Cognitive Styles and Inattentional Blindness
Adam Barnas
Last compiled at 3:01 PM on July 17, 2020
1 Set up R environment
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(plyr)Set the R working drectory to the main experiment directory.
setwd("/Users/adambarnas/Box/CogStyles_IB") 2 Read-in datafiles
Read in the individual subject files (saved automatically on the server as csv files).
Get a count of the number of subjects.
nrow(tbl_all %>% distinct(ID,.keep_all = FALSE))## [1] 183 Cognitive Styles
Split up the cognitive styles stimuli nomenclature to task, answer, and number. Filter the two cognitive styles tasks.
tbl_all_cog_style <- tbl_all %>%
separate(stim1,into=c('task', 'answer', 'number')) %>%
filter(task == 'matching' | task == 'embedded')
tbl_all_cog_style = subset(tbl_all_cog_style, select = -c(ITI,stimFormat,button1,keyboard,key,responseWindow,head,responseType,randomPick,responseOptions,pageBreak,required,responseCode,correct))3.1 Analyze accuracy
In the matching figures task, subjects were instructed to press ‘F’ if the two complex shapes were the same and ‘J’ if the two complex shapes were different. Trials will be labeled 1 for correct responses (‘F’ for same objects and ‘J’ for different objects) and 0 for incorrect responses (‘F’ for different objects and ‘J’ for same objects).
In the embedded figures task, subjects were instructed to press ‘F’ if the simple shape is within the complex shape and ‘J’ if the the simple shape is not within the complex shape. Trials will be labeled 1 for correct responses (‘F’ for simple within complex and ‘J’ for simple not within complex) and 0 for incorrect responses (‘F’ simple not within complex and ‘J’ simple within complex).
tbl_all_cog_style$acc = "filler"
for (i in 1:length(tbl_all_cog_style$ID)){
if (tbl_all_cog_style$task[i] == "matching"){
if (tbl_all_cog_style$answer[i] == "same"){
if (tbl_all_cog_style$response[i] == "f"){
tbl_all_cog_style$acc[i] = 1
} else {
tbl_all_cog_style$acc[i] = 0
}
} else {
if (tbl_all_cog_style$response[i] == "j"){
tbl_all_cog_style$acc[i] = 1
} else {
tbl_all_cog_style$acc[i] = 0
}
}
}
if (tbl_all_cog_style$task[i] == "embedded"){
if (tbl_all_cog_style$answer[i] == "yes"){
if (tbl_all_cog_style$response[i] == "f"){
tbl_all_cog_style$acc[i] = 1
} else {
tbl_all_cog_style$acc[i] = 0
}
} else {
if (tbl_all_cog_style$response[i] == "j"){
tbl_all_cog_style$acc[i] = 1
} else {
tbl_all_cog_style$acc[i] = 0
}
}
}
}3.2 Plot accuracy
tbl_all_cog_style_acc <- tbl_all_cog_style %>%
group_by(ID,task,acc) %>%
dplyr::summarize(counts = n()) %>%
spread(acc,counts) %>%
mutate(total = rowSums(.[3:4], na.rm = TRUE))
colnames(tbl_all_cog_style_acc) <- c("ID", "task", "inacc", "acc", "total")
tbl_all_cog_style_acc[is.na(tbl_all_cog_style_acc)] <- 0
tbl_all_cog_style_acc$rate <- tbl_all_cog_style_acc$acc / tbl_all_cog_style_acc$total
tbl_all_cog_style_acc %>%
ggbarplot("ID", "rate", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2)
tbl_all_cog_style_acc %>%
ggbarplot("task", "rate", add = "mean_se",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + geom_hline(yintercept = .5, linetype = 2)
embedded_chance <- tbl_all_cog_style_acc %>%
filter(task =="embedded")
embedded_chance <-t.test(embedded_chance$rate, mu = .50, alternative="greater")
embedded_chance##
## One Sample t-test
##
## data: embedded_chance$rate
## t = 15.597, df = 17, p-value = 8.324e-12
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.8319395 Inf
## sample estimates:
## mean of x
## 0.8736111matching_chance <- tbl_all_cog_style_acc %>%
filter(task =="matching")
matching_chance <-t.test(matching_chance$rate, mu = .50, alternative="greater")
matching_chance##
## One Sample t-test
##
## data: matching_chance$rate
## t = 14.77, df = 17, p-value = 1.978e-11
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.8014245 Inf
## sample estimates:
## mean of x
## 0.8416667tbl_all_cog_style_acc %>%
with(t.test(rate~task,paired=TRUE))##
## Paired t-test
##
## data: rate by task
## t = 1.2023, df = 17, p-value = 0.2457
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.02411098 0.08799987
## sample estimates:
## mean of the differences
## 0.031944443.3 Plot RTs
tbl_all_cog_style_rts <- tbl_all_cog_style %>%
filter(acc == 1)
tbl_all_cog_style_rts %>%
ggbarplot("ID", "RT", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), add = "median", position = position_dodge(0.8), ylab = "Median RT (ms)", ylim = c(0,8000)) + rotate_x_text()
tbl_all_cog_style_rts %>%
ggbarplot("task", "RT", add = "median",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), position = position_dodge(0.8), order = c("embedded", "matching"), ylab = "Median RT (ms)", ylim = c(0,5000))
tbl_all_cog_style_rts_median <- tbl_all_cog_style_rts %>%
group_by(ID,task) %>%
dplyr::summarize(median_rt = median(RT, na.rm=TRUE))
tbl_all_cog_style_rts_median %>%
with(t.test(median_rt~task,paired=TRUE))##
## Paired t-test
##
## data: median_rt by task
## t = -3.9182, df = 17, p-value = 0.001107
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -2848.1293 -854.4262
## sample estimates:
## mean of the differences
## -1851.2783.4 Compute cognitive style ratio
The wholist-analytic ratio is calculated by dividing the median response latency to items in the matching figures task (wholist) by the median response latency to items in the embedded figures task (analytic) A ratio of below 1 indicates that an individual responded relatively faster to the matching figure items, corresponding to a wholist profile; a ratio of above 1 indicates that an individual responded relatively faster to the embedded figure items, corresponding to a analytic profile.
tbl_all_cog_style_rts_median <- tbl_all_cog_style_rts_median %>%
spread(task, median_rt)
tbl_all_cog_style_rts_median$ratio <- tbl_all_cog_style_rts_median$matching / tbl_all_cog_style_rts_median$embedded
tbl_all_cog_style_rts_median$style = "filler"
for (i in 1:length(tbl_all_cog_style_rts_median$ID)){
if (tbl_all_cog_style_rts_median$ratio[i] > 1){
tbl_all_cog_style_rts_median$style[i] = "analytic"
} else {
tbl_all_cog_style_rts_median$style[i] = "wholist"
}
}
tbl_all_cog_style_rts_median %>%
ggbarplot("ID", "ratio", fill = "#f7a800", color = "#f7a800", ylab = "Median wholist-analytic ratio") + rotate_x_text() + geom_hline(yintercept = 1, linetype = 2)
table(tbl_all_cog_style_rts_median$style)##
## analytic wholist
## 15 34 IB performance
tbl_all_IB <- tbl_all %>%
filter(grepl('Did you notice|item', head))
tbl_all_IB = subset(tbl_all_IB, select = -c(stim1,ITI,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))4.1 Noticers vs. Non-noticers
tbl_all_notice <- tbl_all_IB %>%
filter(grepl('items', head))
table(tbl_all_notice$response)##
## No Not sure Yes
## 4 3 114.2 Unexpected event description
tbl_all_event <- tbl_all_IB %>%
filter(grepl('moved', head))
tbl_all_event <- cbind.data.frame(tbl_all_event[1], tbl_all_event[6])
knitr::kable(tbl_all_event)| ID | response |
|---|---|
| 205396 | it was the plus sign (+) and it moved from the right hand side towards the left hand side of the screen. |
| 205204 | I noticed the plus sign moving across the screen. It moved from right to left across the center line. |
| 205060 | the plus sign, moved from right to left, down the horizontal line |
| 205420 | the + moves horizontally passing the center of the box. |
| 205255 | I feel like I saw the + a few times but did not pay attention to its movement |
| 205189 | Plus sign horizontally |
| 205057 | I might have seen a + sign but I don’t really remember. |
| 204988 | I think I saw + but I am not sure how it moved. |
| 205315 | Ts and Ls |
| 205408 | + |
| 205051 | n/a |
| 204949 | The cross on the right. |
| 205306 | I’m not sure, but the + might have moved with the letters. |
| 205084 | I didn’t see anything unusual |
| 205324 | I have no idea. Maybe the black letters were erased so we only had to focus on the white letters. |
| 205225 | the middle, plus sign |
| 205429 | The cross moved horizontally along the line that the letters were crossing. |
| 205432 | X |
5 Post IB questions
5.1 Expecting an unexpected event
tbl_all_expecting <- tbl_all %>%
filter(grepl('Before', head))
tbl_all_expecting = subset(tbl_all_expecting, select = -c(stim1,ITI,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))
table(tbl_all_expecting$response)##
## No Yes
## 14 45.2 Familiarity with IB experiments
tbl_all_familiarity <- tbl_all %>%
filter(grepl('gorilla', head))
tbl_all_familiarity = subset(tbl_all_familiarity, select = -c(stim1,ITI,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))
table(tbl_all_familiarity$response)##
## No Yes
## 4 146 Cognitive style ratio predicting IB
6.1 Logistic regression (Continuous predictor, dichotomous outcome)
tbl_log_reg <- merge(tbl_all_cog_style_rts_median, tbl_all_notice, by = "ID")
tbl_log_reg = subset(tbl_log_reg, select = -c(rowNo,type,head,timestamp,RT,correct))
tbl_log_reg[tbl_log_reg == "Yes"] <- 1
tbl_log_reg[tbl_log_reg == "No"] <- 0
tbl_log_reg[tbl_log_reg == "Not sure"] <- 0
names(tbl_log_reg)[names(tbl_log_reg)=="response"] <- "notice"
tbl_log_reg$notice <- as.numeric(tbl_log_reg$notice)
log_reg <- glm(notice ~ ratio, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg)##
## Call:
## glm(formula = notice ~ ratio, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0053 -1.1672 0.6951 0.9812 1.2756
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.6924 1.0666 -0.649 0.516
## ratio 0.5755 0.4983 1.155 0.248
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 24.057 on 17 degrees of freedom
## Residual deviance: 22.537 on 16 degrees of freedom
## AIC: 26.537
##
## Number of Fisher Scoring iterations: 46.2 Plot
plot <- ggplot(tbl_log_reg, aes(x=ratio, y=notice)) + geom_point() + stat_smooth(method="glm", method.args=list(family="binomial"), se=TRUE, color="#f7a800") + theme_classic((base_size = 15))
suppressMessages(print(plot))