resp_zcp)
} else {
glmm_E1_resp_zcp <- readRDS(file_E1_resp_zcp)
}
print(summary(glmm_E1_resp_zcp), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || SubjCode) + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24999.6 25398.8 -12450.8 24901.6 25508
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.8801 -0.5983 0.2225 0.5394 5.1358
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 8.502e-02 2.916e-01
## SubjCode.1 Ori_C 4.480e-02 2.116e-01
## SubjCode.2 Con_C 5.272e-08 2.296e-04
## SubjCode.3 Ali_C 2.654e-02 1.629e-01
## SubjCode.4 CA_C 2.701e-01 5.197e-01
## SubjCode.5 Ori_Con 4.239e-03 6.511e-02
## SubjCode.6 Ori_Ali 2.409e-02 1.552e-01
## SubjCode.7 Con_Ali 3.492e-09 5.909e-05
## SubjCode.8 Ori_CA 7.518e-02 2.742e-01
## SubjCode.9 Con_CA 1.839e-01 4.288e-01
## SubjCode.10 Ali_CA 5.837e-08 2.416e-04
## SubjCode.11 Ori_Con_Ali 3.276e-08 1.810e-04
## SubjCode.12 Ori_Con_CA 6.609e-03 8.130e-02
## SubjCode.13 Ori_Ali_CA 2.386e-09 4.885e-05
## SubjCode.14 Con_Ali_CA 3.111e-08 1.764e-04
## SubjCode.15 Ori_Con_Ali_CA 3.912e-07 6.254e-04
## StimGroup (Intercept) 6.463e-03 8.039e-02
## StimGroup.1 Ori_C 1.870e-02 1.368e-01
## StimGroup.2 Con_C 3.020e-02 1.738e-01
## StimGroup.3 Ali_C 2.199e-08 1.483e-04
## StimGroup.4 CA_C 1.725e-02 1.313e-01
## StimGroup.5 Ori_Con 1.669e-02 1.292e-01
## StimGroup.6 Ori_Ali 5.099e-03 7.141e-02
## StimGroup.7 Con_Ali 0.000e+00 0.000e+00
## StimGroup.8 Ori_CA 1.948e-02 1.396e-01
## StimGroup.9 Con_CA 9.260e-02 3.043e-01
## StimGroup.10 Ali_CA 0.000e+00 0.000e+00
## StimGroup.11 Ori_Con_Ali 3.286e-10 1.813e-05
## StimGroup.12 Ori_Con_CA 8.581e-10 2.929e-05
## StimGroup.13 Ori_Ali_CA 3.605e-02 1.899e-01
## StimGroup.14 Con_Ali_CA 2.634e-08 1.623e-04
## StimGroup.15 Ori_Con_Ali_CA 3.625e-09 6.021e-05
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.262082 0.053578 4.892 1.00e-06 ***
## Orientation2-1 0.028174 0.058100 0.485 0.627728
## Congruency2-1 0.018124 0.058268 0.311 0.755767
## Alignment2-1 0.150435 0.032176 4.675 2.93e-06 ***
## CA2-1 -1.653298 0.094256 -17.541 < 2e-16 ***
## Cue2-1 0.007656 0.018442 0.415 0.678064
## Orientation2-1:Congruency2-1 0.099843 0.057067 1.750 0.080193 .
## Orientation2-1:Alignment2-1 0.004285 0.050826 0.084 0.932811
## Congruency2-1:Alignment2-1 0.080800 0.038116 2.120 0.034019 *
## Orientation2-1:CA2-1 0.708224 0.073314 9.660 < 2e-16 ***
## Congruency2-1:CA2-1 0.804835 0.123905 6.496 8.27e-11 ***
## Alignment2-1:CA2-1 -0.102802 0.038569 -2.665 0.007689 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.237890 0.076142 -3.124 0.001782 **
## Orientation2-1:Congruency2-1:CA2-1 -0.797355 0.077845 -10.243 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.226379 0.097490 2.322 0.020229 *
## Congruency2-1:Alignment2-1:CA2-1 -0.399947 0.076088 -5.256 1.47e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.528141 0.152266 3.469 0.000523 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')4.1.1.3 The reduced model
summary(rePCA(glmm_E1_resp_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.5197 0.4288 0.2916 0.2742 0.21165 0.16290 0.15521 0.08130 0.06511 0.0006254 0.0002416 0.0002296 0.000181 0.0001764 5.909e-05 4.885e-05
## Proportion of Variance 0.3749 0.2552 0.1180 0.1043 0.06218 0.03683 0.03344 0.00917 0.00588 0.0000000 0.0000000 0.0000000 0.000000 0.0000000 0.000e+00 0.000e+00
## Cumulative Proportion 0.3749 0.6301 0.7481 0.8525 0.91467 0.95151 0.98494 0.99412 1.00000 1.0000000 1.0000000 1.0000000 1.000000 1.0000000 1.000e+00 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.3043 0.1899 0.1738 0.13956 0.13676 0.13133 0.12918 0.08039 0.07141 0.0001623 0.0001483 6.021e-05 2.929e-05 1.813e-05 0 0
## Proportion of Variance 0.3818 0.1487 0.1245 0.08031 0.07712 0.07112 0.06881 0.02665 0.02103 0.0000000 0.0000000 0.000e+00 0.000e+00 0.000e+00 0 0
## Cumulative Proportion 0.3818 0.5305 0.6550 0.73528 0.81240 0.88352 0.95232 0.97897 1.00000 1.0000000 1.0000000 1.000e+00 1.000e+00 1.000e+00 1 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_Ali,Ori_Ali_CA,Con_C,Ali_CA,Ori_Con_Ali,Con_Ali_CA, andOri_Con_Ali_CA; - by-
StimGroup:Con_Ali,Ali_CA,Ori_Con_Ali,Ori_Con_CA,Ori_Con_Ali_CA,Ali_C, andCon_Ali_CA.
file_E1_resp_rdc <- file.path(dir_lmm, "lmm_E1_resp_rdc.rds")
# fit the reduced model
if (!file.exists(file_E1_resp_rdc)) {
glmm_E1_resp_rdc <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Con_CA || SubjCode # Ori_Ali_CA + Ori_Con_Ali + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Ali_CA || StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA
), # Ori_Con_Ali_CA
data = df_E1_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E1_resp_rdc, file = file_E1_resp_rdc)
} else {
glmm_E1_resp_rdc <- readRDS(file_E1_resp_rdc)
}
print(summary(glmm_E1_resp_rdc), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Ali_CA || StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24971.6 25256.8 -12450.8 24901.6 25522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.8803 -0.5983 0.2225 0.5394 5.1359
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.085014 0.29157
## SubjCode.1 Ori_C 0.044808 0.21168
## SubjCode.2 Ali_C 0.026531 0.16288
## SubjCode.3 CA_C 0.270066 0.51968
## SubjCode.4 Ori_Con 0.004239 0.06511
## SubjCode.5 Ori_Ali 0.024096 0.15523
## SubjCode.6 Ori_CA 0.075207 0.27424
## SubjCode.7 Con_CA 0.183851 0.42878
## SubjCode.8 Ori_Con_CA 0.006620 0.08136
## StimGroup (Intercept) 0.006460 0.08037
## StimGroup.1 Ori_C 0.018703 0.13676
## StimGroup.2 Con_C 0.030193 0.17376
## StimGroup.3 CA_C 0.017240 0.13130
## StimGroup.4 Ori_Con 0.016688 0.12918
## StimGroup.5 Ori_Ali 0.005096 0.07139
## StimGroup.6 Ori_CA 0.019501 0.13964
## StimGroup.7 Con_CA 0.092575 0.30426
## StimGroup.8 Ori_Ali_CA 0.036095 0.18999
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.262103 0.053579 4.892 9.99e-07 ***
## Orientation2-1 0.028192 0.058107 0.485 0.627550
## Congruency2-1 0.018104 0.058267 0.311 0.756023
## Alignment2-1 0.150436 0.032175 4.676 2.93e-06 ***
## CA2-1 -1.653348 0.094258 -17.541 < 2e-16 ***
## Cue2-1 0.007670 0.018443 0.416 0.677490
## Orientation2-1:Congruency2-1 0.099790 0.057071 1.749 0.080372 .
## Orientation2-1:Alignment2-1 0.004318 0.050828 0.085 0.932293
## Congruency2-1:Alignment2-1 0.080803 0.038117 2.120 0.034020 *
## Orientation2-1:CA2-1 0.708126 0.073343 9.655 < 2e-16 ***
## Congruency2-1:CA2-1 0.804914 0.123932 6.495 8.31e-11 ***
## Alignment2-1:CA2-1 -0.102737 0.038570 -2.664 0.007730 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.237928 0.076154 -3.124 0.001782 **
## Orientation2-1:Congruency2-1:CA2-1 -0.797361 0.077861 -10.241 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.226315 0.097528 2.321 0.020313 *
## Congruency2-1:Alignment2-1:CA2-1 -0.399963 0.076095 -5.256 1.47e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.528120 0.152337 3.467 0.000527 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.1.1.4 The extended model
file_E1_resp_etd <- file.path(dir_lmm, "lmm_E1_resp_etd.rds")
# fit the reduced model
if (!file.exists(file_E1_resp_etd)) {
glmm_E1_resp_etd <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Con_CA | SubjCode # Ori_Ali_CA + Ori_Con_Ali + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Ali_CA | StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA
), # Ori_Con_Ali_CA
data = df_E1_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E1_resp_etd, file = file_E1_resp_etd)
} else {
glmm_E1_resp_etd <- readRDS(file_E1_resp_etd)
}
print(summary(glmm_E1_resp_etd), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Ali_CA | StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 25000.8 25872.7 -12393.4 24786.8 25450
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.9807 -0.5969 0.2212 0.5431 7.3743
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.086188 0.29358
## Ori_C 0.048232 0.21962 -0.09
## Ali_C 0.028856 0.16987 0.23 0.16
## CA_C 0.281309 0.53039 -0.25 0.31 -0.49
## Ori_Con 0.021538 0.14676 0.24 -0.34 0.86 -0.47
## Ori_Ali 0.026876 0.16394 -0.44 0.09 -0.27 0.45 -0.23
## Ori_CA 0.087553 0.29589 0.05 -0.12 0.46 -0.16 0.52 -0.03
## Con_CA 0.223566 0.47283 -0.26 0.32 0.35 -0.09 0.17 0.08 0.35
## Ori_Con_CA 0.118309 0.34396 0.35 -0.38 -0.11 -0.16 0.06 -0.08 -0.34 -0.94
## StimGroup (Intercept) 0.006371 0.07982
## Ori_C 0.017382 0.13184 0.04
## Con_C 0.030813 0.17554 -0.21 -0.89
## CA_C 0.016088 0.12684 0.50 0.52 -0.62
## Ori_Con 0.020778 0.14415 -0.12 0.87 -0.76 0.62
## Ori_Ali 0.009075 0.09526 -0.63 -0.33 0.47 -0.19 -0.19
## Ori_CA 0.019575 0.13991 0.44 0.15 0.09 0.03 -0.16 0.01
## Con_CA 0.094485 0.30738 -0.11 -0.77 0.96 -0.55 -0.64 0.35 0.23
## Ori_Ali_CA 0.058064 0.24097 -0.04 0.59 -0.73 0.78 0.79 -0.06 -0.48 -0.75
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.263700 0.053819 4.900 9.60e-07 ***
## Orientation2-1 0.019987 0.057996 0.345 0.730380
## Congruency2-1 0.021907 0.058938 0.372 0.710117
## Alignment2-1 0.159716 0.033263 4.802 1.57e-06 ***
## CA2-1 -1.665096 0.095233 -17.484 < 2e-16 ***
## Cue2-1 0.007496 0.018466 0.406 0.684808
## Orientation2-1:Congruency2-1 0.097563 0.065242 1.495 0.134810
## Orientation2-1:Alignment2-1 -0.017095 0.055843 -0.306 0.759504
## Congruency2-1:Alignment2-1 0.072661 0.038421 1.891 0.058596 .
## Orientation2-1:CA2-1 0.740777 0.076489 9.685 < 2e-16 ***
## Congruency2-1:CA2-1 0.817784 0.128911 6.344 2.24e-10 ***
## Alignment2-1:CA2-1 -0.113906 0.039203 -2.906 0.003666 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.226532 0.076969 -2.943 0.003249 **
## Orientation2-1:Congruency2-1:CA2-1 -0.824606 0.097747 -8.436 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.258646 0.109353 2.365 0.018019 *
## Congruency2-1:Alignment2-1:CA2-1 -0.396665 0.076459 -5.188 2.13e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.508862 0.153307 3.319 0.000903 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(glmm_E1_resp_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Standard deviation 0.6030 0.5688 0.29609 0.26666 0.18759 0.15312 0.13382 2.531e-05 1.192e-06
## Proportion of Variance 0.3942 0.3507 0.09504 0.07709 0.03815 0.02542 0.01941 0.000e+00 0.000e+00
## Cumulative Proportion 0.3942 0.7449 0.83993 0.91702 0.95517 0.98059 1.00000 1.000e+00 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Standard deviation 0.4432 0.1810 0.14504 0.11604 0.09447 0.000622 0.000162 2.581e-05 4.961e-06
## Proportion of Variance 0.7205 0.1202 0.07716 0.04939 0.03274 0.000000 0.000000 0.000e+00 0.000e+00
## Cumulative Proportion 0.7205 0.8407 0.91788 0.96726 1.00000 1.000000 1.000000 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_ConandOri_Ali; - by-
StimGroup:(Intercept),Ori_Ali,CA_CandOri_C.
file_E1_resp_etd2 <- file.path(dir_lmm, "lmm_E1_resp_etd2.rds")
# fit the reduced model
if (!file.exists(file_E1_resp_etd2)) {
glmm_E1_resp_etd2 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_CA | SubjCode # Ori_Ali_CA + Ori_Con_Ali + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + Con_C + # Ali_C + CA_C + Ori_C +
Ori_Con + Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Ali +
Ori_Ali_CA | StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA
), # Ori_Con_Ali_CA
data = df_E1_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E1_resp_etd2, file = file_E1_resp_etd2)
} else {
glmm_E1_resp_etd2 <- readRDS(file_E1_resp_etd2)
}
print(summary(glmm_E1_resp_etd2), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + Con_C + Ori_Con + Ori_CA + Con_CA + Ori_Ali_CA | StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 25069.6 25558.5 -12474.8 24949.6 25497
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.5188 -0.6113 0.2245 0.5400 7.7086
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.08390 0.2897
## Ori_C 0.04465 0.2113 -0.07
## Ali_C 0.02749 0.1658 0.21 0.24
## CA_C 0.26860 0.5183 -0.25 0.26 -0.46
## Ori_CA 0.08194 0.2862 0.02 -0.08 0.40 -0.11
## Con_CA 0.21882 0.4678 -0.26 0.37 0.31 -0.07 0.35
## Ori_Con_CA 0.11888 0.3448 0.35 -0.41 -0.04 -0.24 -0.30 -0.94
## StimGroup Con_C 0.03577 0.1891
## Ori_Con 0.02831 0.1683 -0.84
## Ori_CA 0.02350 0.1533 -0.17 0.20
## Con_CA 0.08697 0.2949 0.97 -0.70 -0.04
## Ori_Ali_CA 0.05914 0.2432 -0.87 0.94 -0.12 -0.78
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.261235 0.046888 5.571 2.53e-08 ***
## Orientation2-1 0.029719 0.038974 0.763 0.445729
## Congruency2-1 0.021434 0.062910 0.341 0.733322
## Alignment2-1 0.157951 0.032588 4.847 1.25e-06 ***
## CA2-1 -1.649155 0.084414 -19.537 < 2e-16 ***
## Cue2-1 0.007871 0.018385 0.428 0.668572
## Orientation2-1:Congruency2-1 0.082133 0.066017 1.244 0.213456
## Orientation2-1:Alignment2-1 -0.003267 0.038160 -0.086 0.931772
## Congruency2-1:Alignment2-1 0.072457 0.038165 1.898 0.057631 .
## Orientation2-1:CA2-1 0.723854 0.077659 9.321 < 2e-16 ***
## Congruency2-1:CA2-1 0.802796 0.125277 6.408 1.47e-10 ***
## Alignment2-1:CA2-1 -0.110011 0.038718 -2.841 0.004492 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.223784 0.076184 -2.937 0.003310 **
## Orientation2-1:Congruency2-1:CA2-1 -0.808224 0.095377 -8.474 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.216229 0.108312 1.996 0.045894 *
## Congruency2-1:Alignment2-1:CA2-1 -0.401548 0.075948 -5.287 1.24e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.557372 0.151898 3.669 0.000243 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0313052 (tol = 0.002, component 1)summary(rePCA(glmm_E1_resp_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.6008 0.5392 0.28075 0.25849 0.1870 0.10915 0.0001569
## Proportion of Variance 0.4276 0.3444 0.09336 0.07914 0.0414 0.01411 0.0000000
## Cumulative Proportion 0.4276 0.7720 0.86535 0.94449 0.9859 1.00000 1.0000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 0.4319 0.1632 0.14310 0.0006368 0.000107
## Proportion of Variance 0.7983 0.1140 0.08763 0.0000000 0.000000
## Cumulative Proportion 0.7983 0.9124 1.00000 1.0000000 1.000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_Con_CA; - by-
StimGroup:Ori_Ali_CA, andCon_C.
file_E1_resp_etd3 <- file.path(dir_lmm, "lmm_E1_resp_etd3.rds")
# fit the reduced model
if (!file.exists(file_E1_resp_etd3)) {
glmm_E1_resp_etd3 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + CA_C + # Con_C + Ali_C +
Ori_CA + Con_CA | SubjCode # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
# Ori_Ali_CA + Ori_Con_Ali + Con_Ali_CA + Ori_Con_CA
) + # Ori_Con_Ali_CA
(0 + # Ali_C + CA_C + Ori_C + Con_C +
Con_CA + Ori_Con + Ori_CA | StimGroup # Con_Ali + Ali_CA + Ori_Ali +
# Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA + Ori_Ali_CA
), # Ori_Con_Ali_CA
data = df_E1_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E1_resp_etd3, file = file_E1_resp_etd3)
} else {
glmm_E1_resp_etd3 <- readRDS(file_E1_resp_etd3)
}
print(summary(glmm_E1_resp_etd3), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + CA_C + Ori_CA + Con_CA | SubjCode) + (0 + Con_CA + Ori_Con + Ori_CA | StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 25198.2 25507.9 -12561.1 25122.2 25519
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.6825 -0.6220 0.2277 0.5397 5.1265
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.08274 0.2876
## Ori_C 0.04391 0.2095 -0.06
## CA_C 0.26123 0.5111 -0.26 0.27
## Ori_CA 0.07305 0.2703 0.05 -0.16 -0.10
## Con_CA 0.18079 0.4252 -0.24 0.35 -0.05 0.20
## StimGroup Con_CA 0.11582 0.3403
## Ori_Con 0.01657 0.1287 -0.62
## Ori_CA 0.02255 0.1502 0.10 0.15
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.264044 0.046558 5.671 1.42e-08 ***
## Orientation2-1 0.026707 0.038652 0.691 0.489588
## Congruency2-1 0.014799 0.019319 0.766 0.443638
## Alignment2-1 0.140870 0.018879 7.462 8.54e-14 ***
## CA2-1 -1.632259 0.083253 -19.606 < 2e-16 ***
## Cue2-1 0.008586 0.018306 0.469 0.639024
## Orientation2-1:Congruency2-1 0.098567 0.055722 1.769 0.076913 .
## Orientation2-1:Alignment2-1 0.011218 0.037754 0.297 0.766362
## Congruency2-1:Alignment2-1 0.089108 0.037761 2.360 0.018286 *
## Orientation2-1:CA2-1 0.697327 0.075247 9.267 < 2e-16 ***
## Congruency2-1:CA2-1 0.793334 0.132646 5.981 2.22e-09 ***
## Alignment2-1:CA2-1 -0.091445 0.037758 -2.422 0.015440 *
## Orientation2-1:Congruency2-1:Alignment2-1 -0.246470 0.075508 -3.264 0.001098 **
## Orientation2-1:Congruency2-1:CA2-1 -0.806779 0.076180 -10.590 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.214806 0.075510 2.845 0.004445 **
## Congruency2-1:Alignment2-1:CA2-1 -0.401671 0.075514 -5.319 1.04e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.542030 0.151006 3.589 0.000331 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.1.1.5 The optimal model
anova(glmm_E1_resp_rdc, glmm_E1_resp_etd3, refit=FALSE)According to BIC, the reduced model is used as the optimal model.
glmm_E1_resp_opt <- glmm_E1_resp_rdc
print(summary(glmm_E1_resp_opt), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Ali_CA || StimGroup)
## Data: df_E1_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24971.6 25256.8 -12450.8 24901.6 25522
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.8803 -0.5983 0.2225 0.5394 5.1359
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.085014 0.29157
## SubjCode.1 Ori_C 0.044808 0.21168
## SubjCode.2 Ali_C 0.026531 0.16288
## SubjCode.3 CA_C 0.270066 0.51968
## SubjCode.4 Ori_Con 0.004239 0.06511
## SubjCode.5 Ori_Ali 0.024096 0.15523
## SubjCode.6 Ori_CA 0.075207 0.27424
## SubjCode.7 Con_CA 0.183851 0.42878
## SubjCode.8 Ori_Con_CA 0.006620 0.08136
## StimGroup (Intercept) 0.006460 0.08037
## StimGroup.1 Ori_C 0.018703 0.13676
## StimGroup.2 Con_C 0.030193 0.17376
## StimGroup.3 CA_C 0.017240 0.13130
## StimGroup.4 Ori_Con 0.016688 0.12918
## StimGroup.5 Ori_Ali 0.005096 0.07139
## StimGroup.6 Ori_CA 0.019501 0.13964
## StimGroup.7 Con_CA 0.092575 0.30426
## StimGroup.8 Ori_Ali_CA 0.036095 0.18999
## Number of obs: 25557, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.262103 0.053579 4.892 9.99e-07 ***
## Orientation2-1 0.028192 0.058107 0.485 0.627550
## Congruency2-1 0.018104 0.058267 0.311 0.756023
## Alignment2-1 0.150436 0.032175 4.676 2.93e-06 ***
## CA2-1 -1.653348 0.094258 -17.541 < 2e-16 ***
## Cue2-1 0.007670 0.018443 0.416 0.677490
## Orientation2-1:Congruency2-1 0.099790 0.057071 1.749 0.080372 .
## Orientation2-1:Alignment2-1 0.004318 0.050828 0.085 0.932293
## Congruency2-1:Alignment2-1 0.080803 0.038117 2.120 0.034020 *
## Orientation2-1:CA2-1 0.708126 0.073343 9.655 < 2e-16 ***
## Congruency2-1:CA2-1 0.804914 0.123932 6.495 8.31e-11 ***
## Alignment2-1:CA2-1 -0.102737 0.038570 -2.664 0.007730 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.237928 0.076154 -3.124 0.001782 **
## Orientation2-1:Congruency2-1:CA2-1 -0.797361 0.077861 -10.241 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.226315 0.097528 2.321 0.020313 *
## Congruency2-1:Alignment2-1:CA2-1 -0.399963 0.076095 -5.256 1.47e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.528120 0.152337 3.467 0.000527 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.1.2 Estimated marginal means
# hit and false alarm
emm_E1_probit <- emmeans(glmm_E1_resp_opt,
~ Orientation + Congruency + Alignment + CA,
type = "response")
emm_E1_probit## Orientation Congruency Alignment CA prob SE df asymp.LCL asymp.UCL
## upr con ali sam 0.942 0.0118 Inf 0.9151 0.962
## inv con ali sam 0.823 0.0251 Inf 0.7697 0.868
## upr inc ali sam 0.750 0.0305 Inf 0.6865 0.806
## inv inc ali sam 0.781 0.0283 Inf 0.7220 0.833
## upr con mis sam 0.941 0.0120 Inf 0.9136 0.961
## inv con mis sam 0.855 0.0222 Inf 0.8075 0.894
## upr inc mis sam 0.884 0.0193 Inf 0.8419 0.918
## inv inc mis sam 0.826 0.0248 Inf 0.7733 0.871
## upr con ali dif 0.137 0.0214 Inf 0.0993 0.183
## inv con ali dif 0.269 0.0316 Inf 0.2113 0.335
## upr inc ali dif 0.324 0.0342 Inf 0.2598 0.393
## inv inc ali dif 0.386 0.0363 Inf 0.3170 0.458
## upr con mis dif 0.162 0.0238 Inf 0.1195 0.213
## inv con mis dif 0.343 0.0350 Inf 0.2776 0.414
## upr inc mis dif 0.314 0.0337 Inf 0.2514 0.383
## inv inc mis dif 0.426 0.0371 Inf 0.3553 0.500
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Intervals are back-transformed from the probit scaleSensitivity d’ in each condition:
# sensitivity d'
emm_E1_d <- contrast(emm_E1_probit, "pairwise", simple="CA")
# emmip(emm_E1_d, Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E1_d[1:8], infer=c(TRUE,FALSE), adjust="none")## contrast Orientation Congruency Alignment estimate SE df asymp.LCL asymp.UCL
## sam - dif upr con ali 2.67 0.132 Inf 2.409 2.93
## sam - dif inv con ali 1.54 0.126 Inf 1.295 1.79
## sam - dif upr inc ali 1.13 0.125 Inf 0.887 1.38
## sam - dif inv inc ali 1.07 0.125 Inf 0.823 1.31
## sam - dif upr con mis 2.55 0.131 Inf 2.294 2.81
## sam - dif inv con mis 1.46 0.126 Inf 1.216 1.71
## sam - dif upr inc mis 1.68 0.127 Inf 1.431 1.93
## sam - dif inv inc mis 1.12 0.125 Inf 0.880 1.37
##
## Results are averaged over the levels of: Cue
## Note: contrasts are still on the probit scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## Confidence level used: 0.954.1.2.1 Composite effect
Composite effect of d’:
emm_E1_cf_d <- contrast(emm_E1_d,
interaction = "pairwise",
simple = c("Congruency", "Alignment"),
infer = TRUE)
summary(emm_E1_cf_d[1:2], side=">")## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.664 0.114 Inf 0.4760 Inf 5.810 <.0001
## con - inc ali - mis sam - dif inv 0.136 0.101 Inf -0.0296 Inf 1.351 0.0884
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedCongruency effect of d’ in aligned condition:
emm_E1_con_d <- contrast(emm_E1_d,
interaction = "pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E1_con_d[1:2], side=">")## Congruency_pairwise contrast Orientation Alignment estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc sam - dif upr ali 1.536 0.143 Inf 1.301 Inf 10.751 <.0001
## con - inc sam - dif inv ali 0.474 0.138 Inf 0.248 Inf 3.448 0.0003
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedplot_E1_cf_d <- emm_E1_d %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, estimate, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(0,3.5)) + # set the limit for y axis c(0, 1100)
labs(y = expression("Sensitivity"~italic("d'")), fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("***", "", "", "", "", "", "", ""),
color = "red",
size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "ccf_d.pdf", plot_ccf_d, width = 8, height = 6)
plot_E1_cf_d
4.1.2.2 Compare composite effects between upright and inverted faces
contrast(emm_E1_d,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.528 0.152 Inf 0.23 0.827 3.467 0.0005
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.954.1.2.3 Facilitation and interference
# facilitation and interference of d'
emm_E1_d_fi <- contrast(emm_E1_d,
"pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E1_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E1_d_fi[1:4]## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.1161 0.0941 Inf -0.0682 0.3005 1.235 0.2170
## ali - mis sam - dif inv con 0.0784 0.0789 Inf -0.0763 0.2330 0.993 0.3205
## ali - mis sam - dif upr inc -0.5479 0.0788 Inf -0.7023 -0.3935 -6.956 <.0001
## ali - mis sam - dif inv inc -0.0575 0.0760 Inf -0.2065 0.0915 -0.757 0.4492
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95summary(emm_E1_d_fi[1:2], side=">")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.1161 0.0941 Inf -0.0386 Inf 1.235 0.1085
## ali - mis sam - dif inv con 0.0784 0.0789 Inf -0.0514 Inf 0.993 0.1603
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedsummary(emm_E1_d_fi[3:4], side="<")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.5479 0.0788 Inf -Inf -0.4183 -6.956 <.0001
## ali - mis sam - dif inv inc -0.0575 0.0760 Inf -Inf 0.0675 -0.757 0.2246
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are left-tailedplot_E1_cffi_d <- summary(emm_E1_d_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=1.1, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin=asymp.LCL, ymax=asymp.UCL),
linewidth=1.5, width=0, alpha=.6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_d) +
labs(x = NULL,
y = expression("Sensitivity"~italic(d)*"' (aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E1_fi_d.pdf", plot_E1_cffi_d, width = 7, height = 4.55)
plot_E1_cffi_d
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E1_d_fi_ui <- contrast(emm_E1_d,
interaction = "pairwise",
simple = c("Orientation", "Alignment"),
infer = TRUE,
adjust = "none")
emm_E1_d_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.0377 0.130 Inf -0.217 0.292 0.291 0.7713
## upr - inv ali - mis sam - dif inc -0.4904 0.117 Inf -0.720 -0.260 -4.179 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.954.1.2.4 Comparisons between facilitation and interference
contrast(emm_E1_d_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## faci-inte upr -0.4318 0.131 Inf -0.688 -0.176 -3.308 0.0009
## faci-inte inv 0.0208 0.118 Inf -0.210 0.252 0.177 0.8596
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.954.1.3 Plot
plot_E1_cf_d_ <- plot_E1_cf_d +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E1_d <- ggarrange(plot_E1_cf_d_, plot_E1_cffi_d,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E1_d
4.2 Correct response times
4.2.1 Fitting the linear mixed-effects models
4.2.1.1 The maximal model
# file_E1_rt_max <- file.path(dir_lmm, "lmm_E1_rt_max.rds")
#
# # fit the max model
# if (!file.exists(file_E1_rt_max)) {
# lmm_E1_rt_max <- lmer(
# log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
# (Orientation * Congruency * Alignment | SubjCode) +
# (Orientation * Congruency * Alignment | StimGroup),
# data = df_E1_lmm |> dplyr::filter(isCorrect==1),
# control = lmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(lmm_E1_rt_max, file = file_E1_rt_max)
# } else {
# lmm_E1_rt_max <- readRDS(file_E1_rt_max)
# }
#
# print(summary(lmm_E1_rt_max), corr = FALSE)4.2.1.2 The zero-correlation-parameter model
file_E1_rt_zcp <- file.path(dir_lmm, "lmm_E1_rt_zcp.rds")
# fit the zcp model
if (!file.exists(file_E1_rt_zcp)) {
lmm_E1_rt_zcp <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || StimGroup),
data = df_E1_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E1_rt_zcp, file = file_E1_rt_zcp)
} else {
lmm_E1_rt_zcp <- readRDS(file_E1_rt_zcp)
}
print(summary(lmm_E1_rt_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17951.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2752 -0.6199 -0.1620 0.4507 7.6784
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.077e-02 2.019e-01
## SubjCode.1 Ori_C 8.976e-02 2.996e-01
## SubjCode.2 Con_C 2.225e-04 1.492e-02
## SubjCode.3 Ali_C 1.157e-03 3.401e-02
## SubjCode.4 Ori_Con 8.418e-04 2.901e-02
## SubjCode.5 Ori_Ali 2.533e-04 1.592e-02
## SubjCode.6 Con_Ali 1.288e-09 3.589e-05
## SubjCode.7 Ori_Con_Ali 0.000e+00 0.000e+00
## StimGroup (Intercept) 2.136e-05 4.622e-03
## StimGroup.1 Ori_C 2.393e-04 1.547e-02
## StimGroup.2 Con_C 0.000e+00 0.000e+00
## StimGroup.3 Ali_C 2.544e-04 1.595e-02
## StimGroup.4 Ori_Con 4.452e-04 2.110e-02
## StimGroup.5 Ori_Ali 1.011e-03 3.179e-02
## StimGroup.6 Con_Ali 2.780e-04 1.667e-02
## StimGroup.7 Ori_Con_Ali 4.331e-03 6.581e-02
## Residual 1.442e-01 3.798e-01
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.750e+00 3.208e-02 3.916e+01 210.402 < 2e-16 ***
## Orientation2-1 2.472e-02 4.794e-02 3.971e+01 0.516 0.6090
## Congruency2-1 3.338e-02 5.984e-03 3.596e+01 5.579 2.55e-06 ***
## Alignment2-1 -7.990e-03 9.200e-03 1.667e+01 -0.869 0.3974
## Cue2-1 1.800e-02 5.472e-03 1.910e+04 3.290 0.0010 **
## CA2-1 6.617e-02 5.532e-03 1.916e+04 11.962 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.377e-02 1.366e-02 9.885e+00 -2.472 0.0332 *
## Orientation2-1:Alignment2-1 -6.845e-03 1.510e-02 9.071e+00 -0.453 0.6610
## Congruency2-1:Alignment2-1 -5.279e-02 1.218e-02 8.624e+00 -4.333 0.0021 **
## Orientation2-1:Congruency2-1:Alignment2-1 3.257e-02 3.026e-02 9.151e+00 1.077 0.3092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')4.2.1.3 The reduced model
summary(rePCA(lmm_E1_rt_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7889 0.5317 0.08956 0.07640 0.04191 0.03928 9.451e-05 0
## Proportion of Variance 0.6748 0.3065 0.00870 0.00633 0.00190 0.00167 0.000e+00 0
## Cumulative Proportion 0.6748 0.9814 0.99009 0.99642 0.99833 1.00000 1.000e+00 1
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.1733 0.08371 0.05556 0.04390 0.04200 0.04073 0.01217 0
## Proportion of Variance 0.6582 0.15359 0.06766 0.04224 0.03866 0.03636 0.00325 0
## Cumulative Proportion 0.6582 0.81183 0.87949 0.92173 0.96039 0.99675 1.00000 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_Ali, andOri_Con_Ali. - by-
StimGroup:Con_C.
file_E1_rt_rdc <- file.path(dir_lmm, "lmm_E1_rt_rdc.rds")
# fit the max model
if (!file.exists(file_E1_rt_rdc)) {
lmm_E1_rt_rdc <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali || SubjCode # Con_Ali +
) + # Ori_Con_Ali
(Ori_C + Ali_C + # Con_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || StimGroup),
data = df_E1_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e8))
)
saveRDS(lmm_E1_rt_rdc, file = file_E1_rt_rdc)
} else {
lmm_E1_rt_rdc <- readRDS(file_E1_rt_rdc)
}
print(summary(lmm_E1_rt_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali || SubjCode) + (Ori_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+08))
##
## REML criterion at convergence: 17951.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2753 -0.6198 -0.1619 0.4507 7.6784
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.077e-02 0.20192
## SubjCode.1 Ori_C 8.974e-02 0.29957
## SubjCode.2 Con_C 2.224e-04 0.01491
## SubjCode.3 Ali_C 1.157e-03 0.03401
## SubjCode.4 Ori_Con 8.459e-04 0.02908
## SubjCode.5 Ori_Ali 2.540e-04 0.01594
## StimGroup (Intercept) 2.135e-05 0.00462
## StimGroup.1 Ori_C 2.391e-04 0.01546
## StimGroup.2 Ali_C 2.543e-04 0.01595
## StimGroup.3 Ori_Con 4.462e-04 0.02112
## StimGroup.4 Ori_Ali 1.011e-03 0.03179
## StimGroup.5 Con_Ali 2.777e-04 0.01666
## StimGroup.6 Ori_Con_Ali 4.323e-03 0.06575
## Residual 1.442e-01 0.37977
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.750e+00 3.208e-02 3.916e+01 210.399 < 2e-16 ***
## Orientation2-1 2.472e-02 4.794e-02 3.972e+01 0.516 0.6090
## Congruency2-1 3.338e-02 5.983e-03 3.596e+01 5.579 2.55e-06 ***
## Alignment2-1 -7.990e-03 9.199e-03 1.667e+01 -0.869 0.3974
## Cue2-1 1.800e-02 5.472e-03 1.910e+04 3.290 0.0010 **
## CA2-1 6.617e-02 5.532e-03 1.916e+04 11.962 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.377e-02 1.367e-02 9.894e+00 -2.471 0.0333 *
## Orientation2-1:Alignment2-1 -6.845e-03 1.510e-02 9.072e+00 -0.453 0.6610
## Congruency2-1:Alignment2-1 -5.279e-02 1.218e-02 8.623e+00 -4.333 0.0021 **
## Orientation2-1:Congruency2-1:Alignment2-1 3.258e-02 3.024e-02 9.159e+00 1.077 0.3090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00255145 (tol = 0.002, component 1)summary(rePCA(lmm_E1_rt_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.7888 0.5317 0.08956 0.07658 0.04197 0.03927
## Proportion of Variance 0.6748 0.3066 0.00870 0.00636 0.00191 0.00167
## Cumulative Proportion 0.6748 0.9814 0.99006 0.99642 0.99833 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.1731 0.08372 0.05562 0.04388 0.04199 0.04071 0.01217
## Proportion of Variance 0.6577 0.15380 0.06789 0.04225 0.03870 0.03637 0.00325
## Cumulative Proportion 0.6577 0.81154 0.87943 0.92168 0.96038 0.99675 1.000004.2.1.4 The extended model
file_E1_rt_etd <- file.path(dir_lmm, "lmm_E1_rt_etd.rds")
# fit the max model
if (!file.exists(file_E1_rt_etd)) {
lmm_E1_rt_etd <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali | SubjCode # Con_Ali +
) + # Ori_Con_Ali
(Ori_C + Ali_C + # Con_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali | StimGroup),
data = df_E1_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e8))
)
saveRDS(lmm_E1_rt_etd, file = file_E1_rt_etd)
} else {
lmm_E1_rt_etd <- readRDS(file_E1_rt_etd)
}
print(summary(lmm_E1_rt_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali | SubjCode) + (Ori_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+08))
##
## REML criterion at convergence: 17928.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3038 -0.6209 -0.1614 0.4462 7.6147
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 4.080e-02 0.201993
## Ori_C 8.973e-02 0.299555 -0.08
## Con_C 2.024e-04 0.014228 0.10 -1.00
## Ali_C 1.211e-03 0.034793 -0.07 0.13 -0.13
## Ori_Con 9.771e-04 0.031258 0.04 -0.34 0.34 -0.02
## Ori_Ali 8.671e-04 0.029447 0.21 -0.46 0.46 0.69 0.55
## StimGroup (Intercept) 4.451e-05 0.006672
## Ori_C 2.786e-04 0.016690 -0.31
## Ali_C 3.236e-04 0.017990 0.05 -0.36
## Ori_Con 6.689e-04 0.025863 -0.99 0.18 0.09
## Ori_Ali 1.321e-03 0.036346 0.59 0.13 0.29 -0.58
## Con_Ali 8.666e-04 0.029437 -0.62 0.18 0.43 0.66 -0.55
## Ori_Con_Ali 5.010e-03 0.070780 0.43 0.25 -0.01 -0.46 -0.07 0.29
## Residual 1.441e-01 0.379572
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.749e+00 3.213e-02 3.935e+01 210.087 < 2e-16 ***
## Orientation2-1 2.488e-02 4.797e-02 3.984e+01 0.519 0.60694
## Congruency2-1 3.330e-02 5.935e-03 1.415e+02 5.611 1.02e-07 ***
## Alignment2-1 -7.968e-03 9.637e-03 1.741e+01 -0.827 0.41956
## Cue2-1 1.799e-02 5.471e-03 1.917e+04 3.289 0.00101 **
## CA2-1 6.618e-02 5.529e-03 1.920e+04 11.970 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.386e-02 1.456e-02 1.177e+01 -2.325 0.03879 *
## Orientation2-1:Alignment2-1 -5.946e-03 1.656e-02 1.139e+01 -0.359 0.72619
## Congruency2-1:Alignment2-1 -5.327e-02 1.439e-02 1.128e+01 -3.701 0.00335 **
## Orientation2-1:Congruency2-1:Alignment2-1 3.180e-02 3.135e-02 9.699e+00 1.015 0.33497
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E1_rt_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.7935 0.5294 0.11203 0.07903 5.102e-05 0
## Proportion of Variance 0.6780 0.3018 0.01352 0.00673 0.000e+00 0
## Cumulative Proportion 0.6780 0.9798 0.99327 1.00000 1.000e+00 1
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.1910 0.1216 0.07104 0.05280 0.0001159 4.38e-05 4.225e-20
## Proportion of Variance 0.6172 0.2502 0.08541 0.04718 0.0000000 0.00e+00 0.000e+00
## Cumulative Proportion 0.6172 0.8674 0.95282 1.00000 1.0000000 1.00e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ori_Ali, andOri_Con; - by-
StimGroup:(Intercept),Ori_CandAli_C.
file_E1_rt_etd2 <- file.path(dir_lmm, "lmm_E1_rt_etd2.rds")
# fit the max model
if (!file.exists(file_E1_rt_etd2)) {
lmm_E1_rt_etd2 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Ali_C | SubjCode # Con_C +
# Con_Ali + Ori_Con + Ori_Ali
) + # Ori_Con_Ali
(0 + # Con_C + Ori_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali | StimGroup),
data = df_E1_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E1_rt_etd2, file = file_E1_rt_etd2)
} else {
lmm_E1_rt_etd2 <- readRDS(file_E1_rt_etd2)
}
print(summary(lmm_E1_rt_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ali_C | SubjCode) + (0 + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17951
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2798 -0.6204 -0.1616 0.4498 7.7318
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0407815 0.20194
## Ori_C 0.0897102 0.29952 -0.07
## Ali_C 0.0011508 0.03392 -0.08 0.13
## StimGroup Ori_Con 0.0006487 0.02547
## Ori_Ali 0.0010711 0.03273 -0.45
## Con_Ali 0.0005217 0.02284 0.14 -0.95
## Ori_Con_Ali 0.0050242 0.07088 -0.82 -0.15 0.46
## Residual 0.1444376 0.38005
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.750e+00 3.205e-02 3.902e+01 210.592 < 2e-16 ***
## Orientation2-1 2.486e-02 4.768e-02 3.899e+01 0.521 0.60498
## Congruency2-1 3.334e-02 5.497e-03 1.923e+04 6.064 1.35e-09 ***
## Alignment2-1 -7.982e-03 7.686e-03 3.935e+01 -1.039 0.30534
## Cue2-1 1.816e-02 5.474e-03 1.923e+04 3.318 0.00091 ***
## CA2-1 6.620e-02 5.533e-03 1.924e+04 11.965 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.395e-02 1.363e-02 1.218e+01 -2.491 0.02810 *
## Orientation2-1:Alignment2-1 -6.894e-03 1.509e-02 9.908e+00 -0.457 0.65772
## Congruency2-1:Alignment2-1 -5.326e-02 1.315e-02 1.280e+01 -4.050 0.00142 **
## Orientation2-1:Congruency2-1:Alignment2-1 3.140e-02 3.139e-02 9.751e+00 1.000 0.34136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E1_rt_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.7900 0.5287 0.08825
## Proportion of Variance 0.6848 0.3067 0.00854
## Cumulative Proportion 0.6848 0.9915 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1967 0.1077 0.000343 4.992e-20
## Proportion of Variance 0.7692 0.2308 0.000000 0.000e+00
## Cumulative Proportion 0.7692 1.0000 1.000000 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ali_C; - by-
StimGroup:Ori_ConandCon_Ali.
file_E1_rt_etd3 <- file.path(dir_lmm, "lmm_E1_rt_etd3.rds")
# fit the max model
if (!file.exists(file_E1_rt_etd3)) {
lmm_E1_rt_etd3 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C | SubjCode # Con_C + + Ali_C
# Con_Ali + Ori_Con + Ori_Ali
) + # Ori_Con_Ali
(0 + # Con_C + Ori_C + Ali_C +
Ori_Ali + # Ori_Con + Con_Ali +
Ori_Con_Ali | StimGroup),
data = df_E1_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E1_rt_etd3, file = file_E1_rt_etd3)
} else {
lmm_E1_rt_etd3 <- readRDS(file_E1_rt_etd3)
}
print(summary(lmm_E1_rt_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C | SubjCode) + (0 + Ori_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17968.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2739 -0.6201 -0.1636 0.4479 7.7175
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0407523 0.20187
## Ori_C 0.0896344 0.29939 -0.07
## StimGroup Ori_Ali 0.0009359 0.03059
## Ori_Con_Ali 0.0044377 0.06662 -0.15
## Residual 0.1447965 0.38052
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.750e+00 3.204e-02 3.902e+01 210.667 < 2e-16 ***
## Orientation2-1 2.468e-02 4.766e-02 3.899e+01 0.518 0.607430
## Congruency2-1 3.350e-02 5.504e-03 1.926e+04 6.088 1.17e-09 ***
## Alignment2-1 -9.034e-03 5.499e-03 1.926e+04 -1.643 0.100397
## Cue2-1 1.809e-02 5.479e-03 1.926e+04 3.302 0.000963 ***
## CA2-1 6.651e-02 5.538e-03 1.926e+04 12.010 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.407e-02 1.100e-02 1.926e+04 -3.096 0.001966 **
## Orientation2-1:Alignment2-1 -6.885e-03 1.465e-02 9.221e+00 -0.470 0.649242
## Congruency2-1:Alignment2-1 -5.365e-02 1.099e-02 1.926e+04 -4.880 1.07e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.309e-02 3.046e-02 9.148e+00 1.086 0.305116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.2.1.5 The optimal model
anova(lmm_E1_rt_rdc, lmm_E1_rt_etd3, refit=FALSE)According to BIC, the extended model is used as the optimal model.
lmm_E1_rt_opt <- lmm_E1_rt_etd3
print(summary(lmm_E1_rt_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C | SubjCode) + (0 + Ori_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E1_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17968.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2739 -0.6201 -0.1636 0.4479 7.7175
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0407523 0.20187
## Ori_C 0.0896344 0.29939 -0.07
## StimGroup Ori_Ali 0.0009359 0.03059
## Ori_Con_Ali 0.0044377 0.06662 -0.15
## Residual 0.1447965 0.38052
## Number of obs: 19357, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.750e+00 3.204e-02 3.902e+01 210.667 < 2e-16 ***
## Orientation2-1 2.468e-02 4.766e-02 3.899e+01 0.518 0.607430
## Congruency2-1 3.350e-02 5.504e-03 1.926e+04 6.088 1.17e-09 ***
## Alignment2-1 -9.034e-03 5.499e-03 1.926e+04 -1.643 0.100397
## Cue2-1 1.809e-02 5.479e-03 1.926e+04 3.302 0.000963 ***
## CA2-1 6.651e-02 5.538e-03 1.926e+04 12.010 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.407e-02 1.100e-02 1.926e+04 -3.096 0.001966 **
## Orientation2-1:Alignment2-1 -6.885e-03 1.465e-02 9.221e+00 -0.470 0.649242
## Congruency2-1:Alignment2-1 -5.365e-02 1.099e-02 1.926e+04 -4.880 1.07e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.309e-02 3.046e-02 9.148e+00 1.086 0.305116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 14.2.2 Estimated marginal means
emm_E1_rt <- emmeans(lmm_E1_rt_opt, ~ Orientation + Congruency + Alignment,
pbkrtest.limit = 2e5,
lmerTest.limit = 2e5)
# emmip(regrid(emm_E1_rt), Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E1_rt, type = "response") # equivalent to regrid(emm_E1_rt)## Orientation Congruency Alignment response SE df lower.CL upper.CL
## upr con ali 810 34.0 41.4 744 882
## inv con ali 855 33.5 42.1 790 925
## upr inc ali 883 37.1 41.9 811 961
## inv inc ali 885 34.8 42.2 818 958
## upr con mis 834 35.0 41.4 767 908
## inv con mis 860 33.8 42.2 794 931
## upr inc mis 847 35.6 41.6 779 922
## inv inc mis 858 33.7 42.2 793 929
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale4.2.2.1 Composite effect
Composite effect of d’:
emm_E1_rt_cf <- contrast(regrid(emm_E1_rt),
interaction = "pairwise",
by = "Orientation",
infer = TRUE)
summary(emm_E1_rt_cf[1:2], side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -59.5 15.8 41.4 -Inf -32.93 -3.772 0.0003
## con - inc ali - mis inv -32.3 16.6 42.1 -Inf -4.37 -1.945 0.0292
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedCongruency effect of rt in aligned condition:
emm_E1_rt_con <- contrast(regrid(emm_E1_rt),
interaction = "pairwise",
by = c("Orientation", "Alignment"),
infer = TRUE)
summary(emm_E1_rt_con[1:2], side="<")## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -72.4 10.6 41.4 -Inf -54.5 -6.803 <.0001
## con - inc inv ali -30.5 10.9 42.1 -Inf -12.2 -2.800 0.0038
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedplot_E1_cf_rt <- summary(emm_E1_rt, type = "response") %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(y = response, x = Alignment, color = Congruency, group = Congruency)) +
geom_point(position = position_dodge(width = 0.1), size = 2) +
geom_line(aes(linetype = Congruency), position = position_dodge(width = 0.1),
linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, position = position_dodge(width = 0.1),
show.legend = F) +
facet_grid(. ~Orientation, switch = "both") +
coord_cartesian(ylim = ylimit_cf_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = "Correct response times (ms)", fill = "Congruency") + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "*", "", "", "", ""), color = sig_color, size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "E1_cf_rt.pdf", plot_E1_cf_rt, width = 8, height = 4.8)
plot_E1_cf_rt
4.2.2.2 Compare composite effects between upright and inverted faces
contrast(regrid(emm_E1_rt),
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -27.2 26.1 41.4 -80 25.6 -1.041 0.3039
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.954.2.2.3 Facilitation and interference
emm_E1_rt_fi <- contrast(regrid(emm_E1_rt),
"pairwise",
by = c("Orientation", "Congruency"),
infer = TRUE,
adjust = "none")
# emmip(emm_E1_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E1_rt_fi[1:4]## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -24.26 10.5 41.4 -45.52 -3.0 -2.304 0.0263
## ali - mis inv con -5.21 11.5 42.1 -28.46 18.0 -0.452 0.6535
## ali - mis upr inc 35.19 11.3 41.6 12.31 58.1 3.105 0.0034
## ali - mis inv inc 27.04 11.6 42.2 3.58 50.5 2.325 0.0249
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95summary(emm_E1_rt_fi[1:2], side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -24.26 10.5 41.4 -Inf -6.54 -2.304 0.0132
## ali - mis inv con -5.21 11.5 42.1 -Inf 14.17 -0.452 0.3267
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedsummary(emm_E1_rt_fi[3:4], side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 35.2 11.3 41.6 16.12 Inf 3.105 0.0017
## ali - mis inv inc 27.0 11.6 42.2 7.48 Inf 2.325 0.0125
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are right-tailedplot_E1_cffi_rt <- summary(emm_E1_rt_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=100, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = expression(RT~"(aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E1_fi_rt.pdf", plot_E1_cffi_rt, width = 7, height = 4.55)
plot_E1_cffi_rt
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of rt
emm_E1_rt_fi_ui <- contrast(emm_E1_rt,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E1_rt_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -0.02343 0.0215 9.22 -0.0718 0.0249 -1.092 0.3025
## upr - inv ali - mis inc 0.00966 0.0208 9.33 -0.0371 0.0565 0.464 0.6530
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: satterthwaite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.954.2.2.4 Comparisons between facilitation and interference
contrast(emm_E1_rt_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2]## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 10.9 15.2 41.4 -19.7 41.6 0.720 0.4754
## faci-inte inv 21.8 16.2 42.1 -10.8 54.4 1.351 0.1839
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.954.2.3 Plot
plot_E1_cf_rt_ <- plot_E1_cf_rt +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E1_rt <- ggarrange(plot_E1_cf_rt_, plot_E1_cffi_rt,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E1_rt
4.3 Plot
plot_E1 <- ggarrange(plot_E1_cf_d_, plot_E1_cffi_d,
plot_E1_cf_rt_, plot_E1_cffi_rt,
labels = c("A", "B", "C", "D"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 2,
ncol = 2)
plot_E1
5 Experiment 2
Visual angle: 5.57°×7.12°
5.1 Behavioral choices (d’)
5.1.1 Fitting the generalized linear mixed-effects models
5.1.1.1 The maximal model
# file_E2_resp_max <- file.path(dir_lmm, "lmm_E2_resp_max.rds")
#
# # fit the max model
# if (!file.exists(file_E2_resp_max)) {
# glmm_E2_resp_max <- glmer(
# isSame ~ Orientation * Congruency * Alignment * CA + Cue +
# (Orientation * Congruency * Alignment * CA | SubjCode) +
# (Orientation * Congruency * Alignment * CA | StimGroup),
# data = df_E2_lmm,
# family = binomial(link = "probit"),
# control = glmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(glmm_E2_resp_max, file = file_E2_resp_max)
# } else {
# glmm_E2_resp_max <- readRDS(file_E2_resp_max)
# }
#
# print(summary(glmm_E2_resp_max), corr = FALSE)5.1.1.2 The zero-correlation-parameter model
file_E2_resp_zcp <- file.path(dir_lmm, "lmm_E2_resp_zcp.rds")
# fit the zcp model
if (!file.exists(file_E2_resp_zcp)) {
glmm_E2_resp_zcp <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || SubjCode) +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || StimGroup),
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_zcp, file = file_E2_resp_zcp)
} else {
glmm_E2_resp_zcp <- readRDS(file_E2_resp_zcp)
}
print(summary(glmm_E2_resp_zcp), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || SubjCode) + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24317.5 24716.9 -12109.8 24219.5 25544
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.3770 -0.5479 0.1944 0.5237 8.3244
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 7.863e-02 2.804e-01
## SubjCode.1 Ori_C 4.847e-02 2.202e-01
## SubjCode.2 Con_C 8.078e-03 8.988e-02
## SubjCode.3 Ali_C 2.818e-02 1.679e-01
## SubjCode.4 CA_C 2.235e-01 4.727e-01
## SubjCode.5 Ori_Con 6.457e-08 2.541e-04
## SubjCode.6 Ori_Ali 1.551e-02 1.245e-01
## SubjCode.7 Con_Ali 3.731e-10 1.932e-05
## SubjCode.8 Ori_CA 2.890e-01 5.376e-01
## SubjCode.9 Con_CA 2.610e-01 5.109e-01
## SubjCode.10 Ali_CA 5.123e-10 2.263e-05
## SubjCode.11 Ori_Con_Ali 4.667e-02 2.160e-01
## SubjCode.12 Ori_Con_CA 2.666e-01 5.163e-01
## SubjCode.13 Ori_Ali_CA 6.641e-10 2.577e-05
## SubjCode.14 Con_Ali_CA 3.270e-02 1.808e-01
## SubjCode.15 Ori_Con_Ali_CA 5.601e-06 2.367e-03
## StimGroup (Intercept) 8.713e-03 9.335e-02
## StimGroup.1 Ori_C 2.001e-02 1.414e-01
## StimGroup.2 Con_C 4.608e-02 2.147e-01
## StimGroup.3 Ali_C 0.000e+00 0.000e+00
## StimGroup.4 CA_C 2.145e-02 1.465e-01
## StimGroup.5 Ori_Con 1.411e-02 1.188e-01
## StimGroup.6 Ori_Ali 0.000e+00 0.000e+00
## StimGroup.7 Con_Ali 2.607e-09 5.106e-05
## StimGroup.8 Ori_CA 2.524e-02 1.589e-01
## StimGroup.9 Con_CA 1.330e-01 3.647e-01
## StimGroup.10 Ali_CA 2.780e-03 5.273e-02
## StimGroup.11 Ori_Con_Ali 4.581e-03 6.768e-02
## StimGroup.12 Ori_Con_CA 7.916e-07 8.897e-04
## StimGroup.13 Ori_Ali_CA 1.845e-09 4.296e-05
## StimGroup.14 Con_Ali_CA 2.824e-02 1.681e-01
## StimGroup.15 Ori_Con_Ali_CA 1.665e-03 4.080e-02
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16071 0.05423 2.964 0.00304 **
## Orientation2-1 0.14941 0.06015 2.484 0.01300 *
## Congruency2-1 0.03120 0.07217 0.432 0.66548
## Alignment2-1 0.18949 0.03297 5.748 9.02e-09 ***
## CA2-1 -1.75056 0.09036 -19.373 < 2e-16 ***
## Cue2-1 0.01578 0.01870 0.844 0.39878
## Orientation2-1:Congruency2-1 0.09971 0.05454 1.828 0.06750 .
## Orientation2-1:Alignment2-1 -0.09997 0.04363 -2.292 0.02193 *
## Congruency2-1:Alignment2-1 0.11981 0.03891 3.079 0.00208 **
## Orientation2-1:CA2-1 0.82057 0.10678 7.685 1.53e-14 ***
## Congruency2-1:CA2-1 1.03144 0.14640 7.045 1.85e-12 ***
## Alignment2-1:CA2-1 -0.03507 0.04262 -0.823 0.41056
## Orientation2-1:Congruency2-1:Alignment2-1 -0.13590 0.08755 -1.552 0.12060
## Orientation2-1:Congruency2-1:CA2-1 -0.91281 0.11385 -8.017 1.08e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.21323 0.07784 2.739 0.00616 **
## Congruency2-1:Alignment2-1:CA2-1 -0.44154 0.09832 -4.491 7.10e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.59126 0.15598 3.791 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')5.1.1.3 The reduced model
summary(rePCA(glmm_E2_resp_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.5376 0.5163 0.5109 0.4727 0.28041 0.22016 0.21602 0.18083 0.1679 0.12454 0.08988 0.002367 0.0002541 2.577e-05 2.263e-05 1.932e-05
## Proportion of Variance 0.2226 0.2053 0.2011 0.1721 0.06056 0.03733 0.03594 0.02518 0.0217 0.01195 0.00622 0.000000 0.0000000 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 0.2226 0.4279 0.6290 0.8011 0.86167 0.89900 0.93494 0.96013 0.9818 0.99377 1.00000 1.000000 1.0000000 1.000e+00 1.000e+00 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.3647 0.2147 0.16806 0.15888 0.14645 0.1414 0.11880 0.09335 0.06768 0.05273 0.04080 0.0008897 5.106e-05 4.296e-05 0 0
## Proportion of Variance 0.4349 0.1506 0.09233 0.08252 0.07011 0.0654 0.04614 0.02848 0.01497 0.00909 0.00544 0.0000000 0.000e+00 0.000e+00 0 0
## Cumulative Proportion 0.4349 0.5855 0.67784 0.76036 0.83047 0.8959 0.94201 0.97049 0.98547 0.99456 1.00000 1.0000000 1.000e+00 1.000e+00 1 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_Ali,Ali_CA,Ori_Con,Ori_Ali_CA, andOri_Con_Ali_CA; - by-
StimGroup:Ali_C,Ori_Ali,Con_Ali,Ori_Con_CA, andOri_Ali_CA.
file_E2_resp_rdc <- file.path(dir_lmm, "lmm_E2_resp_rdc.rds")
# fit the reduced model
if (!file.exists(file_E2_resp_rdc)) {
glmm_E2_resp_rdc <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con +
Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA || SubjCode # + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_CA + Con_CA + Ali_CA + # Ori_Ali + Con_Ali +
Ori_Con_Ali + Con_Ali_CA + # Ori_Con_CA + Ori_Ali_CA +
Ori_Con_Ali_CA || StimGroup),
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_rdc, file = file_E2_resp_rdc)
} else {
glmm_E2_resp_rdc <- readRDS(file_E2_resp_rdc)
}
print(summary(glmm_E2_resp_rdc), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24297.5 24615.4 -12109.8 24219.5 25554
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.3764 -0.5479 0.1944 0.5237 8.3235
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.078661 0.28047
## SubjCode.1 Ori_C 0.048485 0.22019
## SubjCode.2 Con_C 0.008082 0.08990
## SubjCode.3 Ali_C 0.028147 0.16777
## SubjCode.4 CA_C 0.223514 0.47277
## SubjCode.5 Ori_Ali 0.015514 0.12456
## SubjCode.6 Ori_CA 0.289040 0.53762
## SubjCode.7 Con_CA 0.260946 0.51083
## SubjCode.8 Ori_Con_Ali 0.046578 0.21582
## SubjCode.9 Ori_Con_CA 0.266491 0.51623
## SubjCode.10 Con_Ali_CA 0.032721 0.18089
## StimGroup (Intercept) 0.008721 0.09339
## StimGroup.1 Ori_C 0.019992 0.14139
## StimGroup.2 Con_C 0.046057 0.21461
## StimGroup.3 CA_C 0.021446 0.14645
## StimGroup.4 Ori_Con 0.014106 0.11877
## StimGroup.5 Ori_CA 0.025244 0.15888
## StimGroup.6 Con_CA 0.133154 0.36490
## StimGroup.7 Ali_CA 0.002774 0.05267
## StimGroup.8 Ori_Con_Ali 0.004614 0.06793
## StimGroup.9 Con_Ali_CA 0.028261 0.16811
## StimGroup.10 Ori_Con_Ali_CA 0.001513 0.03890
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16069 0.05424 2.963 0.00305 **
## Orientation2-1 0.14950 0.06015 2.486 0.01293 *
## Congruency2-1 0.03122 0.07216 0.433 0.66531
## Alignment2-1 0.18948 0.03295 5.750 8.93e-09 ***
## CA2-1 -1.75077 0.09037 -19.374 < 2e-16 ***
## Cue2-1 0.01576 0.01870 0.843 0.39929
## Orientation2-1:Congruency2-1 0.09969 0.05453 1.828 0.06751 .
## Orientation2-1:Alignment2-1 -0.09994 0.04363 -2.291 0.02198 *
## Congruency2-1:Alignment2-1 0.11985 0.03891 3.080 0.00207 **
## Orientation2-1:CA2-1 0.82052 0.10678 7.684 1.54e-14 ***
## Congruency2-1:CA2-1 1.03147 0.14643 7.044 1.86e-12 ***
## Alignment2-1:CA2-1 -0.03505 0.04261 -0.823 0.41077
## Orientation2-1:Congruency2-1:Alignment2-1 -0.13594 0.08755 -1.553 0.12049
## Orientation2-1:Congruency2-1:CA2-1 -0.91272 0.11381 -8.020 1.06e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.21328 0.07784 2.740 0.00615 **
## Congruency2-1:Alignment2-1:CA2-1 -0.44141 0.09834 -4.489 7.17e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.59115 0.15590 3.792 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.1.1.4 The extended model
file_E2_resp_etd <- file.path(dir_lmm, "lmm_E2_resp_etd.rds")
# fit the reduced model
if (!file.exists(file_E2_resp_etd)) {
glmm_E2_resp_etd <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con +
Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA | SubjCode # + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_CA + Con_CA + Ali_CA + # Ori_Ali + Con_Ali +
Ori_Con_Ali + Con_Ali_CA + # Ori_Con_CA + Ori_Ali_CA +
Ori_Con_Ali_CA | StimGroup),
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_etd, file = file_E2_resp_etd)
} else {
glmm_E2_resp_etd <- readRDS(file_E2_resp_etd)
}
print(summary(glmm_E2_resp_etd), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA | SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Con_Ali_CA + Ori_Con_Ali_CA | StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24364.9 25579.3 -12033.5 24066.9 25444
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.8795 -0.5479 0.1705 0.5252 7.7222
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.080015 0.28287
## Ori_C 0.049231 0.22188 0.04
## Con_C 0.010746 0.10366 -0.10 -0.08
## Ali_C 0.029271 0.17109 0.58 0.01 -0.22
## CA_C 0.232291 0.48197 -0.24 0.27 -0.41 -0.15
## Ori_Ali 0.025487 0.15965 -0.28 0.06 0.60 -0.29 -0.34
## Ori_CA 0.316858 0.56290 0.17 0.11 -0.06 -0.09 -0.36 0.11
## Con_CA 0.277643 0.52692 0.18 0.02 -0.65 0.22 0.00 -0.63 -0.14
## Ori_Con_Ali 0.095177 0.30851 -0.37 0.55 0.29 -0.20 0.38 -0.06 -0.26 -0.05
## Ori_Con_CA 0.327049 0.57188 -0.01 0.03 0.50 -0.37 0.19 0.50 -0.44 -0.47 0.23
## Con_Ali_CA 0.070766 0.26602 -0.25 0.00 -0.42 0.12 0.23 -0.06 -0.65 0.56 -0.06 -0.03
## StimGroup (Intercept) 0.008301 0.09111
## Ori_C 0.019411 0.13932 0.11
## Con_C 0.047449 0.21783 -0.20 -0.57
## CA_C 0.022579 0.15026 0.81 -0.04 -0.46
## Ori_Con 0.016526 0.12855 -0.42 0.47 -0.71 -0.02
## Ori_CA 0.030375 0.17428 -0.28 0.80 -0.42 -0.48 0.38
## Con_CA 0.134526 0.36678 -0.33 -0.45 0.97 -0.55 -0.60 -0.29
## Ali_CA 0.009877 0.09938 0.52 0.25 -0.03 0.31 -0.01 -0.16 -0.14
## Ori_Con_Ali 0.043920 0.20957 -0.04 -0.35 0.43 0.11 -0.35 -0.43 0.52 -0.50
## Con_Ali_CA 0.048000 0.21909 -0.34 0.10 -0.03 -0.49 0.27 0.31 -0.10 0.41 -0.87
## Ori_Con_Ali_CA 0.068844 0.26238 -0.59 -0.44 0.12 -0.31 0.40 -0.27 0.07 0.11 -0.35 0.65
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16377 0.05424 3.020 0.002532 **
## Orientation2-1 0.13990 0.06012 2.327 0.019967 *
## Congruency2-1 0.03053 0.07380 0.414 0.679068
## Alignment2-1 0.19273 0.03380 5.701 1.19e-08 ***
## CA2-1 -1.76452 0.09231 -19.115 < 2e-16 ***
## Cue2-1 0.01553 0.01873 0.829 0.407003
## Orientation2-1:Congruency2-1 0.10403 0.05780 1.800 0.071899 .
## Orientation2-1:Alignment2-1 -0.10273 0.04773 -2.152 0.031366 *
## Congruency2-1:Alignment2-1 0.12646 0.04052 3.121 0.001801 **
## Orientation2-1:CA2-1 0.85247 0.11297 7.546 4.49e-14 ***
## Congruency2-1:CA2-1 1.04972 0.14890 7.050 1.79e-12 ***
## Alignment2-1:CA2-1 -0.05672 0.05103 -1.111 0.266407
## Orientation2-1:Congruency2-1:Alignment2-1 -0.13499 0.11518 -1.172 0.241178
## Orientation2-1:Congruency2-1:CA2-1 -0.93868 0.12263 -7.654 1.94e-14 ***
## Orientation2-1:Alignment2-1:CA2-1 0.22215 0.08063 2.755 0.005866 **
## Congruency2-1:Alignment2-1:CA2-1 -0.44647 0.11383 -3.922 8.77e-05 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.62810 0.18083 3.473 0.000514 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(glmm_E2_resp_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## Standard deviation 0.7428 0.6493 0.4620 0.33199 0.31252 0.26350 0.19634 0.09705 0.05046 0.001481 0.0005527
## Proportion of Variance 0.3643 0.2783 0.1409 0.07277 0.06449 0.04584 0.02545 0.00622 0.00168 0.000000 0.0000000
## Cumulative Proportion 0.3643 0.6426 0.7835 0.85631 0.92080 0.96664 0.99210 0.99832 1.00000 1.000000 1.0000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
## Standard deviation 0.4674 0.3553 0.2466 0.18359 0.10307 0.001793 0.0004636 0.0001787 0.0001428 9.252e-05 1.218e-06
## Proportion of Variance 0.4856 0.2807 0.1352 0.07494 0.02362 0.000010 0.0000000 0.0000000 0.0000000 0.000e+00 0.000e+00
## Cumulative Proportion 0.4856 0.7663 0.9014 0.97638 0.99999 1.000000 1.0000000 1.0000000 1.0000000 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ori_Ali,Ali_C, andOri_C; - by-
StimGroup:(Intercept),Ori_C,Ori_Con,Ali_CA,CA_C, andOri_CA.
file_E2_resp_etd2 <- file.path(dir_lmm, "lmm_E2_resp_etd2.rds")
# fit the reduced model
if (!file.exists(file_E2_resp_etd2)) {
glmm_E2_resp_etd2 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(CA_C + # Con_C + Ali_C + Ori_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA | SubjCode # + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + Con_C + # Ali_C + Ori_C + CA_C +
Con_CA + # Ori_Ali + Con_Ali + Ori_Con + Ali_CA + Ori_CA +
Ori_Con_Ali + Con_Ali_CA + # Ori_Con_CA + Ori_Ali_CA +
Ori_Con_Ali_CA | StimGroup),
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_etd2, file = file_E2_resp_etd2)
} else {
glmm_E2_resp_etd2 <- readRDS(file_E2_resp_etd2)
}
print(summary(glmm_E2_resp_etd2), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (CA_C + Ori_CA + Con_CA + Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA | SubjCode) + (0 + Con_C + Con_CA + Ori_Con_Ali + Con_Ali_CA + Ori_Con_Ali_CA | StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24559.1 25048.1 -12219.5 24439.1 25533
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -13.9049 -0.5604 0.2071 0.5267 8.4867
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07560 0.2750
## CA_C 0.22945 0.4790 -0.24
## Ori_CA 0.29734 0.5453 0.13 -0.34
## Con_CA 0.24662 0.4966 0.17 0.00 -0.14
## Ori_Con_Ali 0.05579 0.2362 -0.54 0.43 -0.25 -0.26
## Ori_Con_CA 0.30738 0.5544 0.04 0.18 -0.46 -0.48 0.61
## Con_Ali_CA 0.05233 0.2287 -0.29 0.23 -0.73 0.55 -0.06 -0.04
## StimGroup Con_C 0.04569 0.2137
## Con_CA 0.12156 0.3487 0.98
## Ori_Con_Ali 0.04025 0.2006 0.51 0.63
## Con_Ali_CA 0.05067 0.2251 -0.05 -0.21 -0.89
## Ori_Con_Ali_CA 0.03302 0.1817 -0.03 -0.05 -0.54 0.64
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16334 0.04461 3.661 0.000251 ***
## Orientation2-1 0.14587 0.01973 7.393 1.43e-13 ***
## Congruency2-1 0.02215 0.07039 0.315 0.752970
## Alignment2-1 0.18006 0.01926 9.347 < 2e-16 ***
## CA2-1 -1.72180 0.07847 -21.942 < 2e-16 ***
## Cue2-1 0.01550 0.01853 0.836 0.403015
## Orientation2-1:Congruency2-1 0.09630 0.03924 2.454 0.014121 *
## Orientation2-1:Alignment2-1 -0.09553 0.03856 -2.478 0.013219 *
## Congruency2-1:Alignment2-1 0.12991 0.03913 3.320 0.000901 ***
## Orientation2-1:CA2-1 0.81874 0.09510 8.609 < 2e-16 ***
## Congruency2-1:CA2-1 1.01926 0.14118 7.219 5.22e-13 ***
## Alignment2-1:CA2-1 -0.01356 0.03851 -0.352 0.724732
## Orientation2-1:Congruency2-1:Alignment2-1 -0.15794 0.10707 -1.475 0.140202
## Orientation2-1:Congruency2-1:CA2-1 -0.92910 0.11872 -7.826 5.04e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.18486 0.07708 2.398 0.016472 *
## Congruency2-1:Alignment2-1:CA2-1 -0.45846 0.11143 -4.114 3.89e-05 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.66619 0.16671 3.996 6.44e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0227189 (tol = 0.002, component 1)summary(rePCA(glmm_E2_resp_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.7232 0.6017 0.4443 0.31396 0.26356 0.11813 0.002299
## Proportion of Variance 0.4136 0.2863 0.1561 0.07795 0.05493 0.01104 0.000000
## Cumulative Proportion 0.4136 0.7000 0.8561 0.93403 0.98896 1.00000 1.000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 0.4362 0.2909 0.12752 0.00148 0.0007104
## Proportion of Variance 0.6535 0.2906 0.05585 0.00001 0.0000000
## Cumulative Proportion 0.6535 0.9441 0.99999 1.00000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_Ali_CA; - by-
StimGroup:Con_C, andOri_Con_Ali.
file_E2_resp_etd3 <- file.path(dir_lmm, "lmm_E2_resp_etd3.rds")
# fit the reduced model
if (!file.exists(file_E2_resp_etd3)) {
glmm_E2_resp_etd3 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(CA_C + # Con_C + Ali_C + Ori_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_Ali + Ori_Con_CA | SubjCode # + Ori_Ali_CA + + Con_Ali_CA
) + # Ori_Con_Ali_CA
(0 + # Ali_C + Ori_C + CA_C + Con_C +
Con_CA + # Ori_Ali + Con_Ali + Ori_Con + Ali_CA + Ori_CA +
Con_Ali_CA + # Ori_Con_CA + Ori_Ali_CA + Ori_Con_Ali +
Ori_Con_Ali_CA | StimGroup),
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_etd3, file = file_E2_resp_etd3)
} else {
glmm_E2_resp_etd3 <- readRDS(file_E2_resp_etd3)
}
print(summary(glmm_E2_resp_etd3), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (CA_C + Ori_CA + Con_CA + Ori_Con_Ali + Ori_Con_CA | SubjCode) + (0 + Con_CA + Con_Ali_CA + Ori_Con_Ali_CA | StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24666.4 25025.0 -12289.2 24578.4 25549
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.5553 -0.5650 0.2032 0.5294 6.9378
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07506 0.2740
## CA_C 0.22593 0.4753 -0.25
## Ori_CA 0.28797 0.5366 0.13 -0.32
## Con_CA 0.24688 0.4969 0.17 0.00 -0.14
## Ori_Con_Ali 0.04712 0.2171 -0.57 0.48 -0.30 -0.27
## Ori_Con_CA 0.30165 0.5492 0.04 0.17 -0.46 -0.48 0.69
## StimGroup Con_CA 0.14647 0.3827
## Con_Ali_CA 0.03001 0.1732 -0.05
## Ori_Con_Ali_CA 0.02413 0.1553 -0.05 1.00
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16790 0.04445 3.778 0.000158 ***
## Orientation2-1 0.14167 0.01962 7.222 5.13e-13 ***
## Congruency2-1 0.01737 0.01958 0.887 0.374897
## Alignment2-1 0.17791 0.01913 9.300 < 2e-16 ***
## CA2-1 -1.71265 0.07788 -21.991 < 2e-16 ***
## Cue2-1 0.01590 0.01847 0.861 0.389412
## Orientation2-1:Congruency2-1 0.10054 0.03911 2.571 0.010154 *
## Orientation2-1:Alignment2-1 -0.09243 0.03832 -2.412 0.015857 *
## Congruency2-1:Alignment2-1 0.12660 0.03837 3.300 0.000969 ***
## Orientation2-1:CA2-1 0.80702 0.09380 8.604 < 2e-16 ***
## Congruency2-1:CA2-1 1.01338 0.14979 6.765 1.33e-11 ***
## Alignment2-1:CA2-1 -0.01584 0.03822 -0.414 0.678607
## Orientation2-1:Congruency2-1:Alignment2-1 -0.16045 0.08470 -1.894 0.058189 .
## Orientation2-1:Congruency2-1:CA2-1 -0.92640 0.11792 -7.856 3.96e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.19515 0.07651 2.551 0.010752 *
## Congruency2-1:Alignment2-1:CA2-1 -0.44413 0.09397 -4.726 2.29e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.63604 0.16274 3.908 9.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0228383 (tol = 0.002, component 1)summary(rePCA(glmm_E2_resp_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.7129 0.5689 0.4414 0.30413 0.25590 0.003967
## Proportion of Variance 0.4290 0.2732 0.1645 0.07808 0.05528 0.000010
## Cumulative Proportion 0.4290 0.7022 0.8666 0.94471 0.99999 1.000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.3830 0.2322 0.0005789
## Proportion of Variance 0.7311 0.2689 0.0000000
## Cumulative Proportion 0.7311 1.0000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_Con_Ali; - by-
StimGroup:Ori_Con_Ali_CA.
file_E2_resp_etd4 <- file.path(dir_lmm, "lmm_E2_resp_etd4.rds")
# fit the reduced model
if (!file.exists(file_E2_resp_etd4)) {
glmm_E2_resp_etd4 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(CA_C + # Con_C + Ali_C + Ori_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_CA | SubjCode # + Ori_Ali_CA + + Con_Ali_CA + Ori_Con_Ali
) + # Ori_Con_Ali_CA
(0 + # Ali_C + Ori_C + CA_C + Con_C +
Con_CA + # Ori_Ali + Con_Ali + Ori_Con + Ali_CA + Ori_CA +
Con_Ali_CA | StimGroup # Ori_Con_CA + Ori_Ali_CA + Ori_Con_Ali +
), # Ori_Con_Ali_CA
data = df_E2_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E2_resp_etd4, file = file_E2_resp_etd4)
} else {
glmm_E2_resp_etd4 <- readRDS(file_E2_resp_etd4)
}
print(summary(glmm_E2_resp_etd4), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (CA_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + Con_CA + Con_Ali_CA | StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24654.8 24940.0 -12292.4 24584.8 25558
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.1589 -0.5663 0.2062 0.5279 6.7438
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07469 0.2733
## CA_C 0.22552 0.4749 -0.25
## Ori_CA 0.28794 0.5366 0.13 -0.32
## Con_CA 0.24538 0.4954 0.18 0.00 -0.14
## Ori_Con_CA 0.30144 0.5490 0.03 0.18 -0.46 -0.48
## StimGroup Con_CA 0.14633 0.3825
## Con_Ali_CA 0.03129 0.1769 -0.05
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16756 0.04434 3.779 0.000158 ***
## Orientation2-1 0.14212 0.01961 7.249 4.20e-13 ***
## Congruency2-1 0.01727 0.01957 0.883 0.377388
## Alignment2-1 0.18074 0.01907 9.479 < 2e-16 ***
## CA2-1 -1.71177 0.07781 -22.000 < 2e-16 ***
## Cue2-1 0.01584 0.01847 0.858 0.390921
## Orientation2-1:Congruency2-1 0.10204 0.03908 2.611 0.009034 **
## Orientation2-1:Alignment2-1 -0.09708 0.03813 -2.546 0.010902 *
## Congruency2-1:Alignment2-1 0.12105 0.03813 3.175 0.001501 **
## Orientation2-1:CA2-1 0.80695 0.09378 8.605 < 2e-16 ***
## Congruency2-1:CA2-1 1.01398 0.14955 6.780 1.20e-11 ***
## Alignment2-1:CA2-1 -0.01781 0.03819 -0.466 0.640886
## Orientation2-1:Congruency2-1:Alignment2-1 -0.13768 0.07626 -1.805 0.071017 .
## Orientation2-1:Congruency2-1:CA2-1 -0.92989 0.11786 -7.890 3.03e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.20020 0.07625 2.625 0.008653 **
## Congruency2-1:Alignment2-1:CA2-1 -0.43929 0.09458 -4.645 3.40e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.58678 0.15253 3.847 0.000120 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.1.1.5 The optimal model
anova(glmm_E2_resp_rdc, glmm_E2_resp_etd4, refit=FALSE)According to BIC, the reduced model is used as the optimal model.
glmm_E2_resp_opt <- glmm_E2_resp_rdc
print(summary(glmm_E2_resp_opt), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E2_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 24297.5 24615.4 -12109.8 24219.5 25554
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.3764 -0.5479 0.1944 0.5237 8.3235
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.078661 0.28047
## SubjCode.1 Ori_C 0.048485 0.22019
## SubjCode.2 Con_C 0.008082 0.08990
## SubjCode.3 Ali_C 0.028147 0.16777
## SubjCode.4 CA_C 0.223514 0.47277
## SubjCode.5 Ori_Ali 0.015514 0.12456
## SubjCode.6 Ori_CA 0.289040 0.53762
## SubjCode.7 Con_CA 0.260946 0.51083
## SubjCode.8 Ori_Con_Ali 0.046578 0.21582
## SubjCode.9 Ori_Con_CA 0.266491 0.51623
## SubjCode.10 Con_Ali_CA 0.032721 0.18089
## StimGroup (Intercept) 0.008721 0.09339
## StimGroup.1 Ori_C 0.019992 0.14139
## StimGroup.2 Con_C 0.046057 0.21461
## StimGroup.3 CA_C 0.021446 0.14645
## StimGroup.4 Ori_Con 0.014106 0.11877
## StimGroup.5 Ori_CA 0.025244 0.15888
## StimGroup.6 Con_CA 0.133154 0.36490
## StimGroup.7 Ali_CA 0.002774 0.05267
## StimGroup.8 Ori_Con_Ali 0.004614 0.06793
## StimGroup.9 Con_Ali_CA 0.028261 0.16811
## StimGroup.10 Ori_Con_Ali_CA 0.001513 0.03890
## Number of obs: 25593, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.16069 0.05424 2.963 0.00305 **
## Orientation2-1 0.14950 0.06015 2.486 0.01293 *
## Congruency2-1 0.03122 0.07216 0.433 0.66531
## Alignment2-1 0.18948 0.03295 5.750 8.93e-09 ***
## CA2-1 -1.75077 0.09037 -19.374 < 2e-16 ***
## Cue2-1 0.01576 0.01870 0.843 0.39929
## Orientation2-1:Congruency2-1 0.09969 0.05453 1.828 0.06751 .
## Orientation2-1:Alignment2-1 -0.09994 0.04363 -2.291 0.02198 *
## Congruency2-1:Alignment2-1 0.11985 0.03891 3.080 0.00207 **
## Orientation2-1:CA2-1 0.82052 0.10678 7.684 1.54e-14 ***
## Congruency2-1:CA2-1 1.03147 0.14643 7.044 1.86e-12 ***
## Alignment2-1:CA2-1 -0.03505 0.04261 -0.823 0.41077
## Orientation2-1:Congruency2-1:Alignment2-1 -0.13594 0.08755 -1.553 0.12049
## Orientation2-1:Congruency2-1:CA2-1 -0.91272 0.11381 -8.020 1.06e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.21328 0.07784 2.740 0.00615 **
## Congruency2-1:Alignment2-1:CA2-1 -0.44141 0.09834 -4.489 7.17e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.59115 0.15590 3.792 0.00015 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.1.2 Estimated marginal means
# hit and false alarm
emm_E2_probit <- emmeans(glmm_E2_resp_opt,
~ Orientation + Congruency + Alignment + CA,
type = "response")
emm_E2_probit## Orientation Congruency Alignment CA prob SE df asymp.LCL asymp.UCL
## upr con ali sam 0.9372 0.0133 Inf 0.9066 0.959
## inv con ali sam 0.8385 0.0252 Inf 0.7842 0.883
## upr inc ali sam 0.6883 0.0359 Inf 0.6149 0.755
## inv inc ali sam 0.7641 0.0314 Inf 0.6984 0.821
## upr con mis sam 0.9411 0.0127 Inf 0.9117 0.962
## inv con mis sam 0.8484 0.0243 Inf 0.7958 0.891
## upr inc mis sam 0.8598 0.0232 Inf 0.8094 0.900
## inv inc mis sam 0.8121 0.0277 Inf 0.7531 0.861
## upr con ali dif 0.0685 0.0142 Inf 0.0449 0.101
## inv con ali dif 0.2382 0.0316 Inf 0.1809 0.304
## upr inc ali dif 0.2511 0.0324 Inf 0.1920 0.319
## inv inc ali dif 0.3696 0.0382 Inf 0.2977 0.446
## upr con mis dif 0.1098 0.0197 Inf 0.0759 0.153
## inv con mis dif 0.2994 0.0353 Inf 0.2341 0.372
## upr inc mis dif 0.2767 0.0341 Inf 0.2141 0.347
## inv inc mis dif 0.4332 0.0397 Inf 0.3572 0.512
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Intervals are back-transformed from the probit scaleSensitivity d’ in each condition:
# sensitivity d'
emm_E2_d <- contrast(emm_E2_probit,
"pairwise",
simple="CA",
infer=c(TRUE,FALSE),
adjust="none")
# emmip(emm_E2_d, Congruency ~ Alignment | Orientation, CIs = TRUE)
emm_E2_d[1:8]## contrast Orientation Congruency Alignment estimate SE df asymp.LCL asymp.UCL
## sam - dif upr con ali 3.02 0.144 Inf 2.736 3.30
## sam - dif inv con ali 1.70 0.137 Inf 1.433 1.97
## sam - dif upr inc ali 1.16 0.135 Inf 0.897 1.43
## sam - dif inv inc ali 1.05 0.135 Inf 0.788 1.32
## sam - dif upr con mis 2.79 0.143 Inf 2.512 3.07
## sam - dif inv con mis 1.56 0.137 Inf 1.288 1.82
## sam - dif upr inc mis 1.67 0.137 Inf 1.404 1.94
## sam - dif inv inc mis 1.05 0.135 Inf 0.788 1.32
##
## Results are averaged over the levels of: Cue
## Note: contrasts are still on the probit scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## Confidence level used: 0.955.1.2.1 Composite effect
Composite effect of d’:
emm_E2_cf_d <- contrast(emm_E2_d,
interaction="pairwise",
simple = c("Congruency", "Alignment"),
by = "Orientation",
infer = TRUE)
summary(emm_E2_cf_d[1:2], side=">")## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.737 0.133 Inf 0.5189 Inf 5.560 <.0001
## con - inc ali - mis sam - dif inv 0.146 0.118 Inf -0.0482 Inf 1.236 0.1082
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedCongruency effect of d’ in aligned condition:
emm_E2_con_d <- contrast(emm_E2_d,
interaction="pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E2_con_d[c(1,2)], side=">")## Congruency_pairwise contrast Orientation Alignment estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc sam - dif upr ali 1.856 0.172 Inf 1.573 Inf 10.779 <.0001
## con - inc sam - dif inv ali 0.648 0.166 Inf 0.375 Inf 3.906 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedplot_E2_cf_d <- emm_E2_d %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, estimate, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(0,3.5)) + # set the limit for y axis c(0, 1100)
labs(y = expression("Sensitivity"~italic("d'")), fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("***", "", "", "", "", "", "", ""),
color = "red",
size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "ccf_d.pdf", plot_ccf_d, width = 8, height = 6)
plot_E2_cf_d
5.1.2.2 Compare composite effects between upright and inverted faces
contrast(emm_E2_d,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.591 0.156 Inf 0.286 0.897 3.792 0.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.955.1.2.3 Facilitation and interference
# facilitation and interference of d'
emm_E2_d_fi <- contrast(emm_E2_d,
"pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E2_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E2_d_fi[1:4]## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.22680 0.0997 Inf 0.0314 0.422 2.275 0.0229
## ali - mis sam - dif inv con 0.14451 0.0818 Inf -0.0158 0.305 1.767 0.0773
## ali - mis sam - dif upr inc -0.51018 0.0803 Inf -0.6675 -0.353 -6.356 <.0001
## ali - mis sam - dif inv inc -0.00132 0.0774 Inf -0.1531 0.150 -0.017 0.9864
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95summary(emm_E2_d_fi[1:2], side=">")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.227 0.0997 Inf 0.06281 Inf 2.275 0.0115
## ali - mis sam - dif inv con 0.145 0.0818 Inf 0.00997 Inf 1.767 0.0386
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedsummary(emm_E2_d_fi[3:4], side="<")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.51018 0.0803 Inf -Inf -0.378 -6.356 <.0001
## ali - mis sam - dif inv inc -0.00132 0.0774 Inf -Inf 0.126 -0.017 0.4932
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are left-tailedplot_E2_cffi_d <- summary(emm_E2_d_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=1.1, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_d) + # set the limit for y axis c(0, 1100)
labs(x = NULL,
y = expression("Sensitivity"~italic(d)*"' (aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E2_fi_d.pdf", plot_E2_cffi_d, width = 7, height = 4.55)
plot_E2_cffi_d
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E2_d_fi_ui <- contrast(emm_E2_d,
interaction = "pairwise",
simple = c("Orientation", "Alignment"),
infer = TRUE,
adjust = "none")
emm_E2_d_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.0823 0.119 Inf -0.151 0.316 0.690 0.4902
## upr - inv ali - mis sam - dif inc -0.5089 0.100 Inf -0.705 -0.312 -5.077 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.955.1.2.4 Comparisons between facilitation and interference
contrast(emm_E2_d_fi,
method = list("faci-inte"=c(1, 1)),
simple = "Congruency",
infer = TRUE)[1:2]## contrast2 contrast1 contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## faci-inte ali - mis sam - dif upr -0.283 0.123 Inf -0.5250 -0.0418 -2.299 0.0215
## faci-inte ali - mis sam - dif inv 0.143 0.107 Inf -0.0665 0.3529 1.338 0.1808
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.955.1.3 Plot
plot_E2_cf_d_ <- plot_E2_cf_d +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E2_d <- ggarrange(plot_E2_cf_d_, plot_E2_cffi_d,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E2_d
5.2 Correct response times
5.2.1 Fitting the linear mixed-effects models
5.2.1.1 The maximal model
# file_E2_rt_max <- file.path(dir_lmm, "lmm_E2_rt_max.rds")
#
# # fit the max model
# if (!file.exists(file_E2_rt_max)) {
# lmm_E2_rt_max <- lmer(
# log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
# (Orientation * Congruency * Alignment | SubjCode) +
# (Orientation * Congruency * Alignment | StimGroup),
# data = df_E2_lmm |> dplyr::filter(isCorrect==1),
# control = lmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(lmm_E2_rt_max, file = file_E2_rt_max)
# } else {
# lmm_E2_rt_max <- readRDS(file_E2_rt_max)
# }
#
# print(summary(lmm_E2_rt_max), corr = FALSE)5.2.1.2 The zero-correlation-parameter model
file_E2_rt_zcp <- file.path(dir_lmm, "lmm_E2_rt_zcp.rds")
# fit the zcp model
if (!file.exists(file_E2_rt_zcp)) {
lmm_E2_rt_zcp <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || StimGroup),
data = df_E2_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E2_rt_zcp, file = file_E2_rt_zcp)
} else {
lmm_E2_rt_zcp <- readRDS(file_E2_rt_zcp)
}
print(summary(lmm_E2_rt_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17838.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5627 -0.6463 -0.1684 0.4609 6.8346
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.189e-02 0.204670
## SubjCode.1 Ori_C 7.936e-02 0.281710
## SubjCode.2 Con_C 3.628e-04 0.019049
## SubjCode.3 Ali_C 6.429e-04 0.025356
## SubjCode.4 Ori_Con 2.163e-03 0.046504
## SubjCode.5 Ori_Ali 1.170e-03 0.034210
## SubjCode.6 Con_Ali 1.557e-03 0.039464
## SubjCode.7 Ori_Con_Ali 8.511e-03 0.092254
## StimGroup (Intercept) 0.000e+00 0.000000
## StimGroup.1 Ori_C 9.803e-05 0.009901
## StimGroup.2 Con_C 6.094e-04 0.024687
## StimGroup.3 Ali_C 0.000e+00 0.000000
## StimGroup.4 Ori_Con 1.577e-04 0.012559
## StimGroup.5 Ori_Ali 0.000e+00 0.000000
## StimGroup.6 Con_Ali 9.796e-05 0.009898
## StimGroup.7 Ori_Con_Ali 0.000e+00 0.000000
## Residual 1.412e-01 0.375754
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.790e+00 3.247e-02 3.897e+01 209.070 < 2e-16 ***
## Orientation2-1 7.759e-02 4.498e-02 3.934e+01 1.725 0.092392 .
## Congruency2-1 4.653e-02 9.965e-03 1.045e+01 4.669 0.000783 ***
## Alignment2-1 -1.283e-02 6.732e-03 3.763e+01 -1.906 0.064345 .
## Cue2-1 2.209e-02 5.371e-03 1.941e+04 4.113 3.92e-05 ***
## CA2-1 5.342e-02 5.419e-03 1.945e+04 9.857 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.636e-02 1.368e-02 1.285e+01 -3.389 0.004912 **
## Orientation2-1:Alignment2-1 -8.647e-03 1.208e-02 3.774e+01 -0.716 0.478643
## Congruency2-1:Alignment2-1 -5.131e-02 1.287e-02 1.131e+01 -3.987 0.002021 **
## Orientation2-1:Congruency2-1:Alignment2-1 4.581e-02 2.608e-02 4.012e+01 1.757 0.086561 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')5.2.1.3 The reduced model
summary(rePCA(lmm_E2_rt_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7497 0.5447 0.24552 0.12376 0.10503 0.09104 0.06748 0.05069
## Proportion of Variance 0.5850 0.3088 0.06274 0.01594 0.01148 0.00863 0.00474 0.00267
## Cumulative Proportion 0.5850 0.8938 0.95654 0.97248 0.98396 0.99259 0.99733 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.0657 0.03342 0.02635 0.02634 0 0 0 0
## Proportion of Variance 0.6327 0.16377 0.10178 0.10171 0 0 0 0
## Cumulative Proportion 0.6327 0.79651 0.89829 1.00000 1 1 1 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
StimGroup:(Intercept),Ali_C,Ori_Ali, andOri_Con_Ali.
file_E2_rt_rdc <- file.path(dir_lmm, "lmm_E2_rt_rdc.rds")
# fit the max model
if (!file.exists(file_E2_rt_rdc)) {
lmm_E2_rt_rdc <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(0 + Ori_C + Con_C + # Ali_C +
Ori_Con + Con_Ali || StimGroup # Ori_Ali +
), # Ori_Con_Ali
data = df_E2_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E2_rt_rdc, file = file_E2_rt_rdc)
} else {
lmm_E2_rt_rdc <- readRDS(file_E2_rt_rdc)
}
print(summary(lmm_E2_rt_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (0 + Ori_C + Con_C + Ori_Con + Con_Ali || StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17838.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5628 -0.6463 -0.1684 0.4609 6.8346
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.188e-02 0.204657
## SubjCode.1 Ori_C 7.932e-02 0.281646
## SubjCode.2 Con_C 3.628e-04 0.019046
## SubjCode.3 Ali_C 6.431e-04 0.025359
## SubjCode.4 Ori_Con 2.162e-03 0.046501
## SubjCode.5 Ori_Ali 1.172e-03 0.034230
## SubjCode.6 Con_Ali 1.558e-03 0.039469
## SubjCode.7 Ori_Con_Ali 8.512e-03 0.092263
## StimGroup Ori_C 9.801e-05 0.009900
## StimGroup.1 Con_C 6.095e-04 0.024688
## StimGroup.2 Ori_Con 1.573e-04 0.012542
## StimGroup.3 Con_Ali 9.871e-05 0.009935
## Residual 1.412e-01 0.375754
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.790e+00 3.247e-02 3.898e+01 209.084 < 2e-16 ***
## Orientation2-1 7.759e-02 4.497e-02 3.936e+01 1.725 0.092317 .
## Congruency2-1 4.653e-02 9.965e-03 1.045e+01 4.669 0.000783 ***
## Alignment2-1 -1.283e-02 6.732e-03 3.763e+01 -1.906 0.064359 .
## Cue2-1 2.209e-02 5.371e-03 1.941e+04 4.113 3.92e-05 ***
## CA2-1 5.342e-02 5.419e-03 1.945e+04 9.857 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.636e-02 1.368e-02 1.285e+01 -3.390 0.004911 **
## Orientation2-1:Alignment2-1 -8.647e-03 1.209e-02 3.775e+01 -0.716 0.478672
## Congruency2-1:Alignment2-1 -5.131e-02 1.287e-02 1.133e+01 -3.986 0.002017 **
## Orientation2-1:Congruency2-1:Alignment2-1 4.581e-02 2.608e-02 4.012e+01 1.757 0.086574 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2.1.4 The extended model
file_E2_rt_etd <- file.path(dir_lmm, "lmm_E2_rt_etd.rds")
# fit the max model
if (!file.exists(file_E2_rt_etd)) {
lmm_E2_rt_etd <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali | SubjCode) +
(0 + Ori_C + Con_C + # Ali_C +
Ori_Con + Con_Ali | StimGroup # Ori_Ali +
), # Ori_Con_Ali
data = df_E2_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E2_rt_etd, file = file_E2_rt_etd)
} else {
lmm_E2_rt_etd <- readRDS(file_E2_rt_etd)
}
print(summary(lmm_E2_rt_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali | SubjCode) + (0 + Ori_C + Con_C + Ori_Con + Con_Ali | StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17776.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6530 -0.6462 -0.1676 0.4622 6.7894
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0419584 0.20484
## Ori_C 0.0792874 0.28158 0.01
## Con_C 0.0007319 0.02705 0.11 0.10
## Ali_C 0.0007451 0.02730 -0.20 -0.15 0.72
## Ori_Con 0.0037017 0.06084 -0.62 -0.01 -0.75 -0.53
## Ori_Ali 0.0020021 0.04474 -0.33 0.22 -0.03 -0.24 0.06
## Con_Ali 0.0031765 0.05636 0.65 -0.38 -0.45 -0.19 -0.19 -0.38
## Ori_Con_Ali 0.0112122 0.10589 0.06 0.03 0.22 0.44 -0.55 0.46 0.22
## StimGroup Ori_C 0.0001343 0.01159
## Con_C 0.0006292 0.02508 -0.59
## Ori_Con 0.0007519 0.02742 0.33 -0.96
## Con_Ali 0.0004054 0.02013 0.62 -1.00 0.95
## Residual 0.1407351 0.37515
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.790e+00 3.250e-02 3.896e+01 208.908 < 2e-16 ***
## Orientation2-1 7.702e-02 4.500e-02 3.946e+01 1.712 0.094826 .
## Congruency2-1 4.685e-02 1.051e-02 1.296e+01 4.459 0.000648 ***
## Alignment2-1 -1.309e-02 6.908e-03 4.491e+01 -1.894 0.064623 .
## Cue2-1 2.191e-02 5.363e-03 1.948e+04 4.085 4.42e-05 ***
## CA2-1 5.324e-02 5.410e-03 1.949e+04 9.842 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.720e-02 1.687e-02 2.205e+01 -2.799 0.010443 *
## Orientation2-1:Alignment2-1 -1.040e-02 1.290e-02 4.478e+01 -0.806 0.424368
## Congruency2-1:Alignment2-1 -4.905e-02 1.537e-02 2.483e+01 -3.191 0.003822 **
## Orientation2-1:Congruency2-1:Alignment2-1 4.560e-02 2.731e-02 4.570e+01 1.669 0.101887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E2_rt_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7535 0.5666 0.30291 0.14978 0.10872 0.0001698 2.632e-05 3.442e-07
## Proportion of Variance 0.5595 0.3164 0.09042 0.02211 0.01165 0.0000000 0.000e+00 0.000e+00
## Cumulative Proportion 0.5595 0.8758 0.96625 0.98835 1.00000 1.0000000 1.000e+00 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1124 0.03169 3.428e-05 1.553e-23
## Proportion of Variance 0.9264 0.07357 0.000e+00 0.000e+00
## Cumulative Proportion 0.9264 1.00000 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ali_C, andOri_Ali; - by-
StimGroup:Ori_CandCon_Ali.
file_E2_rt_etd2 <- file.path(dir_lmm, "lmm_E2_rt_etd2.rds")
# fit the max model
if (!file.exists(file_E2_rt_etd2)) {
lmm_E2_rt_etd2 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + # Con_C + Ali_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali | SubjCode) +
(0 + Con_C + # Ali_C + Ori_C +
Ori_Con | StimGroup # Ori_Ali + + Con_Ali
), # Ori_Con_Ali
data = df_E2_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E2_rt_etd2, file = file_E2_rt_etd2)
} else {
lmm_E2_rt_etd2 <- readRDS(file_E2_rt_etd2)
}
print(summary(lmm_E2_rt_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ori_Con + Con_Ali + Ori_Con_Ali | SubjCode) + (0 + Con_C + Ori_Con | StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17813.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5839 -0.6473 -0.1672 0.4650 6.8219
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0418521 0.20458
## Ori_C 0.0792220 0.28146 0.01
## Ori_Con 0.0030810 0.05551 -0.67 -0.03
## Con_Ali 0.0024920 0.04992 0.72 -0.40 -0.35
## Ori_Con_Ali 0.0106134 0.10302 0.08 0.00 -0.65 0.23
## StimGroup Con_C 0.0005985 0.02446
## Ori_Con 0.0007224 0.02688 -1.00
## Residual 0.1413554 0.37597
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.789e+00 3.246e-02 3.897e+01 209.159 < 2e-16 ***
## Orientation2-1 7.767e-02 4.483e-02 3.903e+01 1.732 0.091108 .
## Congruency2-1 4.626e-02 9.439e-03 9.407e+00 4.901 0.000744 ***
## Alignment2-1 -1.341e-02 5.400e-03 1.954e+04 -2.484 0.013006 *
## Cue2-1 2.220e-02 5.373e-03 1.952e+04 4.133 3.6e-05 ***
## CA2-1 5.309e-02 5.418e-03 1.952e+04 9.799 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.601e-02 1.632e-02 2.053e+01 -2.819 0.010426 *
## Orientation2-1:Alignment2-1 -8.227e-03 1.079e-02 1.954e+04 -0.762 0.445953
## Congruency2-1:Alignment2-1 -5.143e-02 1.338e-02 4.335e+01 -3.844 0.000391 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.721e-02 2.706e-02 4.468e+01 1.745 0.087937 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E2_rt_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 0.7506 0.5628 0.28651 0.09411 3.385e-05
## Proportion of Variance 0.5802 0.3262 0.08454 0.00912 0.000e+00
## Cumulative Proportion 0.5802 0.9063 0.99088 1.00000 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2]
## Standard deviation 0.09667 0
## Proportion of Variance 1.00000 0
## Cumulative Proportion 1.00000 1Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_ConandCon_Ali; - by-
StimGroup:Con_C.
file_E2_rt_etd3 <- file.path(dir_lmm, "lmm_E2_rt_etd3.rds")
# fit the max model
if (!file.exists(file_E2_rt_etd3)) {
lmm_E2_rt_etd3 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + # Con_C + Ali_C +
# Ori_Ali + Ori_Con + Con_Ali +
Ori_Con_Ali | SubjCode) +
(0 + # Ali_C + Ori_C + Con_C +
Ori_Con | StimGroup # Ori_Ali + + Con_Ali
), # Ori_Con_Ali
data = df_E2_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E2_rt_etd3, file = file_E2_rt_etd3)
} else {
lmm_E2_rt_etd3 <- readRDS(file_E2_rt_etd3)
}
print(summary(lmm_E2_rt_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ori_Con_Ali | SubjCode) + (0 + Ori_Con | StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17859.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5165 -0.6468 -0.1687 0.4646 6.8509
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0418598 0.20460
## Ori_C 0.0792968 0.28160 0.02
## Ori_Con_Ali 0.0085761 0.09261 0.00 0.05
## StimGroup Ori_Con 0.0003714 0.01927
## Residual 0.1418814 0.37667
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.789e+00 3.246e-02 3.898e+01 209.133 < 2e-16 ***
## Orientation2-1 7.773e-02 4.486e-02 3.903e+01 1.733 0.09099 .
## Congruency2-1 4.676e-02 5.415e-03 1.955e+04 8.636 < 2e-16 ***
## Alignment2-1 -1.348e-02 5.408e-03 1.956e+04 -2.492 0.01271 *
## Cue2-1 2.205e-02 5.379e-03 1.955e+04 4.099 4.16e-05 ***
## CA2-1 5.295e-02 5.425e-03 1.956e+04 9.761 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.578e-02 1.243e-02 9.177e+00 -3.684 0.00488 **
## Orientation2-1:Alignment2-1 -8.088e-03 1.081e-02 1.958e+04 -0.748 0.45449
## Congruency2-1:Alignment2-1 -5.287e-02 1.081e-02 1.958e+04 -4.889 1.02e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.615e-02 2.613e-02 4.013e+01 1.766 0.08496 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2.1.5 The optimal model
anova(lmm_E2_rt_rdc, lmm_E2_rt_etd3, refit=FALSE)According to BIC, the extended model is used as the optimal model.
lmm_E2_rt_opt <- lmm_E2_rt_etd3
print(summary(lmm_E2_rt_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ori_Con_Ali | SubjCode) + (0 + Ori_Con | StimGroup)
## Data: dplyr::filter(df_E2_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 17859.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5165 -0.6468 -0.1687 0.4646 6.8509
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0418598 0.20460
## Ori_C 0.0792968 0.28160 0.02
## Ori_Con_Ali 0.0085761 0.09261 0.00 0.05
## StimGroup Ori_Con 0.0003714 0.01927
## Residual 0.1418814 0.37667
## Number of obs: 19672, groups: SubjCode, 40; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.789e+00 3.246e-02 3.898e+01 209.133 < 2e-16 ***
## Orientation2-1 7.773e-02 4.486e-02 3.903e+01 1.733 0.09099 .
## Congruency2-1 4.676e-02 5.415e-03 1.955e+04 8.636 < 2e-16 ***
## Alignment2-1 -1.348e-02 5.408e-03 1.956e+04 -2.492 0.01271 *
## Cue2-1 2.205e-02 5.379e-03 1.955e+04 4.099 4.16e-05 ***
## CA2-1 5.295e-02 5.425e-03 1.956e+04 9.761 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.578e-02 1.243e-02 9.177e+00 -3.684 0.00488 **
## Orientation2-1:Alignment2-1 -8.088e-03 1.081e-02 1.958e+04 -0.748 0.45449
## Congruency2-1:Alignment2-1 -5.287e-02 1.081e-02 1.958e+04 -4.889 1.02e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.615e-02 2.613e-02 4.013e+01 1.766 0.08496 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 15.2.2 Estimated marginal means
emm_E2_rt <- emmeans(lmm_E2_rt_opt,
~ Orientation + Congruency + Alignment,
pbkrtest.limit = 2e5,
lmerTest.limit = 2e5)
# emmip(regrid(emm_E2_rt), Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E2_rt, type = "response") # equivalent to regrid(emm_E2_rt)## Orientation Congruency Alignment response SE df lower.CL upper.CL
## upr con ali 813 32.3 40.5 751 881
## inv con ali 914 36.9 40.8 842 991
## upr inc ali 906 36.0 41.3 836 982
## inv inc ali 950 38.4 41.1 875 1031
## upr con mis 837 33.1 40.5 773 907
## inv con mis 911 36.8 40.9 840 989
## upr inc mis 864 34.4 41.1 797 936
## inv inc mis 920 37.3 41.2 847 998
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale5.2.2.1 Composite effect
Composite effect of rt:
emm_E2_rt_cf <- contrast(regrid(emm_E2_rt),
interaction = "pairwise",
by = "Orientation",
infer = TRUE)
summary(emm_E2_rt_cf[1:2], side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -65.5 14.4 40.5 -Inf -41.227 -4.543 <.0001
## con - inc ali - mis inv -27.9 16.1 40.8 -Inf -0.872 -1.737 0.0449
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedemm_E2_rt_con <- contrast(regrid(emm_E2_rt),
interaction = "pairwise",
by = c("Orientation", "Alignment"),
infer = TRUE)
summary(emm_E2_rt_con[1:2], side="<")## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -92.4 10.7 40.5 -Inf -74.5 -8.668 <.0001
## con - inc inv ali -36.1 11.3 40.8 -Inf -17.1 -3.193 0.0014
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedplot_E2_cf_rt <- summary(emm_E2_rt, type = "response") %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(y = response, x = Alignment, color = Congruency, group = Congruency)) +
geom_point(position = position_dodge(width = 0.1), size = 2) +
geom_line(aes(linetype = Congruency), position = position_dodge(width = 0.1),
linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, position = position_dodge(width = 0.1),
show.legend = F) +
facet_grid(. ~Orientation, switch = "both") +
coord_cartesian(ylim = ylimit_cf_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = "Correct response times (ms)", fill = "Congruency") + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "*", "", "", "", ""), color = sig_color, size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "E2_cf_rt.pdf", plot_E2_cf_rt, width = 8, height = 4.8)
plot_E2_cf_rt
5.2.2.2 Compare composite effects between upright and inverted faces
contrast(regrid(emm_E2_rt),
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -37.6 23.4 40.5 -84.9 9.67 -1.607 0.1159
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.955.2.2.3 Facilitation and interference
emm_E2_rt_fi <- contrast(regrid(emm_E2_rt),
"pairwise",
by = c("Orientation", "Congruency"),
infer = TRUE,
adjust = "none")
# emmip(emm_E2_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E2_rt_fi[1:4]## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -23.55 8.78 40.5 -41.28 -5.82 -2.684 0.0105
## ali - mis inv con 2.39 10.39 40.8 -18.60 23.39 0.230 0.8189
## ali - mis upr inc 41.95 10.43 41.1 20.89 63.01 4.022 0.0002
## ali - mis inv inc 30.30 11.28 41.1 7.51 53.08 2.685 0.0104
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95summary(emm_E2_rt_fi[1:2], side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -23.55 8.78 40.5 -Inf -8.78 -2.684 0.0052
## ali - mis inv con 2.39 10.39 40.8 -Inf 19.89 0.230 0.5905
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedsummary(emm_E2_rt_fi[3:4], side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 41.9 10.4 41.1 24.4 Inf 4.022 0.0001
## ali - mis inv inc 30.3 11.3 41.1 11.3 Inf 2.685 0.0052
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are right-tailedplot_E2_cffi_rt <- summary(emm_E2_rt_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=100, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = expression(RT~"(aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E2_fi_rt.pdf", plot_E2_cffi_rt, width = 7, height = 4.55)
plot_E2_cffi_rt
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E2_rt_fi_ui <- contrast(emm_E2_rt,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E2_rt_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -0.0312 0.0164 99.4 -0.0637 0.00135 -1.902 0.0601
## upr - inv ali - mis inc 0.0150 0.0175 128.4 -0.0197 0.04964 0.856 0.3937
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: satterthwaite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.955.2.2.4 Comparisons between facilitation and interference
contrast(emm_E2_rt_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2]## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 18.4 12.8 40.5 -7.45 44.2 1.438 0.1582
## faci-inte inv 32.7 14.6 40.8 3.23 62.2 2.241 0.0305
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.955.2.3 Plot
plot_E2_cf_rt_ <- plot_E2_cf_rt +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E2_rt <- ggarrange(plot_E2_cf_rt_, plot_E2_cffi_rt,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E2_rt
5.3 Plot
plot_E2 <- ggarrange(plot_E2_cf_d_, plot_E2_cffi_d,
plot_E2_cf_rt_, plot_E2_cffi_rt,
labels = c("A", "B", "C", "D"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 2,
ncol = 2)
plot_E2
6 Experiment 1 and 2
6.1 Behavioral choices (d’)
6.1.1 Fitting the generalized linear mixed-effects models
6.1.1.1 The maximal model
# file_E12_resp_max <- file.path(dir_lmm, "lmm_E12_resp_max.rds")
#
# # fit the max model
# if (!file.exists(file_E12_resp_max)) {
# glmm_E12_resp_max <- glmer(
# isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
# (Orientation * Congruency * Alignment * CA | SubjCode) +
# (Orientation * Congruency * Alignment * CA | StimGroup),
# data = df_E12_lmm,
# family = binomial(link = "probit"),
# control = glmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(glmm_E12_resp_max, file = file_E12_resp_max)
# } else {
# glmm_E12_resp_max <- readRDS(file_E12_resp_max)
# }
#
# print(summary(glmm_E12_resp_max), corr = FALSE)6.1.1.2 The zero-correlation-parameter model
file_E12_resp_zcp <- file.path(dir_lmm, "lmm_E12_resp_zcp.rds")
# fit the max model
if (!file.exists(file_E12_resp_zcp)) {
glmm_E12_resp_zcp <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || SubjCode) +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || StimGroup),
data = df_E12_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E12_resp_zcp, file = file_E12_resp_zcp)
} else {
glmm_E12_resp_zcp <- readRDS(file_E12_resp_zcp)
}
print(summary(glmm_E12_resp_zcp), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || SubjCode) + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49187.5 49629.6 -24543.8 49087.5 51100
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.8722 -0.5714 0.2138 0.5290 6.9872
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 8.139e-02 2.853e-01
## SubjCode.1 Ori_C 4.915e-02 2.217e-01
## SubjCode.2 Con_C 3.439e-03 5.864e-02
## SubjCode.3 Ali_C 2.793e-02 1.671e-01
## SubjCode.4 CA_C 2.464e-01 4.964e-01
## SubjCode.5 Ori_Con 2.270e-03 4.764e-02
## SubjCode.6 Ori_Ali 2.288e-02 1.513e-01
## SubjCode.7 Con_Ali 1.073e-10 1.036e-05
## SubjCode.8 Ori_CA 1.821e-01 4.267e-01
## SubjCode.9 Con_CA 2.329e-01 4.826e-01
## SubjCode.10 Ali_CA 5.998e-10 2.449e-05
## SubjCode.11 Ori_Con_Ali 2.754e-09 5.248e-05
## SubjCode.12 Ori_Con_CA 1.368e-01 3.699e-01
## SubjCode.13 Ori_Ali_CA 1.582e-09 3.978e-05
## SubjCode.14 Con_Ali_CA 1.765e-08 1.329e-04
## SubjCode.15 Ori_Con_Ali_CA 3.301e-08 1.817e-04
## StimGroup (Intercept) 6.998e-03 8.366e-02
## StimGroup.1 Ori_C 1.755e-02 1.325e-01
## StimGroup.2 Con_C 3.764e-02 1.940e-01
## StimGroup.3 Ali_C 0.000e+00 0.000e+00
## StimGroup.4 CA_C 1.801e-02 1.342e-01
## StimGroup.5 Ori_Con 1.776e-02 1.333e-01
## StimGroup.6 Ori_Ali 2.817e-08 1.679e-04
## StimGroup.7 Con_Ali 1.209e-09 3.477e-05
## StimGroup.8 Ori_CA 2.171e-02 1.473e-01
## StimGroup.9 Con_CA 1.084e-01 3.292e-01
## StimGroup.10 Ali_CA 8.343e-04 2.888e-02
## StimGroup.11 Ori_Con_Ali 1.282e-09 3.581e-05
## StimGroup.12 Ori_Con_CA 1.229e-08 1.108e-04
## StimGroup.13 Ori_Ali_CA 9.572e-04 3.094e-02
## StimGroup.14 Con_Ali_CA 8.704e-09 9.329e-05
## StimGroup.15 Ori_Con_Ali_CA 1.609e-07 4.011e-04
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21100 0.04205 5.018 5.21e-07 ***
## Orientation2-1 0.08785 0.05066 1.734 0.082926 .
## Congruency2-1 0.02501 0.06325 0.395 0.692580
## Alignment2-1 0.16904 0.02316 7.298 2.93e-13 ***
## CA2-1 -1.69784 0.07134 -23.799 < 2e-16 ***
## Cue2-1 0.01157 0.01312 0.882 0.377837
## VA2-1 0.09670 0.06527 1.481 0.138474
## Orientation2-1:Congruency2-1 0.10027 0.05062 1.981 0.047603 *
## Orientation2-1:Alignment2-1 -0.04610 0.03206 -1.438 0.150496
## Congruency2-1:Alignment2-1 0.10124 0.02712 3.732 0.000190 ***
## Orientation2-1:CA2-1 0.76094 0.07245 10.503 < 2e-16 ***
## Congruency2-1:CA2-1 0.91184 0.12054 7.564 3.89e-14 ***
## Alignment2-1:CA2-1 -0.07199 0.02890 -2.490 0.012759 *
## Orientation2-1:Congruency2-1:Alignment2-1 -0.19070 0.05420 -3.519 0.000434 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.84611 0.06891 -12.278 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.22642 0.05539 4.088 4.35e-05 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.41618 0.05413 -7.689 1.48e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.54948 0.10826 5.076 3.86e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')6.1.1.3 The reduced model
summary(rePCA(glmm_E12_resp_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.4964 0.4826 0.4267 0.3699 0.2853 0.22171 0.16712 0.15126 0.05864 0.04764 0.0001817 0.0001329 5.248e-05 3.978e-05 2.449e-05 1.036e-05
## Proportion of Variance 0.2500 0.2364 0.1848 0.1389 0.0826 0.04989 0.02835 0.02322 0.00349 0.00230 0.0000000 0.0000000 0.000e+00 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 0.2500 0.4865 0.6713 0.8102 0.8928 0.94264 0.97098 0.99421 0.99770 1.00000 1.0000000 1.0000000 1.000e+00 1.000e+00 1.000e+00 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.3292 0.1940 0.14734 0.13419 0.13328 0.13246 0.08366 0.03094 0.02888 0.0004011 0.0001679 0.0001108 9.329e-05 3.581e-05 3.477e-05 0
## Proportion of Variance 0.4715 0.1638 0.09446 0.07835 0.07729 0.07635 0.03045 0.00416 0.00363 0.0000000 0.0000000 0.0000000 0.000e+00 0.000e+00 0.000e+00 0
## Cumulative Proportion 0.4715 0.6353 0.72976 0.80812 0.88541 0.96175 0.99220 0.99637 1.00000 1.0000000 1.0000000 1.0000000 1.000e+00 1.000e+00 1.000e+00 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_Ali,Ori_Con_Ali,Ori_Ali_CA,Con_Ali_CA,Ori_Con_Ali_CA, andAli_CA; - by-
StimGroup:Ali_C,Ori_Ali,Con_Ali,Ori_Con_Ali,Ori_Con_CA,Con_Ali_CA, andOri_Con_Ali_CA.
file_E12_resp_rdc <- file.path(dir_lmm, "lmm_E12_resp_rdc.rds")
# fit the reduced model
if (!file.exists(file_E12_resp_rdc)) {
glmm_E12_resp_rdc <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Con_CA || SubjCode # Ori_Con_Ali + Ori_Ali_CA + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_CA + Con_CA + Ali_CA + # Ori_Ali + Con_Ali +
Ori_Ali_CA || StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E12_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E12_resp_rdc, file = file_E12_resp_rdc)
} else {
glmm_E12_resp_rdc <- readRDS(file_E12_resp_rdc)
}
print(summary(glmm_E12_resp_rdc), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Ali_CA || StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49161.5 49488.7 -24543.8 49087.5 51113
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.8722 -0.5714 0.2138 0.5290 6.9872
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.0813975 0.28530
## SubjCode.1 Ori_C 0.0491501 0.22170
## SubjCode.2 Con_C 0.0034362 0.05862
## SubjCode.3 Ali_C 0.0279300 0.16712
## SubjCode.4 CA_C 0.2464305 0.49642
## SubjCode.5 Ori_Con 0.0022671 0.04761
## SubjCode.6 Ori_Ali 0.0228744 0.15124
## SubjCode.7 Ori_CA 0.1820641 0.42669
## SubjCode.8 Con_CA 0.2329315 0.48263
## SubjCode.9 Ori_Con_CA 0.1368329 0.36991
## StimGroup (Intercept) 0.0069962 0.08364
## StimGroup.1 Ori_C 0.0175483 0.13247
## StimGroup.2 Con_C 0.0376596 0.19406
## StimGroup.3 CA_C 0.0180077 0.13419
## StimGroup.4 Ori_Con 0.0177709 0.13331
## StimGroup.5 Ori_CA 0.0217109 0.14735
## StimGroup.6 Con_CA 0.1083992 0.32924
## StimGroup.7 Ali_CA 0.0008330 0.02886
## StimGroup.8 Ori_Ali_CA 0.0009597 0.03098
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21096 0.04204 5.018 5.21e-07 ***
## Orientation2-1 0.08786 0.05066 1.734 0.082894 .
## Congruency2-1 0.02505 0.06325 0.396 0.692084
## Alignment2-1 0.16908 0.02316 7.300 2.89e-13 ***
## CA2-1 -1.69781 0.07133 -23.804 < 2e-16 ***
## Cue2-1 0.01157 0.01312 0.882 0.377788
## VA2-1 0.09665 0.06526 1.481 0.138571
## Orientation2-1:Congruency2-1 0.10028 0.05062 1.981 0.047580 *
## Orientation2-1:Alignment2-1 -0.04608 0.03206 -1.437 0.150581
## Congruency2-1:Alignment2-1 0.10122 0.02712 3.732 0.000190 ***
## Orientation2-1:CA2-1 0.76087 0.07242 10.506 < 2e-16 ***
## Congruency2-1:CA2-1 0.91178 0.12043 7.571 3.70e-14 ***
## Alignment2-1:CA2-1 -0.07201 0.02890 -2.492 0.012717 *
## Orientation2-1:Congruency2-1:Alignment2-1 -0.19071 0.05419 -3.519 0.000432 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.84615 0.06890 -12.282 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.22645 0.05538 4.089 4.33e-05 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.41612 0.05412 -7.689 1.48e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.54933 0.10815 5.079 3.79e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(glmm_E12_resp_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## Standard deviation 0.4964 0.4826 0.4267 0.3699 0.28530 0.22170 0.16712 0.15124 0.05862 0.04761
## Proportion of Variance 0.2501 0.2364 0.1848 0.1389 0.08261 0.04988 0.02835 0.02322 0.00349 0.00230
## Cumulative Proportion 0.2501 0.4865 0.6713 0.8102 0.89277 0.94265 0.97100 0.99421 0.99770 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Standard deviation 0.3292 0.1941 0.14735 0.13419 0.1333 0.13247 0.08364 0.03098 0.02886
## Proportion of Variance 0.4715 0.1638 0.09444 0.07833 0.0773 0.07633 0.03043 0.00417 0.00362
## Cumulative Proportion 0.4715 0.6353 0.72980 0.80813 0.8854 0.96177 0.99220 0.99638 1.000006.1.1.4 The extended model
file_E12_resp_etd <- file.path(dir_lmm, "lmm_E12_resp_etd.rds")
# fit the reduced model
if (!file.exists(file_E12_resp_etd)) {
glmm_E12_resp_etd <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Ori_CA + Con_CA + # Con_Ali + Ali_CA +
Ori_Con_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Ori_CA + Con_CA + Ali_CA + # Ori_Ali + Con_Ali +
Ori_Ali_CA | StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E12_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E12_resp_etd, file = file_E12_resp_etd)
} else {
glmm_E12_resp_etd <- readRDS(file_E12_resp_etd)
}
print(summary(glmm_E12_resp_etd), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Ali_CA | StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49143.9 50187.4 -24454.0 48907.9 51032
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -10.1167 -0.5734 0.1999 0.5336 7.5829
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.082799 0.28775
## Ori_C 0.048995 0.22135 0.00
## Con_C 0.008251 0.09083 -0.08 -0.28
## Ali_C 0.029213 0.17092 0.41 0.11 -0.15
## CA_C 0.254892 0.50487 -0.24 0.24 -0.53 -0.36
## Ori_Con 0.015598 0.12489 0.18 -0.26 -0.60 0.29 -0.13
## Ori_Ali 0.026440 0.16260 -0.40 0.04 0.43 -0.34 0.16 -0.26
## Ori_CA 0.201202 0.44856 0.14 0.04 -0.26 0.12 -0.25 0.49 -0.05
## Con_CA 0.262939 0.51278 -0.01 0.16 -0.66 0.29 -0.03 0.74 -0.35 0.05
## Ori_Con_CA 0.198518 0.44555 0.09 -0.11 0.70 -0.31 0.01 -0.66 0.44 -0.47 -0.73
## StimGroup (Intercept) 0.006623 0.08138
## Ori_C 0.016775 0.12952 0.06
## Con_C 0.038042 0.19504 -0.19 -0.75
## CA_C 0.017362 0.13177 0.72 0.30 -0.54
## Ori_Con 0.018295 0.13526 -0.24 0.78 -0.73 0.27
## Ori_CA 0.021700 0.14731 0.07 0.54 -0.23 -0.21 0.14
## Con_CA 0.108850 0.32992 -0.26 -0.62 0.97 -0.54 -0.56 -0.16
## Ali_CA 0.004600 0.06782 0.50 0.07 0.29 0.12 -0.38 0.00 0.26
## Ori_Ali_CA 0.022992 0.15163 0.14 0.44 -0.72 0.73 0.76 -0.32 -0.65 -0.36
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21507 0.04186 5.138 2.78e-07 ***
## Orientation2-1 0.07413 0.05009 1.480 0.138879
## Congruency2-1 0.02363 0.06420 0.368 0.712797
## Alignment2-1 0.17848 0.02367 7.539 4.74e-14 ***
## CA2-1 -1.70984 0.07171 -23.842 < 2e-16 ***
## Cue2-1 0.01140 0.01313 0.868 0.385512
## VA2-1 0.12537 0.06284 1.995 0.046041 *
## Orientation2-1:Congruency2-1 0.10947 0.05379 2.035 0.041864 *
## Orientation2-1:Alignment2-1 -0.06206 0.03333 -1.862 0.062631 .
## Congruency2-1:Alignment2-1 0.09047 0.02744 3.297 0.000978 ***
## Orientation2-1:CA2-1 0.79632 0.07465 10.668 < 2e-16 ***
## Congruency2-1:CA2-1 0.92735 0.12258 7.565 3.87e-14 ***
## Alignment2-1:CA2-1 -0.08799 0.03517 -2.502 0.012359 *
## Orientation2-1:Congruency2-1:Alignment2-1 -0.16857 0.05488 -3.071 0.002130 **
## Orientation2-1:Congruency2-1:CA2-1 -0.87764 0.07729 -11.356 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.25299 0.07350 3.442 0.000578 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.41221 0.05466 -7.541 4.67e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.53588 0.10930 4.903 9.44e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.167639 (tol = 0.002, component 1)summary(rePCA(glmm_E12_resp_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## Standard deviation 0.6713 0.5445 0.4265 0.2950 0.21966 0.19769 0.13048 0.09137 0.01150 0.001109
## Proportion of Variance 0.3992 0.2626 0.1611 0.0771 0.04274 0.03462 0.01508 0.00740 0.00012 0.000000
## Cumulative Proportion 0.3992 0.6618 0.8229 0.9000 0.94278 0.97740 0.99249 0.99988 1.00000 1.000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## Standard deviation 0.4244 0.1831 0.14501 0.12200 0.06957 0.02868 0.001474 0.0002811 0.0002621
## Proportion of Variance 0.7057 0.1314 0.08239 0.05831 0.01896 0.00322 0.000010 0.0000000 0.0000000
## Cumulative Proportion 0.7057 0.8371 0.91949 0.97780 0.99677 0.99999 1.000000 1.0000000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ori_Con, andOri_Ali; - by-
StimGroup:(Intercept),Ali_CA,CA_C, andOri_C.
file_E12_resp_etd2 <- file.path(dir_lmm, "lmm_E12_resp_etd2.rds")
# fit the reduced model
if (!file.exists(file_E12_resp_etd2)) {
glmm_E12_resp_etd2 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + Con_C + CA_C + # Ali_C + Ori_C +
Ori_Con + Ori_CA + Con_CA + # Ori_Ali + Con_Ali + Ali_CA +
Ori_Ali_CA | StimGroup # Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E12_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E12_resp_etd2, file = file_E12_resp_etd2)
} else {
glmm_E12_resp_etd2 <- readRDS(file_E12_resp_etd2)
}
print(summary(glmm_E12_resp_etd2), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Ali_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ori_Ali_CA | StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49321.8 49914.2 -24593.9 49187.8 51083
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.3248 -0.5807 0.2078 0.5344 7.0013
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.08073 0.2841
## Ori_C 0.04856 0.2204 0.00
## Ali_C 0.02769 0.1664 0.38 0.15
## CA_C 0.25377 0.5038 -0.25 0.24 -0.34
## Ori_CA 0.19709 0.4439 0.12 0.06 0.11 -0.27
## Con_CA 0.24356 0.4935 -0.03 0.25 0.28 -0.06 0.04
## Ori_Con_CA 0.17579 0.4193 0.15 -0.19 -0.28 0.03 -0.46 -0.72
## StimGroup Con_C 0.03990 0.1998
## CA_C 0.02085 0.1444 -0.41
## Ori_Con 0.02740 0.1655 -0.75 0.05
## Ori_CA 0.02566 0.1602 -0.35 -0.14 0.37
## Con_CA 0.10596 0.3255 0.95 -0.54 -0.51 -0.22
## Ori_Ali_CA 0.02337 0.1529 -0.75 0.68 0.71 -0.10 -0.69
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21325 0.03257 6.548 5.85e-11 ***
## Orientation2-1 0.08276 0.02851 2.903 0.003694 **
## Congruency2-1 0.02204 0.06467 0.341 0.733219
## Alignment2-1 0.17513 0.02314 7.570 3.74e-14 ***
## CA2-1 -1.69662 0.07395 -22.942 < 2e-16 ***
## Cue2-1 0.01220 0.01309 0.932 0.351394
## VA2-1 0.11317 0.06276 1.803 0.071338 .
## Orientation2-1:Congruency2-1 0.09286 0.05933 1.565 0.117548
## Orientation2-1:Alignment2-1 -0.05133 0.02719 -1.888 0.059039 .
## Congruency2-1:Alignment2-1 0.09406 0.02717 3.462 0.000536 ***
## Orientation2-1:CA2-1 0.78373 0.07664 10.226 < 2e-16 ***
## Congruency2-1:CA2-1 0.92335 0.12007 7.690 1.47e-14 ***
## Alignment2-1:CA2-1 -0.08005 0.02753 -2.907 0.003644 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.17787 0.05426 -3.278 0.001044 **
## Orientation2-1:Congruency2-1:CA2-1 -0.87621 0.07319 -11.972 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.21477 0.07269 2.954 0.003133 **
## Congruency2-1:Alignment2-1:CA2-1 -0.41801 0.05406 -7.733 1.05e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.57149 0.10807 5.288 1.23e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0122015 (tol = 0.002, component 1)summary(rePCA(glmm_E12_resp_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.6307 0.5366 0.4210 0.28319 0.20289 0.17142 0.11621
## Proportion of Variance 0.3872 0.2803 0.1725 0.07807 0.04007 0.02861 0.01315
## Cumulative Proportion 0.3872 0.6675 0.8401 0.91817 0.95824 0.98685 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.4183 0.1858 0.14738 0.10933 0.0003776 9.047e-05
## Proportion of Variance 0.7196 0.1419 0.08933 0.04916 0.0000000 0.000e+00
## Cumulative Proportion 0.7196 0.8615 0.95084 1.00000 1.0000000 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
StimGroup:Ori_Ali_CAandCon_C.
file_E12_resp_etd3 <- file.path(dir_lmm, "lmm_E12_resp_etd3.rds")
# fit the reduced model
if (!file.exists(file_E12_resp_etd3)) {
glmm_E12_resp_etd3 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_CA + Con_CA + # Con_Ali + Ali_CA + Ori_Con + Ori_Ali +
Ori_Con_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA + Con_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + CA_C + # Ali_C + Ori_C + Con_C +
Ori_Con + Ori_CA + Con_CA | StimGroup # Ori_Ali + Con_Ali + Ali_CA +
# Ori_Con_Ali + Ori_Con_CA + Con_Ali_CA + Ori_Ali_CA
), # Ori_Con_Ali_CA
data = df_E12_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E12_resp_etd3, file = file_E12_resp_etd3)
} else {
glmm_E12_resp_etd3 <- readRDS(file_E12_resp_etd3)
}
print(summary(glmm_E12_resp_etd3), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Ali_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + CA_C + Ori_Con + Ori_CA + Con_CA | StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49538.1 50033.3 -24713.0 49426.1 51094
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.9717 -0.5871 0.2062 0.5380 7.1299
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.08026 0.2833
## Ori_C 0.04794 0.2189 0.00
## Ali_C 0.02721 0.1649 0.39 0.15
## CA_C 0.25136 0.5014 -0.27 0.25 -0.34
## Ori_CA 0.19279 0.4391 0.12 0.04 0.11 -0.26
## Con_CA 0.24213 0.4921 -0.02 0.25 0.28 -0.06 0.04
## Ori_Con_CA 0.17017 0.4125 0.15 -0.17 -0.28 0.03 -0.46 -0.72
## StimGroup CA_C 0.02313 0.1521
## Ori_Con 0.01739 0.1319 -0.01
## Ori_CA 0.02486 0.1577 -0.22 0.31
## Con_CA 0.13239 0.3639 -0.57 -0.37 -0.13
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21782 0.03247 6.708 1.97e-11 ***
## Orientation2-1 0.07944 0.02835 2.802 0.005079 **
## Congruency2-1 0.01701 0.01392 1.222 0.221645
## Alignment2-1 0.17363 0.02299 7.554 4.23e-14 ***
## CA2-1 -1.69105 0.07527 -22.466 < 2e-16 ***
## Cue2-1 0.01256 0.01306 0.962 0.335981
## VA2-1 0.11412 0.06234 1.831 0.067165 .
## Orientation2-1:Congruency2-1 0.09549 0.05014 1.904 0.056874 .
## Orientation2-1:Alignment2-1 -0.04988 0.02713 -1.839 0.065976 .
## Congruency2-1:Alignment2-1 0.09749 0.02710 3.598 0.000321 ***
## Orientation2-1:CA2-1 0.77198 0.07570 10.198 < 2e-16 ***
## Congruency2-1:CA2-1 0.92266 0.13054 7.068 1.57e-12 ***
## Alignment2-1:CA2-1 -0.08228 0.02747 -2.996 0.002739 **
## Orientation2-1:Congruency2-1:Alignment2-1 -0.18609 0.05409 -3.440 0.000581 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.87360 0.07263 -12.028 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.21948 0.05416 4.052 5.07e-05 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.41294 0.05394 -7.655 1.93e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.56182 0.10780 5.211 1.87e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.1.1.5 The optimal model
anova(glmm_E12_resp_rdc, glmm_E12_resp_etd3, refit=FALSE)According to BIC, the reduced model is used as the optimal model.
glmm_E12_resp_opt <- glmm_E12_resp_rdc
print(summary(glmm_E12_resp_opt), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + VA + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Ori_CA + Con_CA + Ori_Con_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Ori_CA + Con_CA + Ali_CA + Ori_Ali_CA || StimGroup)
## Data: df_E12_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 49161.5 49488.7 -24543.8 49087.5 51113
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.8722 -0.5714 0.2138 0.5290 6.9872
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.0813975 0.28530
## SubjCode.1 Ori_C 0.0491501 0.22170
## SubjCode.2 Con_C 0.0034362 0.05862
## SubjCode.3 Ali_C 0.0279300 0.16712
## SubjCode.4 CA_C 0.2464305 0.49642
## SubjCode.5 Ori_Con 0.0022671 0.04761
## SubjCode.6 Ori_Ali 0.0228744 0.15124
## SubjCode.7 Ori_CA 0.1820641 0.42669
## SubjCode.8 Con_CA 0.2329315 0.48263
## SubjCode.9 Ori_Con_CA 0.1368329 0.36991
## StimGroup (Intercept) 0.0069962 0.08364
## StimGroup.1 Ori_C 0.0175483 0.13247
## StimGroup.2 Con_C 0.0376596 0.19406
## StimGroup.3 CA_C 0.0180077 0.13419
## StimGroup.4 Ori_Con 0.0177709 0.13331
## StimGroup.5 Ori_CA 0.0217109 0.14735
## StimGroup.6 Con_CA 0.1083992 0.32924
## StimGroup.7 Ali_CA 0.0008330 0.02886
## StimGroup.8 Ori_Ali_CA 0.0009597 0.03098
## Number of obs: 51150, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.21096 0.04204 5.018 5.21e-07 ***
## Orientation2-1 0.08786 0.05066 1.734 0.082894 .
## Congruency2-1 0.02505 0.06325 0.396 0.692084
## Alignment2-1 0.16908 0.02316 7.300 2.89e-13 ***
## CA2-1 -1.69781 0.07133 -23.804 < 2e-16 ***
## Cue2-1 0.01157 0.01312 0.882 0.377788
## VA2-1 0.09665 0.06526 1.481 0.138571
## Orientation2-1:Congruency2-1 0.10028 0.05062 1.981 0.047580 *
## Orientation2-1:Alignment2-1 -0.04608 0.03206 -1.437 0.150581
## Congruency2-1:Alignment2-1 0.10122 0.02712 3.732 0.000190 ***
## Orientation2-1:CA2-1 0.76087 0.07242 10.506 < 2e-16 ***
## Congruency2-1:CA2-1 0.91178 0.12043 7.571 3.70e-14 ***
## Alignment2-1:CA2-1 -0.07201 0.02890 -2.492 0.012717 *
## Orientation2-1:Congruency2-1:Alignment2-1 -0.19071 0.05419 -3.519 0.000432 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.84615 0.06890 -12.282 < 2e-16 ***
## Orientation2-1:Alignment2-1:CA2-1 0.22645 0.05538 4.089 4.33e-05 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.41612 0.05412 -7.689 1.48e-14 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.54933 0.10815 5.079 3.79e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.1.2 Estimated marginal means
# hit and false alarm
emm_E12_probit <- emmeans(glmm_E12_resp_opt,
~ Orientation + Congruency + Alignment + CA,
type = "response")
emm_E12_probit## Orientation Congruency Alignment CA prob SE df asymp.LCL asymp.UCL
## upr con ali sam 0.939 0.0102 Inf 0.9162 0.956
## inv con ali sam 0.830 0.0205 Inf 0.7868 0.867
## upr inc ali sam 0.720 0.0271 Inf 0.6643 0.770
## inv inc ali sam 0.773 0.0244 Inf 0.7221 0.817
## upr con mis sam 0.941 0.0100 Inf 0.9183 0.958
## inv con mis sam 0.851 0.0189 Inf 0.8111 0.885
## upr inc mis sam 0.872 0.0172 Inf 0.8356 0.903
## inv inc mis sam 0.819 0.0214 Inf 0.7739 0.858
## upr con ali dif 0.100 0.0145 Inf 0.0746 0.132
## inv con ali dif 0.254 0.0258 Inf 0.2058 0.307
## upr inc ali dif 0.287 0.0274 Inf 0.2355 0.342
## inv inc ali dif 0.378 0.0304 Inf 0.3199 0.439
## upr con mis dif 0.134 0.0177 Inf 0.1026 0.172
## inv con mis dif 0.321 0.0288 Inf 0.2671 0.380
## upr inc mis dif 0.295 0.0277 Inf 0.2432 0.352
## inv inc mis dif 0.430 0.0314 Inf 0.3693 0.492
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95
## Intervals are back-transformed from the probit scaleSensitivity d’ in each condition:
# sensitivity d'
emm_E12_d <- contrast(emm_E12_probit,
"pairwise",
simple="CA",
infer=c(TRUE,FALSE),
adjust="none")
# emmip(emm_E12_d, Congruency ~ Alignment | Orientation, CIs = TRUE)
emm_E12_d[1:8]## contrast Orientation Congruency Alignment estimate SE df asymp.LCL asymp.UCL
## sam - dif upr con ali 2.83 0.109 Inf 2.613 3.04
## sam - dif inv con ali 1.62 0.104 Inf 1.413 1.82
## sam - dif upr inc ali 1.15 0.104 Inf 0.942 1.35
## sam - dif inv inc ali 1.06 0.104 Inf 0.855 1.26
## sam - dif upr con mis 2.67 0.108 Inf 2.454 2.88
## sam - dif inv con mis 1.51 0.105 Inf 1.301 1.71
## sam - dif upr inc mis 1.68 0.105 Inf 1.470 1.88
## sam - dif inv inc mis 1.09 0.104 Inf 0.884 1.29
##
## Results are averaged over the levels of: Cue, VA
## Note: contrasts are still on the probit scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## Confidence level used: 0.956.1.2.1 Composite effect
Composite effect of d’:
emm_E12_cf_d <- contrast(emm_E12_d,
interaction = "pairwise",
simple = c("Congruency", "Alignment"),
infer = TRUE)
summary(emm_E12_cf_d, side = ">")## contrast = sam - dif, Orientation = upr:
## Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis 0.691 0.0815 Inf 0.5568 Inf 8.478 <.0001
##
## contrast = sam - dif, Orientation = inv:
## Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis 0.141 0.0712 Inf 0.0244 Inf 1.987 0.0234
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95
## P values are right-tailedCongruency effect of d’ in aligned condition:
emm_E12_con_d <- contrast(emm_E12_d,
interaction = "pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E12_con_d[1:2], side = ">")## Congruency_pairwise contrast Orientation Alignment estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc sam - dif upr ali 1.680 0.132 Inf 1.462 Inf 12.685 <.0001
## con - inc sam - dif inv ali 0.559 0.129 Inf 0.347 Inf 4.331 <.0001
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95
## P values are right-tailedplot_E12_cf_d <- emm_E12_d %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, estimate, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(0,3.5)) + # set the limit for y axis c(0, 1100)
labs(y = expression("Sensitivity"~italic("d'")), fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("***", "*", "", "", "", "", "", ""),
color = "red",
size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "ccf_d.pdf", plot_ccf_d, width = 8, height = 6)
plot_E12_cf_d
6.1.2.2 Compare composite effects between upright and inverted faces
contrast(emm_E12_d,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.549 0.108 Inf 0.337 0.761 5.079 <.0001
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.956.1.2.3 Facilitation and interference
# facilitation and interference of d'
emm_E12_d_fi <- contrast(emm_E12_d,
interaction = "pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E12_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E12_d_fi[1:4]## Alignment_pairwise contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.1602 0.0649 Inf 0.03295 0.2874 2.468 0.0136
## ali - mis sam - dif inv con 0.1119 0.0529 Inf 0.00826 0.2156 2.116 0.0343
## ali - mis sam - dif upr inc -0.5306 0.0523 Inf -0.63306 -0.4282 -10.152 <.0001
## ali - mis sam - dif inv inc -0.0295 0.0502 Inf -0.12786 0.0688 -0.588 0.5565
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95summary(emm_E12_d_fi[1:2], side=">")## Alignment_pairwise contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.160 0.0649 Inf 0.0534 Inf 2.468 0.0068
## ali - mis sam - dif inv con 0.112 0.0529 Inf 0.0249 Inf 2.116 0.0172
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95
## P values are right-tailedsummary(emm_E12_d_fi[3:4], side="<")## Alignment_pairwise contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.5306 0.0523 Inf -Inf -0.445 -10.152 <.0001
## ali - mis sam - dif inv inc -0.0295 0.0502 Inf -Inf 0.053 -0.588 0.2782
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.95
## P values are left-tailedplot_E12_cffi_d <- summary(emm_E12_d_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=1.1, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_d) + # set the limit for y axis c(0, 1100)
labs(x = NULL,
y = expression("Sensitivity"~italic("d")*"' (aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E12_fi_d.pdf", plot_E12_cffi_d, width = 7, height = 4.55)
plot_E12_cffi_d
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E12_d_fi_ui <- contrast(emm_E12_probit,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E12_d_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise CA_pairwise Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.0482 0.0829 Inf -0.114 0.211 0.582 0.5607
## upr - inv ali - mis sam - dif inc -0.5011 0.0715 Inf -0.641 -0.361 -7.007 <.0001
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.956.1.2.4 Comparisons between facilitation and interference
contrast(emm_E12_d_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## faci-inte upr -0.3705 0.0851 Inf -0.5373 -0.204 -4.351 <.0001
## faci-inte inv 0.0824 0.0746 Inf -0.0638 0.229 1.105 0.2693
##
## Results are averaged over the levels of: Cue, VA
## Confidence level used: 0.956.1.3 Plot
plot_E12_cf_d_ <- plot_E12_cf_d +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E12_d <- ggarrange(plot_E12_cf_d_, plot_E12_cffi_d,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E12_d
6.2 Correct response times
6.2.1 Fitting the linear mixed-effects models
6.2.1.1 The maximal model
# file_E12_rt_max <- file.path(dir_lmm, "lmm_E12_rt_max.rds")
#
# # fit the max model
# if (!file.exists(file_E12_rt_max)) {
# lmm_E12_rt_max <- lmer(
# log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
# (Orientation * Congruency * Alignment | SubjCode) +
# (Orientation * Congruency * Alignment | StimGroup),
# data = df_E12_lmm |> dplyr::filter(isCorrect==1),
# control = lmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(lmm_E12_rt_max, file = file_E12_rt_max)
# } else {
# lmm_E12_rt_max <- readRDS(file_E12_rt_max)
# }
#
# print(summary(lmm_E12_rt_max), corr = FALSE)6.2.1.2 The zero-correlation-parameter model
file_E12_rt_zcp <- file.path(dir_lmm, "lmm_E12_rt_zcp.rds")
# fit the zcp model
if (!file.exists(file_E12_rt_zcp)) {
lmm_E12_rt_zcp <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || StimGroup),
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_zcp, file = file_E12_rt_zcp)
} else {
lmm_E12_rt_zcp <- readRDS(file_E12_rt_zcp)
}
print(summary(lmm_E12_rt_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35753.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2878 -0.6334 -0.1654 0.4552 7.7361
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.133e-02 0.2033065
## SubjCode.1 Ori_C 8.418e-02 0.2901417
## SubjCode.2 Con_C 3.208e-04 0.0179120
## SubjCode.3 Ali_C 8.941e-04 0.0299008
## SubjCode.4 Ori_Con 1.437e-03 0.0379061
## SubjCode.5 Ori_Ali 5.811e-04 0.0241056
## SubjCode.6 Con_Ali 2.724e-04 0.0165057
## SubjCode.7 Ori_Con_Ali 5.268e-04 0.0229523
## StimGroup (Intercept) 1.042e-05 0.0032287
## StimGroup.1 Ori_C 1.002e-04 0.0100088
## StimGroup.2 Con_C 2.891e-04 0.0170033
## StimGroup.3 Ali_C 4.625e-06 0.0021505
## StimGroup.4 Ori_Con 3.934e-04 0.0198331
## StimGroup.5 Ori_Ali 1.754e-04 0.0132447
## StimGroup.6 Con_Ali 1.320e-04 0.0114887
## StimGroup.7 Ori_Con_Ali 3.097e-10 0.0000176
## Residual 1.429e-01 0.3779884
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.770e+00 2.284e-02 7.824e+01 296.441 < 2e-16 ***
## Orientation2-1 5.118e-02 3.282e-02 8.011e+01 1.559 0.122877
## Congruency2-1 3.988e-02 6.916e-03 1.048e+01 5.766 0.000151 ***
## Alignment2-1 -1.036e-02 5.150e-03 2.073e+01 -2.013 0.057322 .
## Cue2-1 2.000e-02 3.837e-03 3.816e+04 5.212 1.88e-07 ***
## VA2-1 -3.988e-02 4.562e-02 7.798e+01 -0.874 0.384786
## CA2-1 5.980e-02 3.875e-03 3.847e+04 15.432 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.005e-02 1.081e-02 1.185e+01 -3.704 0.003077 **
## Orientation2-1:Alignment2-1 -7.841e-03 9.174e-03 1.021e+01 -0.855 0.412303
## Congruency2-1:Alignment2-1 -5.207e-02 8.715e-03 9.031e+00 -5.974 0.000206 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.945e-02 1.562e-02 8.011e+01 2.526 0.013494 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')6.2.1.3 The reduced model
summary(rePCA(lmm_E12_rt_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7676 0.5379 0.10028 0.0791 0.06377 0.06072 0.04739 0.04367
## Proportion of Variance 0.6498 0.3191 0.01109 0.0069 0.00449 0.00407 0.00248 0.00210
## Cumulative Proportion 0.6498 0.9689 0.97997 0.9869 0.99135 0.99542 0.99790 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.05247 0.04498 0.03504 0.03039 0.02648 0.008542 0.005689 4.656e-05
## Proportion of Variance 0.35594 0.26162 0.15874 0.11944 0.09065 0.009430 0.004180 0.000e+00
## Cumulative Proportion 0.35594 0.61756 0.77630 0.89573 0.98638 0.995810 1.000000 1.000e+00Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
StimGroup:Ori_Con_Ali.
file_E12_rt_rdc <- file.path(dir_lmm, "lmm_E12_rt_rdc.rds")
# fit the max model
if (!file.exists(file_E12_rt_rdc)) {
lmm_E12_rt_rdc <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali || StimGroup
), # Ori_Con_Ali
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_rdc, file = file_E12_rt_rdc)
} else {
lmm_E12_rt_rdc <- readRDS(file_E12_rt_rdc)
}
print(summary(lmm_E12_rt_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali || StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35753.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2878 -0.6335 -0.1654 0.4552 7.7361
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.132e-02 0.203267
## SubjCode.1 Ori_C 8.412e-02 0.290041
## SubjCode.2 Con_C 3.214e-04 0.017927
## SubjCode.3 Ali_C 8.945e-04 0.029908
## SubjCode.4 Ori_Con 1.436e-03 0.037895
## SubjCode.5 Ori_Ali 5.795e-04 0.024073
## SubjCode.6 Con_Ali 2.715e-04 0.016478
## SubjCode.7 Ori_Con_Ali 5.250e-04 0.022912
## StimGroup (Intercept) 1.042e-05 0.003228
## StimGroup.1 Ori_C 9.997e-05 0.009999
## StimGroup.2 Con_C 2.890e-04 0.017001
## StimGroup.3 Ali_C 4.767e-06 0.002183
## StimGroup.4 Ori_Con 3.939e-04 0.019847
## StimGroup.5 Ori_Ali 1.754e-04 0.013245
## StimGroup.6 Con_Ali 1.318e-04 0.011481
## Residual 1.429e-01 0.377989
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.770e+00 2.283e-02 7.829e+01 296.498 < 2e-16 ***
## Orientation2-1 5.118e-02 3.281e-02 8.019e+01 1.560 0.122744
## Congruency2-1 3.988e-02 6.916e-03 1.048e+01 5.766 0.000151 ***
## Alignment2-1 -1.036e-02 5.152e-03 2.093e+01 -2.012 0.057283 .
## Cue2-1 2.000e-02 3.837e-03 3.815e+04 5.212 1.88e-07 ***
## VA2-1 -3.988e-02 4.562e-02 7.802e+01 -0.874 0.384691
## CA2-1 5.980e-02 3.875e-03 3.847e+04 15.432 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.005e-02 1.081e-02 1.185e+01 -3.703 0.003084 **
## Orientation2-1:Alignment2-1 -7.841e-03 9.173e-03 1.021e+01 -0.855 0.412275
## Congruency2-1:Alignment2-1 -5.207e-02 8.714e-03 9.026e+00 -5.975 0.000206 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.945e-02 1.561e-02 7.999e+01 2.527 0.013492 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00494732 (tol = 0.002, component 1)summary(rePCA(lmm_E12_rt_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7673 0.5378 0.10025 0.07913 0.06369 0.06062 0.04743 0.04359
## Proportion of Variance 0.6498 0.3191 0.01109 0.00691 0.00448 0.00405 0.00248 0.00210
## Cumulative Proportion 0.6498 0.9689 0.97998 0.98689 0.99137 0.99542 0.99790 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.05251 0.04498 0.03504 0.03037 0.02645 0.008541 0.005776
## Proportion of Variance 0.35636 0.26150 0.15871 0.11925 0.09045 0.009430 0.004310
## Cumulative Proportion 0.35636 0.61786 0.77657 0.89581 0.98626 0.995690 1.000000summary(rePCA(lmm_E12_rt_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7673 0.5378 0.10025 0.07913 0.06369 0.06062 0.04743 0.04359
## Proportion of Variance 0.6498 0.3191 0.01109 0.00691 0.00448 0.00405 0.00248 0.00210
## Cumulative Proportion 0.6498 0.9689 0.97998 0.98689 0.99137 0.99542 0.99790 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.05251 0.04498 0.03504 0.03037 0.02645 0.008541 0.005776
## Proportion of Variance 0.35636 0.26150 0.15871 0.11925 0.09045 0.009430 0.004310
## Cumulative Proportion 0.35636 0.61786 0.77657 0.89581 0.98626 0.995690 1.0000006.2.1.4 The extended model
file_E12_rt_etd <- file.path(dir_lmm, "lmm_E12_rt_etd.rds")
# fit the max model
if (!file.exists(file_E12_rt_etd)) {
lmm_E12_rt_etd <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali | SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali | StimGroup
), # Ori_Con_Ali
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_etd, file = file_E12_rt_etd)
} else {
lmm_E12_rt_etd <- readRDS(file_E12_rt_etd)
}
print(summary(lmm_E12_rt_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali | SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali | StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35704.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3233 -0.6338 -0.1647 0.4544 7.7486
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 4.140e-02 0.203477
## Ori_C 8.419e-02 0.290158 -0.03
## Con_C 5.146e-04 0.022685 0.12 -0.23
## Ali_C 9.304e-04 0.030503 -0.14 0.00 -0.04
## Ori_Con 2.153e-03 0.046397 -0.40 -0.13 -0.71 -0.31
## Ori_Ali 1.158e-03 0.034033 -0.10 -0.07 -0.18 0.08 0.11
## Con_Ali 1.041e-03 0.032258 0.34 -0.47 -0.29 0.21 -0.04 -0.19
## Ori_Con_Ali 4.001e-03 0.063255 -0.03 -0.20 0.49 0.07 -0.58 0.55 0.04
## StimGroup (Intercept) 3.185e-05 0.005644
## Ori_C 1.198e-04 0.010944 -0.14
## Con_C 3.030e-04 0.017405 -0.21 0.05
## Ali_C 5.812e-05 0.007624 -0.51 -0.40 -0.01
## Ori_Con 6.597e-04 0.025685 -0.56 0.17 -0.68 0.41
## Ori_Ali 4.193e-04 0.020477 0.51 0.30 -0.37 0.11 0.06
## Con_Ali 5.095e-04 0.022571 -0.92 -0.09 0.08 0.37 0.55 -0.74
## Residual 1.426e-01 0.377570
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.770e+00 2.290e-02 7.896e+01 295.597 < 2e-16 ***
## Orientation2-1 5.105e-02 3.285e-02 8.038e+01 1.554 0.124165
## Congruency2-1 3.968e-02 7.183e-03 1.197e+01 5.523 0.000132 ***
## Alignment2-1 -1.014e-02 5.684e-03 2.589e+01 -1.784 0.086156 .
## Cue2-1 2.002e-02 3.834e-03 3.827e+04 5.222 1.78e-07 ***
## VA2-1 -2.874e-02 4.349e-02 7.833e+01 -0.661 0.510639
## CA2-1 5.985e-02 3.871e-03 3.853e+04 15.461 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.049e-02 1.234e-02 1.521e+01 -3.280 0.004981 **
## Orientation2-1:Alignment2-1 -7.876e-03 1.076e-02 1.409e+01 -0.732 0.476099
## Congruency2-1:Alignment2-1 -5.106e-02 1.110e-02 1.400e+01 -4.599 0.000413 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.842e-02 1.695e-02 1.233e+02 2.267 0.025116 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E12_rt_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7710 0.5417 0.1932 0.10900 0.08900 0.06904 0.0002651 0
## Proportion of Variance 0.6258 0.3090 0.0393 0.01251 0.00834 0.00502 0.0000000 0
## Cumulative Proportion 0.6258 0.9348 0.9741 0.98664 0.99498 1.00000 1.0000000 1
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.08559 0.07355 0.03474 0.02821 4.121e-05 1.764e-05 8.203e-21
## Proportion of Variance 0.49707 0.36705 0.08187 0.05401 0.000e+00 0.000e+00 0.000e+00
## Cumulative Proportion 0.49707 0.86412 0.94599 1.00000 1.000e+00 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ali_C,Con_Ali, andOri_Ali; - by-
StimGroup:(Intercept),Ali_C, andOri_C.
file_E12_rt_etd2 <- file.path(dir_lmm, "lmm_E12_rt_etd2.rds")
# fit the max model
if (!file.exists(file_E12_rt_etd2)) {
lmm_E12_rt_etd2 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C + # Con_C + Ali_C +
Ori_Con + # Ori_Ali + Con_Ali +
Ori_Con_Ali | SubjCode) +
(0 + Con_C + # Ali_C + Ori_C +
Ori_Con + Ori_Ali + Con_Ali | StimGroup
), # Ori_Con_Ali
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_etd2, file = file_E12_rt_etd2)
} else {
lmm_E12_rt_etd2 <- readRDS(file_E12_rt_etd2)
}
print(summary(lmm_E12_rt_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C + Ori_Con + Ori_Con_Ali | SubjCode) + (0 + Con_C + Ori_Con + Ori_Ali + Con_Ali | StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35756.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2807 -0.6339 -0.1646 0.4563 7.7748
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0413464 0.20334
## Ori_C 0.0840286 0.28988 -0.03
## Ori_Con 0.0017853 0.04225 -0.43 -0.14
## Ori_Con_Ali 0.0029594 0.05440 -0.06 -0.17 -0.82
## StimGroup Con_C 0.0002823 0.01680
## Ori_Con 0.0004521 0.02126 -1.00
## Ori_Ali 0.0003796 0.01948 -0.30 0.30
## Con_Ali 0.0003359 0.01833 -0.14 0.14 -0.90
## Residual 0.1431726 0.37838
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.769e+00 2.282e-02 7.806e+01 296.688 < 2e-16 ***
## Orientation2-1 5.115e-02 3.264e-02 7.903e+01 1.567 0.121099
## Congruency2-1 3.987e-02 6.567e-03 9.143e+00 6.072 0.000174 ***
## Alignment2-1 -1.118e-02 3.854e-03 3.877e+04 -2.901 0.003724 **
## Cue2-1 2.013e-02 3.839e-03 3.880e+04 5.243 1.59e-07 ***
## VA2-1 -3.465e-02 4.432e-02 7.808e+01 -0.782 0.436688
## CA2-1 5.976e-02 3.875e-03 3.876e+04 15.420 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.986e-02 1.128e-02 1.537e+01 -3.535 0.002901 **
## Orientation2-1:Alignment2-1 -7.404e-03 9.866e-03 1.178e+01 -0.750 0.467698
## Congruency2-1:Alignment2-1 -5.328e-02 9.642e-03 1.220e+01 -5.526 0.000123 ***
## Orientation2-1:Congruency2-1:Alignment2-1 3.937e-02 1.657e-02 1.499e+02 2.375 0.018798 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E12_rt_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.7670 0.5393 0.17250 0.0001197
## Proportion of Variance 0.6473 0.3200 0.03274 0.0000000
## Cumulative Proportion 0.6473 0.9673 1.00000 1.0000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.07497 0.06713 2.343e-06 0
## Proportion of Variance 0.55504 0.44496 0.000e+00 0
## Cumulative Proportion 0.55504 1.00000 1.000e+00 1Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_Con; - by-
StimGroup:Con_CandCon_Ali.
file_E12_rt_etd3 <- file.path(dir_lmm, "lmm_E12_rt_etd3.rds")
# fit the max model
if (!file.exists(file_E12_rt_etd3)) {
lmm_E12_rt_etd3 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C + # Con_C + Ali_C +
# Ori_Ali + Con_Ali + Ori_Con +
Ori_Con_Ali | SubjCode) +
(0 + # Ali_C + Ori_C + Con_C +
Ori_Con + Ori_Ali | StimGroup # + Con_Ali
), # Ori_Con_Ali
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_etd3, file = file_E12_rt_etd3)
} else {
lmm_E12_rt_etd3 <- readRDS(file_E12_rt_etd3)
}
print(summary(lmm_E12_rt_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C + Ori_Con_Ali | SubjCode) + (0 + Ori_Con + Ori_Ali | StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35783.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2811 -0.6345 -0.1651 0.4563 7.7589
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0413047 0.20324
## Ori_C 0.0841002 0.29000 -0.03
## Ori_Con_Ali 0.0005221 0.02285 -0.17 -0.39
## StimGroup Ori_Con 0.0004563 0.02136
## Ori_Ali 0.0001667 0.01291 0.39
## Residual 0.1434171 0.37870
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.769e+00 2.280e-02 7.799e+01 296.830 < 2e-16 ***
## Orientation2-1 5.121e-02 3.265e-02 7.902e+01 1.568 0.12081
## Congruency2-1 4.017e-02 3.861e-03 3.880e+04 10.404 < 2e-16 ***
## Alignment2-1 -1.126e-02 3.857e-03 3.882e+04 -2.920 0.00351 **
## Cue2-1 2.005e-02 3.840e-03 3.880e+04 5.223 1.77e-07 ***
## VA2-1 -4.125e-02 4.557e-02 7.798e+01 -0.905 0.36817
## CA2-1 5.964e-02 3.876e-03 3.881e+04 15.384 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.984e-02 1.026e-02 9.170e+00 -3.883 0.00359 **
## Orientation2-1:Alignment2-1 -7.572e-03 8.724e-03 9.278e+00 -0.868 0.40731
## Congruency2-1:Alignment2-1 -5.311e-02 7.710e-03 3.885e+04 -6.888 5.73e-12 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.030e-02 1.563e-02 8.097e+01 2.578 0.01176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00342339 (tol = 0.002, component 1)summary(rePCA(lmm_E12_rt_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.7664 0.5363 0.05445
## Proportion of Variance 0.6690 0.3276 0.00338
## Cumulative Proportion 0.6690 0.9966 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2]
## Standard deviation 0.05852 0.03032
## Proportion of Variance 0.78835 0.21165
## Cumulative Proportion 0.78835 1.00000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_Con_Ali;
file_E12_rt_etd4 <- file.path(dir_lmm, "lmm_E12_rt_etd4.rds")
# fit the max model
if (!file.exists(file_E12_rt_etd4)) {
lmm_E12_rt_etd4 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA +
(Ori_C | SubjCode # Con_C + Ali_C +
# Ori_Ali + Con_Ali + Ori_Con +
) + # Ori_Con_Ali
(0 + # Ali_C + Ori_C + Con_C +
Ori_Con + Ori_Ali | StimGroup # + Con_Ali
), # Ori_Con_Ali
data = df_E12_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_rt_etd4, file = file_E12_rt_etd4)
} else {
lmm_E12_rt_etd4 <- readRDS(file_E12_rt_etd4)
}
print(summary(lmm_E12_rt_etd4), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C | SubjCode) + (0 + Ori_Con + Ori_Ali | StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35783.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2742 -0.6346 -0.1657 0.4563 7.7606
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0413028 0.20323
## Ori_C 0.0840821 0.28997 -0.03
## StimGroup Ori_Con 0.0004562 0.02136
## Ori_Ali 0.0001675 0.01294 0.39
## Residual 0.1434251 0.37872
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.769e+00 2.280e-02 7.799e+01 296.837 < 2e-16 ***
## Orientation2-1 5.119e-02 3.265e-02 7.901e+01 1.568 0.12090
## Congruency2-1 4.018e-02 3.861e-03 3.886e+04 10.405 < 2e-16 ***
## Alignment2-1 -1.127e-02 3.857e-03 3.885e+04 -2.921 0.00349 **
## Cue2-1 2.005e-02 3.840e-03 3.885e+04 5.222 1.77e-07 ***
## VA2-1 -4.068e-02 4.559e-02 7.798e+01 -0.892 0.37494
## CA2-1 5.963e-02 3.876e-03 3.886e+04 15.383 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.983e-02 1.026e-02 9.169e+00 -3.882 0.00359 **
## Orientation2-1:Alignment2-1 -7.605e-03 8.729e-03 9.276e+00 -0.871 0.40560
## Congruency2-1:Alignment2-1 -5.305e-02 7.710e-03 3.885e+04 -6.881 6.04e-12 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.027e-02 1.542e-02 3.879e+04 2.611 0.00903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.2.1.5 The optimal model
anova(lmm_E12_rt_rdc, lmm_E12_rt_etd4, refit=FALSE)According to AIC, the extended model is used as the optimal model.
lmm_E12_rt_opt <- lmm_E12_rt_etd4
print(summary(lmm_E12_rt_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + VA + CA + (Ori_C | SubjCode) + (0 + Ori_Con + Ori_Ali | StimGroup)
## Data: dplyr::filter(df_E12_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 35783.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2742 -0.6346 -0.1657 0.4563 7.7606
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0413028 0.20323
## Ori_C 0.0840821 0.28997 -0.03
## StimGroup Ori_Con 0.0004562 0.02136
## Ori_Ali 0.0001675 0.01294 0.39
## Residual 0.1434251 0.37872
## Number of obs: 39029, groups: SubjCode, 80; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.769e+00 2.280e-02 7.799e+01 296.837 < 2e-16 ***
## Orientation2-1 5.119e-02 3.265e-02 7.901e+01 1.568 0.12090
## Congruency2-1 4.018e-02 3.861e-03 3.886e+04 10.405 < 2e-16 ***
## Alignment2-1 -1.127e-02 3.857e-03 3.885e+04 -2.921 0.00349 **
## Cue2-1 2.005e-02 3.840e-03 3.885e+04 5.222 1.77e-07 ***
## VA2-1 -4.068e-02 4.559e-02 7.798e+01 -0.892 0.37494
## CA2-1 5.963e-02 3.876e-03 3.886e+04 15.383 < 2e-16 ***
## Orientation2-1:Congruency2-1 -3.983e-02 1.026e-02 9.169e+00 -3.882 0.00359 **
## Orientation2-1:Alignment2-1 -7.605e-03 8.729e-03 9.276e+00 -0.871 0.40560
## Congruency2-1:Alignment2-1 -5.305e-02 7.710e-03 3.885e+04 -6.881 6.04e-12 ***
## Orientation2-1:Congruency2-1:Alignment2-1 4.027e-02 1.542e-02 3.879e+04 2.611 0.00903 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 16.2.2 Estimated marginal means
emm_E12_rt <- emmeans(lmm_E12_rt_opt,
~ Orientation + Congruency + Alignment,
pbkrtest.limit = 2e5,
lmerTest.limit = 2e5)
# emmip(regrid(emm_E12_rt), Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E12_rt, type = "response") # equivalent to regrid(emm_E12_rt)## Orientation Congruency Alignment response SE df lower.CL upper.CL
## upr con ali 812 23.4 82.7 766 860
## inv con ali 884 24.9 83.5 836 935
## upr inc ali 894 25.8 83.7 844 947
## inv inc ali 917 25.8 83.8 867 970
## upr con mis 836 24.1 82.3 789 885
## inv con mis 885 24.9 83.3 837 936
## upr inc mis 856 24.7 83.6 808 906
## inv inc mis 888 25.1 84.4 840 940
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale6.2.2.1 Composite effect
Composite effect of rt:
emm_E12_rt_cf <- contrast(regrid(emm_E12_rt),
interaction = "pairwise",
by = "Orientation",
infer = TRUE,
side="<")
emm_E12_rt_cf## Orientation = upr:
## Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis -62.5 9.21 82.3 -Inf -47.2 -6.788 <.0001
##
## Orientation = inv:
## Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis -29.7 10.05 83.3 -Inf -13.0 -2.954 0.0020
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedCongruency effect of rt in aligned conditions:
emm_E12_rt_con <- contrast(regrid(emm_E12_rt),
interaction = "pairwise",
by = c("Orientation", "Alignment"),
infer = TRUE,
side="<")
emm_E12_rt_con[1:2]## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -82.4 7.48 82.7 -Inf -70.0 -11.019 <.0001
## con - inc inv ali -33.1 7.78 83.5 -Inf -20.1 -4.247 <.0001
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedplot_E12_cf_rt <- summary(emm_E12_rt, type = "response") %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(y = response, x = Alignment, color = Congruency, group = Congruency)) +
geom_point(position = position_dodge(width = 0.1), size = 2) +
geom_line(aes(linetype = Congruency), position = position_dodge(width = 0.1),
linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, position = position_dodge(width = 0.1),
show.legend = F) +
facet_grid(. ~Orientation, switch = "both") +
coord_cartesian(ylim = ylimit_cf_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = "Correct response times (ms)", fill = "Congruency") + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "**", "", "", "", ""), color = sig_color, size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "E12_cf_rt.pdf", plot_E12_cf_rt, width = 8, height = 4.8)
plot_E12_cf_rt
6.2.2.2 Compare composite effects between upright and inverted faces
contrast(regrid(emm_E12_rt),
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -32.8 13.6 82.3 -59.9 -5.8 -2.416 0.0179
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.956.2.2.3 Facilitation and interference
emm_E12_rt_fi <- contrast(regrid(emm_E12_rt),
"pairwise",
by = c("Orientation", "Congruency"),
infer = TRUE,
adjust = "none")
# emmip(emm_E12_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E12_rt_fi[1:4]## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -23.99 6.13 82.3 -36.2 -11.8 -3.912 0.0002
## ali - mis inv con -1.23 7.06 83.3 -15.3 12.8 -0.174 0.8621
## ali - mis upr inc 38.54 7.19 83.6 24.2 52.8 5.361 <.0001
## ali - mis inv inc 28.45 7.59 83.8 13.3 43.6 3.746 0.0003
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95summary(emm_E12_rt_fi[1:2], side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -23.99 6.13 82.3 -Inf -13.8 -3.912 0.0001
## ali - mis inv con -1.23 7.06 83.3 -Inf 10.5 -0.174 0.4310
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedsummary(emm_E12_rt_fi[3:4], side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 38.5 7.19 83.6 26.6 Inf 5.361 <.0001
## ali - mis inv inc 28.5 7.59 83.8 15.8 Inf 3.746 0.0002
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are right-tailedplot_E12_cffi_rt <- summary(emm_E12_rt_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=100, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = expression(RT~"(aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E12_fi_rt.pdf", plot_E12_cffi_rt, width = 7, height = 4.55)
plot_E12_cffi_rt
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E12_rt_fi_ui <- contrast(emm_E12_rt,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E12_rt_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -0.0277 0.0113 25.7 -0.0509 -0.00457 -2.462 0.0208
## upr - inv ali - mis inc 0.0125 0.0120 33.3 -0.0119 0.03697 1.043 0.3046
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: satterthwaite
## Results are given on the log (not the response) scale.
## Confidence level used: 0.956.2.2.4 Comparisons between facilitation and interference
contrast(emm_E12_rt_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 14.5 9.68 82.3 -4.72 33.8 1.502 0.1369
## faci-inte inv 27.2 10.68 83.3 5.98 48.5 2.549 0.0127
##
## Results are averaged over the levels of: Cue, VA, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.956.2.3 Plot
plot_E12_cf_rt_ <- plot_E12_cf_rt +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E12_rt <- ggarrange(plot_E12_cf_rt_, plot_E12_cffi_rt,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E12_rt
6.3 Plot
plot_E12 <- ggarrange(plot_E12_cf_d_, plot_E12_cffi_d,
plot_E12_cf_rt_, plot_E12_cffi_rt,
labels = c("A", "B", "C", "D"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 2,
ncol = 2)
plot_E12
7 Experiment 3 (pre-registered analysis)
Visual angle: 3.2°×4°
7.1 Behavioral choices (d’)
7.1.1 Fitting the generalized linear mixed-effects models
7.1.1.1 The maximal model
# file_E3_resp_max <- file.path(dir_lmm, "lmm_E3_resp_max.rds")
#
# # fit the max model
# if (!file.exists(file_E3_resp_max)) {
# glmm_E3_resp_max <- glmer(
# isSame ~ Orientation * Congruency * Alignment * CA + Cue +
# (Orientation * Congruency * Alignment * CA | SubjCode) +
# (Orientation * Congruency * Alignment * CA | StimGroup),
# data = df_E3_lmm,
# family = binomial(link = "probit"),
# control = glmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(glmm_E3_resp_max, file = file_E3_resp_max)
# } else {
# glmm_E3_resp_max <- readRDS(file_E3_resp_max)
# }
#
# print(summary(glmm_E3_resp_max), corr = FALSE)7.1.1.2 The zero-correlation-parameter model
file_E3_resp_zcp <- file.path(dir_lmm, "lmm_E3_resp_zcp.rds")
# fit the max model
if (!file.exists(file_E3_resp_zcp)) {
glmm_E3_resp_zcp <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || SubjCode) +
(Ori_C + Con_C + Ali_C + CA_C +
Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA +
Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA +
Ori_Con_Ali_CA || StimGroup),
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_zcp, file = file_E3_resp_zcp)
} else {
glmm_E3_resp_zcp <- readRDS(file_E3_resp_zcp)
}
print(summary(glmm_E3_resp_zcp), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || SubjCode) + (Ori_C + Con_C + Ali_C + CA_C + Ori_Con + Ori_Ali + Con_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_Ali + Ori_Con_CA + Ori_Ali_CA + Con_Ali_CA + Ori_Con_Ali_CA || StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 71328.8 71781.9 -35615.4 71230.8 76629
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -10.6819 -0.5410 0.2114 0.4992 7.3080
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 7.006e-02 2.647e-01
## SubjCode.1 Ori_C 6.830e-02 2.614e-01
## SubjCode.2 Con_C 1.209e-04 1.100e-02
## SubjCode.3 Ali_C 2.540e-02 1.594e-01
## SubjCode.4 CA_C 1.893e-01 4.350e-01
## SubjCode.5 Ori_Con 0.000e+00 0.000e+00
## SubjCode.6 Ori_Ali 2.109e-02 1.452e-01
## SubjCode.7 Con_Ali 2.969e-09 5.449e-05
## SubjCode.8 Ori_CA 1.072e-01 3.275e-01
## SubjCode.9 Con_CA 2.203e-01 4.694e-01
## SubjCode.10 Ali_CA 3.163e-03 5.624e-02
## SubjCode.11 Ori_Con_Ali 1.224e-09 3.499e-05
## SubjCode.12 Ori_Con_CA 4.222e-02 2.055e-01
## SubjCode.13 Ori_Ali_CA 1.918e-09 4.379e-05
## SubjCode.14 Con_Ali_CA 4.048e-02 2.012e-01
## SubjCode.15 Ori_Con_Ali_CA 2.126e-10 1.458e-05
## StimGroup (Intercept) 1.328e-02 1.152e-01
## StimGroup.1 Ori_C 1.744e-02 1.321e-01
## StimGroup.2 Con_C 4.844e-02 2.201e-01
## StimGroup.3 Ali_C 0.000e+00 0.000e+00
## StimGroup.4 CA_C 3.738e-02 1.933e-01
## StimGroup.5 Ori_Con 1.453e-02 1.205e-01
## StimGroup.6 Ori_Ali 1.421e-09 3.770e-05
## StimGroup.7 Con_Ali 6.957e-04 2.638e-02
## StimGroup.8 Ori_CA 2.700e-02 1.643e-01
## StimGroup.9 Con_CA 9.342e-02 3.056e-01
## StimGroup.10 Ali_CA 1.404e-10 1.185e-05
## StimGroup.11 Ori_Con_Ali 4.522e-09 6.725e-05
## StimGroup.12 Ori_Con_CA 4.308e-02 2.076e-01
## StimGroup.13 Ori_Ali_CA 2.414e-02 1.554e-01
## StimGroup.14 Con_Ali_CA 3.377e-09 5.811e-05
## StimGroup.15 Ori_Con_Ali_CA 1.394e-09 3.733e-05
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.251191 0.044121 5.693 1.25e-08 ***
## Orientation2-1 -0.004616 0.049488 -0.093 0.925689
## Congruency2-1 0.027899 0.070558 0.395 0.692538
## Alignment2-1 0.155321 0.018507 8.392 < 2e-16 ***
## CA2-1 -1.775409 0.073897 -24.026 < 2e-16 ***
## Cue2-1 0.095623 0.010865 8.801 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.118771 0.044475 2.670 0.007574 **
## Orientation2-1:Alignment2-1 -0.015723 0.026318 -0.597 0.550223
## Congruency2-1:Alignment2-1 0.084999 0.024195 3.513 0.000443 ***
## Orientation2-1:CA2-1 0.753740 0.064362 11.711 < 2e-16 ***
## Congruency2-1:CA2-1 0.993299 0.108289 9.173 < 2e-16 ***
## Alignment2-1:CA2-1 -0.106580 0.023462 -4.543 5.56e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.149685 0.045299 -3.304 0.000952 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.641746 0.082209 -7.806 5.89e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.165783 0.066994 2.475 0.013338 *
## Congruency2-1:Alignment2-1:CA2-1 -0.471182 0.048925 -9.631 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.620971 0.090569 6.856 7.06e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')7.1.1.3 The reduced model
summary(rePCA(glmm_E3_resp_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.4694 0.4350 0.3275 0.26470 0.26135 0.2055 0.20120 0.15939 0.14523 0.05624 0.01100 5.449e-05 4.379e-05 3.499e-05 1.458e-05 0
## Proportion of Variance 0.2797 0.2403 0.1362 0.08895 0.08672 0.0536 0.05139 0.03225 0.02678 0.00402 0.00015 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0
## Cumulative Proportion 0.2797 0.5200 0.6561 0.74509 0.83181 0.8854 0.93680 0.96905 0.99583 0.99985 1.00000 1.000e+00 1.000e+00 1.000e+00 1.000e+00 1
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation 0.3056 0.2201 0.2076 0.1933 0.16432 0.15538 0.13205 0.1205 0.11524 0.02638 6.725e-05 5.811e-05 3.77e-05 3.733e-05 1.185e-05 0
## Proportion of Variance 0.2925 0.1517 0.1349 0.1170 0.08454 0.07558 0.05459 0.0455 0.04158 0.00218 0.000e+00 0.000e+00 0.00e+00 0.000e+00 0.000e+00 0
## Cumulative Proportion 0.2925 0.4441 0.5790 0.6960 0.78057 0.85615 0.91075 0.9562 0.99782 1.00000 1.000e+00 1.000e+00 1.00e+00 1.000e+00 1.000e+00 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_C,Ori_Con,Con_Ali,Ori_Con_Ali,Ori_Ali_CA, andOri_Con_Ali_CA; - by-
StimGroup:Ali_C,Ori_Ali,Ali_CA,Ori_Con_Ali,Con_Ali_CA, andOri_Con_Ali_CA.
file_E3_resp_rdc <- file.path(dir_lmm, "lmm_E3_resp_rdc.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_rdc)) {
glmm_E3_resp_rdc <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_Ali + Ori_CA + Con_CA + Ali_CA + # Ori_Con + Con_Ali +
Ori_Con_CA + Con_Ali_CA || SubjCode # Ori_Con_Ali + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Con_Ali + Ori_CA + Con_CA + # Ori_Ali + Ali_CA +
Ori_Con_CA + Ori_Ali_CA || StimGroup # Ori_Con_Ali + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_rdc, file = file_E3_resp_rdc)
} else {
glmm_E3_resp_rdc <- readRDS(file_E3_resp_rdc)
}
print(summary(glmm_E3_resp_rdc), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_CA + Con_Ali_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Con_Ali + Ori_CA + Con_CA + Ori_Con_CA + Ori_Ali_CA || StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 71304.8 71647.0 -35615.4 71230.8 76641
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -10.6910 -0.5409 0.2115 0.4993 7.3167
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.070040 0.26465
## SubjCode.1 Ori_C 0.068311 0.26136
## SubjCode.2 Ali_C 0.025407 0.15939
## SubjCode.3 CA_C 0.189252 0.43503
## SubjCode.4 Ori_Ali 0.021090 0.14522
## SubjCode.5 Ori_CA 0.107253 0.32750
## SubjCode.6 Con_CA 0.220262 0.46932
## SubjCode.7 Ali_CA 0.003169 0.05629
## SubjCode.8 Ori_Con_CA 0.042261 0.20557
## SubjCode.9 Con_Ali_CA 0.040514 0.20128
## StimGroup (Intercept) 0.013287 0.11527
## StimGroup.1 Ori_C 0.017443 0.13207
## StimGroup.2 Con_C 0.048426 0.22006
## StimGroup.3 CA_C 0.037367 0.19331
## StimGroup.4 Ori_Con 0.014522 0.12051
## StimGroup.5 Con_Ali 0.000695 0.02636
## StimGroup.6 Ori_CA 0.027001 0.16432
## StimGroup.7 Con_CA 0.093397 0.30561
## StimGroup.8 Ori_Con_CA 0.043115 0.20764
## StimGroup.9 Ori_Ali_CA 0.024149 0.15540
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.251177 0.044124 5.693 1.25e-08 ***
## Orientation2-1 -0.004564 0.049492 -0.092 0.926518
## Congruency2-1 0.027936 0.070539 0.396 0.692075
## Alignment2-1 0.155320 0.018507 8.392 < 2e-16 ***
## CA2-1 -1.775326 0.073875 -24.032 < 2e-16 ***
## Cue2-1 0.095627 0.010865 8.802 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.118759 0.044459 2.671 0.007558 **
## Orientation2-1:Alignment2-1 -0.015732 0.026317 -0.598 0.549973
## Congruency2-1:Alignment2-1 0.085009 0.024192 3.514 0.000442 ***
## Orientation2-1:CA2-1 0.753719 0.064359 11.711 < 2e-16 ***
## Congruency2-1:CA2-1 0.993139 0.108285 9.172 < 2e-16 ***
## Alignment2-1:CA2-1 -0.106607 0.023462 -4.544 5.52e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.149687 0.045293 -3.305 0.000950 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.641655 0.082207 -7.805 5.93e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.165826 0.066985 2.476 0.013303 *
## Congruency2-1:Alignment2-1:CA2-1 -0.471165 0.048928 -9.630 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.620925 0.090550 6.857 7.02e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.1.1.4 The extended model
file_E3_resp_etd <- file.path(dir_lmm, "lmm_E3_resp_etd.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_etd)) {
glmm_E3_resp_etd <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_Ali + Ori_CA + Con_CA + Ali_CA + # Ori_Con + Con_Ali +
Ori_Con_CA + Con_Ali_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(Ori_C + Con_C + CA_C + # Ali_C +
Ori_Con + Con_Ali + Ori_CA + Con_CA + # Ori_Ali + Ali_CA +
Ori_Con_CA + Ori_Ali_CA | StimGroup # Ori_Con_Ali + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_etd, file = file_E3_resp_etd)
} else {
glmm_E3_resp_etd <- readRDS(file_E3_resp_etd)
}
print(summary(glmm_E3_resp_etd), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_CA + Con_Ali_CA | SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Con_Ali + Ori_CA + Con_CA + Ori_Con_CA + Ori_Ali_CA | StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 71302.8 72477.2 -35524.4 71048.8 76551
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -13.5135 -0.5419 0.2069 0.5022 7.6015
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.070482 0.26548
## Ori_C 0.068056 0.26088 0.07
## Ali_C 0.026977 0.16425 0.18 0.00
## CA_C 0.197809 0.44476 0.08 -0.08 -0.16
## Ori_Ali 0.024359 0.15607 0.03 -0.06 -0.14 0.12
## Ori_CA 0.117181 0.34232 -0.02 -0.14 0.19 -0.57 -0.07
## Con_CA 0.225724 0.47510 0.05 0.12 -0.03 0.00 0.33 0.02
## Ali_CA 0.014621 0.12092 -0.67 -0.25 -0.37 0.23 0.01 -0.14 0.28
## Ori_Con_CA 0.081224 0.28500 0.17 -0.15 -0.34 0.32 -0.28 -0.22 -0.35 -0.38
## Con_Ali_CA 0.060546 0.24606 0.20 -0.05 -0.52 0.47 0.70 -0.28 0.02 -0.18 0.43
## StimGroup (Intercept) 0.012985 0.11395
## Ori_C 0.016351 0.12787 0.02
## Con_C 0.048557 0.22036 -0.05 -0.72
## CA_C 0.036713 0.19161 0.76 0.30 -0.43
## Ori_Con 0.016269 0.12755 -0.12 0.57 -0.64 0.13
## Con_Ali 0.004501 0.06709 -0.35 0.75 -0.71 0.26 0.40
## Ori_CA 0.027619 0.16619 -0.29 0.76 -0.56 0.06 0.32 0.78
## Con_CA 0.094037 0.30665 -0.45 -0.68 0.90 -0.68 -0.45 -0.52 -0.47
## Ori_Con_CA 0.048152 0.21944 -0.43 -0.02 0.09 -0.59 0.60 -0.22 -0.19 0.33
## Ori_Ali_CA 0.032402 0.18001 -0.17 0.14 -0.47 0.35 0.63 0.47 0.23 -0.28 0.13
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.251703 0.043836 5.742 9.36e-09 ***
## Orientation2-1 -0.005973 0.048427 -0.123 0.90183
## Congruency2-1 0.032701 0.070639 0.463 0.64342
## Alignment2-1 0.166603 0.019101 8.722 < 2e-16 ***
## CA2-1 -1.783417 0.073907 -24.130 < 2e-16 ***
## Cue2-1 0.095441 0.010873 8.778 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.110615 0.046665 2.370 0.01777 *
## Orientation2-1:Alignment2-1 -0.022137 0.027335 -0.810 0.41802
## Congruency2-1:Alignment2-1 0.079511 0.031492 2.525 0.01158 *
## Orientation2-1:CA2-1 0.773073 0.065721 11.763 < 2e-16 ***
## Congruency2-1:CA2-1 1.000041 0.108745 9.196 < 2e-16 ***
## Alignment2-1:CA2-1 -0.115127 0.026168 -4.400 1.08e-05 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.147458 0.046023 -3.204 0.00136 **
## Orientation2-1:Congruency2-1:CA2-1 -0.655134 0.087690 -7.471 7.96e-14 ***
## Orientation2-1:Alignment2-1:CA2-1 0.162795 0.073729 2.208 0.02724 *
## Congruency2-1:Alignment2-1:CA2-1 -0.485340 0.051449 -9.433 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.638437 0.091668 6.965 3.29e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(glmm_E3_resp_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## Standard deviation 0.5369 0.4923 0.3071 0.28989 0.25213 0.22551 0.21565 0.13058 0.00237 0.0004827
## Proportion of Variance 0.3250 0.2732 0.1064 0.09474 0.07167 0.05733 0.05243 0.01922 0.00001 0.0000000
## Cumulative Proportion 0.3250 0.5982 0.7046 0.79934 0.87101 0.92834 0.98077 0.99999 1.00000 1.0000000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## Standard deviation 0.4317 0.2716 0.1896 0.16523 0.10334 0.05920 0.0003448 9.67e-05 3.518e-05 1.605e-06
## Proportion of Variance 0.5522 0.2185 0.1065 0.08087 0.03164 0.01038 0.0000000 0.00e+00 0.000e+00 0.000e+00
## Cumulative Proportion 0.5522 0.7707 0.8771 0.95798 0.98962 1.00000 1.0000000 1.00e+00 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ali_CAandOri_Ali; - by-
StimGroup:Con_Ali,(Intercept),Ori_C, andOri_Con.
file_E3_resp_etd2 <- file.path(dir_lmm, "lmm_E3_resp_etd2.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_etd2)) {
glmm_E3_resp_etd2 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + CA_C + # Con_C +
Ori_CA + Con_CA + # Ori_Con + Con_Ali + Ali_CA + Ori_Ali +
Ori_Con_CA + Con_Ali_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + Con_C + CA_C + # Ali_C + Ori_C +
Ori_CA + Con_CA + # Ori_Ali + Ali_CA + Con_Ali + Ori_Con +
Ori_Con_CA + Ori_Ali_CA | StimGroup # Ori_Con_Ali + Con_Ali_CA +
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_etd2, file = file_E3_resp_etd2)
} else {
glmm_E3_resp_etd2 <- readRDS(file_E3_resp_etd2)
}
print(summary(glmm_E3_resp_etd2), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA + Con_Ali_CA | SubjCode) + (0 + Con_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA + Ori_Ali_CA | StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 71802.4 72486.7 -35827.2 71654.4 76604
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -15.0164 -0.5528 0.2079 0.5047 8.0870
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.06823 0.2612
## Ori_C 0.06720 0.2592 0.07
## Ali_C 0.02544 0.1595 0.15 -0.03
## CA_C 0.19340 0.4398 0.07 -0.07 -0.13
## Ori_CA 0.11582 0.3403 -0.02 -0.17 0.18 -0.56
## Con_CA 0.22023 0.4693 0.07 0.12 -0.01 0.00 0.03
## Ori_Con_CA 0.06001 0.2450 0.19 -0.16 -0.47 0.40 -0.26 -0.38
## Con_Ali_CA 0.04202 0.2050 0.14 -0.11 -0.65 0.61 -0.38 0.12 0.76
## StimGroup Con_C 0.04939 0.2222
## CA_C 0.04565 0.2136 -0.25
## Ori_CA 0.03845 0.1961 -0.62 -0.08
## Con_CA 0.09817 0.3133 0.81 -0.72 -0.37
## Ori_Con_CA 0.04537 0.2130 0.03 -0.46 -0.19 0.30
## Ori_Ali_CA 0.03217 0.1794 -0.61 0.32 0.43 -0.41 0.17
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.256484 0.024573 10.438 < 2e-16 ***
## Orientation2-1 -0.007856 0.026460 -0.297 0.766549
## Congruency2-1 0.021422 0.071258 0.301 0.763703
## Alignment2-1 0.156989 0.018514 8.479 < 2e-16 ***
## CA2-1 -1.769699 0.079511 -22.257 < 2e-16 ***
## Cue2-1 0.096443 0.010821 8.912 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.115914 0.023244 4.987 6.14e-07 ***
## Orientation2-1:Alignment2-1 -0.022020 0.022711 -0.970 0.332270
## Congruency2-1:Alignment2-1 0.085782 0.022975 3.734 0.000189 ***
## Orientation2-1:CA2-1 0.765300 0.073352 10.433 < 2e-16 ***
## Congruency2-1:CA2-1 0.999037 0.110465 9.044 < 2e-16 ***
## Alignment2-1:CA2-1 -0.110035 0.023012 -4.782 1.74e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.137248 0.045504 -3.016 0.002560 **
## Orientation2-1:Congruency2-1:CA2-1 -0.676251 0.084902 -7.965 1.65e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.159594 0.072766 2.193 0.028290 *
## Congruency2-1:Alignment2-1:CA2-1 -0.481587 0.049366 -9.755 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.635024 0.090815 6.992 2.70e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0405417 (tol = 0.002, component 1)summary(rePCA(glmm_E3_resp_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.5320 0.4799 0.2948 0.27133 0.24381 0.2123 0.10765 0.04877
## Proportion of Variance 0.3572 0.2907 0.1096 0.09292 0.07502 0.0569 0.01463 0.00300
## Cumulative Proportion 0.3572 0.6479 0.7575 0.85046 0.92548 0.9824 0.99700 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.4197 0.2543 0.1997 0.14644 0.08399 0.0008002
## Proportion of Variance 0.5697 0.2092 0.1289 0.06935 0.02282 0.0000000
## Cumulative Proportion 0.5697 0.7789 0.9078 0.97718 1.00000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:CA_C; - by-
StimGroup:Ori_Ali_CA.
file_E3_resp_etd3 <- file.path(dir_lmm, "lmm_E3_resp_etd3.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_etd3)) {
glmm_E3_resp_etd3 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + # Con_C + CA_C +
Ori_CA + Con_CA + # Ori_Con + Con_Ali + Ali_CA + Ori_Ali +
Ori_Con_CA + Con_Ali_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA +
) + # Ori_Con_Ali_CA
(0 + Con_C + CA_C + # Ali_C + Ori_C +
Ori_CA + Con_CA + # Ori_Ali + Ali_CA + Con_Ali + Ori_Con +
Ori_Con_CA | StimGroup # Ori_Con_Ali + Con_Ali_CA + + Ori_Ali_CA
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_etd3, file = file_E3_resp_etd3)
} else {
glmm_E3_resp_etd3 <- readRDS(file_E3_resp_etd3)
}
print(summary(glmm_E3_resp_etd3), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + Ori_CA + Con_CA + Ori_Con_CA + Con_Ali_CA | SubjCode) + (0 + Con_C + CA_C + Ori_CA + Con_CA + Ori_Con_CA | StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 72934.9 73489.7 -36407.4 72814.9 76618
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.8428 -0.5681 0.2251 0.5140 9.6449
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07172 0.2678
## Ori_C 0.07865 0.2804 -0.02
## Ali_C 0.02378 0.1542 0.16 -0.02
## Ori_CA 0.08889 0.2981 0.03 -0.30 0.13
## Con_CA 0.27307 0.5226 0.14 0.05 -0.02 0.03
## Ori_Con_CA 0.04287 0.2070 0.06 -0.11 -0.39 0.00 -0.61
## Con_Ali_CA 0.02025 0.1423 0.24 -0.10 -0.72 -0.18 0.37 0.30
## StimGroup Con_C 0.04769 0.2184
## CA_C 0.04272 0.2067 -0.25
## Ori_CA 0.03696 0.1923 -0.63 -0.05
## Con_CA 0.09208 0.3035 0.81 -0.72 -0.38
## Ori_Con_CA 0.04534 0.2129 0.09 -0.52 -0.24 0.34
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.253248 0.025093 10.092 < 2e-16 ***
## Orientation2-1 -0.006717 0.028028 -0.240 0.810600
## Congruency2-1 0.014614 0.070008 0.209 0.834648
## Alignment2-1 0.150481 0.017974 8.372 < 2e-16 ***
## CA2-1 -1.733588 0.066390 -26.112 < 2e-16 ***
## Cue2-1 0.094888 0.010715 8.855 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.118080 0.022884 5.160 2.47e-07 ***
## Orientation2-1:Alignment2-1 -0.016306 0.022380 -0.729 0.466251
## Congruency2-1:Alignment2-1 0.081706 0.022643 3.608 0.000308 ***
## Orientation2-1:CA2-1 0.728984 0.070450 10.347 < 2e-16 ***
## Congruency2-1:CA2-1 0.970787 0.109545 8.862 < 2e-16 ***
## Alignment2-1:CA2-1 -0.108883 0.022647 -4.808 1.53e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.139687 0.044846 -3.115 0.001841 **
## Orientation2-1:Congruency2-1:CA2-1 -0.631891 0.083299 -7.586 3.30e-14 ***
## Orientation2-1:Alignment2-1:CA2-1 0.160220 0.044866 3.571 0.000355 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.447678 0.046531 -9.621 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.594008 0.089405 6.644 3.05e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0143617 (tol = 0.002, component 1)summary(rePCA(glmm_E3_resp_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 0.5428 0.3317 0.2714 0.2502 0.21436 0.10082 0.04712
## Proportion of Variance 0.4917 0.1836 0.1229 0.1045 0.07668 0.01696 0.00371
## Cumulative Proportion 0.4917 0.6753 0.7982 0.9026 0.97933 0.99629 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 0.4022 0.2370 0.1920 0.08953 0.04491
## Proportion of Variance 0.6107 0.2122 0.1392 0.03027 0.00762
## Cumulative Proportion 0.6107 0.8229 0.9621 0.99238 1.00000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_Ali_CA; - by-
StimGroup:Ori_CA.
file_E3_resp_etd4 <- file.path(dir_lmm, "lmm_E3_resp_etd4.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_etd4)) {
glmm_E3_resp_etd4 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + # Con_C + CA_C +
Ori_CA + Con_CA + # Ori_Con + Con_Ali + Ali_CA + Ori_Ali +
Ori_Con_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA + + Con_Ali_CA
) + # Ori_Con_Ali_CA
(0 + Con_C + CA_C + # Ali_C + Ori_C +
Con_CA + # Ori_Ali + Ali_CA + Con_Ali + Ori_Con + Ori_CA +
Ori_Con_CA | StimGroup # Ori_Con_Ali + Con_Ali_CA + + Ori_Ali_CA
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_etd4, file = file_E3_resp_etd4)
} else {
glmm_E3_resp_etd4 <- readRDS(file_E3_resp_etd4)
}
print(summary(glmm_E3_resp_etd4), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + Con_C + CA_C + Con_CA + Ori_Con_CA | StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 72976.1 73419.9 -36440.0 72880.1 76630
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -14.1409 -0.5713 0.2259 0.5147 10.6594
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07161 0.2676
## Ori_C 0.07841 0.2800 -0.02
## Ali_C 0.02379 0.1542 0.16 -0.02
## Ori_CA 0.08752 0.2958 0.03 -0.30 0.13
## Con_CA 0.27182 0.5214 0.14 0.06 -0.03 0.04
## Ori_Con_CA 0.04358 0.2088 0.09 -0.11 -0.38 0.01 -0.61
## StimGroup Con_C 0.04659 0.2158
## CA_C 0.04193 0.2048 -0.29
## Con_CA 0.09552 0.3091 0.82 -0.73
## Ori_Con_CA 0.04341 0.2083 -0.08 -0.56 0.24
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.253074 0.025076 10.092 < 2e-16 ***
## Orientation2-1 -0.005758 0.027991 -0.206 0.837029
## Congruency2-1 0.015103 0.069252 0.218 0.827360
## Alignment2-1 0.149531 0.017959 8.326 < 2e-16 ***
## CA2-1 -1.731578 0.065793 -26.319 < 2e-16 ***
## Cue2-1 0.094798 0.010709 8.852 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.111425 0.022821 4.883 1.05e-06 ***
## Orientation2-1:Alignment2-1 -0.016526 0.022361 -0.739 0.459893
## Congruency2-1:Alignment2-1 0.084231 0.022350 3.769 0.000164 ***
## Orientation2-1:CA2-1 0.728006 0.035393 20.569 < 2e-16 ***
## Congruency2-1:CA2-1 0.971155 0.111106 8.741 < 2e-16 ***
## Alignment2-1:CA2-1 -0.106468 0.022547 -4.722 2.33e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.140301 0.044627 -3.144 0.001667 **
## Orientation2-1:Congruency2-1:CA2-1 -0.632521 0.082085 -7.706 1.30e-14 ***
## Orientation2-1:Alignment2-1:CA2-1 0.159840 0.044756 3.571 0.000355 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.449333 0.044587 -10.078 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.594099 0.089072 6.670 2.56e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00720821 (tol = 0.002, component 1)summary(rePCA(glmm_E3_resp_etd4))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.5399 0.3298 0.2690 0.2406 0.19103 0.09870
## Proportion of Variance 0.5054 0.1886 0.1255 0.1003 0.06327 0.01689
## Cumulative Proportion 0.5054 0.6940 0.8195 0.9198 0.98311 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.3928 0.2369 0.12246 0.04533
## Proportion of Variance 0.6784 0.2467 0.06594 0.00903
## Cumulative Proportion 0.6784 0.9250 0.99097 1.00000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
StimGroup:Con_CA.
file_E3_resp_etd5 <- file.path(dir_lmm, "lmm_E3_resp_etd5.rds")
# fit the reduced model
if (!file.exists(file_E3_resp_etd5)) {
glmm_E3_resp_etd5 <- glmer(
isSame ~ Orientation * Congruency * Alignment * CA + Cue +
(Ori_C + Ali_C + # Con_C + CA_C +
Ori_CA + Con_CA + # Ori_Con + Con_Ali + Ali_CA + Ori_Ali +
Ori_Con_CA | SubjCode # Ori_Con_Ali + Ori_Ali_CA + + Con_Ali_CA
) + # Ori_Con_Ali_CA
(0 + Con_C + CA_C + # Ali_C + Ori_C +
# Ori_Ali + Ali_CA + Con_Ali + Ori_Con + Ori_CA + Con_CA +
Ori_Con_CA | StimGroup # Ori_Con_Ali + Con_Ali_CA + + Ori_Ali_CA
), # Ori_Con_Ali_CA
data = df_E3_lmm,
family = binomial(link = "probit"),
control = glmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(glmm_E3_resp_etd5, file = file_E3_resp_etd5)
} else {
glmm_E3_resp_etd5 <- readRDS(file_E3_resp_etd5)
}
print(summary(glmm_E3_resp_etd5), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + Ori_CA + Con_CA + Ori_Con_CA | SubjCode) + (0 + Con_C + CA_C + Ori_Con_CA | StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 73168.0 73574.9 -36540.0 73080.0 76634
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -13.2584 -0.5765 0.2243 0.5101 10.0948
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.07121 0.2669
## Ori_C 0.07829 0.2798 -0.02
## Ali_C 0.02359 0.1536 0.16 -0.02
## Ori_CA 0.08760 0.2960 0.03 -0.32 0.12
## Con_CA 0.27046 0.5201 0.14 0.06 -0.03 0.04
## Ori_Con_CA 0.04122 0.2030 0.10 -0.11 -0.38 0.05 -0.63
## StimGroup Con_C 0.05498 0.2345
## CA_C 0.03613 0.1901 -0.28
## Ori_Con_CA 0.05490 0.2343 0.19 -0.70
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.256698 0.025004 10.266 < 2e-16 ***
## Orientation2-1 -0.008846 0.027968 -0.316 0.751793
## Congruency2-1 0.013956 0.075000 0.186 0.852380
## Alignment2-1 0.149328 0.017903 8.341 < 2e-16 ***
## CA2-1 -1.728440 0.061180 -28.252 < 2e-16 ***
## Cue2-1 0.093884 0.010695 8.778 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.112733 0.022796 4.945 7.61e-07 ***
## Orientation2-1:Alignment2-1 -0.017243 0.022337 -0.772 0.440142
## Congruency2-1:Alignment2-1 0.086831 0.022324 3.890 0.000100 ***
## Orientation2-1:CA2-1 0.726408 0.035369 20.538 < 2e-16 ***
## Congruency2-1:CA2-1 0.957404 0.052504 18.235 < 2e-16 ***
## Alignment2-1:CA2-1 -0.109033 0.022520 -4.842 1.29e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.141927 0.044573 -3.184 0.001452 **
## Orientation2-1:Congruency2-1:CA2-1 -0.624941 0.088653 -7.049 1.80e-12 ***
## Orientation2-1:Alignment2-1:CA2-1 0.161710 0.044702 3.618 0.000297 ***
## Congruency2-1:Alignment2-1:CA2-1 -0.448507 0.044528 -10.072 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.595044 0.088916 6.692 2.20e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.1.1.5 The optimal model
anova(glmm_E3_resp_rdc, glmm_E3_resp_etd5, refit=FALSE)According to BIC, the reduced model is used as the optimal model.
glmm_E3_resp_opt <- glmm_E3_resp_rdc
print(summary(glmm_E3_resp_opt), corr = FALSE)## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: binomial ( probit )
## Formula: isSame ~ Orientation * Congruency * Alignment * CA + Cue + (Ori_C + Ali_C + CA_C + Ori_Ali + Ori_CA + Con_CA + Ali_CA + Ori_Con_CA + Con_Ali_CA || SubjCode) + (Ori_C + Con_C + CA_C + Ori_Con + Con_Ali + Ori_CA + Con_CA + Ori_Con_CA + Ori_Ali_CA || StimGroup)
## Data: df_E3_lmm
## Control: glmerControl(optCtrl = list(maxfun = 1e+07))
##
## AIC BIC logLik deviance df.resid
## 71304.8 71647.0 -35615.4 71230.8 76641
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -10.6910 -0.5409 0.2115 0.4993 7.3167
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 0.070040 0.26465
## SubjCode.1 Ori_C 0.068311 0.26136
## SubjCode.2 Ali_C 0.025407 0.15939
## SubjCode.3 CA_C 0.189252 0.43503
## SubjCode.4 Ori_Ali 0.021090 0.14522
## SubjCode.5 Ori_CA 0.107253 0.32750
## SubjCode.6 Con_CA 0.220262 0.46932
## SubjCode.7 Ali_CA 0.003169 0.05629
## SubjCode.8 Ori_Con_CA 0.042261 0.20557
## SubjCode.9 Con_Ali_CA 0.040514 0.20128
## StimGroup (Intercept) 0.013287 0.11527
## StimGroup.1 Ori_C 0.017443 0.13207
## StimGroup.2 Con_C 0.048426 0.22006
## StimGroup.3 CA_C 0.037367 0.19331
## StimGroup.4 Ori_Con 0.014522 0.12051
## StimGroup.5 Con_Ali 0.000695 0.02636
## StimGroup.6 Ori_CA 0.027001 0.16432
## StimGroup.7 Con_CA 0.093397 0.30561
## StimGroup.8 Ori_Con_CA 0.043115 0.20764
## StimGroup.9 Ori_Ali_CA 0.024149 0.15540
## Number of obs: 76678, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.251177 0.044124 5.693 1.25e-08 ***
## Orientation2-1 -0.004564 0.049492 -0.092 0.926518
## Congruency2-1 0.027936 0.070539 0.396 0.692075
## Alignment2-1 0.155320 0.018507 8.392 < 2e-16 ***
## CA2-1 -1.775326 0.073875 -24.032 < 2e-16 ***
## Cue2-1 0.095627 0.010865 8.802 < 2e-16 ***
## Orientation2-1:Congruency2-1 0.118759 0.044459 2.671 0.007558 **
## Orientation2-1:Alignment2-1 -0.015732 0.026317 -0.598 0.549973
## Congruency2-1:Alignment2-1 0.085009 0.024192 3.514 0.000442 ***
## Orientation2-1:CA2-1 0.753719 0.064359 11.711 < 2e-16 ***
## Congruency2-1:CA2-1 0.993139 0.108285 9.172 < 2e-16 ***
## Alignment2-1:CA2-1 -0.106607 0.023462 -4.544 5.52e-06 ***
## Orientation2-1:Congruency2-1:Alignment2-1 -0.149687 0.045293 -3.305 0.000950 ***
## Orientation2-1:Congruency2-1:CA2-1 -0.641655 0.082207 -7.805 5.93e-15 ***
## Orientation2-1:Alignment2-1:CA2-1 0.165826 0.066985 2.476 0.013303 *
## Congruency2-1:Alignment2-1:CA2-1 -0.471165 0.048928 -9.630 < 2e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1:CA2-1 0.620925 0.090550 6.857 7.02e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 17.1.2 Estimated marginal means
# hit and false alarm
emm_E3_probit <- emmeans(glmm_E3_resp_opt,
~ Orientation + Congruency + Alignment + CA,
type = "response")
emm_E3_probit## Orientation Congruency Alignment CA prob SE df asymp.LCL asymp.UCL
## upr con ali sam 0.954 0.00822 Inf 0.9353 0.968
## inv con ali sam 0.845 0.01955 Inf 0.8036 0.880
## upr inc ali sam 0.764 0.02514 Inf 0.7118 0.810
## inv inc ali sam 0.765 0.02508 Inf 0.7128 0.811
## upr con mis sam 0.952 0.00845 Inf 0.9331 0.966
## inv con mis sam 0.871 0.01736 Inf 0.8335 0.902
## upr inc mis sam 0.895 0.01509 Inf 0.8621 0.921
## inv inc mis sam 0.823 0.02128 Inf 0.7780 0.861
## upr con ali dif 0.110 0.01555 Inf 0.0823 0.143
## inv con ali dif 0.227 0.02465 Inf 0.1821 0.279
## upr inc ali dif 0.313 0.02885 Inf 0.2592 0.372
## inv inc ali dif 0.386 0.03107 Inf 0.3271 0.448
## upr con mis dif 0.148 0.01904 Inf 0.1142 0.189
## inv con mis dif 0.282 0.02753 Inf 0.2307 0.338
## upr inc mis dif 0.297 0.02818 Inf 0.2441 0.354
## inv inc mis dif 0.425 0.03181 Inf 0.3641 0.488
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Intervals are back-transformed from the probit scaleSensitivity d’ in each condition:
# sensitivity d'
emm_E3_d <- contrast(emm_E3_probit, "pairwise", simple="CA")
# emmip(emm_E3_d, Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E3_d[1:8], infer=c(TRUE,FALSE), adjust="none")## contrast Orientation Congruency Alignment estimate SE df asymp.LCL asymp.UCL
## sam - dif upr con ali 2.91 0.105 Inf 2.703 3.12
## sam - dif inv con ali 1.76 0.102 Inf 1.563 1.96
## sam - dif upr inc ali 1.21 0.101 Inf 1.006 1.40
## sam - dif inv inc ali 1.01 0.101 Inf 0.812 1.21
## sam - dif upr con mis 2.71 0.105 Inf 2.503 2.91
## sam - dif inv con mis 1.71 0.102 Inf 1.506 1.91
## sam - dif upr inc mis 1.79 0.103 Inf 1.584 1.99
## sam - dif inv inc mis 1.11 0.102 Inf 0.915 1.31
##
## Results are averaged over the levels of: Cue
## Note: contrasts are still on the probit scale. Consider using
## regrid() if you want contrasts of back-transformed estimates.
## Confidence level used: 0.957.1.2.1 Composite effect
Composite effect of d’:
emm_E3_cf_d <- contrast(emm_E3_d,
interaction = "pairwise",
simple = c("Congruency", "Alignment"),
infer = TRUE)
summary(emm_E3_cf_d[1:2], side = ">")## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.782 0.0713 Inf 0.6643 Inf 10.957 <.0001
## con - inc ali - mis sam - dif inv 0.161 0.0616 Inf 0.0593 Inf 2.607 0.0046
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedCongruency effect of d’ in aligned condition:
emm_E3_con_d <- contrast(emm_E3_d,
interaction = "pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E3_con_d[1:2], side=">")## Congruency_pairwise contrast Orientation Alignment estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc sam - dif upr ali 1.705 0.122 Inf 1.504 Inf 13.998 <.0001
## con - inc sam - dif inv ali 0.753 0.119 Inf 0.557 Inf 6.326 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedEquivalence tests for composite effect:
summary(emm_E3_cf_d[1:2], delta = .2, level=.9)## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.782 0.0713 Inf 0.6643 0.899 8.154 1.0000
## con - inc ali - mis sam - dif inv 0.161 0.0616 Inf 0.0593 0.262 -0.638 0.2619
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 0.2
## P values are left-tailedsummary(emm_E3_cf_d[1:2], delta = .2, side = ">") # > -.2## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.782 0.0713 Inf 0.6643 Inf 13.761 <.0001
## con - inc ali - mis sam - dif inv 0.161 0.0616 Inf 0.0593 Inf 5.852 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 0.2
## P values are right-tailedsummary(emm_E3_cf_d[1:2], delta = .2, side = "<") # < .2## Congruency_pairwise Alignment_pairwise contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## con - inc ali - mis sam - dif upr 0.782 0.0713 Inf -Inf 0.899 8.154 1.0000
## con - inc ali - mis sam - dif inv 0.161 0.0616 Inf -Inf 0.262 -0.638 0.2619
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 0.2
## P values are left-tailedplot_E3_cf_d <- emm_E3_d %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, estimate, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = asymp.LCL, ymax = asymp.UCL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(0,3.5)) + # set the limit for y axis c(0, 1100)
labs(y = expression("Sensitivity"~italic("d'")), fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("***", "***", "", "", "", "", "", ""),
color = "red",
size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "ccf_d.pdf", plot_ccf_d, width = 8, height = 6)
plot_E3_cf_d
7.1.2.2 Compare composite effects between upright and inverted faces
emm_E3_cf_ui_d <- contrast(emm_E3_d,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)
summary(emm_E3_cf_ui_d, side=">")## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.621 0.0906 Inf 0.472 Inf 6.857 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedEquivalence tests
summary(emm_E3_cf_ui_d, delta = .25, level=.9)## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.621 0.0906 Inf 0.472 0.77 4.096 1.0000
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 0.25
## P values are left-tailedsummary(emm_E3_cf_ui_d, delta = .25, side = ">") # > -.25## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.621 0.0906 Inf 0.472 Inf 9.618 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 0.25
## P values are right-tailedsummary(emm_E3_cf_ui_d, delta = .25, side = "<") # < .25## contrast = sam - dif:
## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv con - inc ali - mis 0.621 0.0906 Inf -Inf 0.77 4.096 1.0000
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 0.25
## P values are left-tailed7.1.2.3 Facilitation
# facilitation and interference of d'
emm_E3_d_fi <- contrast(emm_E3_d,
"pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E3_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E3_d_fi[1:4]## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.2013 0.0607 Inf 0.0824 0.32021 3.318 0.0009
## ali - mis sam - dif inv con 0.0567 0.0510 Inf -0.0432 0.15652 1.112 0.2661
## ali - mis sam - dif upr inc -0.5803 0.0504 Inf -0.6791 -0.48154 -11.514 <.0001
## ali - mis sam - dif inv inc -0.1040 0.0481 Inf -0.1984 -0.00974 -2.162 0.0306
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95summary(emm_E3_d_fi[1:2], side=">")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.2013 0.0607 Inf 0.1015 Inf 3.318 0.0005
## ali - mis sam - dif inv con 0.0567 0.0510 Inf -0.0271 Inf 1.112 0.1331
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are right-tailedEquivalence tests:
summary(emm_E3_d_fi[1:2], delta=.15, level=.9)## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.2013 0.0607 Inf 0.1015 0.301 0.845 0.8011
## ali - mis sam - dif inv con 0.0567 0.0510 Inf -0.0271 0.140 -1.832 0.0335
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 0.15
## P values are left-tailedsummary(emm_E3_d_fi[1:2], delta=.15, side=">")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.2013 0.0607 Inf 0.1015 Inf 5.790 <.0001
## ali - mis sam - dif inv con 0.0567 0.0510 Inf -0.0271 Inf 4.056 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 0.15
## P values are right-tailedsummary(emm_E3_d_fi[1:2], delta=.15, side="<")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr con 0.2013 0.0607 Inf -Inf 0.301 0.845 0.8011
## ali - mis sam - dif inv con 0.0567 0.0510 Inf -Inf 0.140 -1.832 0.0335
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 0.15
## P values are left-tailed7.1.2.4 Interference
summary(emm_E3_d_fi[3:4], side="<")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.580 0.0504 Inf -Inf -0.4974 -11.514 <.0001
## ali - mis sam - dif inv inc -0.104 0.0481 Inf -Inf -0.0249 -2.162 0.0153
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## P values are left-tailedEquivalence tests:
summary(emm_E3_d_fi[3:4], delta=.15, level=.9)## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.580 0.0504 Inf -0.663 -0.4974 8.538 1.0000
## ali - mis sam - dif inv inc -0.104 0.0481 Inf -0.183 -0.0249 -0.955 0.1698
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 0.15
## P values are left-tailedsummary(emm_E3_d_fi[3:4], delta=.15, side=">")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.580 0.0504 Inf -0.663 Inf -8.538 1.0000
## ali - mis sam - dif inv inc -0.104 0.0481 Inf -0.183 Inf 0.955 0.1698
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 0.15
## P values are right-tailedsummary(emm_E3_d_fi[3:4], delta=.15, side="<")## contrast1 contrast Orientation Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ali - mis sam - dif upr inc -0.580 0.0504 Inf -Inf -0.4974 -14.490 <.0001
## ali - mis sam - dif inv inc -0.104 0.0481 Inf -Inf -0.0249 -5.280 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 0.15
## P values are left-tailedplot_E3_cffi_d <- summary(emm_E3_d_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=1.1, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin=asymp.LCL, ymax=asymp.UCL),
linewidth=1.5, width=0, alpha=.6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_d) + # set the limit for y axis c(0, 1100)
labs(x = NULL,
y = expression("Sensitivity"~italic(d)*"' (aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E3_fi_d.pdf", plot_E3_cffi_d, width = 7, height = 4.55)
plot_E3_cffi_d
7.1.2.5 Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of d'
emm_E3_d_fi_ui <- contrast(emm_E3_d,
interaction = "pairwise",
simple = c("Orientation", "Alignment"),
infer = TRUE,
adjust = "none")
emm_E3_d_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.145 0.0851 Inf -0.0222 0.311 1.699 0.0893
## upr - inv ali - mis sam - dif inc -0.476 0.0763 Inf -0.6259 -0.327 -6.239 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95Equivalence tests:
summary(emm_E3_d_fi_ui[1:2], delta=.2, level=.9)## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.145 0.0851 Inf 0.00463 0.285 -0.650 0.2577
## upr - inv ali - mis sam - dif inc -0.476 0.0763 Inf -0.60187 -0.351 3.619 0.9999
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 0.2
## P values are left-tailedsummary(emm_E3_d_fi_ui[1:2], delta=.2, side=">")## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.145 0.0851 Inf 0.00463 Inf 4.049 <.0001
## upr - inv ali - mis sam - dif inc -0.476 0.0763 Inf -0.60187 Inf -3.619 0.9999
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 0.2
## P values are right-tailedsummary(emm_E3_d_fi_ui[1:2], delta=.2, side="<")## Orientation_pairwise Alignment_pairwise contrast Congruency estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## upr - inv ali - mis sam - dif con 0.145 0.0851 Inf -Inf 0.285 -0.650 0.2577
## upr - inv ali - mis sam - dif inc -0.476 0.0763 Inf -Inf -0.351 -8.858 <.0001
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 0.2
## P values are left-tailed7.1.2.6 Comparisons between facilitation and interference
contrast(emm_E3_d_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## faci-inte upr -0.3790 0.0858 Inf -0.547 -0.211 -4.420 <.0001
## faci-inte inv -0.0474 0.0776 Inf -0.200 0.105 -0.611 0.5415
##
## Results are averaged over the levels of: Cue
## Confidence level used: 0.957.1.3 Plot
plot_E3_cf_d_ <- plot_E3_cf_d +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E3_d <- ggarrange(plot_E3_cf_d_, plot_E3_cffi_d,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E3_d
7.2 Correct response times
7.2.1 Fitting the linear mixed-effects models
7.2.1.1 The maximal model
# file_E3_rt_max <- file.path(dir_lmm, "lmm_E3_rt_max.rds")
#
# # fit the max model
# if (!file.exists(file_E3_rt_max)) {
# lmm_E3_rt_max <- lmer(
# log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
# (Orientation * Congruency * Alignment | SubjCode) +
# (Orientation * Congruency * Alignment | StimGroup),
# data = df_E3_lmm |> dplyr::filter(isCorrect==1),
# control = lmerControl(optCtrl = list(maxfun = 1e7))
# )
#
# saveRDS(lmm_E3_rt_max, file = file_E3_rt_max)
# } else {
# lmm_E3_rt_max <- readRDS(file_E3_rt_max)
# }
#
# print(summary(lmm_E3_rt_max), corr = FALSE)7.2.1.2 The zero-correlation-parameter model
file_E3_rt_zcp <- file.path(dir_lmm, "lmm_E3_rt_zcp.rds")
# fit the zcp model
if (!file.exists(file_E3_rt_zcp)) {
lmm_E3_rt_zcp <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode) +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_rt_zcp, file = file_E3_rt_zcp)
} else {
lmm_E3_rt_zcp <- readRDS(file_E3_rt_zcp)
}
print(summary(lmm_E3_rt_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode) + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 61831.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3954 -0.6326 -0.1829 0.4209 8.3008
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.630e-02 0.215175
## SubjCode.1 Ori_C 8.678e-02 0.294589
## SubjCode.2 Con_C 1.662e-04 0.012893
## SubjCode.3 Ali_C 1.503e-03 0.038771
## SubjCode.4 Ori_Con 9.227e-04 0.030376
## SubjCode.5 Ori_Ali 8.266e-04 0.028751
## SubjCode.6 Con_Ali 0.000e+00 0.000000
## SubjCode.7 Ori_Con_Ali 3.239e-10 0.000018
## StimGroup (Intercept) 7.894e-05 0.008885
## StimGroup.1 Ori_C 6.339e-05 0.007961
## StimGroup.2 Con_C 2.067e-04 0.014378
## StimGroup.3 Ali_C 0.000e+00 0.000000
## StimGroup.4 Ori_Con 2.558e-04 0.015994
## StimGroup.5 Ori_Ali 0.000e+00 0.000000
## StimGroup.6 Con_Ali 1.640e-04 0.012805
## StimGroup.7 Ori_Con_Ali 1.705e-03 0.041293
## Residual 1.623e-01 0.402845
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.818e+00 1.991e-02 1.227e+02 342.354 < 2e-16 ***
## Orientation2-1 3.934e-02 2.722e-02 1.203e+02 1.445 0.150943
## Congruency2-1 4.904e-02 5.761e-03 9.599e+00 8.512 8.9e-06 ***
## Alignment2-1 -2.122e-03 4.863e-03 1.174e+02 -0.436 0.663400
## Cue2-1 4.162e-02 3.324e-03 5.872e+04 12.520 < 2e-16 ***
## CA2-1 5.586e-02 3.353e-03 5.851e+04 16.657 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.909e-02 8.818e-03 1.109e+01 -5.567 0.000164 ***
## Orientation2-1:Alignment2-1 1.780e-02 7.157e-03 1.175e+02 2.487 0.014283 *
## Congruency2-1:Alignment2-1 -4.337e-02 7.793e-03 9.147e+00 -5.566 0.000330 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.824e-02 1.865e-02 9.038e+00 3.123 0.012193 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')7.2.1.3 The reduced model
summary(rePCA(lmm_E3_rt_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.7313 0.5341 0.09624 0.07540 0.07137 0.03200 4.468e-05 0
## Proportion of Variance 0.6358 0.3392 0.01101 0.00676 0.00606 0.00122 0.000e+00 0
## Cumulative Proportion 0.6358 0.9749 0.98597 0.99273 0.99878 1.00000 1.000e+00 1
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 0.1025 0.0397 0.03569 0.03179 0.02206 0.01976 0 0
## Proportion of Variance 0.6892 0.1034 0.08356 0.06627 0.03191 0.02562 0 0
## Cumulative Proportion 0.6892 0.7926 0.87620 0.94247 0.97438 1.00000 1 1Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_AliandOri_Con_Ali. - by-
StimGroup:Ali_CandOri_Ali.
file_E3_rt_rdc <- file.path(dir_lmm, "lmm_E3_rt_rdc.rds")
# fit the max model
if (!file.exists(file_E3_rt_rdc)) {
lmm_E3_rt_rdc <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali || SubjCode # + Con_Ali +
) + # Ori_Con_Ali
(Ori_C + Con_C + # Ali_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali || StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e8))
)
saveRDS(lmm_E3_rt_rdc, file = file_E3_rt_rdc)
} else {
lmm_E3_rt_rdc <- readRDS(file_E3_rt_rdc)
}
print(summary(lmm_E3_rt_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali || SubjCode) + (Ori_C + Con_C + Ori_Con + Con_Ali + Ori_Con_Ali || StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+08))
##
## REML criterion at convergence: 61831.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3954 -0.6326 -0.1829 0.4209 8.3007
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 4.628e-02 0.215136
## SubjCode.1 Ori_C 8.684e-02 0.294683
## SubjCode.2 Con_C 1.666e-04 0.012905
## SubjCode.3 Ali_C 1.503e-03 0.038766
## SubjCode.4 Ori_Con 9.231e-04 0.030383
## SubjCode.5 Ori_Ali 8.276e-04 0.028768
## StimGroup (Intercept) 7.889e-05 0.008882
## StimGroup.1 Ori_C 6.344e-05 0.007965
## StimGroup.2 Con_C 2.063e-04 0.014365
## StimGroup.3 Ori_Con 2.564e-04 0.016012
## StimGroup.4 Con_Ali 1.642e-04 0.012813
## StimGroup.5 Ori_Con_Ali 1.706e-03 0.041305
## Residual 1.623e-01 0.402845
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.818e+00 1.991e-02 1.228e+02 342.417 < 2e-16 ***
## Orientation2-1 3.934e-02 2.722e-02 1.202e+02 1.445 0.151073
## Congruency2-1 4.904e-02 5.758e-03 9.616e+00 8.516 8.76e-06 ***
## Alignment2-1 -2.122e-03 4.862e-03 1.174e+02 -0.436 0.663366
## Cue2-1 4.162e-02 3.324e-03 5.872e+04 12.520 < 2e-16 ***
## CA2-1 5.585e-02 3.353e-03 5.851e+04 16.657 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.909e-02 8.822e-03 1.108e+01 -5.565 0.000164 ***
## Orientation2-1:Alignment2-1 1.780e-02 7.158e-03 1.175e+02 2.487 0.014291 *
## Congruency2-1:Alignment2-1 -4.337e-02 7.795e-03 9.147e+00 -5.565 0.000330 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.824e-02 1.865e-02 9.036e+00 3.123 0.012206 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00554055 (tol = 0.002, component 1)summary(rePCA(lmm_E3_rt_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.7315 0.534 0.09623 0.07542 0.07141 0.03204
## Proportion of Variance 0.6360 0.339 0.01101 0.00676 0.00606 0.00122
## Cumulative Proportion 0.6360 0.975 0.98596 0.99272 0.99878 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.1025 0.03975 0.03566 0.03181 0.02205 0.01977
## Proportion of Variance 0.6892 0.10357 0.08336 0.06633 0.03187 0.02563
## Cumulative Proportion 0.6892 0.79281 0.87617 0.94250 0.97437 1.000007.2.1.4 The extended model
file_E3_rt_etd <- file.path(dir_lmm, "lmm_E3_rt_etd.rds")
# fit the max model
if (!file.exists(file_E3_rt_etd)) {
lmm_E3_rt_etd <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali | SubjCode # + Con_Ali +
) + # Ori_Con_Ali
(Ori_C + Con_C + # Ali_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali | StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e8))
)
saveRDS(lmm_E3_rt_etd, file = file_E3_rt_etd)
} else {
lmm_E3_rt_etd <- readRDS(file_E3_rt_etd)
}
print(summary(lmm_E3_rt_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali | SubjCode) + (Ori_C + Con_C + Ori_Con + Con_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+08))
##
## REML criterion at convergence: 61793.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4252 -0.6321 -0.1845 0.4208 8.3172
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 4.637e-02 0.21535
## Ori_C 8.716e-02 0.29523 -0.02
## Con_C 2.681e-04 0.01637 0.19 -0.11
## Ali_C 1.525e-03 0.03906 0.16 0.13 0.34
## Ori_Con 9.836e-04 0.03136 -0.31 0.42 0.37 0.05
## Ori_Ali 1.350e-03 0.03674 0.32 -0.44 0.92 0.28 0.20
## StimGroup (Intercept) 7.903e-05 0.00889
## Ori_C 6.147e-05 0.00784 -0.28
## Con_C 2.132e-04 0.01460 -0.79 0.30
## Ori_Con 3.693e-04 0.01922 0.06 -0.31 -0.13
## Con_Ali 3.808e-04 0.01951 -0.14 -0.36 -0.29 0.81
## Ori_Con_Ali 1.850e-03 0.04302 -0.24 0.40 -0.29 -0.38 0.15
## Residual 1.622e-01 0.40274
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.818e+00 1.993e-02 1.227e+02 342.092 < 2e-16 ***
## Orientation2-1 3.929e-02 2.727e-02 1.201e+02 1.441 0.152193
## Congruency2-1 4.908e-02 5.889e-03 1.060e+01 8.334 5.64e-06 ***
## Alignment2-1 -2.224e-03 4.881e-03 1.171e+02 -0.456 0.649448
## Cue2-1 4.163e-02 3.320e-03 5.786e+04 12.540 < 2e-16 ***
## CA2-1 5.582e-02 3.350e-03 5.816e+04 16.661 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.918e-02 9.464e-03 1.270e+01 -5.197 0.000186 ***
## Orientation2-1:Alignment2-1 1.797e-02 7.454e-03 1.728e+02 2.411 0.016957 *
## Congruency2-1:Alignment2-1 -4.314e-02 9.078e-03 1.144e+01 -4.752 0.000536 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.878e-02 1.903e-02 9.512e+00 3.089 0.012145 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E3_rt_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.7353 0.5359 0.11014 0.08402 0.04081 2.955e-05
## Proportion of Variance 0.6371 0.3383 0.01429 0.00832 0.00196 0.000e+00
## Cumulative Proportion 0.6371 0.9754 0.98972 0.99804 1.00000 1.000e+00
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6]
## Standard deviation 0.1096 0.06538 0.03961 0.01879 2.258e-05 2.151e-21
## Proportion of Variance 0.6598 0.23470 0.08613 0.01939 0.000e+00 0.000e+00
## Cumulative Proportion 0.6598 0.89448 0.98061 1.00000 1.000e+00 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_C,Ori_Con, andOri_Ali; - by-
StimGroup:(Intercept)andOri_C.
file_E3_rt_etd2 <- file.path(dir_lmm, "lmm_E3_rt_etd2.rds")
# fit the max model
if (!file.exists(file_E3_rt_etd2)) {
lmm_E3_rt_etd2 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Ali_C | SubjCode # Con_C +
# + Con_Ali + Ori_Con + Ori_Ali
) + # Ori_Con_Ali
(0 + Con_C + # Ali_C + Ori_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali | StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e8))
)
saveRDS(lmm_E3_rt_etd2, file = file_E3_rt_etd2)
} else {
lmm_E3_rt_etd2 <- readRDS(file_E3_rt_etd2)
}
print(summary(lmm_E3_rt_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ali_C | SubjCode) + (0 + Con_C + Ori_Con + Con_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+08))
##
## REML criterion at convergence: 61841.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3733 -0.6324 -0.1838 0.4234 8.3293
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0463005 0.21518
## Ori_C 0.0868050 0.29463 -0.02
## Ali_C 0.0014959 0.03868 0.16 0.14
## StimGroup Con_C 0.0002268 0.01506
## Ori_Con 0.0003985 0.01996 -0.09
## Con_Ali 0.0003179 0.01783 -0.44 0.94
## Ori_Con_Ali 0.0017055 0.04130 -0.29 -0.26 -0.14
## Residual 0.1625015 0.40311
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.817e+00 1.971e-02 1.190e+02 345.805 < 2e-16 ***
## Orientation2-1 3.919e-02 2.710e-02 1.190e+02 1.446 0.150767
## Congruency2-1 4.910e-02 5.814e-03 8.944e+00 8.445 1.49e-05 ***
## Alignment2-1 -2.189e-03 4.857e-03 1.174e+02 -0.451 0.653000
## Cue2-1 4.170e-02 3.323e-03 5.889e+04 12.550 < 2e-16 ***
## CA2-1 5.560e-02 3.352e-03 5.884e+04 16.589 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.893e-02 9.182e-03 1.095e+01 -5.328 0.000245 ***
## Orientation2-1:Alignment2-1 1.770e-02 6.659e-03 5.895e+04 2.658 0.007868 **
## Congruency2-1:Alignment2-1 -4.308e-02 8.728e-03 1.159e+01 -4.936 0.000382 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.813e-02 1.865e-02 9.012e+00 3.116 0.012374 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E3_rt_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.7312 0.5337 0.09367
## Proportion of Variance 0.6455 0.3439 0.01059
## Cumulative Proportion 0.6455 0.9894 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 0.1045 0.06485 0.03434 1.64e-18
## Proportion of Variance 0.6696 0.25801 0.07236 0.00e+00
## Cumulative Proportion 0.6696 0.92764 1.00000 1.00e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
StimGroup:Con_C.
file_E3_rt_etd3 <- file.path(dir_lmm, "lmm_E3_rt_etd3.rds")
# fit the max model
if (!file.exists(file_E3_rt_etd3)) {
lmm_E3_rt_etd3 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Ali_C | SubjCode # Con_C +
# + Con_Ali + Ori_Con + Ori_Ali
) + # Ori_Con_Ali
(0 + # Ali_C + Ori_C + Con_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali | StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_rt_etd3, file = file_E3_rt_etd3)
} else {
lmm_E3_rt_etd3 <- readRDS(file_E3_rt_etd3)
}
print(summary(lmm_E3_rt_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ali_C | SubjCode) + (0 + Ori_Con + Con_Ali + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 61850.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3914 -0.6324 -0.1843 0.4224 8.3160
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0463073 0.21519
## Ori_C 0.0867992 0.29462 -0.02
## Ali_C 0.0014940 0.03865 0.16 0.14
## StimGroup Ori_Con 0.0004248 0.02061
## Con_Ali 0.0003214 0.01793 0.92
## Ori_Con_Ali 0.0018542 0.04306 -0.34 0.06
## Residual 0.1625496 0.40317
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.817e+00 1.972e-02 1.190e+02 345.772 < 2e-16 ***
## Orientation2-1 3.905e-02 2.710e-02 1.190e+02 1.441 0.152201
## Congruency2-1 4.941e-02 3.334e-03 5.897e+04 14.817 < 2e-16 ***
## Alignment2-1 -2.198e-03 4.856e-03 1.174e+02 -0.453 0.651706
## Cue2-1 4.154e-02 3.321e-03 5.897e+04 12.505 < 2e-16 ***
## CA2-1 5.521e-02 3.350e-03 5.902e+04 16.482 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.868e-02 9.325e-03 1.093e+01 -5.221 0.000291 ***
## Orientation2-1:Alignment2-1 1.786e-02 6.660e-03 5.896e+04 2.682 0.007326 **
## Congruency2-1:Alignment2-1 -4.327e-02 8.749e-03 1.169e+01 -4.945 0.000367 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.813e-02 1.905e-02 9.163e+00 3.052 0.013476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00264032 (tol = 0.002, component 1)summary(rePCA(lmm_E3_rt_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.7311 0.5337 0.09359
## Proportion of Variance 0.6454 0.3440 0.01058
## Cumulative Proportion 0.6454 0.9894 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.1085 0.06498 1.584e-05
## Proportion of Variance 0.7361 0.26393 0.000e+00
## Cumulative Proportion 0.7361 1.00000 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
StimGroup:Con_Ali.
file_E3_rt_etd4 <- file.path(dir_lmm, "lmm_E3_rt_etd4.rds")
# fit the max model
if (!file.exists(file_E3_rt_etd4)) {
lmm_E3_rt_etd4 <- lmer(
log(RT) ~ Orientation * Congruency * Alignment + Cue + CA +
(Ori_C + Ali_C | SubjCode # Con_C +
# + Con_Ali + Ori_Con + Ori_Ali
) + # Ori_Con_Ali
(0 + # Ali_C + Ori_C + Con_C +
Ori_Con + # Ori_Ali + Con_Ali +
Ori_Con_Ali | StimGroup),
data = df_E3_lmm |> dplyr::filter(isCorrect==1),
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_rt_etd4, file = file_E3_rt_etd4)
} else {
lmm_E3_rt_etd4 <- readRDS(file_E3_rt_etd4)
}
print(summary(lmm_E3_rt_etd4), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ali_C | SubjCode) + (0 + Ori_Con + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 61854.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3874 -0.6325 -0.1841 0.4227 8.3233
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0462913 0.21515
## Ori_C 0.0867930 0.29461 -0.02
## Ali_C 0.0014940 0.03865 0.16 0.14
## StimGroup Ori_Con 0.0002798 0.01673
## Ori_Con_Ali 0.0017631 0.04199 -0.64
## Residual 0.1625771 0.40321
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.817e+00 1.971e-02 1.190e+02 345.832 < 2e-16 ***
## Orientation2-1 3.907e-02 2.710e-02 1.191e+02 1.442 0.151971
## Congruency2-1 4.943e-02 3.335e-03 5.898e+04 14.823 < 2e-16 ***
## Alignment2-1 -2.174e-03 4.856e-03 1.174e+02 -0.448 0.655166
## Cue2-1 4.154e-02 3.322e-03 5.897e+04 12.506 < 2e-16 ***
## CA2-1 5.523e-02 3.350e-03 5.902e+04 16.488 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.881e-02 8.512e-03 9.424e+00 -5.734 0.000237 ***
## Orientation2-1:Alignment2-1 1.799e-02 6.660e-03 5.896e+04 2.701 0.006923 **
## Congruency2-1:Alignment2-1 -4.351e-02 6.663e-03 5.906e+04 -6.531 6.59e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.822e-02 1.881e-02 9.037e+00 3.095 0.012753 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00373015 (tol = 0.002, component 1)summary(rePCA(lmm_E3_rt_etd4))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 0.7310 0.5336 0.09359
## Proportion of Variance 0.6455 0.3439 0.01058
## Cumulative Proportion 0.6455 0.9894 1.00000
##
## $StimGroup
## Importance of components:
## [,1] [,2]
## Standard deviation 0.1078 0.03071
## Proportion of Variance 0.9249 0.07507
## Cumulative Proportion 0.9249 1.000007.2.1.5 The optimal model
anova(lmm_E3_rt_rdc, lmm_E3_rt_etd4, refit=FALSE)According to BIC, the extended model is used as the optimal model.
lmm_E3_rt_opt <- lmm_E3_rt_etd4
print(summary(lmm_E3_rt_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: log(RT) ~ Orientation * Congruency * Alignment + Cue + CA + (Ori_C + Ali_C | SubjCode) + (0 + Ori_Con + Ori_Con_Ali | StimGroup)
## Data: dplyr::filter(df_E3_lmm, isCorrect == 1)
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 61854.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3874 -0.6325 -0.1841 0.4227 8.3233
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 0.0462913 0.21515
## Ori_C 0.0867930 0.29461 -0.02
## Ali_C 0.0014940 0.03865 0.16 0.14
## StimGroup Ori_Con 0.0002798 0.01673
## Ori_Con_Ali 0.0017631 0.04199 -0.64
## Residual 0.1625771 0.40321
## Number of obs: 59327, groups: SubjCode, 120; StimGroup, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.817e+00 1.971e-02 1.190e+02 345.832 < 2e-16 ***
## Orientation2-1 3.907e-02 2.710e-02 1.191e+02 1.442 0.151971
## Congruency2-1 4.943e-02 3.335e-03 5.898e+04 14.823 < 2e-16 ***
## Alignment2-1 -2.174e-03 4.856e-03 1.174e+02 -0.448 0.655166
## Cue2-1 4.154e-02 3.322e-03 5.897e+04 12.506 < 2e-16 ***
## CA2-1 5.523e-02 3.350e-03 5.902e+04 16.488 < 2e-16 ***
## Orientation2-1:Congruency2-1 -4.881e-02 8.512e-03 9.424e+00 -5.734 0.000237 ***
## Orientation2-1:Alignment2-1 1.799e-02 6.660e-03 5.896e+04 2.701 0.006923 **
## Congruency2-1:Alignment2-1 -4.351e-02 6.663e-03 5.906e+04 -6.531 6.59e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 5.822e-02 1.881e-02 9.037e+00 3.095 0.012753 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.00373015 (tol = 0.002, component 1)7.2.2 Estimated marginal means
emm_E3_rt <- emmeans(lmm_E3_rt_opt, ~ Orientation + Congruency + Alignment,
pbkrtest.limit = 2e5,
lmerTest.limit = 2e5)
# emmip(regrid(emm_E3_rt), Congruency ~ Alignment | Orientation, CIs = TRUE)
summary(emm_E3_rt, type = "response") # equivalent to regrid(emm_E3_rt)## Orientation Congruency Alignment response SE df lower.CL upper.CL
## upr con ali 853 20.9 126 812 895
## inv con ali 913 21.8 127 871 958
## upr inc ali 952 23.5 128 906 999
## inv inc ali 943 22.6 129 900 989
## upr con mis 874 21.5 124 833 918
## inv con mis 926 22.7 125 883 972
## upr inc mis 908 22.4 125 864 953
## inv inc mis 943 23.1 126 898 990
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## Intervals are back-transformed from the log scale7.2.2.1 Composite effect
Composite effect of RT:
emm_E3_rt_cf <- contrast(regrid(emm_E3_rt),
interaction = "pairwise",
by = "Orientation",
infer = TRUE)
summary(emm_E3_rt_cf[1:2], side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -65.8 10.3 124 -Inf -48.74 -6.382 <.0001
## con - inc ali - mis inv -13.3 10.9 125 -Inf 4.85 -1.213 0.1137
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedCongruency effect of rt in aligned condition:
emm_E3_rt_con <- contrast(regrid(emm_E3_rt),
interaction = "pairwise",
by = c("Orientation", "Alignment"),
infer = TRUE)
summary(emm_E3_rt_con[1:2], side="<")## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -99.3 8.07 126 -Inf -85.9 -12.306 <.0001
## con - inc inv ali -29.9 8.13 127 -Inf -16.4 -3.678 0.0002
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedEquivalence tests for composite effect:
summary(emm_E3_rt_cf[1:2], delta=50, level=.9)## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -65.8 10.3 124 -82.9 -48.74 1.535 0.9364
## con - inc ali - mis inv -13.3 10.9 125 -31.4 4.85 -3.362 0.0005
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 50
## P values are left-tailedsummary(emm_E3_rt_cf[1:2], delta=50, side=">")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -65.8 10.3 124 -82.9 Inf -1.535 0.9364
## con - inc ali - mis inv -13.3 10.9 125 -31.4 Inf 3.362 0.0005
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 50
## P values are right-tailedsummary(emm_E3_rt_cf[1:2], delta=50, side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -65.8 10.3 124 -Inf -48.74 -11.228 <.0001
## con - inc ali - mis inv -13.3 10.9 125 -Inf 4.85 -5.788 <.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 50
## P values are left-tailedplot_E3_cf_rt <- summary(emm_E3_rt, type = "response") %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(y = response, x = Alignment, color = Congruency, group = Congruency)) +
geom_point(position = position_dodge(width = 0.1), size = 2) +
geom_line(aes(linetype = Congruency), position = position_dodge(width = 0.1),
linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, position = position_dodge(width = 0.1),
show.legend = F) +
facet_grid(. ~Orientation, switch = "both") +
coord_cartesian(ylim = ylimit_cf_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = "Correct response times (ms)", fill = "Congruency") + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "", "", "", "", ""), color = sig_color, size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
NULL
# ggsave(filename = "E3_cf_rt.pdf", plot_E3_cf_rt, width = 8, height = 4.8)
plot_E3_cf_rt
7.2.2.2 Compare composite effects between upright and inverted faces
emm_E3_rt_cf_uni <- contrast(regrid(emm_E3_rt),
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)
summary(emm_E3_rt_cf_uni, side="<")## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -52.6 17.3 124 -Inf -23.9 -3.040 0.0014
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedEquivalence tests:
summary(emm_E3_rt_cf_uni, delta=60, level=.9)## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -52.6 17.3 124 -81.2 -23.9 -0.429 0.3344
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 60
## P values are left-tailedsummary(emm_E3_rt_cf_uni, delta=60, side=">")## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -52.6 17.3 124 -81.2 Inf 0.429 0.3344
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 60
## P values are right-tailedsummary(emm_E3_rt_cf_uni, delta=60, side="<")## Orientation_pairwise Congruency_pairwise Alignment_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv con - inc ali - mis -52.6 17.3 124 -Inf -23.9 -6.509 <.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 60
## P values are left-tailed7.2.2.3 Facilitation
emm_E3_rt_fi <- contrast(regrid(emm_E3_rt),
"pairwise",
by = c("Orientation", "Congruency"),
infer = TRUE,
adjust = "none")
# emmip(emm_E3_rt_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E3_rt_fi[1:4]## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -21.708 6.80 124 -35.2 -8.24 -3.190 0.0018
## ali - mis inv con -12.898 7.56 125 -27.9 2.07 -1.705 0.0906
## ali - mis upr inc 44.134 7.82 125 28.7 59.61 5.644 <.0001
## ali - mis inv inc 0.361 8.08 126 -15.6 16.35 0.045 0.9645
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95summary(emm_E3_rt_fi[1:2], side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -21.7 6.80 124 -Inf -10.432 -3.190 0.0009
## ali - mis inv con -12.9 7.56 125 -Inf -0.364 -1.705 0.0453
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are left-tailedEquivalence tests:
summary(emm_E3_rt_fi[1:2], delta=30, level=.9)## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -21.7 6.80 124 -33.0 -10.432 -1.219 0.1126
## ali - mis inv con -12.9 7.56 125 -25.4 -0.364 -2.261 0.0127
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 30
## P values are left-tailedsummary(emm_E3_rt_fi[1:2], delta=30, side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -21.7 6.80 124 -33.0 Inf 1.219 0.1126
## ali - mis inv con -12.9 7.56 125 -25.4 Inf 2.261 0.0127
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 30
## P values are right-tailedsummary(emm_E3_rt_fi[1:2], delta=30, side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -21.7 6.80 124 -Inf -10.432 -7.600 <.0001
## ali - mis inv con -12.9 7.56 125 -Inf -0.364 -5.672 <.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 30
## P values are left-tailed7.2.2.4 Interference
summary(emm_E3_rt_fi[3:4], side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 44.134 7.82 125 31.2 Inf 5.644 <.0001
## ali - mis inv inc 0.361 8.08 126 -13.0 Inf 0.045 0.4822
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## P values are right-tailedEquivalence tests:
summary(emm_E3_rt_fi[3:4], delta=30, level=.9)## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 44.134 7.82 125 31.2 57.1 1.807 0.9635
## ali - mis inv inc 0.361 8.08 126 -13.0 13.7 -3.669 0.0002
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 30
## P values are left-tailedsummary(emm_E3_rt_fi[3:4], delta=30, side=">")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 44.134 7.82 125 31.2 Inf 9.480 <.0001
## ali - mis inv inc 0.361 8.08 126 -13.0 Inf 3.759 0.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 30
## P values are right-tailedsummary(emm_E3_rt_fi[3:4], delta=30, side="<")## contrast Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 44.134 7.82 125 -Inf 57.1 1.807 0.9635
## ali - mis inv inc 0.361 8.08 126 -Inf 13.7 -3.669 0.0002
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 30
## P values are left-tailedplot_E3_cffi_rt <- summary(emm_E3_rt_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=100, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_rt) + # set the limit for y axis c(0, 1100)
labs(x = NULL, y = expression(RT~"(aligned-misaligned)")) + # set the names for main, x and y axis
theme(legend.position = "none") +
NULL
# ggsave(filename = "E3_fi_rt.pdf", plot_E3_cffi_rt, width = 7, height = 4.55)
plot_E3_cffi_rt
7.2.2.5 Comparing facilitation/interference between upright and inverted faces
# facilitation and interference of rt
emm_E3_rt_fi_ui <- contrast(regrid(emm_E3_rt),
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E3_rt_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -8.81 10.0 124 -28.6 11.0 -0.879 0.3809
## upr - inv ali - mis inc 43.77 11.1 125 21.7 65.8 3.926 0.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95Equivalence tests:
summary(emm_E3_rt_fi_ui[1:2], delta=50, level=.9)## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -8.81 10.0 124 -25.4 7.79 -4.112 <.0001
## upr - inv ali - mis inc 43.77 11.1 125 25.3 62.25 -0.558 0.2888
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.9
## Statistics are tests of equivalence with a threshold of 50
## P values are left-tailedsummary(emm_E3_rt_fi_ui[1:2], delta=50, side=">")## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -8.81 10.0 124 -25.4 Inf 4.112 <.0001
## upr - inv ali - mis inc 43.77 11.1 125 25.3 Inf 8.410 <.0001
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of noninferiority with a threshold of 50
## P values are right-tailedsummary(emm_E3_rt_fi_ui[1:2], delta=50, side="<")## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con -8.81 10.0 124 -Inf 7.79 -5.870 <.0001
## upr - inv ali - mis inc 43.77 11.1 125 -Inf 62.25 -0.558 0.2888
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.95
## Statistics are tests of nonsuperiority with a threshold of 50
## P values are left-tailed7.2.2.6 Comparisons between facilitation and interference
contrast(emm_E3_rt_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2]## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 22.4 10.4 124 1.81 43.04 2.154 0.0332
## faci-inte inv -12.5 11.2 125 -34.71 9.63 -1.119 0.2652
##
## Results are averaged over the levels of: Cue, CA
## Degrees-of-freedom method: inherited from satterthwaite when re-gridding
## Confidence level used: 0.957.2.3 Plot
plot_E3_cf_rt_ <- plot_E3_cf_rt +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E3_rt <- ggarrange(plot_E3_cf_rt_, plot_E3_cffi_rt,
labels = c("a", "b"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E3_rt
7.3 Plot
plot_E3 <- ggarrange(plot_E3_cf_d_, plot_E3_cffi_d,
plot_E3_cf_rt_, plot_E3_cffi_rt,
labels = c("A", "B", "C", "D"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 2,
ncol = 2)
plot_E3
8 Exploratory analysis: Inverse efficiency
8.1 Experiment 1 & 2
RM-ANOVA is used to analyze the inverse efficiency.
8.1.1 Calculate inverse efficiency
df_E12_acc <- df_E12_lmm %>%
group_by(SubjCode, Orientation, Congruency, Alignment) %>%
summarise(acc = mean(isCorrect),
.groups = "drop")
df_E12_rt <- df_E12_lmm %>%
filter(isCorrect==1) %>%
group_by(SubjCode, Orientation, Congruency, Alignment) %>%
summarise(rt = mean(RT),
.groups = "drop")
df_E12_ie <- left_join(df_E12_acc, df_E12_rt,
by = c("SubjCode", "Orientation", "Congruency", "Alignment")) %>%
mutate(IE = rt/acc) %>%
select(SubjCode, Orientation, Congruency, Alignment, IE)
df_E12_ie df_E12_ie <- df_E12_ie %>%
psychr::set_contr(c(Orientation, Congruency, Alignment)) %>%
psychr::add_lmmcols(IE ~ Orientation * Congruency * Alignment) ## old_name
## Ori_C Orientation2-1
## Con_C Congruency2-1
## Ali_C Alignment2-1
## Ori_Con Orientation2-1:Congruency2-1
## Ori_Ali Orientation2-1:Alignment2-1
## Con_Ali Congruency2-1:Alignment2-1
## Ori_Con_Ali Orientation2-1:Congruency2-1:Alignment2-1## [1] "Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali"8.1.2 Fitting linear mixed-effects models
8.1.2.1 The zero-correlation-parameter model
file_E12_ie_zcp <- file.path(dir_lmm, "lmm_E12_ie_zcp.rds")
# fit the max model
if (!file.exists(file_E12_ie_zcp)) {
lmm_E12_ie_zcp <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_zcp, file = file_E12_ie_zcp)
} else {
lmm_E12_ie_zcp <- readRDS(file_E12_ie_zcp)
}
print(summary(lmm_E12_ie_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9157.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4496 -0.3196 -0.0310 0.2332 7.1264
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 1.757e+05 419.13798
## SubjCode.1 Ori_C 3.185e+05 564.34717
## SubjCode.2 Con_C 6.671e+04 258.28403
## SubjCode.3 Ali_C 5.541e+03 74.43744
## SubjCode.4 Ori_Con 8.352e+04 288.99234
## SubjCode.5 Ori_Ali 8.874e-04 0.02979
## SubjCode.6 Con_Ali 2.880e+03 53.66181
## SubjCode.7 Ori_Con_Ali 6.694e+04 258.73607
## Residual 3.117e+04 176.56247
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.38 79.00 28.588 < 2e-16 ***
## Orientation2-1 235.59 64.62 79.00 3.646 0.000476 ***
## Congruency2-1 272.64 32.07 79.00 8.500 9.20e-13 ***
## Alignment2-1 -27.30 16.25 79.00 -1.680 0.096878 .
## Orientation2-1:Congruency2-1 -217.06 42.70 79.00 -5.083 2.43e-06 ***
## Orientation2-1:Alignment2-1 77.93 27.92 79.00 2.791 0.006577 **
## Congruency2-1:Alignment2-1 -167.41 28.55 79.00 -5.863 1.01e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 62.88 79.00 1.788 0.077637 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues8.1.2.2 The reduced model
summary(rePCA(lmm_E12_ie_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 3.1963 2.3739 1.6368 1.46541 1.46285 0.4216 0.3039 0.0001687
## Proportion of Variance 0.4425 0.2441 0.1160 0.09301 0.09269 0.0077 0.0040 0.0000000
## Cumulative Proportion 0.4425 0.6866 0.8026 0.89562 0.98830 0.9960 1.0000 1.0000000Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Ori_Ali;
file_E12_ie_rdc <- file.path(dir_lmm, "lmm_E12_ie_rdc.rds")
# fit the reduced model
if (!file.exists(file_E12_ie_rdc)) {
lmm_E12_ie_rdc <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali || SubjCode),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_rdc, file = file_E12_ie_rdc)
} else {
lmm_E12_ie_rdc <- readRDS(file_E12_ie_rdc)
}
print(summary(lmm_E12_ie_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Con_Ali + Ori_Con_Ali || SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9157.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4496 -0.3196 -0.0310 0.2332 7.1265
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 175679 419.14
## SubjCode.1 Ori_C 318488 564.35
## SubjCode.2 Con_C 66709 258.28
## SubjCode.3 Ali_C 5541 74.44
## SubjCode.4 Ori_Con 83518 288.99
## SubjCode.5 Con_Ali 2879 53.66
## SubjCode.6 Ori_Con_Ali 66942 258.73
## Residual 31174 176.56
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.38 79.00 28.588 < 2e-16 ***
## Orientation2-1 235.59 64.62 79.00 3.646 0.000476 ***
## Congruency2-1 272.64 32.07 79.00 8.501 9.20e-13 ***
## Alignment2-1 -27.30 16.25 79.00 -1.680 0.096877 .
## Orientation2-1:Congruency2-1 -217.06 42.70 79.00 -5.083 2.43e-06 ***
## Orientation2-1:Alignment2-1 77.93 27.92 79.00 2.791 0.006577 **
## Congruency2-1:Alignment2-1 -167.41 28.55 79.00 -5.863 1.01e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 62.88 79.00 1.788 0.077636 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(lmm_E12_ie_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 3.1963 2.3739 1.6368 1.46538 1.46283 0.4216 0.3039
## Proportion of Variance 0.4425 0.2441 0.1160 0.09301 0.09268 0.0077 0.0040
## Cumulative Proportion 0.4425 0.6866 0.8026 0.89562 0.98830 0.9960 1.00008.1.2.3 The extended model
file_E12_ie_etd <- file.path(dir_lmm, "lmm_E12_ie_etd.rds")
# fit the reduced model
if (!file.exists(file_E12_ie_etd)) {
lmm_E12_ie_etd <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Con_Ali + # Ori_Ali +
Ori_Con_Ali | SubjCode),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_etd, file = file_E12_ie_etd)
} else {
lmm_E12_ie_etd <- readRDS(file_E12_ie_etd)
}
print(summary(lmm_E12_ie_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Con_Ali + Ori_Con_Ali | SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 8968.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3901 -0.3550 -0.0354 0.3611 4.3024
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 177200 421.0
## Ori_C 324947 570.0 0.32
## Con_C 73115 270.4 0.31 -0.44
## Ali_C 13465 116.0 0.45 0.39 0.13
## Ori_Con 109267 330.6 -0.77 -0.03 -0.53 -0.67
## Con_Ali 40765 201.9 -0.35 -0.51 0.29 0.18 0.01
## Ori_Con_Ali 193700 440.1 -0.24 -0.56 0.15 -0.36 0.19 0.75
## Residual 18457 135.9
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.37 79.04 28.593 < 2e-16 ***
## Orientation2-1 235.59 64.63 78.97 3.645 0.000477 ***
## Congruency2-1 272.64 32.08 78.97 8.498 9.32e-13 ***
## Alignment2-1 -27.30 16.84 83.63 -1.621 0.108743
## Orientation2-1:Congruency2-1 -217.06 42.75 79.12 -5.078 2.48e-06 ***
## Orientation2-1:Alignment2-1 77.93 21.48 158.06 3.628 0.000385 ***
## Congruency2-1:Alignment2-1 -167.41 31.16 89.49 -5.373 6.10e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 65.32 84.22 1.721 0.088908 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## Model failed to converge with max|grad| = 0.0046076 (tol = 0.002, component 1)summary(rePCA(lmm_E12_ie_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 5.1015 3.7399 2.4886 1.54805 1.2393 0.61993 0.0002099
## Proportion of Variance 0.5151 0.2768 0.1226 0.04743 0.0304 0.00761 0.0000000
## Cumulative Proportion 0.5151 0.7920 0.9146 0.96199 0.9924 1.00000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ali_CandCon_Ali;
file_E12_ie_etd2 <- file.path(dir_lmm, "lmm_E12_ie_etd2.rds")
# fit the reduced model
if (!file.exists(file_E12_ie_etd2)) {
lmm_E12_ie_etd2 <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + # Ali_C +
Ori_Con + # Con_Ali + Ori_Ali +
Ori_Con_Ali | SubjCode),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_etd2, file = file_E12_ie_etd2)
} else {
lmm_E12_ie_etd2 <- readRDS(file_E12_ie_etd2)
}
print(summary(lmm_E12_ie_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ori_Con + Ori_Con_Ali | SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9044.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8971 -0.3912 -0.0504 0.3572 6.9800
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 175314 418.7
## Ori_C 317206 563.2 0.33
## Con_C 65356 255.6 0.33 -0.47
## Ori_Con 80284 283.3 -0.90 -0.04 -0.66
## Ori_Con_Ali 70035 264.6 -0.41 -0.94 0.25 0.24
## Residual 33907 184.1
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.38 79.02 28.590 < 2e-16 ***
## Orientation2-1 235.59 64.63 78.98 3.645 0.000477 ***
## Congruency2-1 272.64 32.08 79.00 8.500 9.23e-13 ***
## Alignment2-1 -27.30 14.56 315.95 -1.876 0.061625 .
## Orientation2-1:Congruency2-1 -217.06 43.03 84.44 -5.045 2.56e-06 ***
## Orientation2-1:Alignment2-1 77.93 29.11 315.95 2.677 0.007824 **
## Congruency2-1:Alignment2-1 -167.41 29.11 315.95 -5.750 2.10e-08 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 65.32 135.78 1.721 0.087483 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(lmm_E12_ie_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 3.5466 2.6827 0.97892 0.39110 0.0001945
## Proportion of Variance 0.6022 0.3446 0.04588 0.00732 0.0000000
## Cumulative Proportion 0.6022 0.9468 0.99268 1.00000 1.0000000Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Con_CandOri_Con_Ali;
file_E12_ie_etd3 <- file.path(dir_lmm, "lmm_E12_ie_etd3.rds")
# fit the reduced model
if (!file.exists(file_E12_ie_etd3)) {
lmm_E12_ie_etd3 <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + # Ali_C + Con_C +
Ori_Con | SubjCode # Con_Ali + Ori_Ali + Ori_Con_Ali
),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_etd3, file = file_E12_ie_etd3)
} else {
lmm_E12_ie_etd3 <- readRDS(file_E12_ie_etd3)
}
print(summary(lmm_E12_ie_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Ori_Con | SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9200.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2966 -0.4005 -0.0051 0.3281 7.2966
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 172408 415.2
## Ori_C 305181 552.4 0.33
## Ori_Con 70047 264.7 -0.96 -0.05
## Residual 57902 240.6
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.39 79.00 28.582 < 2e-16 ***
## Orientation2-1 235.59 64.63 79.00 3.645 0.000476 ***
## Congruency2-1 272.64 19.02 474.00 14.332 < 2e-16 ***
## Alignment2-1 -27.30 19.02 474.00 -1.435 0.151856
## Orientation2-1:Congruency2-1 -217.06 48.20 114.80 -4.504 1.62e-05 ***
## Orientation2-1:Alignment2-1 77.93 38.05 474.00 2.048 0.041084 *
## Congruency2-1:Alignment2-1 -167.41 38.05 474.00 -4.400 1.34e-05 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 76.09 474.00 1.477 0.140219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E12_ie_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3]
## Standard deviation 2.4610 1.8443 6.284e-05
## Proportion of Variance 0.6404 0.3596 0.000e+00
## Cumulative Proportion 0.6404 1.0000 1.000e+00file_E12_ie_etd4 <- file.path(dir_lmm, "lmm_E12_ie_etd4.rds")
# fit the reduced model
if (!file.exists(file_E12_ie_etd4)) {
lmm_E12_ie_etd4 <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C | SubjCode# Ali_C + Con_C +
# Con_Ali + Ori_Ali + Ori_Con_Ali + Ori_Con
),
data = df_E12_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E12_ie_etd4, file = file_E12_ie_etd4)
} else {
lmm_E12_ie_etd4 <- readRDS(file_E12_ie_etd4)
}
print(summary(lmm_E12_ie_etd4), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C | SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9244.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6424 -0.4165 -0.0259 0.3123 7.9382
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 171590 414.2
## Ori_C 302150 549.7 0.34
## Residual 63852 252.7
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.38 79.00 28.588 < 2e-16 ***
## Orientation2-1 235.59 64.62 79.00 3.646 0.000476 ***
## Congruency2-1 272.64 19.98 474.00 13.648 < 2e-16 ***
## Alignment2-1 -27.30 19.98 474.00 -1.367 0.172335
## Orientation2-1:Congruency2-1 -217.06 39.95 474.00 -5.433 8.88e-08 ***
## Orientation2-1:Alignment2-1 77.93 39.95 474.00 1.950 0.051706 .
## Congruency2-1:Alignment2-1 -167.41 39.95 474.00 -4.190 3.33e-05 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 79.91 474.00 1.407 0.160105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.1.2.4 The optimal model
anova(lmm_E12_ie_rdc, lmm_E12_ie_etd4, refit=FALSE)According to BIC, the extended model is used as the optimal model.
lmm_E12_ie_opt <- lmm_E12_ie_rdc
print(summary(lmm_E12_ie_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Con_Ali + Ori_Con_Ali || SubjCode)
## Data: df_E12_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 9157.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4496 -0.3196 -0.0310 0.2332 7.1265
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 175679 419.14
## SubjCode.1 Ori_C 318488 564.35
## SubjCode.2 Con_C 66709 258.28
## SubjCode.3 Ali_C 5541 74.44
## SubjCode.4 Ori_Con 83518 288.99
## SubjCode.5 Con_Ali 2879 53.66
## SubjCode.6 Ori_Con_Ali 66942 258.73
## Residual 31174 176.56
## Number of obs: 640, groups: SubjCode, 80
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1354.45 47.38 79.00 28.588 < 2e-16 ***
## Orientation2-1 235.59 64.62 79.00 3.646 0.000476 ***
## Congruency2-1 272.64 32.07 79.00 8.501 9.20e-13 ***
## Alignment2-1 -27.30 16.25 79.00 -1.680 0.096877 .
## Orientation2-1:Congruency2-1 -217.06 42.70 79.00 -5.083 2.43e-06 ***
## Orientation2-1:Alignment2-1 77.93 27.92 79.00 2.791 0.006577 **
## Congruency2-1:Alignment2-1 -167.41 28.55 79.00 -5.863 1.01e-07 ***
## Orientation2-1:Congruency2-1:Alignment2-1 112.42 62.88 79.00 1.788 0.077636 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.1.3 Estimated marginal means
# hit and false alarm
emm_E12_ie <- emmeans(lmm_E12_ie_opt,
~ Orientation + Congruency + Alignment)
emm_E12_ie## Orientation Congruency Alignment emmean SE df lower.CL upper.CL
## upr con ali 1023 62.3 192 900 1146
## inv con ali 1357 62.3 192 1234 1480
## upr inc ali 1516 62.3 192 1393 1639
## inv inc ali 1576 62.3 192 1453 1699
## upr con mis 1069 62.3 192 946 1192
## inv con mis 1424 62.3 192 1301 1547
## upr inc mis 1338 62.3 192 1215 1461
## inv inc mis 1532 62.3 192 1409 1655
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.1.3.1 Composite effect
Composite effect of d’:
emm_E12_cf_ie <- contrast(emm_E12_ie,
interaction = "pairwise",
simple = c("Congruency", "Alignment"),
infer = TRUE)
summary(emm_E12_cf_ie[1:2], side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -224 42.5 157 -Inf -153.3 -5.265 <.0001
## con - inc ali - mis inv -111 42.5 157 -Inf -40.9 -2.618 0.0049
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedCongruency effect of d’ in aligned condition:
emm_E12_con_ie <- contrast(emm_E12_ie,
interaction = "pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E12_con_ie[1:2], side="<")## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -493 44 216 -Inf -420 -11.206 <.0001
## con - inc inv ali -220 44 216 -Inf -147 -4.994 <.0001
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedplot_E12_cf_ie <- emm_E12_ie %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, emmean, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(800, 1800)) + # set the limit for y axis c(0, 1100)
labs(y = "Inverse efficiency (RT/Accuracy)", fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "**", "", "", "", ""),
color = "red",
size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
theme(axis.title.x = element_text(margin = margin(t = 0))) +
NULL
# ggsave(filename = "ccf_ie.pdf", plot_ccf_ie, width = 8, height = 6)
plot_E12_cf_ie
8.1.3.2 Compare composite effects between upright and inverted faces
contrast(emm_E12_cf_ie,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## Congruency_pairwise = con - inc, Alignment_pairwise = ali - mis:
## Orientation_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv -112 62.9 79 -238 12.7 -1.788 0.0776
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.1.3.3 Facilitation and interference
# facilitation and interference
emm_E12_ie_fi <- contrast(emm_E12_ie,
interaction = "pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E12_ie_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E12_ie_fi[1:4]## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -45.5 30.2 311 -104.9 13.81 -1.510 0.1321
## ali - mis inv con -67.3 30.2 311 -126.6 -7.91 -2.230 0.0265
## ali - mis upr inc 178.1 30.2 311 118.7 237.43 5.904 <.0001
## ali - mis inv inc 43.9 30.2 311 -15.4 103.29 1.457 0.1462
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95summary(emm_E12_ie_fi[1:2], side="<")## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -45.5 30.2 311 -Inf 4.22 -1.510 0.0661
## ali - mis inv con -67.3 30.2 311 -Inf -17.50 -2.230 0.0132
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedsummary(emm_E12_ie_fi[3:4], side=">")## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 178.1 30.2 311 128.32 Inf 5.904 <.0001
## ali - mis inv inc 43.9 30.2 311 -5.82 Inf 1.457 0.0731
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are right-tailedplot_E12_cffi_ie <- summary(emm_E12_ie_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=300, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_ie) +
labs(x = NULL,
y = "Inverse efficiency (aligned-misaligned)") + # set the names for main, x and y axis
theme(legend.position = "none",
axis.title.x = element_text(margin = margin(t = 0))) +
NULL
# ggsave(filename = "E12_fi_ie.pdf", plot_E12_cffi_ie, width = 7, height = 4.55)
plot_E12_cffi_ie
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of
emm_E12_ie_fi_ui <- contrast(emm_E12_ie,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E12_ie_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con 21.7 42 156 -61.3 105 0.517 0.6062
## upr - inv ali - mis inc 134.1 42 156 51.1 217 3.190 0.0017
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.1.3.4 Comparisons between facilitation and interference
contrast(emm_E12_ie_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 132.5 42.8 154 47.9 217.2 3.093 0.0023
## faci-inte inv -23.3 42.8 154 -108.0 61.3 -0.544 0.5870
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.1.4 Bayesian hierarchical model
8.1.4.1 Prior predictive check
brm_E12_ie_ppc <- brm(IE ~ Orientation * Congruency * Alignment +
(Orientation * Congruency * Alignment | SubjCode),
data = df_E12_ie,
prior = c(prior(normal(1000, 300), class = Intercept),
prior(normal(0, 200), class = b),
prior(normal(0, 50), class = sigma),
prior(normal(0, 50), class = sd),
prior(lkj(2), class = cor)),
sample_prior = "only",
chains = 10,
iter = 4000,
warmup = 2000,
file = file.path("brms", "brmsfit_E12_ie_ppc.rds"))
summary(brm_E12_ie_ppc)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: IE ~ Orientation * Congruency * Alignment + (Orientation * Congruency * Alignment | SubjCode)
## Data: df_E12_ie (Number of observations: 640)
## Draws: 10 chains, each with iter = 4000; warmup = 2000; thin = 1;
## total post-warmup draws = 20000
##
## Multilevel Hyperparameters:
## ~SubjCode (Number of levels: 80)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 39.63 29.97 1.56 111.33 1.00 15630 8438
## sd(Orientation2M1) 40.08 30.48 1.63 113.06 1.00 17280 9289
## sd(Congruency2M1) 39.92 30.33 1.57 112.80 1.00 16332 8743
## sd(Alignment2M1) 40.08 30.15 1.79 112.81 1.00 17415 9193
## sd(Orientation2M1:Congruency2M1) 39.69 29.97 1.51 112.38 1.00 14535 8333
## sd(Orientation2M1:Alignment2M1) 39.95 29.93 1.72 111.68 1.00 16103 9231
## sd(Congruency2M1:Alignment2M1) 39.56 29.88 1.59 110.92 1.00 16179 9445
## sd(Orientation2M1:Congruency2M1:Alignment2M1) 40.05 30.46 1.61 112.88 1.00 17638 9178
## cor(Intercept,Orientation2M1) 0.00 0.30 -0.57 0.57 1.00 28668 13982
## cor(Intercept,Congruency2M1) -0.00 0.30 -0.57 0.57 1.00 30070 13812
## cor(Orientation2M1,Congruency2M1) 0.00 0.30 -0.58 0.58 1.00 24092 15209
## cor(Intercept,Alignment2M1) -0.00 0.30 -0.58 0.57 1.00 28267 14574
## cor(Orientation2M1,Alignment2M1) 0.00 0.30 -0.56 0.57 1.00 24148 14547
## cor(Congruency2M1,Alignment2M1) 0.00 0.30 -0.58 0.57 1.00 19516 14754
## cor(Intercept,Orientation2M1:Congruency2M1) -0.00 0.30 -0.58 0.57 1.00 27467 14928
## cor(Orientation2M1,Orientation2M1:Congruency2M1) -0.00 0.30 -0.57 0.57 1.00 23699 14819
## cor(Congruency2M1,Orientation2M1:Congruency2M1) 0.00 0.30 -0.58 0.58 1.00 20550 14380
## cor(Alignment2M1,Orientation2M1:Congruency2M1) -0.00 0.30 -0.58 0.58 1.00 16124 14644
## cor(Intercept,Orientation2M1:Alignment2M1) 0.00 0.30 -0.57 0.59 1.00 26812 13840
## cor(Orientation2M1,Orientation2M1:Alignment2M1) 0.00 0.30 -0.57 0.58 1.00 21154 14237
## cor(Congruency2M1,Orientation2M1:Alignment2M1) 0.00 0.30 -0.57 0.57 1.00 19221 15847
## cor(Alignment2M1,Orientation2M1:Alignment2M1) -0.00 0.30 -0.58 0.58 1.00 17584 15328
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Alignment2M1) 0.00 0.30 -0.57 0.58 1.00 16083 15156
## cor(Intercept,Congruency2M1:Alignment2M1) -0.00 0.30 -0.57 0.57 1.00 27683 14358
## cor(Orientation2M1,Congruency2M1:Alignment2M1) -0.00 0.30 -0.59 0.58 1.00 24323 14628
## cor(Congruency2M1,Congruency2M1:Alignment2M1) -0.00 0.30 -0.58 0.58 1.00 18217 14574
## cor(Alignment2M1,Congruency2M1:Alignment2M1) -0.00 0.30 -0.57 0.57 1.00 17445 15553
## cor(Orientation2M1:Congruency2M1,Congruency2M1:Alignment2M1) -0.00 0.30 -0.58 0.58 1.00 15259 14664
## cor(Orientation2M1:Alignment2M1,Congruency2M1:Alignment2M1) 0.00 0.30 -0.57 0.58 1.00 14013 15916
## cor(Intercept,Orientation2M1:Congruency2M1:Alignment2M1) -0.00 0.30 -0.58 0.58 1.00 27715 14723
## cor(Orientation2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.00 0.30 -0.58 0.58 1.00 23357 14473
## cor(Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.00 0.30 -0.58 0.57 1.00 19399 14483
## cor(Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.00 0.30 -0.57 0.57 1.00 17248 15611
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.00 0.30 -0.58 0.59 1.00 15744 15176
## cor(Orientation2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.00 0.30 -0.58 0.58 1.00 14121 15532
## cor(Congruency2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.00 0.30 -0.58 0.58 1.00 12435 15481
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 999.27 299.90 408.48 1578.08 1.00 28902 14709
## Orientation2M1 0.14 199.77 -388.79 395.13 1.00 26676 13990
## Congruency2M1 -1.18 198.90 -390.17 382.86 1.00 29174 14902
## Alignment2M1 -1.16 200.46 -389.76 393.41 1.00 27481 14741
## Orientation2M1:Congruency2M1 -1.29 201.06 -390.64 392.75 1.00 27597 15491
## Orientation2M1:Alignment2M1 0.02 200.13 -392.74 392.80 1.00 28575 14815
## Congruency2M1:Alignment2M1 1.34 199.69 -390.69 393.17 1.00 27035 14735
## Orientation2M1:Congruency2M1:Alignment2M1 -0.50 201.99 -396.50 394.45 1.00 24974 15198
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 40.04 29.59 1.60 110.27 1.00 14794 7048
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).emmb_E12_ie_ppc <- emmeans(brm_E12_ie_ppc, ~ Orientation + Congruency + Alignment)
emmb_E12_ie_ppc## Orientation Congruency Alignment emmean lower.HPD upper.HPD
## upr con ali 1003 290 1691
## inv con ali 1004 307 1704
## upr inc ali 1001 295 1702
## inv inc ali 998 302 1702
## upr con mis 1001 290 1693
## inv con mis 997 299 1702
## upr inc mis 996 285 1672
## inv inc mis 1000 257 1667
##
## Point estimate displayed: median
## HPD interval probability: 0.95emmip(brm_E12_ie_ppc,
Congruency ~ Alignment | Orientation,
CIs = TRUE,
adjust = "sidak")
8.1.4.2 Model fitting
brm_E12_ie <- brm(IE ~ Orientation * Congruency * Alignment +
(Orientation * Congruency * Alignment | SubjCode),
data = df_E12_ie,
prior = c(prior(normal(1000, 300), class = Intercept),
prior(normal(0, 200), class = b),
prior(normal(0, 50), class = sigma),
prior(normal(0, 50), class = sd),
prior(lkj(2), class = cor)),
save_pars = save_pars(all = TRUE),
chains = 10,
iter = 8000,
warmup = 2000,
control = list(adapt_delta = .999,
max_treedepth = 15),
file = file.path("brms", "brmsfit_E12_ie.rds"))
summary(brm_E12_ie)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: IE ~ Orientation * Congruency * Alignment + (Orientation * Congruency * Alignment | SubjCode)
## Data: df_E12_ie (Number of observations: 640)
## Draws: 10 chains, each with iter = 8000; warmup = 2000; thin = 1;
## total post-warmup draws = 60000
##
## Multilevel Hyperparameters:
## ~SubjCode (Number of levels: 80)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 316.01 16.81 284.57 350.27 1.00 7193 14422
## sd(Orientation2M1) 395.74 19.90 358.60 436.40 1.00 17782 31977
## sd(Congruency2M1) 210.81 16.37 180.05 244.36 1.00 21816 36357
## sd(Alignment2M1) 83.36 16.03 50.88 114.24 1.00 12113 18455
## sd(Orientation2M1:Congruency2M1) 211.61 23.70 165.13 258.09 1.00 15207 30125
## sd(Orientation2M1:Alignment2M1) 103.34 28.19 45.13 156.41 1.00 11616 13341
## sd(Congruency2M1:Alignment2M1) 106.05 29.06 47.09 161.29 1.00 10046 15971
## sd(Orientation2M1:Congruency2M1:Alignment2M1) 132.17 50.48 26.89 229.69 1.00 7725 11063
## cor(Intercept,Orientation2M1) 0.23 0.07 0.09 0.36 1.00 10908 20666
## cor(Intercept,Congruency2M1) 0.28 0.08 0.11 0.43 1.00 20780 34548
## cor(Orientation2M1,Congruency2M1) -0.36 0.08 -0.51 -0.21 1.00 23962 35450
## cor(Intercept,Alignment2M1) 0.32 0.13 0.06 0.58 1.00 28550 29278
## cor(Orientation2M1,Alignment2M1) 0.31 0.12 0.07 0.56 1.00 37506 32347
## cor(Congruency2M1,Alignment2M1) 0.12 0.15 -0.18 0.41 1.00 41045 39834
## cor(Intercept,Orientation2M1:Congruency2M1) -0.69 0.08 -0.84 -0.51 1.00 24930 34266
## cor(Orientation2M1,Orientation2M1:Congruency2M1) 0.01 0.10 -0.19 0.21 1.00 38258 42188
## cor(Congruency2M1,Orientation2M1:Congruency2M1) -0.47 0.11 -0.67 -0.25 1.00 39222 39977
## cor(Alignment2M1,Orientation2M1:Congruency2M1) -0.53 0.14 -0.77 -0.23 1.00 36607 42319
## cor(Intercept,Orientation2M1:Alignment2M1) 0.41 0.16 0.08 0.69 1.00 50462 35374
## cor(Orientation2M1,Orientation2M1:Alignment2M1) 0.29 0.16 -0.03 0.60 1.00 55389 36368
## cor(Congruency2M1,Orientation2M1:Alignment2M1) -0.31 0.18 -0.62 0.07 1.00 47142 33238
## cor(Alignment2M1,Orientation2M1:Alignment2M1) 0.36 0.20 -0.08 0.71 1.00 40795 34329
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Alignment2M1) -0.31 0.18 -0.65 0.07 1.00 54457 38566
## cor(Intercept,Congruency2M1:Alignment2M1) -0.31 0.16 -0.61 0.01 1.00 48334 38995
## cor(Orientation2M1,Congruency2M1:Alignment2M1) -0.45 0.15 -0.73 -0.14 1.00 41046 35275
## cor(Congruency2M1,Congruency2M1:Alignment2M1) 0.25 0.18 -0.11 0.58 1.00 54833 41011
## cor(Alignment2M1,Congruency2M1:Alignment2M1) 0.14 0.23 -0.35 0.53 1.00 18440 26660
## cor(Orientation2M1:Congruency2M1,Congruency2M1:Alignment2M1) -0.00 0.19 -0.37 0.38 1.00 50873 38870
## cor(Orientation2M1:Alignment2M1,Congruency2M1:Alignment2M1) -0.14 0.24 -0.60 0.32 1.00 26664 40430
## cor(Intercept,Orientation2M1:Congruency2M1:Alignment2M1) -0.20 0.20 -0.57 0.20 1.00 57365 33983
## cor(Orientation2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.52 0.18 -0.81 -0.09 1.00 35068 18871
## cor(Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.11 0.21 -0.31 0.52 1.00 63975 37881
## cor(Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.24 0.23 -0.65 0.22 1.00 45164 36664
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.11 0.21 -0.32 0.51 1.00 55894 39575
## cor(Orientation2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.08 0.25 -0.56 0.40 1.00 30329 42955
## cor(Congruency2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.41 0.23 -0.12 0.78 1.00 21439 26695
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1342.07 34.95 1272.09 1409.72 1.00 2912 5560
## Orientation2M1 225.61 44.69 137.64 312.63 1.00 5761 13335
## Congruency2M1 267.88 26.27 216.27 319.45 1.00 10812 23631
## Alignment2M1 -28.92 15.59 -59.50 1.48 1.00 30300 42435
## Orientation2M1:Congruency2M1 -206.54 33.87 -272.24 -139.45 1.00 10628 23509
## Orientation2M1:Alignment2M1 75.04 27.27 21.22 128.38 1.00 38179 40119
## Congruency2M1:Alignment2M1 -163.24 27.57 -217.34 -109.21 1.00 40608 42814
## Orientation2M1:Congruency2M1:Alignment2M1 107.70 50.59 8.37 206.63 1.00 59753 44676
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 157.69 11.01 135.30 178.74 1.00 4641 7839
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).8.1.4.3 Bayes factor
emmb_E12_ie <- emmeans(brm_E12_ie, ~ Orientation + Congruency + Alignment)
emmip(brm_E12_ie,
Congruency ~ Alignment | Orientation,
CIs = TRUE,
adjust = "sidak")
8.1.4.3.1 Composite effect
emm_E12_cfb <- contrast(emmb_E12_ie,
interaction = "pairwise",
simple = c("Congruency", "Alignment"))
emm_E12_cfb## Orientation = upr:
## Congruency_pairwise Alignment_pairwise estimate lower.HPD upper.HPD
## con - inc ali - mis -217 -290 -146.9
##
## Orientation = inv:
## Congruency_pairwise Alignment_pairwise estimate lower.HPD upper.HPD
## con - inc ali - mis -109 -186 -35.4
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E12_cfb <- gather_emmeans_draws(emm_E12_cfb) %>%
pivot_wider(names_from = Orientation,
values_from = .value) %>%
mutate(cf_diff = upr - inv) %>%
select(-c(.chain, .iteration))
head(draws_E12_cfb)emm_E12_conb <- contrast(emmb_E12_ie,
"pairwise",
simple = "Congruency")
emm_E12_conb## Orientation = upr, Alignment = ali:
## contrast estimate lower.HPD upper.HPD
## con - inc -480 -556 -403.6
##
## Orientation = inv, Alignment = ali:
## contrast estimate lower.HPD upper.HPD
## con - inc -219 -280 -155.5
##
## Orientation = upr, Alignment = mis:
## contrast estimate lower.HPD upper.HPD
## con - inc -263 -343 -185.1
##
## Orientation = inv, Alignment = mis:
## contrast estimate lower.HPD upper.HPD
## con - inc -110 -179 -43.6
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E12_conb <- gather_emmeans_draws(emm_E12_conb) %>%
filter(Alignment == "ali") %>%
pivot_wider(names_from = Orientation,
values_from = .value) %>%
mutate(con_diff = upr - inv) %>%
select(-c(.chain, .iteration))
head(draws_E12_conb)bf_E12_cf_upr <- bayesfactor_pointnull(draws_E12_cfb$upr,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_cf_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.01e+05
##
## * Evidence Against The Null: 0as.numeric(bf_E12_cf_upr)## [1] 101151.5bf_E12_con_upr <- bayesfactor_pointnull(draws_E12_conb$upr,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_con_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.40e+12
##
## * Evidence Against The Null: 0as.numeric(bf_E12_con_upr)## [1] 1.397503e+12bf_E12_cf_inv <- bayesfactor_pointnull(draws_E12_cfb$inv,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_cf_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 10.81
##
## * Evidence Against The Null: 0bf_E12_con_inv <- bayesfactor_pointnull(draws_E12_conb$inv,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_con_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.89e+07
##
## * Evidence Against The Null: 0as.numeric(bf_E12_con_inv)## [1] 18912082bf_E12_cf_diff <- bayesfactor_pointnull(draws_E12_cfb$cf_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_cf_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## ----
## 2.47
##
## * Evidence Against The Null: 08.1.4.3.2 Facilitation and interference
emm_E12_fib <- contrast(emmb_E12_ie,
"pairwise",
simple = "Alignment")
emm_E12_fib## Orientation = upr, Congruency = con:
## contrast estimate lower.HPD upper.HPD
## ali - mis -42.1 -92.6 9.95
##
## Orientation = inv, Congruency = con:
## contrast estimate lower.HPD upper.HPD
## ali - mis -63.3 -118.4 -4.84
##
## Orientation = upr, Congruency = inc:
## contrast estimate lower.HPD upper.HPD
## ali - mis 175.1 120.7 229.22
##
## Orientation = inv, Congruency = inc:
## contrast estimate lower.HPD upper.HPD
## ali - mis 46.0 -12.0 100.76
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E12_fib <- gather_emmeans_draws(emm_E12_fib) %>%
mutate(Congruency = fct_recode(Congruency, fac="con", int="inc")) %>%
pivot_wider(names_from = c(Orientation, Congruency),
values_from = .value) %>%
select(-c(.chain, .iteration)) %>%
mutate(fac_diff = upr_fac - inv_fac,
int_diff = upr_int - inv_int)
head(draws_E12_fib)Facilitation:
bf_E12_fac_upr <- bayesfactor_pointnull(draws_E12_fib$upr_fac,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_fac_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.491
##
## * Evidence Against The Null: 0bf_E12_fac_inv <- bayesfactor_pointnull(draws_E12_fib$inv_fac,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_fac_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## ----
## 1.64
##
## * Evidence Against The Null: 0bf_E12_fac_diff <- bayesfactor_pointnull(draws_E12_fib$fac_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_fac_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.218
##
## * Evidence Against The Null: 0Interference:
bf_E12_int_upr <- bayesfactor_pointnull(draws_E12_fib$upr_int,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_int_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.27e+05
##
## * Evidence Against The Null: 0bf_E12_int_inv <- bayesfactor_pointnull(draws_E12_fib$inv_int,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_int_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.514
##
## * Evidence Against The Null: 0bf_E12_int_diff <- bayesfactor_pointnull(draws_E12_fib$int_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E12_int_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 70.98
##
## * Evidence Against The Null: 08.1.5 Plot
plot_E12_cf_ie_ <- plot_E12_cf_ie +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E12_ie <- ggarrange(plot_E12_cf_ie_, plot_E12_cffi_ie,
labels = c("A", "B"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E12_ie
8.2 Experiment 3
RM-ANOVA is used to analyze the inverse efficiency.
8.2.1 Calculate inverse efficiency
df_E3_acc <- df_E3_lmm %>%
group_by(SubjCode, Orientation, Congruency, Alignment) %>%
summarise(acc = mean(isCorrect),
.groups = "drop")
df_E3_rt <- df_E3_lmm %>%
filter(isCorrect==1) %>%
group_by(SubjCode, Orientation, Congruency, Alignment) %>%
summarise(rt = mean(RT),
.groups = "drop")
df_E3_ie <- left_join(df_E3_acc, df_E3_rt,
by = c("SubjCode", "Orientation", "Congruency", "Alignment")) %>%
mutate(IE = rt/acc) %>%
select(SubjCode, Orientation, Congruency, Alignment, IE)
df_E3_ie df_E3_ie <- df_E3_ie %>%
psychr::set_contr(c(Orientation, Congruency, Alignment)) %>%
psychr::add_lmmcols(IE ~ Orientation * Congruency * Alignment) ## old_name
## Ori_C Orientation2-1
## Con_C Congruency2-1
## Ali_C Alignment2-1
## Ori_Con Orientation2-1:Congruency2-1
## Ori_Ali Orientation2-1:Alignment2-1
## Con_Ali Congruency2-1:Alignment2-1
## Ori_Con_Ali Orientation2-1:Congruency2-1:Alignment2-1## [1] "Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali"8.2.2 Fitting linear mixed-effects models
8.2.2.1 The zero-correlation-parameter model
file_E3_ie_zcp <- file.path(dir_lmm, "lmm_E3_ie_zcp.rds")
# fit the max model
if (!file.exists(file_E3_ie_zcp)) {
lmm_E3_ie_zcp <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + Con_Ali +
Ori_Con_Ali || SubjCode),
data = df_E3_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_ie_zcp, file = file_E3_ie_zcp)
} else {
lmm_E3_ie_zcp <- readRDS(file_E3_ie_zcp)
}
print(summary(lmm_E3_ie_zcp), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Con_Ali + Ori_Con_Ali || SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13735.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8412 -0.3705 -0.0477 0.3250 5.0298
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 172436.26 415.254
## SubjCode.1 Ori_C 301748.22 549.316
## SubjCode.2 Con_C 54382.36 233.200
## SubjCode.3 Ali_C 3062.05 55.336
## SubjCode.4 Ori_Con 23399.32 152.968
## SubjCode.5 Ori_Ali 6967.48 83.471
## SubjCode.6 Con_Ali 32.32 5.685
## SubjCode.7 Ori_Con_Ali 134338.79 366.523
## Residual 33064.10 181.835
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.00 36.904 < 2e-16 ***
## Orientation2-1 218.15 51.50 119.00 4.236 4.51e-05 ***
## Congruency2-1 326.19 24.31 119.01 13.418 < 2e-16 ***
## Alignment2-1 -24.60 12.78 119.00 -1.925 0.056559 .
## Orientation2-1:Congruency2-1 -95.10 27.31 119.00 -3.482 0.000698 ***
## Orientation2-1:Alignment2-1 102.39 24.68 119.02 4.149 6.30e-05 ***
## Congruency2-1:Alignment2-1 -170.35 23.48 119.02 -7.255 4.45e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 57.65 119.00 2.618 0.010006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.2.2.2 The reduced model
summary(rePCA(lmm_E3_ie_zcp))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## Standard deviation 3.0210 2.2837 2.0157 1.28248 0.8412 0.45905 0.3043 0.03127
## Proportion of Variance 0.4333 0.2476 0.1929 0.07809 0.0336 0.01001 0.0044 0.00005
## Cumulative Proportion 0.4333 0.6809 0.8739 0.95195 0.9856 0.99556 1.0000 1.00000Following random effects were removed due to their explained variances being smaller than 0.1%:
- by-
SubjCode:Con_Ali;
file_E3_ie_rdc <- file.path(dir_lmm, "lmm_E3_ie_rdc.rds")
# fit the reduced model
if (!file.exists(file_E3_ie_rdc)) {
lmm_E3_ie_rdc <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + # Con_Ali +
Ori_Con_Ali || SubjCode),
data = df_E3_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_ie_rdc, file = file_E3_ie_rdc)
} else {
lmm_E3_ie_rdc <- readRDS(file_E3_ie_rdc)
}
print(summary(lmm_E3_ie_rdc), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Ori_Con_Ali || SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13735.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8421 -0.3706 -0.0477 0.3250 5.0310
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjCode (Intercept) 172434 415.25
## SubjCode.1 Ori_C 301746 549.31
## SubjCode.2 Con_C 54378 233.19
## SubjCode.3 Ali_C 3054 55.27
## SubjCode.4 Ori_Con 23366 152.86
## SubjCode.5 Ori_Ali 6933 83.27
## SubjCode.6 Ori_Con_Ali 134222 366.36
## Residual 33080 181.88
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.00 36.904 < 2e-16 ***
## Orientation2-1 218.15 51.50 119.00 4.236 4.51e-05 ***
## Congruency2-1 326.19 24.31 119.00 13.418 < 2e-16 ***
## Alignment2-1 -24.60 12.78 119.00 -1.925 0.056559 .
## Orientation2-1:Congruency2-1 -95.10 27.31 119.00 -3.482 0.000698 ***
## Orientation2-1:Alignment2-1 102.39 24.68 119.00 4.149 6.30e-05 ***
## Congruency2-1:Alignment2-1 -170.35 23.48 119.00 -7.255 4.45e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 57.65 119.00 2.618 0.010006 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(lmm_E3_ie_rdc))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 3.0202 2.2831 2.0143 1.28213 0.84045 0.45782 0.30387
## Proportion of Variance 0.4335 0.2477 0.1928 0.07811 0.03357 0.00996 0.00439
## Cumulative Proportion 0.4335 0.6812 0.8740 0.95209 0.98565 0.99561 1.000008.2.2.3 The extended model
file_E3_ie_etd <- file.path(dir_lmm, "lmm_E3_ie_etd.rds")
# fit the reduced model
if (!file.exists(file_E3_ie_etd)) {
lmm_E3_ie_etd <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + Ali_C +
Ori_Con + Ori_Ali + # Con_Ali +
Ori_Con_Ali | SubjCode),
data = df_E3_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_ie_etd, file = file_E3_ie_etd)
} else {
lmm_E3_ie_etd <- readRDS(file_E3_ie_etd)
}
print(summary(lmm_E3_ie_etd), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ali_C + Ori_Con + Ori_Ali + Ori_Con_Ali | SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13556.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8108 -0.3692 -0.0456 0.3202 4.0014
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 173752 416.84
## Ori_C 307022 554.10 0.16
## Con_C 59688 244.31 0.62 0.06
## Ali_C 9106 95.43 0.39 0.31 0.46
## Ori_Con 44987 212.10 0.00 0.78 0.28 0.30
## Ori_Ali 38079 195.14 -0.01 -0.01 0.22 0.35 -0.11
## Ori_Con_Ali 246726 496.72 -0.28 -0.10 0.04 -0.14 -0.19 0.86
## Residual 22547 150.16
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.00 36.904 < 2e-16 ***
## Orientation2-1 218.15 51.50 119.00 4.236 4.51e-05 ***
## Congruency2-1 326.19 24.32 119.03 13.414 < 2e-16 ***
## Alignment2-1 -24.60 13.03 126.15 -1.888 0.061325 .
## Orientation2-1:Congruency2-1 -95.10 27.40 120.30 -3.471 0.000721 ***
## Orientation2-1:Alignment2-1 102.39 26.33 136.17 3.889 0.000157 ***
## Congruency2-1:Alignment2-1 -170.35 19.38 237.98 -8.788 3.09e-16 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 59.66 126.13 2.530 0.012653 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(lmm_E3_ie_etd))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Standard deviation 4.0525 3.477 2.8229 1.32993 0.74525 0.44534 6.186e-05
## Proportion of Variance 0.4211 0.310 0.2043 0.04535 0.01424 0.00509 0.000e+00
## Cumulative Proportion 0.4211 0.731 0.9353 0.98067 0.99491 1.00000 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ali_CandOri_Ali;
file_E3_ie_etd2 <- file.path(dir_lmm, "lmm_E3_ie_etd2.rds")
# fit the reduced model
if (!file.exists(file_E3_ie_etd2)) {
lmm_E3_ie_etd2 <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + # Ali_C +
Ori_Con + # Con_Ali + Ori_Ali +
Ori_Con_Ali | SubjCode),
data = df_E3_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_ie_etd2, file = file_E3_ie_etd2)
} else {
lmm_E3_ie_etd2 <- readRDS(file_E3_ie_etd2)
}
print(summary(lmm_E3_ie_etd2), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ori_Con + Ori_Con_Ali | SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13626.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1391 -0.3798 -0.0546 0.3373 5.0929
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 172385 415.2
## Ori_C 301784 549.3 0.16
## Con_C 54642 233.8 0.65 0.06
## Ori_Con 37650 194.0 0.01 0.85 0.28
## Ori_Con_Ali 142006 376.8 -0.38 -0.14 0.05 -0.18
## Residual 33196 182.2
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.04 36.908 < 2e-16 ***
## Orientation2-1 218.15 51.51 119.00 4.235 4.52e-05 ***
## Congruency2-1 326.19 24.37 119.36 13.388 < 2e-16 ***
## Alignment2-1 -24.60 11.76 475.99 -2.092 0.0370 *
## Orientation2-1:Congruency2-1 -95.10 29.45 160.15 -3.230 0.0015 **
## Orientation2-1:Alignment2-1 102.39 23.52 475.99 4.353 1.64e-05 ***
## Congruency2-1:Alignment2-1 -170.35 23.52 475.99 -7.242 1.79e-12 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 58.28 125.62 2.589 0.0107 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')summary(rePCA(lmm_E3_ie_etd2))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4] [,5]
## Standard deviation 3.2460 2.5247 1.8789 0.94911 4.804e-06
## Proportion of Variance 0.4937 0.2987 0.1654 0.04221 0.000e+00
## Cumulative Proportion 0.4937 0.7924 0.9578 1.00000 1.000e+00Following random effects were removed due to their explained variances being smaller than 1%:
- by-
SubjCode:Ori_Con;
file_E3_ie_etd3 <- file.path(dir_lmm, "lmm_E3_ie_etd3.rds")
# fit the reduced model
if (!file.exists(file_E3_ie_etd3)) {
lmm_E3_ie_etd3 <- lmer(
IE ~ Orientation * Congruency * Alignment +
(Ori_C + Con_C + # Ali_C +
# Con_Ali + Ori_Ali + Ori_Con +
Ori_Con_Ali | SubjCode),
data = df_E3_ie,
control = lmerControl(optCtrl = list(maxfun = 1e7))
)
saveRDS(lmm_E3_ie_etd3, file = file_E3_ie_etd3)
} else {
lmm_E3_ie_etd3 <- readRDS(file_E3_ie_etd3)
}
print(summary(lmm_E3_ie_etd3), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ori_Con_Ali | SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13679.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1812 -0.4171 -0.0406 0.3727 5.3668
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 171769 414.4
## Ori_C 299098 546.9 0.17
## Con_C 51719 227.4 0.67 0.06
## Ori_Con_Ali 91712 302.8 -0.47 -0.17 0.09
## Residual 38394 195.9
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.00 36.904 < 2e-16 ***
## Orientation2-1 218.15 51.50 119.00 4.236 4.51e-05 ***
## Congruency2-1 326.19 24.31 119.00 13.418 < 2e-16 ***
## Alignment2-1 -24.60 12.65 476.00 -1.945 0.052331 .
## Orientation2-1:Congruency2-1 -95.10 25.30 476.00 -3.759 0.000192 ***
## Orientation2-1:Alignment2-1 102.39 25.30 476.00 4.048 6.03e-05 ***
## Congruency2-1:Alignment2-1 -170.35 25.30 476.00 -6.734 4.76e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 57.65 119.00 2.618 0.010007 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rePCA(lmm_E3_ie_etd3))## $SubjCode
## Importance of components:
## [,1] [,2] [,3] [,4]
## Standard deviation 2.8895 2.2796 1.4632 0.55928
## Proportion of Variance 0.5218 0.3248 0.1338 0.01955
## Cumulative Proportion 0.5218 0.8466 0.9805 1.000008.2.2.4 The optimal model
anova(lmm_E3_ie_rdc, lmm_E3_ie_etd3, refit=FALSE)According to BIC, the extended model is used as the optimal model.
lmm_E3_ie_opt <- lmm_E3_ie_etd3
print(summary(lmm_E3_ie_opt), corr = FALSE)## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: IE ~ Orientation * Congruency * Alignment + (Ori_C + Con_C + Ori_Con_Ali | SubjCode)
## Data: df_E3_ie
## Control: lmerControl(optCtrl = list(maxfun = 1e+07))
##
## REML criterion at convergence: 13679.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1812 -0.4171 -0.0406 0.3727 5.3668
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## SubjCode (Intercept) 171769 414.4
## Ori_C 299098 546.9 0.17
## Con_C 51719 227.4 0.67 0.06
## Ori_Con_Ali 91712 302.8 -0.47 -0.17 0.09
## Residual 38394 195.9
## Number of obs: 960, groups: SubjCode, 120
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1415.61 38.36 119.00 36.904 < 2e-16 ***
## Orientation2-1 218.15 51.50 119.00 4.236 4.51e-05 ***
## Congruency2-1 326.19 24.31 119.00 13.418 < 2e-16 ***
## Alignment2-1 -24.60 12.65 476.00 -1.945 0.052331 .
## Orientation2-1:Congruency2-1 -95.10 25.30 476.00 -3.759 0.000192 ***
## Orientation2-1:Alignment2-1 102.39 25.30 476.00 4.048 6.03e-05 ***
## Congruency2-1:Alignment2-1 -170.35 25.30 476.00 -6.734 4.76e-11 ***
## Orientation2-1:Congruency2-1:Alignment2-1 150.91 57.65 119.00 2.618 0.010007 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 18.2.3 Estimated marginal means
# hit and false alarm
emm_E3_ie <- emmeans(lmm_E3_ie_opt,
~ Orientation + Congruency + Alignment)
emm_E3_ie## Orientation Congruency Alignment emmean SE df lower.CL upper.CL
## upr con ali 1096 42.3 143 1012 1180
## inv con ali 1348 45.7 139 1258 1439
## upr inc ali 1593 50.8 135 1492 1693
## inv inc ali 1674 59.2 131 1557 1792
## upr con mis 1143 39.9 147 1064 1222
## inv con mis 1422 49.1 136 1325 1519
## upr inc mis 1394 52.5 134 1290 1498
## inv inc mis 1654 56.7 132 1541 1766
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.2.3.1 Composite effect
Composite effect of d’:
emm_E3_cf_ie <- contrast(emm_E3_ie,
interaction = "pairwise",
simple = c("Congruency", "Alignment"),
infer = TRUE)
summary(emm_E3_cf_ie[1:2], side="<")## Congruency_pairwise Alignment_pairwise Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc ali - mis upr -245.8 38.4 325 -Inf -182.5 -6.409 <.0001
## con - inc ali - mis inv -94.9 38.4 325 -Inf -31.6 -2.474 0.0069
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedCongruency effect of d’ in aligned condition:
emm_E3_con_ie <- contrast(emm_E3_ie,
interaction = "pairwise",
simple = "Congruency",
infer = TRUE)
summary(emm_E3_con_ie[1:2], side="<")## Congruency_pairwise Orientation Alignment estimate SE df lower.CL upper.CL t.ratio p.value
## con - inc upr ali -497 33.8 221 -Inf -441 -14.680 <.0001
## con - inc inv ali -326 33.1 228 -Inf -271 -9.865 <.0001
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedplot_E3_cf_ie <- emm_E3_ie %>%
summary(infer=TRUE) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Alignment = fct_recode(Alignment, aligned="ali", misaligned="mis"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv")) %>%
ggplot(aes(Alignment, emmean, color=Congruency, group=Congruency)) +
geom_point(size = 2) + # position = position_dodge(width = 0.1),
geom_line(aes(linetype = Congruency), linewidth = 0.8) +
scale_linetype_manual(values=c("solid", "dashed")) +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Orientation, switch = "both") +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6, # position = position_dodge(width = 0.1),
show.legend = F) +
coord_cartesian(ylim = c(800, 1800)) + # set the limit for y axis c(0, 1100)
labs(y = "Inverse efficiency (RT/Accuracy)", fill = "Congruency",
x = NULL) + # set the names for main, x and y axises
geom_text(label = c("", "", "***", "**", "", "", "", ""),
color = "red",
size = 6, nudge_y = 50, nudge_x = 0.5) + # add starts to the significant columns
theme(axis.title.x = element_text(margin = margin(t = 0))) +
NULL
# ggsave(filename = "ccf_ie.pdf", plot_ccf_ie, width = 8, height = 6)
plot_E3_cf_ie
8.2.3.2 Compare composite effects between upright and inverted faces
contrast(emm_E3_cf_ie,
interaction="pairwise",
simple = c("Orientation", "Congruency", "Alignment"),
infer = TRUE)## Congruency_pairwise = con - inc, Alignment_pairwise = ali - mis:
## Orientation_pairwise estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv -151 57.7 119 -265 -36.7 -2.618 0.0100
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.2.3.3 Facilitation and interference
# facilitation and interference
emm_E3_ie_fi <- contrast(emm_E3_ie,
interaction = "pairwise",
simple = "Alignment",
infer = TRUE,
adjust = "none")
# emmip(emm_E3_ie_fi[1:4], ~ Orientation | Congruency, CIs = TRUE, adjust = "sidak")
emm_E3_ie_fi[1:4]## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -47.1 26.2 559 -98.6 4.41 -1.796 0.0730
## ali - mis inv con -74.0 26.2 559 -125.5 -22.53 -2.823 0.0049
## ali - mis upr inc 198.7 26.2 559 147.2 250.21 7.577 <.0001
## ali - mis inv inc 20.9 26.2 559 -30.7 72.36 0.795 0.4268
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95summary(emm_E3_ie_fi[1:2], side="<")## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr con -47.1 26.2 559 -Inf -3.9 -1.796 0.0365
## ali - mis inv con -74.0 26.2 559 -Inf -30.8 -2.823 0.0025
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are left-tailedsummary(emm_E3_ie_fi[3:4], side=">")## Alignment_pairwise Orientation Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## ali - mis upr inc 198.7 26.2 559 155.5 Inf 7.577 <.0001
## ali - mis inv inc 20.9 26.2 559 -22.4 Inf 0.795 0.2134
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.95
## P values are right-tailedplot_E3_cffi_ie <- summary(emm_E3_ie_fi[1:4], level=.95) %>%
as_tibble() %>%
mutate(Congruency = fct_recode(Congruency, congruent="con", incongruent="inc"),
Orientation = fct_recode(Orientation, upright="upr", inverted="inv"),
Label = ifelse(Orientation=="upright",
ifelse(Congruency=="congruent", "facilitation",
"interference"), "")) %>%
ggplot(aes(y = estimate, x = Orientation, color = Congruency)) +
geom_point(size = 2) +
geom_text(aes(label = Label), y=300, x=1.5, fontface="bold", size=5) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), linewidth=1.5, width=0,
alpha = .6) +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_color_manual(values=two_colors) +
facet_grid(. ~ Congruency, switch = "both") +
coord_cartesian(ylim = ylimit_cf_fi_ie) +
labs(x = NULL,
y = "Inverse efficiency (aligned-misaligned)") + # set the names for main, x and y axis
theme(legend.position = "none",
axis.title.x = element_text(margin = margin(t = 0))) +
NULL
# ggsave(filename = "E3_fi_ie.pdf", plot_E3_cffi_ie, width = 7, height = 4.55)
plot_E3_cffi_ie
Comparing facilitation/interference between upright and inverted faces:
# facilitation and interference of
emm_E3_ie_fi_ui <- contrast(emm_E3_ie,
interaction = "pairwise",
by = "Congruency",
infer = TRUE,
adjust = "none")
emm_E3_ie_fi_ui[1:2]## Orientation_pairwise Alignment_pairwise Congruency estimate SE df lower.CL upper.CL t.ratio p.value
## upr - inv ali - mis con 26.9 38.4 325 -48.5 102 0.702 0.4829
## upr - inv ali - mis inc 177.8 38.4 325 102.4 253 4.637 <.0001
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.2.3.4 Comparisons between facilitation and interference
contrast(emm_E3_ie_fi,
method = list("faci-inte"=c(1, 1)),
by = "Orientation",
infer = TRUE)[1:2] ## contrast Orientation estimate SE df lower.CL upper.CL t.ratio p.value
## faci-inte upr 151.6 35.8 476 81.3 221.9 4.238 <.0001
## faci-inte inv -53.2 35.8 476 -123.5 17.1 -1.487 0.1378
##
## Degrees-of-freedom method: satterthwaite
## Confidence level used: 0.958.2.4 Bayesian hierarchical model
8.2.4.1 Model fitting
brm_E3_ie <- brm(IE ~ Orientation * Congruency * Alignment +
(Orientation * Congruency * Alignment | SubjCode),
data = df_E3_ie,
prior = c(prior(normal(1000, 300), class = Intercept),
prior(normal(0, 200), class = b),
prior(normal(0, 50), class = sigma),
prior(normal(0, 50), class = sd),
prior(lkj(2), class = cor)),
save_pars = save_pars(all = TRUE),
chains = 10,
iter = 8000,
warmup = 2000,
control = list(adapt_delta = .999,
max_treedepth = 15),
file = file.path("brms", "brmsfit_E3_ie.rds"))
summary(brm_E3_ie)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: IE ~ Orientation * Congruency * Alignment + (Orientation * Congruency * Alignment | SubjCode)
## Data: df_E3_ie (Number of observations: 960)
## Draws: 10 chains, each with iter = 8000; warmup = 2000; thin = 1;
## total post-warmup draws = 60000
##
## Multilevel Hyperparameters:
## ~SubjCode (Number of levels: 120)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 341.91 16.47 311.11 375.71 1.00 9562 18892
## sd(Orientation2M1) 416.02 19.46 379.05 455.66 1.00 18569 31351
## sd(Congruency2M1) 196.03 15.28 167.06 227.14 1.00 25709 38256
## sd(Alignment2M1) 52.91 16.52 19.73 85.31 1.00 17671 17603
## sd(Orientation2M1:Congruency2M1) 131.90 22.66 87.71 176.62 1.00 45538 40369
## sd(Orientation2M1:Alignment2M1) 44.80 32.47 1.83 119.74 1.00 11106 17569
## sd(Congruency2M1:Alignment2M1) 39.14 27.41 1.67 101.39 1.00 18930 24795
## sd(Orientation2M1:Congruency2M1:Alignment2M1) 83.13 51.49 4.36 193.79 1.00 12863 18823
## cor(Intercept,Orientation2M1) 0.12 0.07 -0.01 0.25 1.00 12460 23971
## cor(Intercept,Congruency2M1) 0.55 0.07 0.41 0.68 1.00 29146 40221
## cor(Orientation2M1,Congruency2M1) 0.02 0.08 -0.14 0.18 1.00 27558 39694
## cor(Intercept,Alignment2M1) 0.37 0.17 0.02 0.68 1.00 56332 37890
## cor(Orientation2M1,Alignment2M1) 0.29 0.17 -0.05 0.62 1.00 60815 38070
## cor(Congruency2M1,Alignment2M1) 0.44 0.18 0.04 0.76 1.00 52597 35067
## cor(Intercept,Orientation2M1:Congruency2M1) -0.03 0.14 -0.31 0.25 1.00 63032 44407
## cor(Orientation2M1,Orientation2M1:Congruency2M1) 0.69 0.10 0.47 0.87 1.00 59475 42743
## cor(Congruency2M1,Orientation2M1:Congruency2M1) 0.21 0.15 -0.10 0.51 1.00 65339 47960
## cor(Alignment2M1,Orientation2M1:Congruency2M1) 0.26 0.21 -0.18 0.66 1.00 50249 46307
## cor(Intercept,Orientation2M1:Alignment2M1) -0.01 0.26 -0.52 0.51 1.00 84581 38162
## cor(Orientation2M1,Orientation2M1:Alignment2M1) -0.01 0.25 -0.51 0.50 1.00 87792 42799
## cor(Congruency2M1,Orientation2M1:Alignment2M1) 0.15 0.28 -0.44 0.64 1.00 60015 41341
## cor(Alignment2M1,Orientation2M1:Alignment2M1) 0.13 0.29 -0.47 0.65 1.00 42692 44698
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Alignment2M1) 0.03 0.27 -0.51 0.55 1.00 64987 47462
## cor(Intercept,Congruency2M1:Alignment2M1) -0.16 0.27 -0.64 0.42 1.00 70608 40048
## cor(Orientation2M1,Congruency2M1:Alignment2M1) -0.03 0.26 -0.53 0.49 1.00 80416 41253
## cor(Congruency2M1,Congruency2M1:Alignment2M1) 0.02 0.28 -0.52 0.54 1.00 86848 42652
## cor(Alignment2M1,Congruency2M1:Alignment2M1) 0.10 0.30 -0.50 0.63 1.00 46589 46340
## cor(Orientation2M1:Congruency2M1,Congruency2M1:Alignment2M1) 0.05 0.28 -0.51 0.58 1.00 69579 46805
## cor(Orientation2M1:Alignment2M1,Congruency2M1:Alignment2M1) 0.03 0.30 -0.56 0.59 1.00 52690 50417
## cor(Intercept,Orientation2M1:Congruency2M1:Alignment2M1) -0.28 0.26 -0.71 0.31 1.00 55835 35685
## cor(Orientation2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.10 0.25 -0.57 0.42 1.00 79343 40003
## cor(Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.00 0.26 -0.51 0.50 1.00 81695 41346
## cor(Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.09 0.28 -0.61 0.47 1.00 61479 47759
## cor(Orientation2M1:Congruency2M1,Orientation2M1:Congruency2M1:Alignment2M1) -0.03 0.27 -0.54 0.50 1.00 65121 45357
## cor(Orientation2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.16 0.32 -0.48 0.72 1.00 18949 27435
## cor(Congruency2M1:Alignment2M1,Orientation2M1:Congruency2M1:Alignment2M1) 0.07 0.30 -0.52 0.62 1.00 46922 51338
##
## Regression Coefficients:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1408.86 31.11 1348.00 1469.41 1.00 3782 7441
## Orientation2M1 209.65 38.78 133.85 285.25 1.00 8859 18293
## Congruency2M1 321.17 21.46 278.93 363.08 1.00 12119 28447
## Alignment2M1 -25.22 13.09 -51.03 0.12 1.00 65638 44453
## Orientation2M1:Congruency2M1 -95.22 26.82 -147.74 -42.33 1.00 42334 41812
## Orientation2M1:Alignment2M1 100.78 24.49 52.76 148.80 1.00 98016 43943
## Congruency2M1:Alignment2M1 -167.86 24.39 -215.89 -119.96 1.00 98369 42919
## Orientation2M1:Congruency2M1:Alignment2M1 142.79 47.60 48.67 235.75 1.00 93718 42405
##
## Further Distributional Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 186.86 6.87 172.87 200.05 1.00 12952 13889
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).8.2.4.2 Bayes factor
emmb_E3_ie <- emmeans(brm_E3_ie, ~ Orientation + Congruency + Alignment)
emmip(brm_E3_ie,
Congruency ~ Alignment | Orientation,
CIs = TRUE,
adjust = "sidak")
8.2.4.2.1 Composite effect
emm_E3_cfb <- contrast(emmb_E3_ie,
interaction = "pairwise",
simple = c("Congruency", "Alignment"))
emm_E3_cfb## Orientation = upr:
## Congruency_pairwise Alignment_pairwise estimate lower.HPD upper.HPD
## con - inc ali - mis -239.3 -306 -173.0
##
## Orientation = inv:
## Congruency_pairwise Alignment_pairwise estimate lower.HPD upper.HPD
## con - inc ali - mis -96.5 -163 -29.3
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E3_cfb <- gather_emmeans_draws(emm_E3_cfb) %>%
pivot_wider(names_from = Orientation,
values_from = .value) %>%
mutate(cf_diff = upr - inv) %>%
select(-c(.chain, .iteration))
head(draws_E3_cfb)emm_E3_conb <- contrast(emmb_E3_ie,
"pairwise",
simple = "Congruency")
emm_E3_conb## Orientation = upr, Alignment = ali:
## contrast estimate lower.HPD upper.HPD
## con - inc -489 -546 -430
##
## Orientation = inv, Alignment = ali:
## contrast estimate lower.HPD upper.HPD
## con - inc -322 -383 -261
##
## Orientation = upr, Alignment = mis:
## contrast estimate lower.HPD upper.HPD
## con - inc -249 -307 -191
##
## Orientation = inv, Alignment = mis:
## contrast estimate lower.HPD upper.HPD
## con - inc -225 -286 -163
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E3_conb <- gather_emmeans_draws(emm_E3_conb) %>%
filter(Alignment == "ali") %>%
pivot_wider(names_from = Orientation,
values_from = .value) %>%
mutate(con_diff = upr - inv) %>%
select(-c(.chain, .iteration))
head(draws_E3_conb)bf_E3_cf_upr <- bayesfactor_pointnull(draws_E3_cfb$upr,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_cf_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 2.95e+07
##
## * Evidence Against The Null: 0bf_E3_con_upr <- bayesfactor_pointnull(draws_E3_conb$upr,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_con_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.54e+21
##
## * Evidence Against The Null: 0as.numeric(bf_E3_con_upr)## [1] 1.537454e+21bf_E3_cf_inv <- bayesfactor_pointnull(draws_E3_cfb$inv,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_cf_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## ----
## 9.29
##
## * Evidence Against The Null: 0bf_E3_con_inv <- bayesfactor_pointnull(draws_E3_conb$inv,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_con_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.35e+11
##
## * Evidence Against The Null: 0as.numeric(bf_E3_con_inv)## [1] 135069338637bf_E3_cf_diff <- bayesfactor_pointnull(draws_E3_cfb$cf_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_cf_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 24.56
##
## * Evidence Against The Null: 08.2.4.2.2 Facilitation and interference
emm_E3_fib <- contrast(emmb_E3_ie,
"pairwise",
simple = "Alignment")
emm_E3_fib## Orientation = upr, Congruency = con:
## contrast estimate lower.HPD upper.HPD
## ali - mis -44.1 -92.4 4.16
##
## Orientation = inv, Congruency = con:
## contrast estimate lower.HPD upper.HPD
## ali - mis -73.4 -121.4 -24.97
##
## Orientation = upr, Congruency = inc:
## contrast estimate lower.HPD upper.HPD
## ali - mis 195.4 146.1 242.72
##
## Orientation = inv, Congruency = inc:
## contrast estimate lower.HPD upper.HPD
## ali - mis 23.0 -26.8 70.72
##
## Point estimate displayed: median
## HPD interval probability: 0.95# Extract the draws
draws_E3_fib <- gather_emmeans_draws(emm_E3_fib) %>%
mutate(Congruency = fct_recode(Congruency, fac="con", int="inc")) %>%
pivot_wider(names_from = c(Orientation, Congruency),
values_from = .value) %>%
select(-c(.chain, .iteration)) %>%
mutate(fac_diff = upr_fac - inv_fac,
int_diff = upr_int - inv_int)
head(draws_E3_fib)Facilitation:
bf_E3_fac_upr <- bayesfactor_pointnull(draws_E3_fib$upr_fac,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_fac_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.612
##
## * Evidence Against The Null: 0bf_E3_fac_inv <- bayesfactor_pointnull(draws_E3_fib$inv_fac,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_fac_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 10.58
##
## * Evidence Against The Null: 0bf_E3_fac_diff <- bayesfactor_pointnull(draws_E3_fib$fac_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_fac_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.247
##
## * Evidence Against The Null: 0Interference:
bf_E3_int_upr <- bayesfactor_pointnull(draws_E3_fib$upr_int,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_int_upr## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 3.24e+07
##
## * Evidence Against The Null: 0bf_E3_int_inv <- bayesfactor_pointnull(draws_E3_fib$inv_int,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_int_inv## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## -----
## 0.192
##
## * Evidence Against The Null: 0bf_E3_int_diff <- bayesfactor_pointnull(draws_E3_fib$int_diff,
prior = distribution_normal(1000, 0, 200),
null = 0)
bf_E3_int_diff## Bayes Factor (Savage-Dickey density ratio)
##
## BF
## --------
## 1.14e+04
##
## * Evidence Against The Null: 08.2.5 Plot
plot_E3_cf_ie_ <- plot_E3_cf_ie +
guides(color = guide_legend(nrow = 1, title.position = "left"),
linetype = guide_legend(nrow = 1, title.position = "left")) +
theme(legend.position = "inside",
legend.position.inside = c(0.5, 0.1),
legend.box = "horizontal",
legend.key.height = unit(0.01, "cm"))
plot_E3_ie <- ggarrange(plot_E3_cf_ie_, plot_E3_cffi_ie,
labels = c("A", "B"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 1)
plot_E3_ie
8.3 Plot (Experiment 1&2 and Experiment 3)
plot_ie <- ggarrange(plot_E3_cf_ie_, plot_E3_cffi_ie,
plot_E3_cf_ie_, plot_E3_cffi_ie,
labels = c("A", "B", "C", "D"),
font.label = (list(size = 18)),
widths = c(1.5, 1),
nrow = 2,
ncol = 2)
plot_ie
Session information
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Asia/Shanghai
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidybayes_3.0.7 bayestestR_0.14.0 brms_2.22.0 Rcpp_1.0.13 afex_1.4-1 psychr_0.0.1 here_1.0.1 ggpubr_0.6.0 emmeans_1.10.4 optimx_2023-10.21 lmerTest_3.1-3 lme4_1.1-35.5 Matrix_1.7-0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] mnormt_2.1.1 gridExtra_2.3 inline_0.3.19 rlang_1.1.4 magrittr_2.0.3 matrixStats_1.4.1 compiler_4.4.1 loo_2.8.0 vctrs_0.6.5 reshape2_1.4.4 pkgconfig_2.0.3 arrayhelpers_1.1-0 fastmap_1.2.0 backports_1.5.0 labeling_0.4.3 logspline_2.1.22 effectsize_0.8.9 utf8_1.2.4 rmarkdown_2.28 tzdb_0.4.0 pracma_2.4.4 nloptr_2.1.1 xfun_0.48 cachem_1.1.0 jsonlite_1.8.9 highr_0.11 uuid_1.2-1 psych_2.4.6.26 broom_1.0.7 parallel_4.4.1 R6_2.5.1 StanHeaders_2.32.10 bslib_0.8.0 stringi_1.8.4 car_3.1-3 boot_1.3-30 jquerylib_0.1.4 numDeriv_2016.8-1.1 estimability_1.5.1 rstan_2.32.6 knitr_1.48 parameters_0.22.2 tinylabels_0.2.4 bayesplot_1.11.1 splines_4.4.1 timechange_0.3.0 tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-8 yaml_2.3.10 codetools_0.2-20 pkgbuild_1.4.4 lattice_0.22-6 plyr_1.8.9 withr_3.0.1 bridgesampling_1.1-2 posterior_1.6.0 coda_0.19-4.1 evaluate_1.0.0 RcppParallel_5.1.9 ggdist_3.3.2 pillar_1.9.0 carData_3.0-5 tensorA_0.36.2.1 stats4_4.4.1 checkmate_2.3.2 insight_0.20.5 distributional_0.5.0 generics_0.1.3 rprojroot_2.0.4 papaja_0.1.2
## [72] hms_1.1.3 rstantools_2.4.0 munsell_0.5.1 scales_1.3.0 minqa_1.2.8 xtable_1.8-4 glue_1.8.0 tools_4.4.1 xaringanExtra_0.8.0 ggsignif_0.6.4 mvtnorm_1.3-1 cowplot_1.1.3 grid_4.4.1 QuickJSR_1.4.0 datawizard_0.12.3 colorspace_2.1-1 nlme_3.1-164 Formula_1.2-5 cli_3.6.3 fansi_1.0.6 svUnit_1.0.6 Brobdingnag_1.2-9 gtable_0.3.5 rstatix_0.7.2 sass_0.4.9 digest_0.6.37 farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4 MASS_7.3-60.2