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analyze_RTs_by_block-MA.R
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## load dataset
source('dmc/models/RW/arw-RL-mag.R')
source('utils.R')
pdf(file='figures/across-block3.pdf', width=6, height=6)
par(oma=c(3,1,1,0), mar=c(0, 4, 1, 0.5) + 0.1, mfrow=c(3,2), mgp=c(2.75,.75,0), las=1, bty='l')
# Experiment 3 ------------------------------------------------------------
dataName <- 'exp3'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
for(pp in unique(dat$pp)) {
for(block in unique(dat$block_nr[dat$pp==pp])) {
idx <- dat$pp==pp&dat$block_nr==block
dat[idx,]$trialBin <- cut(dat[idx,'trial_nr'], breaks=10, labels = FALSE)
}
}
## RTs
rtByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), mean)
sertByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.45, .8), xlab='Trial bin', ylab='Mean RT (s)', xaxt='n')
title('Mean RT (s)', line = .25)
mtext('Experiment 2',
side=2, cex=0.66*1.2, las=0, line=4, font=2)
axis(side=1, labels=FALSE)
abline(h=seq(0, 1, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
for(block in unique(rtByBlock$block_nr) ) {
idx = rtByBlock$block_nr == block
x <- rtByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, rtByBlock[idx, 'rt'], col=block, lwd=2)
arrows(x,
y0=rtByBlock[idx, 'rt']-sertByBlock[idx, 'rt'],
y1=rtByBlock[idx, 'rt']+sertByBlock[idx, 'rt'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('topright', paste0('Block ', 1:nBlocks), col=1:3, lty=c(1,1,1), lwd=2, bty='n')
## accuracies
accByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), mean)
seaccByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.45, .9), xlab='Trial bin', ylab='Accuracy', xaxt='n')
title('Accuracy', line = .25)
axis(side=1, labels=FALSE)
abline(h=seq(0, 1, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
for(block in unique(rtByBlock$block_nr) ) {
idx = accByBlock$block_nr == block
x <- accByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, accByBlock[idx, 'choiceIsHighP'], col=block, lwd=2)
arrows(x,
y0=accByBlock[idx, 'choiceIsHighP']-seaccByBlock[idx, 'choiceIsHighP'],
y1=accByBlock[idx, 'choiceIsHighP']+seaccByBlock[idx, 'choiceIsHighP'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('bottomright', paste0('Block ', 1:nBlocks), col=1:3, lty=c(1,1,1), lwd=2, bty='n')
# test for SAT effect
library(lme4)
library(lmerTest)
library(report)
dat$lTrialBin <- log(dat$trialBin)
mod1 <- glmer(choiceIsHighP~lTrialBin*cue + (1|pp), data=dat[!dat$excl,], family='binomial')
summary(mod1)
report(mod1)
table_long(report(mod1))
mod2 <- lmer(rt~trialBin* cue + (1|pp), data=dat[!dat$excl,])
summary(mod2)
report(mod2)
table_long(report(mod2))
dat$RT <- dat$rt*1000 # to get more decimals; report rounds it to 2 digits
table_long(report(lmer(RT~trialBin* cue + (1|pp), data=dat[!dat$excl,])))
# Experiment 2 ------------------------------------------------------------
dataName <- 'exp2'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
dat$block_nr <- dat$block
dat$choiceIsHighP <- dat$choiceIsHighP_orig
## RTs
rtByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), mean)
sertByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.525, .8), xlab='Trial bin', ylab='Mean RT (s)', xaxt='s')
axis(side=1, labels=FALSE)
abline(h=seq(0, 1, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
mtext('Experiment 3',
side=2, cex=0.66*1.2, las=0, line=4, font=2)
for(block in unique(rtByBlock$block_nr) ) {
idx = rtByBlock$block_nr == block
x <- rtByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, rtByBlock[idx, 'rt'], col=block, lwd=2)
arrows(x,
y0=rtByBlock[idx, 'rt']-sertByBlock[idx, 'rt'],
y1=rtByBlock[idx, 'rt']+sertByBlock[idx, 'rt'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('topright', paste0('Block ', 1:nBlocks), col=1:nBlocks, lty=rep(1, nBlocks), lwd=2, bty='n')
## accuracies
accByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), mean)
seaccByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.25, .9), xlab='Trial bin', ylab='Accuracy', xaxt='s')
axis(side=1, labels=FALSE)
abline(h=seq(0, 1, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
for(block in unique(rtByBlock$block_nr) ) {
idx = accByBlock$block_nr == block
x <- accByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, accByBlock[idx, 'choiceIsHighP'], col=block, lwd=2)
arrows(x,
y0=accByBlock[idx, 'choiceIsHighP']-seaccByBlock[idx, 'choiceIsHighP'],
y1=accByBlock[idx, 'choiceIsHighP']+seaccByBlock[idx, 'choiceIsHighP'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('bottomleft', paste0('Block ', 1:nBlocks), col=1:nBlocks, lty=rep(1, nBlocks), lwd=2, bty='n')
# Experiment MA ------------------------------------------------------------
dataName <- 'expMA'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
dat$block_nr <- dat$block
dat$trialBin <- NA
for(pp in unique(dat$pp)) {
for(block in unique(dat$block_nr[dat$pp==pp])) {
idx <- dat$pp==pp&dat$block_nr==block
dat[idx,]$trialBin <- cut(dat[idx,'trial_nr'], breaks=10, labels = FALSE)
}
}
#dat$choiceIsHighP <- dat$choiceIsHighP_orig
## RTs
rtByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), mean)
sertByBlock <- aggregate(rt~block_nr*trialBin, aggregate(rt~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.85, 1.3), xlab='Trial bin', ylab='Mean RT (s)', xaxt='s')
axis(side=1, labels=FALSE)
abline(h=seq(0, 3, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
mtext('Experiment 4',
side=2, cex=0.66*1.2, las=0, line=4, font=2)
for(block in unique(rtByBlock$block_nr) ) {
idx = rtByBlock$block_nr == block
x <- rtByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, rtByBlock[idx, 'rt'], col=block, lwd=2)
arrows(x,
y0=rtByBlock[idx, 'rt']-sertByBlock[idx, 'rt'],
y1=rtByBlock[idx, 'rt']+sertByBlock[idx, 'rt'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('topright', paste0('Block ', 1:nBlocks), col=1:nBlocks, lty=rep(1, nBlocks), lwd=2, bty='n')
## accuracies
accByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), mean)
seaccByBlock <- aggregate(choiceIsHighP~block_nr*trialBin, aggregate(choiceIsHighP~block_nr*trialBin*pp, dat, mean), function(x) sd(x)/sqrt(length(x)))
nBlocks <- max(dat$block_nr)
plot(0,0,type='n', xlim=c(1, 10), ylim=c(.25, .9), xlab='Trial bin', ylab='Accuracy', xaxt='s')
axis(side=1, labels=FALSE)
abline(h=seq(0, 1, .05), col='grey', lty=3)
abline(v=seq(0, 10, 1), col='grey', lty=3)
for(block in unique(rtByBlock$block_nr) ) {
idx = accByBlock$block_nr == block
x <- accByBlock[idx, 'trialBin'] - block/10 + (nBlocks/20)
lines(x, accByBlock[idx, 'choiceIsHighP'], col=block, lwd=2)
arrows(x,
y0=accByBlock[idx, 'choiceIsHighP']-seaccByBlock[idx, 'choiceIsHighP'],
y1=accByBlock[idx, 'choiceIsHighP']+seaccByBlock[idx, 'choiceIsHighP'],
length = .05, angle = 90, code=3, col=block, lwd=2)
}
legend('topleft', paste0('Block ', 1:nBlocks), col=1:nBlocks, lty=rep(1, nBlocks), lwd=2, bty='n')
dev.off()
# Statistics? -------------------------------------------------------------
library(lme4)
library(lmerTest)
library(report)
# Exp 2
dataName <- 'exp2'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
dat$block_nr <- dat$block
dat$lTrialBin <- log(dat$trialBin)
mod2rt = lmer(rt~block_nr*trialBin + (1|pp), data=dat)
summary(mod2rt)
report(mod2rt)
table_long(report(mod2rt))
mod2acc = glmer(choiceIsHighP_orig~block_nr*lTrialBin + (1|pp), data=dat, family='binomial')
summary(mod2acc)
report(mod2acc)
table_long(report(mod2acc))
# Exp 3
dataName <- 'exp3'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
for(pp in unique(dat$pp)) {
for(block in unique(dat$block_nr[dat$pp==pp])) {
idx <- dat$pp==pp&dat$block_nr==block
dat[idx,]$trialBin <- cut(dat[idx,'trial_nr'], breaks=10, labels = FALSE)
}
}
dat$lTrialBin <- log(dat$trialBin)
mod3rt = lmer(rt~block_nr*trialBin + (1|pp), data=dat)
summary(mod3rt)
report(mod3rt)
mod3acc = glmer(choiceIsHighP~block_nr*lTrialBin + (1|pp), data=dat, family='binomial')
summary(mod3acc)
report(mod3acc)
table_short(report(mod3acc))
# Exp MA
dataName <- 'expMA'
tmp <- loadData(dataName)
data <- tmp[['data']]
dat <- tmp[['dat']]
dat$block_nr <- dat$block
dat$trialBin <- NA
for(pp in unique(dat$pp)) {
for(block in unique(dat$block_nr[dat$pp==pp])) {
idx <- dat$pp==pp&dat$block_nr==block
dat[idx,]$trialBin <- cut(dat[idx,'trial_nr'], breaks=10, labels = FALSE)
}
}
dat$lTrialBin <- log(dat$trialBin)
mod4rt = lmer(rt~block_nr*trialBin + (1|pp), data=dat)
summary(mod4rt)
report(mod4rt)
table_short(report(mod4rt))
mod4acc = glmer(choiceIsHighP~block_nr*lTrialBin + (1|pp), data=dat, family='binomial')
summary(mod4acc)
treport(mod4acc)
table_short(report(mod4acc))