Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § MATERIALS AND METHODS › Data analysis › Nonlinear mixed-effects learning curve models ↔ scripts/run_nlme_learningcurve.R, lines 1–35 · score 0.70 · nonlinear mixed, fit separately, 11–200, 1–10, curve, model
[2] § MATERIALS AND METHODS › Data analysis › Analysis of survey scores ↔ cave/pixi/pixi.min.js, the whole file · a weak match · score 0.62 · ABC, Composite, reverse, domains
[3] § MATERIALS AND METHODS › Data analysis › Analysis of survey scores ↔ data_analysis_final.R, lines 233–283 · score 0.59 · sensory seeking, sensory avoiding, reverse, raw, AASP
[4] § MATERIALS AND METHODS › Video game development ↔ cave/js/plugins.js, the whole file · a weak match · score 0.56 · 2.5 s, modified, touches, ms, video, clicks
[5] § MATERIALS AND METHODS › Participants ↔ scripts/run_learningcurve_3group_ks.R, lines 1–47 · score 0.54 · missing VABS, 11–200, accuracy, SRS, score, game
[6] § MATERIALS AND METHODS › Data analysis › SDT model ↔ data_analysis_final.R, lines 813–888 · score 0.53 · lose switch, win stay, bs, SRS, fit, Model
[7] § MATERIALS AND METHODS › Data analysis › SDT model ↔ data_analysis_final.R, lines 813–888 · score 0.53 · lose switch, win stay, bs, SRS, fit, Model
[8] § RESULTS › Integration noise correlates with symptom severity in social and adaptive domains ↔ data_analysis_final.R, lines 2221–2261 · score 0.52 · sensation seeking, perceptual noise, BIS, AASP, VABS, k1
[9] § RESULTS › Integration noise correlates with symptom severity in social and adaptive domains ↔ data_analysis_final.R, lines 2222–2262 · score 0.52 · sensation seeking, perceptual noise, BIS, AASP, VABS, k1

Paper

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The authors' code

R · 2,591 lines · 90 KB · CC0-1.0 · 3 matches

# Created: 2020-07-07 15:00:00
# Last modified: 2020-07-07 15:00:00
setwd("/Users/suchetachakravarty/Documents/projects/geode/")
rm(list=ls())
# Load the required libraries
library(data.table)
library(ggplot2)
library(dplyr)
library(ggpubr)
library(rstatix)
library(gridExtra)
library(Hmisc)
library(rstan)
library(zoo)
library(latex2exp)
library(afex)
library(emmeans)
library(minpack.lm)
library(tidyverse)
library(glmnet)
library(lme4)
library(broom.mixed)
# load the data
load("data/preprocessed_data.RData")
# create a binary group variable
alldata[, binary_group := fifelse(grepl("ASD", group, ignore.case = TRUE), "ASD", "TD")]
# create a verbal group variable
alldata[, group_verbal := fcase(
is.na(vinabcstd), "flag",
binary_group == "ASD" & vinabcstd >= 75, "ASD",
binary_group == "TD" & vinabcstd >= 75, "TD",
binary_group == "ASD" & vinabcstd < 75, "ASD (low verbal)",
binary_group == "TD" & vinabcstd < 75, "flag_low_td",
default = "Uncategorized"
)]
# create a srs normal group variable
alldata[, srs_normal := fifelse(
(binary_group == "ASD" & srs2total > 20) | (binary_group == "TD" & srs2total < 120),1,0)]
# alldata[, table(srs_normal)]
# create a above criteria group variable
alldata[, above_crit := fifelse(
(nTrials == 200 & perf > .616),1,0)]
# data summary
summary <- unique(alldata[, .(username, subID, game_version, source, binary_group, group_verbal, srs_normal, gender, race, age, ethnicity, vinabcstd, srs2total, bistotal,aasp_low_reg_raw,
aasp_sen_seek_raw,aasp_sen_ses_raw,aasp_sen_avoid_raw, perf, trainperf, nTrials, above_crit)])
###### table for subject numbers ####
summary[srs_normal == 1 & game_version=="ft", table(binary_group)]
summary[srs_normal == 1 & game_version=="ft" & group_verbal!="flag", table(binary_group)]
summary[above_crit==1 & srs_normal == 1 & game_version=="ft" & group_verbal!="flag" & !is.na(bistotal) & !is.na(aasp_low_reg_raw) & !is.na(aasp_sen_seek_raw) & !is.na(aasp_sen_ses_raw) & !is.na(aasp_sen_avoid_raw), table(binary_group)]
############################################################################################################
# Data for Supplemenatry Figure 1
# total number of participants in each binary group for game_version = "ft"
summary[game_version == "ft", table(binary_group)] #+1 ASD subject whose game data we could not track
# total number of participants in each binary group for game_version = "ft" and srs_normal = 1
summary[game_version == "ft" & srs_normal == 1, table(binary_group)]
# total number of participants in each binary group for game_version = "ft" and srs_normal = 1 and above_crit = 1
summary[game_version == "ft" & srs_normal == 1 & above_crit == 1, table(binary_group)]
# number of participants in binary_group = "ASD" for game_version = "ft" and srs_normal = 1 and above_crit = 0
summary[game_version == "ft" & srs_normal == 1 & above_crit == 0, table(binary_group)]
# subIDs of the ASD participants who did not meet the criteria
id1 = summary[game_version == "ft" & above_crit == 0 & binary_group == "ASD", .(subID)]
# subIDs ofparticipants who played game again
id2 = summary[game_version != "ft", .(subID)]
# remove the "_2" and "_3" at the end of the subID for id2 only where it is present
id2[, subID := gsub("_2$|_3$", "", subID)]
# common subIDs between id1 and id2
common_ids = id1[subID %in% id2$subID, .(subID)]
# subIDs of the ASD participants who played game_version = "ft-2" and met the criteria
id3 = summary1[game_version == "ft-2" & srs_normal == 1 & above_crit == 1 & binary_group == "ASD", .(subID)]
# clear id1, id2, common_ids
rm(id1,id2,common_ids)
############################################################################################################
# Supplemenatry Figure 2
# only include subjects with game_version = "ft" and srs_normal = 1
df = summary[game_version == "ft" & srs_normal==1]
# gender distribution
# If gender = "M", Male, if "F", Female, otherwise call it Other/Unknown
df[,gender := fcase(gender == "M", "Male",gender == "F","Female", default = "Other/Unknown")]
p1<-
ggplot(df,aes(x=gender, fill=binary_group, color=binary_group)) +
geom_bar(position="dodge") +
theme_bw()+labs(fill="",color="",xlab="",
# ylab="Frequency"
)+
theme(legend.position = c(.7,.8),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 1, hjust=1),
legend.background=element_blank())
# age distribution
p2 <-
ggplot(df,aes(x=age,fill=binary_group,color=binary_group))+
geom_bar(width=.5,position=position_dodge(width = .5),alpha=.7)+
theme_bw()+
scale_x_continuous(breaks = c(11, 12, 13,14,15,16,17),limits = c(10,18))+
# ylab("Frequency")+
xlab("Age (y)")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background=element_blank())
# race distribution
unique(df$race)
df[,race := fcase(race=="LA6156-9","Asian",
race=="LA10610-6","Black or African American",
race=="LA10611-4","Native Hawaiian or Other Pacific Islander",
race=="LA4457-3","White",
race=="LA4489-6","Native Hawaiian or Other Pacific Islander",default="Unknown")]
df$race<-factor(df$race,levels = c("White","Black or African American", "Asian",
"Native Hawaiian or Other Pacific Islander","Unknown"))
p3 <-
ggplot(df,aes(x=race,fill=binary_group,color=binary_group))+
geom_bar(width=.5,position=position_dodge(width = .5),alpha=.7)+
theme_bw()+
# ylab("Frequency")+
xlab("")+
labs(fill="",color="")+
scale_x_discrete(labels = c("White","Black/African\nAmerican","Asian",
"Native Hawaiian/\nPacific Islander","Unknown"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 1, hjust=1),
panel.grid.minor = element_blank())
# ethnicity distribution
unique(df$ethnicity)
df[,ethnicity := fcase(ethnicity=="2135-2","Hispanic or Latino",
ethnicity=="2186-5","Not Hispanic or Latino",
default="Unknown")]
df$ethnicity<-factor(df$ethnicity,levels = c("Hispanic or Latino","Not Hispanic or Latino","Unknown"))
p4 <-
ggplot(df,aes(x=ethnicity,fill=binary_group,color=binary_group))+
geom_bar(width=.5,position=position_dodge(width = .5),alpha=.7)+
theme_bw()+
# ylab("Frequency")+
xlab("")+
labs(fill="",color="")+
scale_x_discrete(labels = c("Hispanic/Latino","Not Hispanic/Latino","Unknown"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
axis.text.x = element_text(angle = 90, vjust = 1, hjust=1),
panel.grid.minor = element_blank())
# srs2total distribution
p5 <-
ggplot(df,
aes(x=srs2total))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("SRS-2")+
geom_vline(xintercept=70,linetype="dashed")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# bistotal distribution
p6 <-
ggplot(df,
aes(x=bistotal))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("BIS")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# reverse scale vinabcstd distribution
df[,vinabcstd := 140 - vinabcstd]
# vinabcstd distribution
p7 <-
ggplot(df,
aes(x=vinabcstd))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("VABS-3")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# aasp_low_reg_raw distribution
p8 <-
ggplot(df,
aes(x=aasp_low_reg_raw))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("AASP\n(Low Registration)")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# aasp_sen_avoid_raw distribution
p9 <-
ggplot(df,
aes(x=aasp_sen_avoid_raw))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("AASP\n(Sensory Avoiding)")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# reverse scale aasp_sen_seek_raw distribution
df[,aasp_sen_seek_raw := 75 - aasp_sen_seek_raw]
p10 <-
ggplot(df,
aes(x=aasp_sen_seek_raw))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("AASP\n(Sensory Seeking)")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# aasp_sen_ses_raw distribution
p11 <-
ggplot(df,
aes(x=aasp_sen_ses_raw))+
geom_histogram(aes(#y = after_stat(density),
fill=binary_group,
color=binary_group),
position="identity",
alpha=.5)+
theme_bw()+
xlab("AASP\n(Sensory Sensitivity)")+
# ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# combine all the plots into publication ready format, add labels and save the plot
ggarrange(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,ncol=4,nrow=3,common.legend = TRUE,align = "v",
labels = c("A","B","C","D","E","F","G","H","I","J","K")) %>% ggexport(filename = "results/figures/geodems/demographics_v2.pdf")
# remove plot objects
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11)
############################################################################################################
# Main Figure 2
# distribution of vinabcstc for group_verbal (remove flagged subjects) and for srs_normal=1 and game_version = "ft"
df = summary[game_version == "ft" & srs_normal==1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df$group_verbal <- factor(df$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
# plot distribution of vinabcstd for group_verbal
p1 <-
ggplot(df,
aes(x=vinabcstd))+
geom_histogram(aes(
fill=group_verbal,color=group_verbal),
position="identity",
alpha=.5)+
theme_bw()+
scale_fill_manual(values = c("blue","orange","red"))+
scale_color_manual(values = c("blue","orange","red"))+
xlab("VABS-3")+
ylab("Frequency")+
labs(fill="",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.key = element_rect(colour = "transparent"),
legend.text=element_text(size=4))
# scatter plot perf by vinabcstd
p2 <-
ggplot(df,aes(x=vinabcstd,y=perf*100))+
geom_point(aes(color=group_verbal))+
ylab("%Correct")+
xlab("VABS-3")+
scale_color_manual(values = c("blue","orange","red"))+
geom_hline(yintercept = 62,linetype="dashed",size=.5)+
theme_bw()+
labs(color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# plot percentage of subjects above the criteria for each group_verbal, show the percentage on the plot
df = df[, .(above_crit = mean(above_crit)), by = .(group_verbal)]
df$group_verbal <- factor(df$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
p3 <-
ggplot(df,
aes(x=group_verbal,y=above_crit))+
geom_bar(stat="identity",aes(fill=group_verbal),position="dodge")+
geom_text(aes(label = paste0(round(above_crit*100,1),"%")),position = position_stack(vjust = 0.5), col = "white")+
theme_bw()+
scale_fill_manual(values = c("blue","orange","red"))+
xlab("")+
ylab("%above criteria")+
labs(fill="")+
theme(legend.position = "None",
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.key = element_rect(colour = "transparent"),
legend.text=element_text(size=4))
# score for trials 1 through 10 for each group_verbal from alldata, for game_version = "ft" and srs_normal = 1 and above_crit = 1
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","group_verbal","trial","score")]
df = df[trial %in% 1:10]
# plot the average scores and se for each group_verbal
df = df[, .(mean_score = mean(score), se = sd(score)/sqrt(.N)), by = .(group_verbal,trial)]
df$group_verbal <- factor(df$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
pairwise_test <- df %>%
pairwise_t_test(mean_score ~ group_verbal, p.adjust.method = "bonferroni")
pairwise_test <- pairwise_test %>%
add_xy_position(x = "group_verbal")
fit_learning_curve <- function(data) {
nls(mean_score ~ pinf - (pinf - p0) * exp(-alpha * trial),
data = data,
start = list(pinf = 1, p0 = 0.5, alpha = 0.1),
control = nls.control(maxiter = 100))
}
td_fit <- df %>%
filter(group_verbal == "TD") %>%
fit_learning_curve()
asd_fit <- df %>%
filter(group_verbal == "ASD") %>%
fit_learning_curve()
asd_low_verbal_fit <- df %>%
filter(group_verbal == "ASD (low verbal)") %>%
fit_learning_curve()
new_data <- data.frame(trial = seq(1, 10, 0.1))
td_pred <- data.frame(
trial = new_data$trial,
mean_score = predict(td_fit, newdata = new_data),
group_verbal = "TD"
)
asd_pred <- data.frame(
trial = new_data$trial,
mean_score = predict(asd_fit, newdata = new_data),
group_verbal = "ASD"
)
asd_low_verbal_pred <- data.frame(
trial = new_data$trial,
mean_score = predict(asd_low_verbal_fit, newdata = new_data),
group_verbal = "ASD (low verbal)"
)
pred_data <- rbind(td_pred, asd_pred)
pred_data <- rbind(pred_data, asd_low_verbal_pred)
p4 <-
ggplot() +
geom_point(data = df, aes(x = trial, y = 100*mean_score, color = group_verbal)) +
geom_errorbar(data = df, aes(x = trial, ymin = 100*(mean_score - se), ymax = 100*(mean_score + se), color = group_verbal), width = 0.2) +
geom_line(data = pred_data, aes(x = trial, y = 100*mean_score, color = group_verbal)) +
# stat_pvalue_manual(pairwise_test, label = "p.adj.signif",
# y.position = max(df$mean_score*100) + 0.1) +
scale_x_continuous(breaks = 1:10) +
labs(title = "Level 1",
x = "Trial",
y = "%Correct",
color = "Group") +
theme_bw() +
ylim(60,105)+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.key = element_rect(colour = "transparent"),
legend.text=element_text(size=4))
# rolling average (trials 11 through 200) for each group_verbal
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","group_verbal","trial","score")]
df = df[trial %in% 11:200]
window_size <- 50
df_rolling <- df %>%
group_by(subID, group_verbal) %>%
arrange(trial) %>%
mutate(rolling_avg = rollmean(score, k = window_size, fill = NA, align = "left")) %>% ungroup()
df_summary <- df_rolling %>%
group_by(group_verbal, trial) %>%
summarise(
avg_score = mean(rolling_avg, na.rm = TRUE),
se = sd(rolling_avg, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# fit_learning_curve <- function(data) {
# nls(avg_score ~ pinf - (pinf - p0) * exp(-alpha * trial),
# data = data,
# start = list(pinf = 1, p0 = 0.5, alpha = 0.1),
# control = nls.control(maxiter = 100))
# }
# td_fit <- df_summary %>%
# filter(group_verbal == "TD") %>%
# fit_learning_curve()
# asd_fit <- df_summary %>%
# filter(group_verbal == "ASD") %>%
# fit_learning_curve()
# asd_low_verbal_fit <- df_summary %>%
# filter(group_verbal == "ASD (low verbal)") %>%
# fit_learning_curve()
p5 <-
ggplot(df_summary, aes(x = trial, y = 100*avg_score, color = group_verbal)) +
# geom_point() +
geom_smooth(method = "loess",span=1,se=F,size=.5) +
geom_ribbon(aes(ymin = 100*(avg_score - se), ymax = 100*(avg_score + se), fill = group_verbal), alpha = .4) +
labs(title = "Rolling Average Score by Group (Trials 11-200)",
x = "Trial",
y = "Rolling Average Score",
color = "Group",
fill = "Group") +
theme_bw() +
ylim(75,90)+
labs(title = "Level 2-10",
x = "Trial",
y = "%Correct",
color = "Group") +
scale_x_continuous(breaks = seq(10, 150, 20),limits = c(10,150)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.key = element_rect(colour = "transparent"),
legend.text=element_text(size=4))
# psychometric curve (unsigned)
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","group_verbal","trial","score","dflash")]
df = df[trial %in% 101:200 & dflash != 0]
df$dflash <- abs(df$dflash)
df_subject_avg <- df %>%
group_by(subID, group_verbal, dflash) %>%
summarise(avg_score = mean(score, na.rm = TRUE), .groups = "drop")
df_long <- df_subject_avg %>%
select(subID, group_verbal, dflash, avg_score) %>%
mutate(dflash = factor(dflash,levels = c(1:10))) # Convert dflash to a factor
aov_model <- aov_ez(
id = "subID",
dv = "avg_score",
data = df_long,
between = "group_verbal",
within = "dflash",
type = 3
)
print(aov_model)
posthoc <- emmeans(aov_model, ~ group_verbal | dflash)
group_comparisons <- pairs(posthoc)
print(group_comparisons)
significant_dflash <- group_comparisons %>%
as.data.frame() %>%
filter(p.value < 0.05) %>%
pull(dflash) %>%
unique()
df_group_avg <- df_subject_avg %>%
group_by(group_verbal, dflash) %>%
summarise(
mean_score = mean(avg_score, na.rm = TRUE),
se = sd(avg_score, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
p6<-
ggplot(df_group_avg, aes(x = dflash, y = mean_score, color = group_verbal)) +
geom_point(size=.5)+
geom_smooth(se=F,method="glm",method.args = list(family = "binomial"),size=.5) +
# geom_ribbon(aes(ymin = mean_score - se, ymax = mean_score + se, fill = group_verbal), alpha = 0.2) +
geom_errorbar(aes(ymin = mean_score - se, ymax = mean_score + se), alpha = 0.8, size=.5,width=.5) +
labs(title = "Trial 101-200",
x = "Cue difference",
y = "Accuracy",
color = "Group",
fill = "Group") +
ylim(.65,1.05)+
theme_bw() +
scale_x_continuous(breaks = seq(1, 10),limits = c(.5,10.5)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# p <- p + geom_point(data = df_group_avg %>%
# filter(dflash %in% significant_dflash) %>%
# group_by(dflash) %>%
# summarise(max_y = max(mean_score + se)),
# aes(x = dflash, y = max_y + 0.05),
# shape = 8, size = 3, color = "black")
ggarrange(p1,p2,p3,p4,p5,p6,ncol=3,nrow=3,common.legend = TRUE,align = "v",
labels = c("A","B","C","D","E","F")) %>% ggexport(filename = "results/figures/geodems/descriptives_v2.pdf")
# remove variables
rm(df,df_group_avg,df_long,df_rolling,df_subject_avg,group_comparisons,pairwise_test,posthoc,significant_dflash,td_fit,asd_fit,asd_low_verbal_fit)
rm(td_pred,asd_pred,asd_low_verbal_pred,df_summary,new_data,pred_data)
rm(aov_model,window_size,fit_learning_curve)
# remove plot objects
rm(p1,p2,p3,p4,p5,p6)
####### Supplementary Figure 3 ######################
# the full psychometric curve
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","group_verbal","trial","choice","dflash","score")]
df = df[trial %in% 11:200]
df_subject_avg <- df %>%
group_by(subID, group_verbal, dflash) %>%
summarise(avg_choice = mean(choice, na.rm = TRUE), .groups = "drop")
df_group_avg <- df_subject_avg %>% group_by(group_verbal, dflash) %>% summarise(
mean_choice = mean(avg_choice, na.rm = TRUE),
se = sd(avg_choice, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
df_group_avg$group_verbal <- factor(df_group_avg$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
p1<-
ggplot(df_group_avg, aes(x = dflash, y = mean_choice, color = group_verbal)) +
geom_point(size=.5)+
geom_smooth(se=F,method="glm",method.args = list(family = "binomial"),size=.5) +
# geom_ribbon(aes(ymin = mean_choice - se, ymax = mean_choice + se, fill = group_verbal), alpha = 0.2) +
geom_errorbar(aes(ymin = mean_choice - se, ymax = mean_choice + se), alpha = 0.8, size=.5,width=.5) +
labs(title = "Trial 11-200",
x = "Cue difference",
y = "%Went right",
color = "",
fill = ""
) +
ylim(-.05,1.05)+
theme_bw() +
scale_x_continuous(breaks = seq(-10, 10,3),limits = c(-11,11)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
df$dflash <- abs(df$dflash)
df = df[df$dflash != 0]
df_subject_avg <- df %>%
group_by(subID, group_verbal, dflash) %>%
summarise(avg_score = mean(score, na.rm = TRUE), .groups = "drop")
df_group_avg <- df_subject_avg %>% group_by(group_verbal, dflash) %>% summarise(
mean_score = mean(avg_score, na.rm = TRUE),
se = sd(avg_score, na.rm = TRUE) / sqrt(n()),
.groups = "drop")
df_group_avg$group_verbal <- factor(df_group_avg$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
p2<-
ggplot(df_group_avg, aes(x = dflash, y = mean_score, color = group_verbal)) +
geom_point(size=.5)+
geom_smooth(se=F,method="glm",method.args = list(family = "binomial"),size=.5) +
# geom_ribbon(aes(ymin = mean_choice - se, ymax = mean_choice + se, fill = group_verbal), alpha = 0.2) +
geom_errorbar(aes(ymin = mean_score - se, ymax = mean_score + se), alpha = 0.8, size=.5,width=.5) +
labs(title = "Trial 11-200",
x = "Cue difference (absolute)",
y = "Accuracy",
color = "",
fill = ""
) +
ylim(.65,1.05)+
theme_bw() +
scale_x_continuous(breaks = seq(1, 10),limits = c(.5,10.5)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
df <- alldata[trial>10 & srs_normal == 1 & above_crit == 1 & game_version == "ft",c("username","subID","choice","dflash","winstay","loseswitch","binary_group","group_verbal")]
df <- df[group_verbal != "flag" & group_verbal != "flag_low_td"]
# create trial bins
df <- df %>%
group_by(subID) %>%
mutate(trial= row_number(),
trial_bin = cut(trial, breaks = 19, labels = FALSE)) %>%
ungroup()
# function to fit a glm for each trial bin
fit_bin_glm <- function(bin_data) {
glm(choice ~ dflash + winstay + loseswitch,
data = bin_data,
family = binomial(link = "logit"))
}
# fit the models
model_fits <- df %>%
group_by(group_verbal, trial_bin) %>%
nest() %>%
mutate(model = map(data, fit_bin_glm))
# get parameter estimates for each bin and group
param_estimates <- model_fits %>%
mutate(params = map(model, broom::tidy)) %>%
unnest(params) %>%
select(group_verbal, trial_bin, term, estimate, std.error)
param_estimates <- param_estimates %>%
mutate(significant = abs(estimate / std.error) > 1.96) # p < 0.05
# Define the learning curve function
learning_curve <- function(n, p0, pinf, alpha) {
pinf - (pinf - p0) * exp(-alpha * n)
}
# Filter data for dflash and split by group
dflash_data <- param_estimates %>%
filter(term == "dflash") %>%
split(.$group_verbal)
# Function to fit the learning curve for a group
fit_learning_curve <- function(data) {
nlsLM(estimate ~ learning_curve(trial_bin, p0, pinf, alpha),
data = data,
start = list(p0 = min(data$estimate),
pinf = max(data$estimate),
alpha = 0.1),
lower = c(0.5, 0.5, 0.1),
upper = c(1, 1, 1))
}
# Fit the model for each group
fits <- map(dflash_data, safely(fit_learning_curve))
# Extract parameters and create predictions
results <- imap_dfr(fits, function(fit, group_verbal) {
if (is.null(fit$error)) {
params <- coef(fit$result)
new_data <- data.frame(trial_bin = seq(1, 19, length.out = 100))
new_data$predicted <- predict(fit$result, newdata = new_data)
new_data$group_verbal <- group_verbal
return(new_data)
} else {
return(NULL)
}
})
# Print the fitted parameters
walk2(fits, names(fits), function(fit, group_verbal) {
if (is.null(fit$error)) {
params <- coef(fit$result)
cat(sprintf("%s: p0 = %.4f, pinf = %.4f, alpha = %.4f\n",
group_verbal, params["p0"], params["pinf"], params["alpha"]))
} else {
cat(sprintf("%s: Fitting failed\n", binary_group))
}
})
# Plot
p3 <-
ggplot() +
geom_point(data = param_estimates %>% filter(term == "dflash"),
aes(x = trial_bin*10, y = estimate, color = group_verbal),size=.5) +
geom_ribbon(data = param_estimates %>% filter(term == "dflash"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, fill = group_verbal),
alpha = 0.2) +
geom_errorbar(data = param_estimates %>% filter(term == "dflash"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, color = group_verbal),
width = .5,size=.5) +
geom_line(data = results,
aes(x = trial_bin*10, y = predicted, color = group_verbal)) +
labs(title = "",
x = "Trial",
y = "Psychometric Slope",
color="") +
theme_bw()+
scale_x_continuous(breaks = seq(11, 200,30),limits = c(5,205)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p4 <-
ggplot() +
geom_point(data = param_estimates %>% filter(term == "(Intercept)"), aes(x = trial_bin*10, y = estimate, color = group_verbal),size=.5) +
# geom_line(data = new_data, aes(x = trial_bin, y = predicted), color = "red") +
geom_ribbon(data = param_estimates %>% filter(term == "(Intercept)"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, fill = group_verbal),
alpha = 0.2) +
geom_errorbar(data = param_estimates %>% filter(term == "(Intercept)"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, color = group_verbal),
width = .5,size=.5) +
geom_smooth(data = param_estimates %>% filter(term == "(Intercept)"),
aes(x = trial_bin*10, y = estimate, color = group_verbal),
method = "lm",
# formula = y ~ s(x, bs = "cs"),
se = F,size=.5) +
# facet_wrap(~ term, scales = "free_y", ncol = 3, nrow = 3) +
labs(title = "",
x = "Trial",
y = "Side-bias",
color="",
fill="") +
ylim(-.8,.8)+
theme_bw()+
scale_x_continuous(breaks = seq(11, 200,30),limits = c(5,205)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5 <-
ggplot() +
geom_point(data = param_estimates %>% filter(term == "winstay"), aes(x = trial_bin*10, y = estimate, color = group_verbal),size=.5) +
# geom_line(data = new_data, aes(x = trial_bin, y = predicted), color = "red") +
geom_ribbon(data = param_estimates %>% filter(term == "winstay"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, fill = group_verbal),
alpha = 0.2) +
geom_errorbar(data = param_estimates %>% filter(term == "winstay"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, color = group_verbal),
width = .5,size=.5) +
geom_smooth(data = param_estimates %>% filter(term == "winstay"),
aes(x = trial_bin*10, y = estimate, color = group_verbal),
method = "lm",
# formula = y ~ s(x, bs = "cs"),
se = F,size=.5) +
# facet_wrap(~ term, scales = "free_y", ncol = 3, nrow = 3) +
labs(title = "",
x = "Trial",
y = "Win-stay",
color="",
fill="") +
ylim(-.8,.8)+
theme_bw()+
scale_x_continuous(breaks = seq(11, 200,30),limits = c(5,205)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p6 <-
ggplot() +
geom_point(data = param_estimates %>% filter(term == "loseswitch"), aes(x = trial_bin*10, y = estimate, color = group_verbal),size=.5) +
# geom_line(data = new_data, aes(x = trial_bin, y = predicted), color = "red") +
geom_ribbon(data = param_estimates %>% filter(term == "loseswitch"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, fill = group_verbal),
alpha = 0.2) +
geom_errorbar(data = param_estimates %>% filter(term == "loseswitch"),
aes(x = trial_bin*10, ymin = estimate - std.error, ymax = estimate + std.error, color = group_verbal),
width = .5,size=.5) +
geom_smooth(data = param_estimates %>% filter(term == "loseswitch"),
aes(x = trial_bin*10, y = estimate, color = group_verbal),
method = "lm",
# formula = y ~ s(x, bs = "cs"),
se = F,size=.5) +
# facet_wrap(~ term, scales = "free_y", ncol = 3, nrow = 3) +
labs(title = "",
x = "Trial",
y = "Lose-switch",
color="",
fill="") +
ylim(-.8,1.8)+
theme_bw()+
scale_x_continuous(breaks = seq(11, 200,30),limits = c(5,205)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5,p6,labels = c("A","B","C","D","E","F"),ncol=3,nrow=3,common.legend = TRUE,align = "v") %>% ggexport(filename = "results/figures/geodems/suppl_figure3.pdf")
# remove variables, functions and objects
rm(df,df_group_avg,df_subject_avg,dflash_data,fits,results)
rm(param_estimates,fit_bin_glm,model_fits)
rm(p1,p2,p3,p4,p5,p6)
rm(learning_curve,fit_learning_curve)
########### Figure 3 (flash weights) ######################
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","group_verbal","trial","choice","dflash","lbin1","lbin2","lbin3","lbin4","lbin5","lbin6","lbin7","lbin8","lbin9","lbin10","rbin1","rbin2","rbin3","rbin4","rbin5","rbin6","rbin7","rbin8","rbin9","rbin10")]
df = df[trial %in% 101:200]
# Fit logistic regression model for each group
fit_model <- function(data) {
glm(choice ~ lbin1 + lbin2 + lbin3 + lbin4 + lbin5 + lbin6 + lbin7 + lbin8 + lbin9 + lbin10 +
rbin1 + rbin2 + rbin3 + rbin4 + rbin5 + rbin6 + rbin7 + rbin8 + rbin9 + rbin10,
family = binomial,
data = data)
}
models <- df %>%
group_by(group_verbal) %>%
nest() %>%
mutate(model = map(data, fit_model))
# Extract coefficients
coef_data <- models %>%
mutate(coef = map(model, tidy)) %>%
select(group_verbal, coef) %>%
unnest(coef) %>%
filter(term != "(Intercept)") %>%
mutate(
side = ifelse(grepl("^l", term), "left", "right"),
bin = as.numeric(gsub("^[lr]bin", "", term)),
weight = estimate * ifelse(side == "left", -1, 1)
) %>%
group_by(group_verbal, bin) %>%
reframe(avg_weight = mean(weight),se_avg_weight = sqrt(sum(std.error^2)) / 2) # Using propagation of error method)
model <- aov(avg_weight ~ group_verbal * bin, data = coef_data)
summary(model)
# For group comparisons
emmeans(model, pairwise ~ group_verbal)
# For bin comparisons
emmeans(model, pairwise ~ bin)
# For side comparisons
# emmeans(lm_model, pairwise ~ side)
# For group:bin interaction
emmeans(model, pairwise ~ group_verbal | bin)
# For group:side interaction
# emmeans(lm_model, pairwise ~ group | side)
# pairwise_test <- coef_data %>%
# pairwise_t_test(avg_weight ~ group_verbal, p.adjust.method = "bonferroni")
# pairwise_test <- pairwise_test %>%
# add_xy_position(x = "group_verbal")
# Plot model weights
# p <-
# ggplot(coef_data, aes(x = bin, y = estimate, color = group_verbal,shape=side)) +
# geom_line() +
# geom_point() +
# labs(x = "Bin", y = "Weight", color = "Group") +
# theme_minimal()
coef_data$group_verbal <- factor(coef_data$group_verbal, levels = c("TD","ASD","ASD (low verbal)"),ordered = TRUE)
p1<-
ggplot(coef_data, aes(x = bin/4, y = avg_weight, color = group_verbal),size=.5) +
# geom_ribbon(aes(ymin=weight-std.error,ymax=weight+std.error,fill=group_verbal),alpha=.5) +
# geom_point() +
geom_errorbar(aes(ymin=avg_weight-se_avg_weight,ymax=avg_weight+se_avg_weight,color=group_verbal),size=.5,width=.1) +
geom_smooth(span=1,se=F,size=.5)+
ylim(0.25,1)+
labs(x = "Flash time (s)", y = TeX("$\\beta$"), color = "") +
theme_bw()+
scale_x_continuous(breaks = seq(0, 2.5, .5),limits = c(0,2.8)) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# Function to group bins
# group_bins <- function(data) {
# data %>%
# mutate(
# l_early = as.integer(lbin1 + lbin2 + lbin3>0),
# l_middle = as.integer(lbin4 + lbin5 + lbin6 + lbin7>0),
# l_late = as.integer(lbin8 + lbin9 + lbin10>0),
# r_early = as.integer(rbin1 + rbin2 + rbin3>0),
# r_middle = as.integer(rbin4 + rbin5 + rbin6 + rbin7>0),
# r_late = as.integer(rbin8 + rbin9 + rbin10>0)
# ) %>%
# select(-starts_with("lbin"), -starts_with("rbin"))
# }
group_bins <- function(data) {
data %>%
mutate(
l_early = lbin1 + lbin2 + lbin3,
l_middle = lbin4 + lbin5 + lbin6 + lbin7,
l_late = lbin8 + lbin9 + lbin10,
r_early = rbin1 + rbin2 + rbin3,
r_middle = rbin4 + rbin5 + rbin6 + rbin7,
r_late = rbin8 + rbin9 + rbin10
) %>%
select(-starts_with("lbin"), -starts_with("rbin"))
}
# Fit logistic regression model for each group
fit_model <- function(data) {
data <- group_bins(data)
glm(choice ~ l_early + l_middle + l_late + r_early + r_middle + r_late,
family = binomial,
data = data)
}
models <- df %>%
group_by(group_verbal) %>%
nest() %>%
mutate(model = map(data, fit_model))
# Extract coefficients
coef_data <- models %>%
mutate(coef = map(model, tidy)) %>%
select(group_verbal, coef) %>%
unnest(coef) %>%
filter(term != "(Intercept)") %>%
mutate(
side = ifelse(grepl("^l", term), "left", "right"),
bin = gsub("^[lr]_", "", term),
weight = estimate * ifelse(side == "left", -1, 1)
) %>%
group_by(group_verbal, bin) %>%
reframe(avg_weight = mean(weight),se_avg_weight = sqrt(sum(std.error^2)) / 2) # Using propagation of error method)
coef_data$bin <- factor(coef_data$bin, levels = c("early", "middle", "late"),ordered = TRUE)
coef_data$bin_num <- as.numeric(coef_data$bin)
p2<-
ggplot(coef_data, aes(x = bin_num, y = avg_weight, color = group_verbal),size=.5) +
# geom_ribbon(aes(ymin=weight-std.error,ymax=weight+std.error,fill=group_verbal),alpha=.5) +
geom_point(size=.5) +
geom_errorbar(aes(ymin=avg_weight-se_avg_weight,ymax=avg_weight+se_avg_weight,color=group_verbal),size=.5,width=.2) +
geom_smooth(span=1,se=F,size=.5)+
ylim(0.25,1)+
labs(x = "Flash count", y = TeX("$\\beta$"), color = "") +
theme_bw()+
scale_x_discrete(limits = c("early", "middle", "late")) +
scale_color_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue")) +
scale_fill_manual(values = c("ASD" = "orange", "ASD (low verbal)" = "red", "TD" = "blue"))+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v",
labels = c("B","C")) %>% ggexport(filename = "results/figures/geodems/flash_weights.pdf")
# remove variables, functions and objects
rm(df,coef_data,models,fit_model)
rm(p1,p2)
rm(group_bins)
########### Figure 3 (heatmaps) ############################
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","binary_group","group_verbal","trial","score","dflash","tflash","choice","numflashright","numflashleft")]
# Function to create heatmap for a single group
create_heatmap <- function(data, group_name) {
# Aggregate data
agg_data <- data[, .(
accuracy = mean(score),
n = .N
), by = .(numflashright, numflashleft)]
# Keep only upper triangular matrix (including diagonal)
agg_data <- agg_data[numflashright > numflashleft]
# agg_data <- agg_data[numflashright >0]
# agg_data <- agg_data[numflashleft >0]
# Create heatmap
ggplot(agg_data, aes(x = numflashleft, y = numflashright, fill = accuracy)) +
geom_tile() +
scale_fill_gradient(low = "white", #mid = "white",
high = "mediumblue",
# midpoint = 0.75,
limits = c(0.6, 1),
name = "Accuracy") +
labs(title = paste(group_name),
x = "Smaller Number",
y = "Larger Number") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5)) +
# geom_text(aes(label = sprintf("%.2f\n(n=%d)", accuracy, n)),
# color = "black", size = 3) +
scale_x_continuous(breaks = seq(0, max(data$numflashleft)-1, by = 1)) +
scale_y_continuous(breaks = seq(1, max(data$numflashright), by = 1)) +
coord_fixed(ratio = 1) +
theme(legend.position = c(.8, .3),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# + # Make the plot square
# geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "black") # Add diagonal line
}
# Create heatmaps for each group
asd_heatmap <- create_heatmap(df[binary_group == "ASD"], "ASD")
td_heatmap <- create_heatmap(df[binary_group == "TD"], "TD")
# asd_low_heatmap <- create_heatmap(df[group_verbal == "ASD (low verbal)"], "ASD (low verbal)")
# Display heatmaps
print(asd_heatmap)
print(td_heatmap)
# print(asd_low_heatmap)
agg_data_td <- df[binary_group=="TD", .(
accuracy = mean(score),
n = .N
), by = .(numflashright, numflashleft)]
agg_data_asd <- df[binary_group=="ASD", .(
accuracy = mean(score),
n = .N
), by = .(numflashright, numflashleft)]
agg_data <- merge(agg_data_td,agg_data_asd,by=c("numflashleft","numflashright"))
agg_data$accuracy = agg_data$accuracy.x - agg_data$accuracy.y
agg_data <- agg_data[numflashright > numflashleft]
td_asd_heatmap <- ggplot(agg_data, aes(x = numflashleft, y = numflashright, fill = accuracy)) +
geom_tile() +
scale_fill_gradient2(low = "red", mid = "white",
high = "mediumblue",
# midpoint = 0.75,
limits = c(-0.15, .15),
name = "Accuracy") +
labs(title = paste("TD - ASD"),
x = "Smaller Number",
y = "Larger Number") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5)) +
# geom_text(aes(label = sprintf("%.2f\n(n=%d)", accuracy, n)),
# color = "black", size = 3) +
scale_x_continuous(breaks = seq(0, max(agg_data$numflashleft)-1, by = 1)) +
scale_y_continuous(breaks = seq(1, max(agg_data$numflashright), by = 1)) +
coord_fixed(ratio = 1) +
theme(legend.position = c(.8, .3),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# + # Make the plot square
# geom_abline(i
ggarrange(td_heatmap, asd_heatmap, nrow=2,ncol = 3,
common.legend = TRUE,legend = "top",
align = "h",
labels = c("D","E","F")) %>% ggexport(filename = "results/figures/geodems/heatmaps.pdf")
ggarrange(td_asd_heatmap, nrow=2,ncol = 3,
common.legend = TRUE,legend = "right",
align = "h",
labels = c("F")) %>% ggexport(filename = "results/figures/geodems/diff_heatmaps.pdf")
rm(df,asd_heatmap,td_heatmap,asd_low_heatmap,create_heatmap,agg_data,agg_data_asd,agg_data_asd_low,agg_data_td)
############### Figure 4 #################################
library(optimx)
library(boot)
df = alldata[game_version == "ft" & srs_normal == 1 & above_crit == 1 & group_verbal != "flag" & group_verbal != "flag_low_td"]
df = df[,c("subID","binary_group","group_verbal","trial","score","dflash","tflash","choice","numflashright","numflashleft","winstay","loseswitch")]
df = df[trial %in% 11:200]
df$r <- df$numflashright
df$l <- df$numflashleft
df <- as.data.frame(df[,c("subID","binary_group","choice","r","l","trial","winstay","loseswitch")])
sdt_mdl <- function(params,data){
p_choose_l = rep(0, length(data$choice))
p_choose_r = rep(0, length(data$choice))
if (min(params)<0){
aic<-1000000
}
else{
for (i in 1:length(data$r)){
k0=params[1]
k1=params[2]
bs=params[3]
b1=params[4]
b2=params[5]
p=pnorm(0,mean=data$r[i]-data$l[i]+bs+b1*data$winstay[i]+b2*data$loseswitch[i],
sd=sqrt(k1*(data$r[i]^2+data$l[i]^2)+k0))
if (length(p)==0){
p=0
}
p_choose_l[i]=p
p_choose_r[i]<-1-p_choose_l[i]
}
lhd<-rep(NA,length(data$choice))
lhd[data$choice==1]<-p_choose_r[data$choice==1]
lhd[data$choice==0]<-p_choose_l[data$choice==0]
lhd[lhd==0]<-.0001
log.lhd<-log(lhd)
aic<- -2*sum(log.lhd)+2*length(params) # since there are 5 params
}
return(aic)
}
fit_model <- function(data) {
result <- optim(par = c(0.5, 0.5, 0, 0, 0),
fn = sdt_mdl,
data = data,
method = "SANN",
hessian = TRUE
)
}
# Fit model for ASD group
asd_data <- df[df$binary_group == "ASD", ]
asd_model <- fit_model(asd_data)
asd_se <- sqrt(diag(solve(asd_model$hessian)))
# Fit model for TD group
td_data <- df[df$binary_group == "TD", ]
td_model <- fit_model(td_data)
td_se <- sqrt(diag(solve(td_model$hessian)))
params_df <- data.frame(
group = rep(c("ASD", "TD"), each = 5),
parameter = rep(c("k0", "k1","bs", "b1", "b2"), 2),
value = c(asd_model$par, td_model$par),
se = c(asd_se, td_se)
)
params_df$parameter <- factor(params_df$parameter,
levels = c("k0","k1","bs", "b1", "b2"))
# p<-
# ggplot(params_df, aes(x = group, y = value)) +
# geom_point(aes(color = group), size = 2) +
# geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
# # geom_bar(stat = "identity", position = "dodge") +
# facet_wrap(~ parameter, scales = "free_y", nrow = 3,ncol=3,shrink = T,dir="v") +
# theme_bw() +
# labs(title = "SDT Model Parameters by Group", x = "Group", y = "Value",color="") +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))+
# theme(#legend.position = c(.8, .3),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank())
# ggexport(p, filename = "results/figures/geodems/sdt_model_params.pdf")
# Generate predictions for ASD group
mdl_func <- function(params,data){
p_choose_l = rep(0, length(data$choice))
p_choose_r = rep(0, length(data$choice))
for (i in 1:length(data$r)){
k0=params[1]
k1=params[2]
bs=params[3]
b1=params[4]
b2=params[5]
p=pnorm(0,mean=data$r[i]-data$l[i]+bs+b1*data$winstay[i]+b2*data$loseswitch[i],
sd=sqrt(k1*(data$r[i]^2+data$l[i]^2)+k0))
if (length(p)==0){
p=0
}
p_choose_l[i]=p
p_choose_r[i]<-1-p_choose_l[i]
}
return(p_choose_r)
}
asd_pred <- mdl_func(asd_model$par, asd_data)
td_pred <- mdl_func(td_model$par, td_data)
fit_data <- rbind(
data.frame(subID = asd_data$subID,group = "ASD", observed = asd_data$choice, predicted = asd_pred, dflash=asd_data$r-asd_data$l),
data.frame(subID = td_data$subID,group = "TD", observed = td_data$choice, predicted = td_pred, dflash=td_data$r-td_data$l)
)
df_sub_avg <- fit_data %>%
group_by(subID,group, dflash) %>%
summarise(
avg_choice = mean(observed, na.rm = TRUE),
avg_pred = mean(predicted, na.rm=TRUE),
.groups = "drop")
df_group_avg <- df_sub_avg %>%
group_by(group, dflash) %>%
summarise(
mean_choice = mean(avg_choice, na.rm = TRUE),
mean_pred = mean(avg_pred, na.rm = TRUE),
choice_se = sd(avg_choice, na.rm = TRUE) / sqrt(n()),
pred_se = sd(avg_pred, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# p_fit <-
# ggplot(df_group_avg, aes(x=dflash,color = group)) +
# geom_point(aes(y=mean_choice),alpha = 0.5) +
# geom_errorbar(aes(y=mean_choice, ymin = mean_choice - choice_se, ymax = mean_choice + choice_se), width = 0.5) +
# geom_line(aes(y=mean_pred),alpha = 1) +
# # geom_smooth(method = "glm",method.args = list(family = "binomial") ,se = FALSE) +
# # facet_wrap(~group,nrow = 3,ncol=3) +
# theme_bw() +
# labs(title = "Model Fit vs Original Data", x = "Cue difference", y = "%Went right",color="")+
# theme(legend.position = "None",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank())
# ggarrange(p_fit, nrow=3, ncol=3) %>%
# ggexport(p_fit, filename = "results/figures/geodems/sdt_model_fit.pdf")
# rm(p,p_fit)
setDT(df_group_avg)
p1 <-
ggplot(df_group_avg[group=="ASD"],aes(x=dflash))+
geom_point(aes(y=mean_choice),alpha = 0.5,color="red") +
geom_errorbar(aes(y=mean_choice, ymin = mean_choice - choice_se, ymax = mean_choice + choice_se), width = 1,color="red") +
geom_line(aes(y=mean_pred),alpha = 1,color="black") +
theme_bw() +
labs(title = "Model Fit: ASD", x = "Cue difference", y = "%Went right",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p2 <-
ggplot(df_group_avg[group=="TD"],aes(x=dflash))+
geom_point(aes(y=mean_choice),alpha = 0.5,color="blue") +
geom_errorbar(aes(y=mean_choice, ymin = mean_choice - choice_se, ymax = mean_choice + choice_se), width = 1,color="blue") +
geom_line(aes(y=mean_pred),alpha = 1,color="black") +
theme_bw() +
labs(title = "TD", x = "Cue difference", y = "%Went right",color="")+
theme(legend.position = "None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "h",
labels = c("E","F")) %>% ggexport(filename = "results/figures/geodems/sdt_model_fit.pdf")
rm(p1,p2)
setDT(params_df)
p1 <-
ggplot(params_df[parameter=="k0"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
# facet_wrap(~ parameter, scales = "free_y", nrow = 3,ncol=3,shrink = T,dir="v") +
theme_bw() +
ylim(0.5,2) +
labs(title = "", x="", y = TeX("$k_0$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(#legend.position = c(.8, .3),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p2 <-
ggplot(params_df[parameter=="k1"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0.05,0.15) +
labs(x = "", y = TeX("$k_1$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p3 <-
ggplot(params_df[parameter=="bs"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(-0.1,0.1) +
labs(x = "", y = TeX("$bs$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p4 <-
ggplot(params_df[parameter=="b1"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0,0.3) +
labs(x = "", y = TeX("$b1$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5 <-
ggplot(params_df[parameter=="b2"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0.3,0.7) +
labs(x = "", y = TeX("$b2$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v",
labels = c("G","H","I","J","K")) %>% ggexport(filename = "results/figures/geodems/sdt_model_params.pdf")
################ Supplementary figure 4 #################################
sdt_mdl_linear <- function(params,data){
p_choose_l = rep(0, length(data$choice))
p_choose_r = rep(0, length(data$choice))
if (min(params)<0){
aic<-1000000
}
else{
for (i in 1:length(data$r)){
k0=params[1]
k1=params[2]
bs=params[3]
b1=params[4]
b2=params[5]
p=pnorm(0,mean=data$r[i]-data$l[i]+bs+b1*data$winstay[i]+b2*data$loseswitch[i],
sd=sqrt(k1*(data$r[i]+data$l[i])+k0))
if (length(p)==0){
p=0
}
p_choose_l[i]=p
p_choose_r[i]<-1-p_choose_l[i]
}
lhd<-rep(NA,length(data$choice))
lhd[data$choice==1]<-p_choose_r[data$choice==1]
lhd[data$choice==0]<-p_choose_l[data$choice==0]
lhd[lhd==0]<-.0001
log.lhd<-log(lhd)
aic<- -2*sum(log.lhd)+2*length(params) # since there are 5 params
}
return(aic)
}
fit_model_linear <- function(data) {
result <- optim(par = c(0.5, 0.5, 0, 0, 0),
fn = sdt_mdl_linear,
data = data,
method = "SANN",
hessian = TRUE
)
}
asd_model_linear <- fit_model_linear(asd_data)
asd_se_linear <- sqrt(diag(solve(asd_model_linear$hessian)))
td_model_linear <- fit_model_linear(td_data)
td_se_linear <- sqrt(diag(solve(td_model_linear$hessian)))
params_df_linear <- data.frame(
group = rep(c("ASD", "TD"), each = 5),
parameter = rep(c("k0", "k1","bs", "b1", "b2"), 2),
value = c(asd_model_linear$par, td_model_linear$par),
se = c(asd_se_linear, td_se_linear)
)
params_df_linear$parameter <- factor(params_df_linear$parameter,
levels = c("k0","k1","bs", "b1", "b2"))
mdl_func_linear <- function(params,data){
p_choose_l = rep(0, length(data$choice))
p_choose_r = rep(0, length(data$choice))
for (i in 1:length(data$r)){
k0=params[1]
k1=params[2]
bs=params[3]
b1=params[4]
b2=params[5]
p=pnorm(0,mean=data$r[i]-data$l[i]+bs+b1*data$winstay[i]+b2*data$loseswitch[i],
sd=sqrt(k1*(data$r[i]+data$l[i])+k0))
if (length(p)==0){
p=0
}
p_choose_l[i]=p
p_choose_r[i]<-1-p_choose_l[i]
}
return(p_choose_r)
}
asd_pred_linear <- mdl_func_linear(asd_model_linear$par, asd_data)
td_pred_linear <- mdl_func_linear(td_model_linear$par, td_data)
fit_data_linear <- rbind(
data.frame(subID = asd_data$subID,group = "ASD", observed = asd_data$choice, predicted = asd_pred_linear, dflash=asd_data$r-asd_data$l),
data.frame(subID = td_data$subID,group = "TD", observed = td_data$choice, predicted = td_pred_linear, dflash=td_data$r-td_data$l)
)
df_sub_avg_linear <- fit_data_linear %>%
group_by(subID,group, dflash) %>%
summarise(
avg_choice = mean(observed, na.rm = TRUE),
avg_pred = mean(predicted, na.rm=TRUE),
.groups = "drop")
df_group_avg_linear <- df_sub_avg_linear %>%
group_by(group, dflash) %>%
summarise(
mean_choice = mean(avg_choice, na.rm = TRUE),
mean_pred = mean(avg_pred, na.rm = TRUE),
choice_se = sd(avg_choice, na.rm = TRUE) / sqrt(n()),
pred_se = sd(avg_pred, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
tmp <- data.frame(
group = rep(c("ASD", "TD"), each = 2),
mdl = rep(c("scalar noise","linear noise"), 2),
aic = c(asd_model$value,
asd_model_linear$value,
td_model$value,
td_model_linear$value)
)
p <-
ggplot(tmp, aes(x = group, y = log(aic),color = mdl)) +
geom_point(position = position_dodge(width = 0.5),size=.5) +
theme_bw() +
ylim(9,11)+
scale_color_manual(values = c("scalar noise" = "darkgreen", "linear noise" = "maroon")) +
geom_text(position = position_dodge(width = 1),aes(label = round(log(aic), 3)), vjust = -0.5,size=2) +
labs(title = "Model comparison: Scalar vs Linear Noise", x = "", y = "log(AIC)",color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(legend.position = "bottom",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank()
)
ggarrange(p, nrow=3, ncol=3,align="h") %>% ggexport(filename = "results/figures/geodems/sdt_model_fit_linear.pdf")
setDT(params_df_linear)
p1 <-
ggplot(params_df_linear[parameter=="k0"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
# facet_wrap(~ parameter, scales = "free_y", nrow = 3,ncol=3,shrink = T,dir="v") +
theme_bw() +
ylim(0,1.5) +
labs(title = "", x="", y = TeX("$k_0$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(#legend.position = c(.8, .3),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p2 <-
ggplot(params_df_linear[parameter=="k1"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0.5,.9) +
labs(x = "", y = TeX("$k_1$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p3 <-
ggplot(params_df_linear[parameter=="bs"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(-0.1,0.1) +
labs(x = "", y = TeX("$bs$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p4 <-
ggplot(params_df_linear[parameter=="b1"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0,0.3) +
labs(x = "", y = TeX("$b1$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5 <-
ggplot(params_df_linear[parameter=="b2"], aes(x = group, y = value)) +
geom_point(aes(color = group), size = 2) +
geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
theme_bw() +
ylim(0.3,0.7) +
labs(x = "", y = TeX("$b2$"),color="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v") %>% ggexport(filename = "results/figures/geodems/sdt_model_params_linear.pdf")
# p <-
# ggplot(params_df_linear, aes(x = group, y = value)) +
# geom_point(aes(color = group), size = 2) +
# geom_errorbar(aes(ymin = value - se, ymax = value + se,color = group), width = 0.2) +
# # geom_bar(stat = "identity", position = "dodge") +
# facet_wrap(~ parameter, scales = "free_y", nrow = 3,ncol=3,shrink = T,dir="v") +
# theme_bw() +
# labs(title = "Linear Noise Model Parameters by Group", x = "Group", y = "Value",color="") +
# theme(axis.text.x = element_text(angle = 45, hjust = 1))+
# theme(#legend.position = c(.8, .3),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank())
# ggexport(p, filename = "results/figures/geodems/sdt_model_params_linear.pdf")
# setDT(df_group_avg_linear)
# setDT(df_group_avg)
# df_group_avg$mdl <- "scalar noise"
# df_group_avg_linear$mdl <- "linear noise"
# tmp <- rbind(df_group_avg,df_group_avg_linear)
# setDT(tmp)
# # p_fit_linear <-
# ggplot(tmp[group=="ASD"],aes(x=dflash,color=mdl)) +
# geom_point(aes(y=mean_choice),color="red",alpha = 0.5,size=.5) +
# geom_errorbar(aes(y=mean_choice,ymin = mean_choice - choice_se, ymax = mean_choice + choice_se), color="red",width = 0.5) +
# geom_line(aes(y=mean_pred),alpha = 1) +
# theme_bw() +
# scale_color_manual(values = c("scalar noise" = "darkgreen", "linear noise" = "maroon")) +
# labs(title = "Scalar vs Linear Noise: ASD", x = "Cue difference", y = "%Went right",color="")+
# theme(#legend.position = "None",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank())
# ggarrange(p, nrow=3, ncol=3,align="h") %>%
# ggexport(filename = "results/figures/geodems/sdt_model_fit_linear.pdf")
rm(p_fit_linear,p_fit,params_df_linear,asd_model_linear,asd_se_linear,td_model_linear,td_se_linear,fit_data_linear,df_sub_avg_linear,df_group_avg_linear)
rm(asd_data,td_data,asd_model,asd_se,td_model,td_se,fit_data,df_sub_avg,df_group_avg)
rm(tmp,p,fit_model_linear,sdt_mdl_linear,params_df_linear,mdl_func_linear,asd_pred_linear,td_pred_linear)
rm(fit_model,sdt_mdl,params_df,mdl_func,asd_pred,td_pred,fit_model_linear,sdt_mdl_linear,params_df_linear,mdl_func_linear,asd_pred_linear,td_pred_linear)
rm(df,df_avg)
### Figure 5 ########
library(rstan)
load("results/files/params_expdata_svnarrow.RData")
asdhbmfit <- rstan::extract(fitasd)
tdhbmfit <- rstan::extract(fittd)
params_df <- data.frame(group=rep(c("ASD","TD"),each=length(asdhbmfit$mu0)),
mu0=c(asdhbmfit$mu0,tdhbmfit$mu0),
mu1=c(asdhbmfit$mu1,tdhbmfit$mu1),
sig0=c(asdhbmfit$sig0,tdhbmfit$sig0),
sig1=c(asdhbmfit$sig1,tdhbmfit$sig1),
k0=c(asdhbmfit$k0,tdhbmfit$k0))
p1<-ggplot(params_df, aes(color = group, x = k0,fill=group)) +
geom_histogram(aes(y=after_stat(density)), alpha=0.5,
position="identity")+
labs(fill="",color="")+
xlab(TeX("$k_{0}$"))+
geom_density(alpha=.2,show.legend = F)+
theme_bw()+
theme(legend.position =c(.8,.7),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p2<-ggplot(params_df, aes(color = group, x = mu0,fill=group)) +
geom_histogram(aes(y=after_stat(density)), alpha=0.5,
position="identity")+
labs(fill="",color="")+
xlab(TeX("$\\mu_{0}$"))+
geom_density(alpha=.2,show.legend = F)+
theme_bw()+
theme(legend.position =c(.8,.7),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p3<-ggplot(params_df, aes(color = group, x = sig0,fill=group)) +
geom_histogram(aes(y=after_stat(density)), alpha=0.5,
position="identity")+
labs(fill="",color="")+
xlab(TeX("$\\sigma_{0}$"))+
geom_density(alpha=.2,show.legend = F)+
theme_bw()+
theme(legend.position =c(.8,.7),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
df = alldata[srs_normal == 1 & above_crit == 1 & game_version == "ft"]
df1 = data.frame("k1"=colMeans(asdhbmfit$k1),
"b1"=colMeans(asdhbmfit$b1),
"b2"=colMeans(asdhbmfit$b2),
"bs"=colMeans(asdhbmfit$bs),
"gr"="ASD")
df2 = data.frame("k1"=colMeans(tdhbmfit$k1),
"b1"=colMeans(tdhbmfit$b1),
"b2"=colMeans(tdhbmfit$b2),
"bs"=colMeans(tdhbmfit$bs),
"gr"="TD")
dt1 = df[binary_group=="ASD"]
dt2 = df[binary_group=="TD"]
dt1$subj_id<-as.numeric(factor(dt1$username,
levels=unique(dt1$username)))
dt2$subj_id<-as.numeric(factor(dt2$username,
levels=unique(dt2$username)))
df1$subj_id = as.numeric(c(1:length(unique(dt1$subj_id))))
df2$subj_id = as.numeric(c(1:length(unique(dt2$subj_id))))
dt1 = merge(dt1,df1,by="subj_id")
dt2 = merge(dt2,df2,by="subj_id")
dt = rbind(dt1,dt2)
dt = unique(dt[,.(username,k1,b1,b2,bs,binary_group)])
rm(dt1,dt2,df1,df2,df)
params_df2 <- dt
colnames(params_df2)[colnames(params_df2)=="binary_group"] <- "group"
# p4<-
ggplot(params_df2, aes(color = group, y = k1, x = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(color="")+xlab("")+
ylab(TeX("$k_{1}$"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5<-ggplot(params_df, aes(color = group, x = mu1,fill=group)) +
geom_histogram(aes(y=after_stat(density)), alpha=0.5,
position="identity")+
labs(fill="",color="")+
xlab(TeX("$\\mu_{1}$"))+
geom_density(alpha=.2,show.legend = F)+
theme_bw()+
theme(legend.position =c(.8,.7),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p6<-ggplot(params_df, aes(color = group, x = sig1,fill=group)) +
geom_histogram(aes(y=after_stat(density)), alpha=0.5,
position="identity")+
labs(fill="",color="")+
xlab(TeX("$\\sigma_{1}$"))+
geom_density(alpha=.2,show.legend = F)+
theme_bw()+
theme(legend.position =c(.8,.7),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p7<-
ggplot(params_df2, aes(color = group, y = abs(bs), x = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(color="")+xlab("")+
ylab(TeX("Side-bias"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p8<-
ggplot(params_df2, aes(color = group, y = abs(b1), x = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(color="")+xlab("")+
ylab(TeX("WS"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p9<-
ggplot(params_df2, aes(color = group, y = abs(b2), x = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(color="")+xlab("")+
ylab(TeX("LS"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5,p6,p7,p8,p9, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v") %>%
ggexport(filename = "results/figures/geodems/hbm_params.pdf")
t.test(params_df2$k1~params_df2$group,var.equal = F)
t.test(abs(params_df2$bs)~params_df2$group)
t.test(abs(params_df2$b1)~params_df2$group)
t.test(abs(params_df2$b2)~params_df2$group)
summary2 = summary[game_version=="ft"]
# rename column binary_group to group
colnames(summary2)[colnames(summary2)=="binary_group"] <- "group"
summary2 = merge(summary2,params_df2,by=c("username","group"),all=TRUE)
colnames(summary2)
head(summary2)
df = alldata[above_crit == 1 & srs_normal == 1 & game_version == "ft"] %>%
group_by(username) %>% summarise(mean_rt = mean(rt,na.rm = TRUE),
mean_iti = mean(iti,na.rm = TRUE),
.groups = "drop")
summary2 = merge(summary2,df,by="username",all=TRUE)
rm(df,dt)
rm(p1,p2,p3,p4,p5,p6,p7,p8,p9,params_df,params_df2,asdhbmfit,tdhbmfit)
rm(asd_subid,td_subid,fitasd,fittd)
###################### individual glms ###############################################
# Fit a GLM model to the choice data for each subject
df <- alldata[trial>10 & srs_normal == 1 & above_crit == 1 & game_version == "ft",c("username","subID","choice","dflash","winstay","loseswitch","binary_group","group_verbal")]
# Function to fit a GLM model to the choice data for a single subject
fit_subject_glm <- function(subject_data) {
glm(choice ~ dflash + winstay + loseswitch,
data = subject_data,
family = binomial(link = "logit"))
}
# fit the model for each subject
model_fits <- df %>%
group_by(subID) %>%
nest() %>%
mutate(model = map(data, fit_subject_glm))
# get the parameter estimates for each subject
param_estimates <- model_fits %>%
mutate(params = map(model, broom::tidy)) %>%
unnest(params) %>%
select(subID, term, estimate) %>%
pivot_wider(names_from = term, values_from = estimate)
# Merge with subject group information
param_estimates <- param_estimates %>%
left_join(df %>% distinct(subID, binary_group), by = "subID")
# # Plot the parameter estimates by group
# param_long <- param_estimates %>%
# pivot_longer(cols = c(dflash, winstay, loseswitch),
# names_to = "parameter",
# values_to = "estimate")
colnames(param_estimates) <- c("subID","Sidebias","Slope","WS","LS","group")
p2<-
ggplot(param_estimates,
aes(x = group, y = abs(Sidebias), color = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(x = "", y = "Side-bias") +
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p1<-
ggplot(param_estimates,
aes(x = group, y = Slope, color = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(x = "", y = "Slope") +
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p3<-
ggplot(param_estimates,
aes(x = group, y = abs(WS), color = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(x = "", y = "WS") +
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p4<-
ggplot(param_estimates,
aes(x = group, y = abs(LS), color = group)) +
geom_boxplot(outlier.shape = NA,show.legend = F,alpha=1,width=.5)+
geom_jitter(width=.1,show.legend = FALSE,size=.5)+
labs(x = "", y = "LS") +
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
summary2 = merge(summary2,param_estimates,by=c("subID","group"),all=TRUE)
colnames(summary2)
p5 <-
ggplot(summary2, aes(x = Slope, y = k1, color = group)) +
geom_point(size=.5) +
labs(x = "Psychometric slope", y = "Perceptual noise",color="") +
theme_bw() +
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
cor.test(summary2$Slope,summary2$k1)
cor.test(summary2$k1,abs(summary2$WS))
cor.test(summary2$k1,abs(summary2$LS))
cor.test(summary2$k1,abs(summary2$Sidebias))
ggplot(summary2, aes(x = k1, y = abs(LS), color = group)) +
geom_point(size=.5) +
labs(x = "Psychometric slope", y = "Perceptual noise",color="") +
theme_bw() +
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# p6 <-
# ggplot(summary2, aes(x = abs(LS), y = abs(b2), color = group)) +
# geom_point() +
# # labs(title = "Sidebias vs. Performance", x = "Sidebias", y = "Performance",color="") +
# theme_bw() +
# theme(legend.position ="None",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v",
labels = c("A","B","C","D","E")) %>%
ggexport(filename = "results/figures/geodems/indglm_params.pdf")
t.test(param_estimates$Slope~param_estimates$group)
t.test(abs(param_estimates$Sidebias)~param_estimates$group)
t.test(abs(param_estimates$WS)~param_estimates$group)
t.test(abs(param_estimates$LS)~param_estimates$group)
rm(p1,p2,p3,p4,p5,param_estimates,model_fits,fit_subject_glm,df)
# # Merge with the summary data
# summary <- summary %>%
# right_join(param_estimates, by = c("subID","binary_group"))
# # Plot the fitted choice probabilities for selected subjects
# plot_subject_fit <- function(subject_id, group_data, full_data) {
# subject_model <- group_data %>%
# filter(subject_id == !!subject_id) %>%
# pull(model) %>%
# .[[1]]
# subject_data <- full_data %>% filter(subject_id == !!subject_id)
# new_data <- expand.grid(
# dflash = seq(min(subject_data$dflash), max(subject_data$dflash), length.out = 100),
# winstay = mean(subject_data$winstay),
# loseswitch = mean(subject_data$loseswitch)
# )
# new_data$predicted_prob <- predict(subject_model, newdata = new_data, type = "response")
# ggplot(subject_data, aes(x = dflash, y = choice)) +
# geom_point(alpha = 0.5) +
# geom_line(data = new_data, aes(y = predicted_prob), color = "red") +
# labs(title = paste("Subject", subject_id, "(", group_data$group[1], ")"),
# x = "dflash", y = "Choice Probability") +
# theme_minimal()
# }
# # Select two subjects from each group
# asd_subjects <- param_estimates %>% filter(binary_group == "ASD") %>% slice_sample(n = 1) %>% pull(subID)
# td_subjects <- param_estimates %>% filter(binary_group == "TD") %>% slice_sample(n = 1) %>% pull(subID)
# # Plot for selected subjects
# selected_plots <- map(c(asd_subjects, td_subjects),
# ~plot_subject_fit(.x, model_fits, df))
# # Arrange plots in a grid
# grid.arrange(grobs = selected_plots, ncol = 2)
# rm(model_fits, param_estimates, param_long, selected_plots,plot_subject_fit,asd_subjects,td_subjects,fit_subject_glm)
#### correlations ####
library(Hmisc)
library(tidyverse)
library(gridExtra)
# Select the columns of interest
df1 <- summary2[#binary_group == "ASD"
,c("perf","trainperf","Slope","k1","bs","b1","b2","mean_rt","mean_iti")]
df1$bs <- abs(df1$bs)
df1$b1 <- abs(df1$b1)
df1$b2 <- abs(df1$b2)
df2 <- summary2[,c("vinabcstd","srs2total","bistotal","aasp_low_reg_raw","aasp_sen_seek_raw","aasp_sen_ses_raw","aasp_sen_avoid_raw","group_verbal","group")]
df <- cbind(df1,df2)
df <- df[group_verbal != "flag" & group_verbal!="flag_low_td",]
# cor_test <- cor.test(df$vinabcstd, df$perf)
df$group_verbal <- factor(df$group_verbal, levels = c("TD", "ASD", "ASD (low verbal)"),ordered = T)
p1<-
ggplot(df,aes(x=vinabcstd,y=perf*100))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "bottom")+
labs(color="",x="VABS-3",y="%Correct")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
# annotate("text", x = 75, y = .5,
# label = paste("r =", round(cor_test$estimate, 2),
# ", p =", round(cor_test$p.value, 6)),
# hjust = 1, vjust = 1)+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p2 <-
ggplot(df,aes(x=vinabcstd,y=Slope))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "top")+
labs(color="",x="VABS-3",y="Psychometric Slope")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p3 <-
ggplot(df,aes(x=vinabcstd,y=k1))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "top")+
labs(color="",x="VABS-3",y="Perceptual Noise")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p4 <-
ggplot(df,aes(x=srs2total,y=perf*100))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "bottom")+
labs(color="",x="SRS-2",y="%Correct")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p5 <-
ggplot(df,aes(x=srs2total,y=Slope))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "top")+
labs(color="",x="SRS-2",y="Psychometric Slope")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
p6 <-
ggplot(df,aes(x=srs2total,y=k1))+
geom_point(aes(color=group_verbal),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5)+
stat_cor(method = "pearson",label.y.npc = "top")+
labs(color="",x="SRS-2",y="Perceptual Noise")+
scale_color_manual(values = c("TD" = "blue", "ASD" = "orange", "ASD (low verbal)" = "red"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p1,p2,p3,p4,p5,p6, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v",
labels = c("A","B","C","D","E","F")) %>%
ggexport(filename = "results/figures/geodems/correlations.pdf")
rm(df)
###### cross correlation ######
library(corrplot)
library(psych)
library(coin)
library(reshape2)
df <- summary2
# df <- df[complete.cases(df),]
df1 <- df[#binary_group == "ASD"
,c("perf","trainperf","Slope","k1","bs","b1","b2","mean_rt","mean_iti")]
df1$bs <- abs(df1$bs)
df1$b1 <- abs(df1$b1)
df1$b2 <- abs(df1$b2)
df2 <- df[,c("vinabcstd","srs2total","bistotal","aasp_low_reg_raw","aasp_sen_seek_raw","aasp_sen_ses_raw","aasp_sen_avoid_raw","group_verbal","group")]
df1 <- df1[,-c("trainperf","mean_rt")]
df2 <- df2[,-c("group_verbal","group")]
# df1 <- as.matrix(df1)
# df2 <- as.matrix(df2)
impute_mean <- function(df) {
for (col in names(df)) {
df[[col]][is.na(df[[col]])] <- median(df[[col]], na.rm = TRUE)
}
return(df)
}
# Impute missing values in both datasets
df1_imputed <- impute_mean(df1)
df2_imputed <- impute_mean(df2)
colnames(df1_imputed) <- c("Accuracy","Psychometric Slope","Perceptual Noise","Side-bias","WS","LS","ITI")
colnames(df2_imputed) <- c("VABS-3","SRS-2","BIS","AASP (Low Registration)","AASP (Sensation Seeking)","AASP (Sensory Sensitivity)","AASP (Sensation Avoiding)")
observed_corr <- corr.test(df1_imputed, df2_imputed, method = "spearman", adjust = "none")
set.seed(123) # For reproducibility
permutation_test <- function(A, B, n_perm = 1000) {
m <- ncol(A)
n <- ncol(B)
p_values <- matrix(NA, nrow = m, ncol = n)
for (i in 1:m) {
for (j in 1:n) {
observed_cor <- cor(A[[i]], B[[j]], method = "spearman", use = "complete.obs")
permuted_cors <- numeric(n_perm)
for (k in 1:n_perm) {
permuted_A <- A[sample(nrow(A)), ] # Permute entire rows of A
permuted_cors[k] <- cor(permuted_A[[i]], B[[j]], method = "spearman", use = "complete.obs")
}
p_values[i, j] <- mean(abs(permuted_cors) >= abs(observed_cor))
}
}
return(p_values)
}
# Run permutation test
p_values <- permutation_test(df1_imputed, df2_imputed)
p_adjusted <- p.adjust(p_values, method = "fdr")
sig_mask <- p_adjusted < 0.05
sig_corr <- observed_corr$r * sig_mask
melted_corr <- melt(sig_corr)
colnames(melted_corr) <- c("VarA", "VarB", "Correlation")
# p1 <-
ggplot(melted_corr[melted_corr$VarB %in% c("VABS-3","SRS-2","BIS"),], aes(x=VarA, VarB, fill = Correlation)) +
geom_tile(color="black",lwd = .2,
linetype = 2) +
scale_fill_gradient2(low = "yellow", high = "red", mid = "white",
midpoint = 0,
limit = c(-.35, .35), space = "Lab",
name="Spearman\nCorrelation") +
theme_bw() +
# coord_fixed()+
theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) +
coord_fixed() +
labs(x = "", y = "",
title = "Significant correlations between game measures and survey scores")
# p2 <-
ggplot(melted_corr[!(melted_corr$VarB %in% c("VABS-3","SRS-2","BIS")),], aes(x=VarA, VarB, fill = Correlation)) +
geom_tile(color="black",lwd = .2,
linetype = 2) +
scale_fill_gradient2(low = "yellow", high = "red", mid = "white",
midpoint = 0,
limit = c(-.35, .35), space = "Lab",
name="Spearman\nCorrelation") +
theme_bw() +
# coord_fixed()+
theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust = 1)) +
coord_fixed() +
labs(x = "", y = "")
ggarrange(p1,p2, nrow=2,ncol = 2,common.legend = TRUE,legend = "top",align = "h",labels = c("G")) %>%
ggexport(filename = "results/figures/geodems/cross_correlations.pdf")
############################################################################################################
# Load necessary libraries
# library(tidyverse)
# library(FactoMineR)
# library(factoextra)
# install.packages("leaps", type = "source")
# Assuming your data frame is called 'df'
# and has a column 'group' with values 'ASD' or 'TD'
data <- summary2[,c("group","srs2total","vinabcstd","bistotal","aasp_low_reg_raw","aasp_sen_seek_raw","aasp_sen_ses_raw","aasp_sen_avoid_raw",
"perf","trainperf","Slope","k1",
# "Sidebias","WS","LS",
"bs","b1","b2",
"mean_iti"
# ,"mean_rt"
)]
data <- data[complete.cases(data),]
# data$WS <- abs(data$WS)
# data$LS <- abs(data$LS)
# data$Sidebias <- abs(data$Sidebias)
data$bs <- abs(data$bs)
data$b1 <- abs(data$b1)
data$b2 <- abs(data$b2)
setDT(data)
colnames(data) <- c("group","SRS-2","VABS-3","BIS","AASP (Low Registration)","AASP (Sensation Seeking)","AASP (Sensory Sensitivity)","AASP (Sensation Avoiding)",
"Accuracy","Training Accuracy","Psychometric Slope","Perceptual Noise","Side-bias","WS","LS","Mean ITI"
# ,"Mean RT"
)
# Separate the group column and features
features <- data[,-c("group")]
scaled_features <- scale(features)
condition <- data$group
# 2. Perform PCA
pca_result <- prcomp(features, #center = TRUE,
scale. = TRUE)
# 3. Summary of PCA results
summary(pca_result)
print(pca_result$rotation)
# 4. Calculate variance explained by each component
var_explained <- pca_result$sdev^2 / sum(pca_result$sdev^2)
scree_data <- data.frame(PC = 1:length(var_explained),
VarExplained = var_explained)
p1<-
ggplot(scree_data, aes(x = PC*100, y = VarExplained)) +
geom_line() +
geom_point() +
theme_bw() +
labs(x = "Principal Component",
y = "%Variance Explained",
title = "Scree Plot") +
scale_x_continuous(breaks = 1:length(var_explained))
pc_scores <- pca_result$x
pca_data <- data.frame(pc_scores, condition = condition)
p2 <-
ggplot(pca_data, aes(x = PC1, y = PC2, color = condition)) +
geom_point(alpha = 0.7,size=.5) +
theme_bw() +
labs(x = "PC1", y = "PC2", color="",
title = "PC1 vs PC2 colored by group") +
scale_color_manual(values = c("ASD" = "red", "TD" = "blue")) +
# stat_ellipse(level = 0.95)+
theme(legend.position =c(.8,.8),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
loadings <- data.frame(pca_result$rotation)
# loadings$feature <- rownames(loadings)
loadings_sorted <- loadings %>%
arrange(desc(abs(PC1)))
# loadings_sorted$PC1<- factor(loadings_sorted$PC1, levels = loadings_sorted$PC1)
loadings_sorted$feature <- rownames(loadings_sorted)
loadings_sorted$feature <- factor(loadings_sorted$feature,levels = loadings_sorted$feature)
# loadings_long <- tidyr::pivot_longer(loadings,
# cols = c(PC1, PC2),
# names_to = "PC",
# values_to = "loading")
loadings_long <- loadings_sorted %>%
pivot_longer(cols = c(PC1, PC2), names_to = "PC", values_to = "loading")
# ggplot(loadings_long, aes(x = feature, y = Loading, fill = PC)) +
# geom_col(position = "dodge") +
# coord_flip() +
# labs(x = "Variables", y = "Loadings") +
# theme_minimal()
p3 <-
ggplot(loadings_long, aes(x = feature, y = abs(loading), fill = PC)) +
geom_bar(stat = "identity", position = "dodge") +
theme_bw() +
labs(x = "Features", y = "Loading", fill="",
title = "Loadings for PC1 and PC2") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values = c("PC1" = "#F8766D", "PC2" = "#00BFC4"))+
theme(legend.position =c(.8,.8),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
biplot_data <- data.frame(pca_result$x[,1:2])
biplot_data$condition <- condition
loadings_scaled <- pca_result$rotation[,1:2] *
(max(abs(pca_result$x[,1:2])) /
max(abs(pca_result$rotation[,1:2])))
p4 <-
ggplot(biplot_data, aes(x = PC1, y = PC2, color = condition)) +
geom_point(alpha = 0.7,size=.5) +
geom_segment(data = data.frame(loadings_scaled),
aes(x = 0, y = 0, xend = PC1, yend = PC2),
arrow = arrow(length = unit(0.1, "cm")),
linewidth = 0.5,
color = "black") +
xlim(-10,10) + ylim(-10,10) +
geom_text(data = data.frame(loadings_scaled),
aes(x = PC1, y = PC2, label = rownames(loadings_scaled)),
color = "black", vjust = 1, hjust = 1,size=2) +
theme_bw() +
labs(x = "PC1", y = "PC2", title = "Biplot",color="") +
scale_color_manual(values = c("ASD" = "red", "TD" = "blue")) +
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
t_test_pc1 <- t.test(abs(pc_scores[,1]) ~ condition)
print("T-test results for PC1:")
print(t_test_pc1)
t_test_pc2 <- t.test(abs(pc_scores[,2]) ~ condition)
print("T-test results for PC2:")
print(t_test_pc2)
ggarrange(p1,p2,p3, nrow=2,ncol = 2,common.legend = FALSE,legend = "top",
align = "v",labels = c("A","B","C")) %>%
ggexport(filename = "results/figures/geodems/pca.pdf")
rm(p1,p2,p3,p4,scree_data,pc_scores,pca_data,loadings,loadings_long,biplot_data,loadings_scaled)
rm(data,features,condition,pca_result,var_explained)
##### classification analysis #####
library(caret)
library(pROC)
library(reshape2)
set.seed(123)
data <- summary2[,c("group","perf","trainperf","Slope","k1","mean_iti",
"bs","b1","b2",
# "WS","LS","Sidebias",
"aasp_low_reg_raw",
"aasp_sen_seek_raw","aasp_sen_ses_raw","aasp_sen_avoid_raw"
)]
data <- data[complete.cases(data),]
data$group <- as.factor(data$group)
data$bs <- abs(data$bs)
data$b1 <- abs(data$b1)
data$b2 <- abs(data$b2)
# data$WS <- abs(data$WS)
# data$LS <- abs(data$b1)
# data$b2 <- abs(data$b2)
data2 <- data[,c("group","perf","trainperf","Slope","k1","mean_iti",
# "WS","LS","Sidebias"
"bs","b1","b2"
)]
data3 <- data[,c("group",
"aasp_low_reg_raw",
"aasp_sen_seek_raw","aasp_sen_ses_raw","aasp_sen_avoid_raw"
)]
ctrl <- trainControl(method = "cv", number = 5, classProbs = TRUE, summaryFunction = twoClassSummary,savePredictions = "all")
mdl_name = "rf"
# "rf" - Random Forest
# "svmRadial" - Support Vector Machines with Radial Basis Function Kernel
# "glm" - Generalized Linear Model (including Logistic Regression)
# "knn" - k-Nearest Neighbors
# "nb" - Naive Bayes
# "nnet" - Neural Network
# "gbm" - Gradient Boosting Machine
# "rpart" - Decision Trees
# "lda" - Linear Discriminant Analysis
# "glmnet" - Regularized Generalized Linear Models
mdl <- train(group ~ .,
data = data,
method = mdl_name,
trControl = ctrl,
metric = "ROC")
mdl2 <- train(group ~ .,
data = data2,
method = mdl_name,
trControl = ctrl,
metric = "ROC")
mdl3 <- train(group ~ .,
data = data3,
method = mdl_name,
trControl = ctrl,
metric = "ROC")
roc_obj <- roc(mdl$pred$obs, mdl$pred$ASD)
roc_obj2 <- roc(mdl2$pred$obs, mdl2$pred$ASD)
roc_obj3 <- roc(mdl3$pred$obs, mdl3$pred$ASD)
roc_data1 = data.frame(
specificity = roc_obj$specificities,
sensitivity = roc_obj$sensitivities
)
# list(
roc_data2 = data.frame(
specificity = roc_obj2$specificities,
sensitivity = roc_obj2$sensitivities
)
# auc = auc(roc_obj2)
# )
# list(
roc_data3 = data.frame(
specificity = roc_obj3$specificities,
sensitivity = roc_obj3$sensitivities
)#,
# auc = auc(roc_obj3)
# )
# Combine ROC data
roc_data_all <- rbind(
cbind(roc_data1, set = "Game + AASP"),
cbind(roc_data2, set = "Game"),
cbind(roc_data3, set = "AASP")
)
aucs <- c(auc(roc_obj),auc(roc_obj2),auc(roc_obj3))
print(aucs)
# Plot ROC curves
p4<- ggplot(roc_data_all, aes(x = 1 - specificity, y = sensitivity, color = set)) +
geom_line(size = 1) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "gray") +
coord_equal() +
scale_color_brewer(palette = "Set1") +
labs(x = "False Positive Rate", y = "True Positive Rate",
# title = "ROC Curves for Different Feature Sets (10-fold CV)",
color = "") +
theme_bw() +
# annotate("text", x = 0.75, y = 0.25,
# label = sprintf("AUC Set 1: %.3f\nAUC Set 2: %.3f\nAUC Set 3: %.3f",
# aucs[1], aucs[2], aucs[3]),
# hjust = 0, vjust = 0, size = 3) +
theme(legend.position ="bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
ggarrange(p4, nrow=2,ncol = 2,common.legend = FALSE,legend = "top",
align = "hv",labels = c("D")) %>%
ggexport(filename = "results/figures/geodems/classify.pdf")
##### multiple sessions #####
df1 = summary[game_version=="ft" & above_crit ==1,c("username","subID","binary_group","perf")]
df2 = summary[game_version!="ft" & above_crit ==1,c("username","subID","binary_group","perf")]
df2[, subID := gsub("_2$|_3$", "", subID)]
common_ids = intersect(df1$subID, df2$subID)
df1 = df1[subID %in% common_ids]
df2 = df2[subID %in% common_ids]
df2 = df2[!duplicated(df2$subID),]
df2$perf2 <- df2$perf
subs <- union(df1$username,df2$username)
df1 <- df1[,-c("username")]
df2 <- df2[,-c("username","perf")]
df <- merge(df1,df2,by=c("subID","binary_group"),all=TRUE)
rm(df1,df2)
p1 <-
ggplot(df,aes(x=perf,y=perf2))+
geom_point(aes(color=binary_group),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5,linetype="dashed")+
stat_cor(method = "pearson",label.y.npc = "bottom")+
labs(title="Accuracy",color="",x="First attempt",y="Second attempt")+
scale_color_manual(values = c("ASD" = "red", "TD" = "blue"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
df = alldata[username %in% subs] %>%
group_by(username,subID,binary_group,game_version) %>% summarise(mean_rt = mean(rt,na.rm = TRUE),
.groups = "drop")
df$mean_rt <- df$mean_rt/1000
df1 = df[df$game_version=="ft",]
df2 = df[df$game_version!="ft",]
setDT(df1)
setDT(df2)
df2[, subID := gsub("_2$|_3$", "", subID)]
# rename column mean_rt to mean_rt2
colnames(df2)[colnames(df2)=="mean_rt"] <- "mean_rt2"
df1 <- df1[,-c("username","game_version")]
df2 <- df2[,-c("username","game_version")]
# merge the two dataframes on subID and binary_group, remove username
df = merge(df1,df2,by=c("subID","binary_group"),all=TRUE)
rm(df1,df2)
p2 <-
ggplot(df,aes(x=mean_rt,y=mean_rt2))+
geom_point(aes(color=binary_group),size=.5)+
geom_smooth(method = "lm",se = T,color="black",size=.5,linetype="dashed")+
stat_cor(method = "pearson",label.y.npc = "bottom")+
labs(title="RT (s)",color="",x="First attempt",y="Second attempt")+
scale_color_manual(values = c("ASD" = "red", "TD" = "blue"))+
theme_bw()+
theme(legend.position ="None",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
# make common x and y axis in the grid
rm(df)
ggarrange(p1,p2, nrow=3,ncol = 3,common.legend = TRUE,legend = "top",align = "v",labels = c("A")) %>%
ggexport(filename = "results/figures/geodems/multi_session.pdf")

data_analysis_final.R at commit 906c9be, under CC0-1.0 · at the source

Overview

Authors: Sucheta Chakravarty¹, Quan Do², Yutong Li¹, Vanessa Torres-Lacarra¹, Helen Tager-Flusberg¹, Joseph T McGuire^1,3, Benjamin B Scott^1,3

ORCID iDs: Sucheta Chakravarty, Quan Do, Yutong Li, Helen Tager-Flusberg, Joseph T McGuire, Benjamin B Scott

Psychological & Brain Sciences, Boston University, Boston, MA, USA
Graduate Program for Neuroscience, Boston University, Boston, MA, USA
Center for Systems Neuroscience, Boston University, Boston, MA, USA

Institutions: Boston University (United States)

Journal: Science advances, volume 12, issue 37, article eaec9291

Dates: received 8 October 2025; accepted 3 August 2026; published online 11 September 2026; in print September 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1126/sciadv.aec9291 · PMID 42726881 · PMCID PMC13564830 · OpenAlex W7212241919

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), autism (population), cognitive (subfield)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging

MeSH: Autism Spectrum Disorder*, Autistic Disorder*, Conditioning, Operant*, Video Games*, Visual Perception*, Adolescent, Animals, Female, Humans, Learning, Male (* major topic)

Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: Simons Foundation (874568)

Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Altered perception is a hallmark of autism spectrum disorder (ASD), yet its underlying mechanisms remain unclear, in part because individuals with greater impairments are often excluded from psychophysics research. Here, we introduce GEODE (gathering evidence to optimize decisions), an online video game designed to measure visual perception across the full autism spectrum. GEODE incorporates feedback-based shaping from animal training to teach task mechanics, customized graphics and storyline elements to sustain engagement, and touchscreen compatibility for broader accessibility, enabling participation from adolescents including those with profound autism. Across a large, heterogeneous cohort, autistic participants successfully played GEODE but showed slower learning and reduced accuracy compared with typically developing siblings. These deficits were best explained by increased noise in the integration of sensory evidence, which scaled nonlinearly with stimulus complexity and tracked standardized survey measures of adaptive functioning more closely than diagnostic category alone. Injecting equivalent noise into artificial neural networks produced agents that recapitulated ASD-like patterns of learning and decision-making. These findings implicate noisy evidence integration as a computational mechanism for altered visual perception across the autism spectrum and establish a scalable, accessible platform for probing altered perception and learning in neurodevelopmental and neuropsychiatric disorders.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

Zenodo 16998131

License: CC0-1.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Size: 1 file

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Found in: “Data, code, and materials availability:”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: afex (1 file), broom (1 file), caret (1 file), data.table (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), glmnet (1 file), lme4 (1 file), pROC (1 file), psych (1 file), reshape2 (1 file), rstatix (1 file), Stan (1 file), tidyverse (1 file)

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README.md — Text, 393 lines

At the source: dx.doi.org/10.5281/zenodo.16998131

Zenodo 20545339

License: CC-BY-4.0

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At the source: doi.org/10.5281/zenodo.20545339

ratacad/geode_data

License: CC0-1.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 906c9beeee3eab63dda2abd50f918cc940316ab7, 20 August 2026

Languages: R (9)

Size: 14 files, 9 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: data.table (8 files), tidyverse (5 files), ggplot2 (3 files), ggpubr (3 files), lme4 (3 files), broom (2 files), nlme (2 files), Stan (2 files), afex (1 file), car (1 file), caret (1 file), easystats (1 file), emmeans (1 file), glmnet (1 file), patchwork (1 file), pROC (1 file), psych (1 file), reshape2 (1 file), rstatix (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

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11 files

additional_stats_geode.R — R, 426 lines
data_analysis_final.R — R, 2,591 lines, 3 matches
learningcurveASDTDfit.R — R, 237 lines
scripts/run_VABS-3matched_suppfig9.R — R, 459 lines
scripts/run_heterogeneity_deep2.R — R, 118 lines
scripts/run_learningcurve_3group_ks.R — R, 150 lines, 1 match
scripts/run_learningcurve_bootstrap.R — R, 227 lines
scripts/run_nlme_learningcurve.R — R, 68 lines, 1 match
scripts/run_scalar_param_stats.R — R, 113 lines
LICENSE — License, 121 lines
README.md — Text, 452 lines

At the source: github.com/RatAcad/Geode_Data/tree/v1.0.1

ratacad/geode

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 2a95c90a647fe2e4e920f688e5ed22c5f41541aa, 4 June 2026

Languages: JavaScript (366), TypeScript (8)

Size: 1,353 files, 374 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: CITATION.cff

Not found: README, license file, environment file, tests, continuous integration, documentation

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At the source: github.com/RatAcad/Geode/tree/v1.0.0

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
384 scripts, each with its path and the digest of its content;
9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. All deidentified data and relevant analysis code can be found here: doi.org/10.5281/zenodo.16998131 (http://dx.doi.org/10.5281/zenodo.16998131). The source code for the game is available here: https://doi.org/10.5281/zenodo.20545339. This study did not generate new materials.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 3, 28 September 2026

Funding: added Simons Foundation Autism Research Initiative: 874568

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 11 MeSH terms, 54 references.

Cite

This paper

Chakravarty, S., Do, Q., Li, Y., Torres-Lacarra, V., Tager-Flusberg, H., McGuire, J. T., & Scott, B. B. (2026). Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks. Science advances, 12(37), eaec9291. https://doi.org/10.1126/sciadv.aec9291

BibTeX

@article{chakravarty2026computational, author = {Chakravarty, Sucheta and Do, Quan and Li, Yutong and Torres-Lacarra, Vanessa and Tager-Flusberg, Helen and McGuire, Joseph T and Scott, Benjamin B}, title = {{Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks}}, journal = {Science advances}, year = {2026}, month = sep, volume = {12}, number = {37}, pages = {eaec9291}, publisher = {American Association for the Advancement of Science}, issn = {2375-2548}, doi = {10.1126/sciadv.aec9291}, url = {https://doi.org/10.1126/sciadv.aec9291}, pmid = {42726881}, pmcid = {PMC13564830} }

RIS

TY - JOUR AU - Chakravarty, Sucheta AU - Do, Quan AU - Li, Yutong AU - Torres-Lacarra, Vanessa AU - Tager-Flusberg, Helen AU - McGuire, Joseph T AU - Scott, Benjamin B TI - Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks T2 - Science advances J2 - Sci Adv PY - 2026 DA - 2026/09/11 VL - 12 IS - 37 SP - eaec9291 SN - 2375-2548 PB - American Association for the Advancement of Science DO - 10.1126/sciadv.aec9291 UR - https://doi.org/10.1126/sciadv.aec9291 LA - en ER -

CSL-JSON

{ "id": "10.1126/sciadv.aec9291", "type": "article-journal", "title": "Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks", "container-title": "Science advances", "author": [ { "family": "Chakravarty", "given": "Sucheta" }, { "family": "Do", "given": "Quan" }, { "family": "Li", "given": "Yutong" }, { "family": "Torres-Lacarra", "given": "Vanessa" }, { "family": "Tager-Flusberg", "given": "Helen" }, { "family": "McGuire", "given": "Joseph T" }, { "family": "Scott", "given": "Benjamin B" } ], "container-title-short": "Sci Adv", "volume": "12", "issue": "37", "page": "eaec9291", "DOI": "10.1126/sciadv.aec9291", "PMID": "42726881", "PMCID": "PMC13564830", "ISSN": "2375-2548", "publisher": "American Association for the Advancement of Science", "URL": "https://doi.org/10.1126/sciadv.aec9291", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 11 ] ] } }

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Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks.

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