1 Set up R environment
2 Read-in datafiles
3 Cognitive Styles
4 IB performance
5 Post IB questions
- 5.1 Expecting an unexpected event
- 5.2 Familiarity with IB experiments
6 Cognitive style ratio predicting IB

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.

Get a count of the number of subjects.

nrow(tbl_all %>% distinct(ID,.keep_all = FALSE))

## [1] 127

3 Cognitive Styles

Organize data

tbl_all_simple = subset(tbl_all, select = -c(rowNo,responseWindow,presTime,ISI,ITI,stimFormat,button1,keyboard,key,responseType,randomPick,responseOptions,pageBreak,required,if.,then,ITI_ms,ITI_f,ITI_fDuration,presTime_ms,presTime_f,presTime_fDuration,timestamp,responseCode,correct))

tbl_all_simple <- tbl_all_simple %>% 
  separate(response,into=c('response'))

tbl_all_simple <- tbl_all_simple %>% 
  separate(RTkeys,into=c('RTkeys'))

tbl_all_simple[tbl_all_simple==""]<-NA

tbl_all_simple$rt<- tbl_all_simple$RTkeys
tbl_all_simple$rt[is.na(tbl_all_simple$rt)] <- tbl_all_simple$RT[is.na(tbl_all_simple$rt)]

tbl_all_simple$rt  <- as.numeric(tbl_all_simple$rt)
tbl_all_simple$ID  <- as.character(tbl_all_simple$ID)
tbl_all_simple$stim1  <- as.character(tbl_all_simple$stim1)

tbl_all_blank_removed <- tbl_all_simple %>% 
  filter(stim1 != "blank")

tbl_all_cog_style <- tbl_all_blank_removed %>% 
  filter(grepl('embedded|matching', stim1))

tbl_all_cog_style[tbl_all_cog_style== "embedded_prompt" ] <- NA
tbl_all_cog_style[tbl_all_cog_style== "matching_prompt" ] <- NA
tbl_all_cog_style[tbl_all_cog_style== "timeout" ] <- NA

tbl_all_cog_style <- tbl_all_cog_style %>% 
  group_by(ID) %>% 
  fill(stim1) %>% #default direction down
  fill(stim1, .direction = "up")

tbl_all_cog_style_condensed <- tbl_all_cog_style %>% 
  group_by(ID, stim1) %>% 
  dplyr::summarise(RT = sum(rt), response = first(na.omit(response)))

Split up the cognitive styles stimuli nomenclature to task, answer, and number. Filter the two cognitive styles tasks.

tbl_all_cog_style_condensed <- tbl_all_cog_style_condensed %>%
  separate(stim1,into=c('task', 'answer', 'number'))

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_condensed$acc = "filler"

for (i in 1:length(tbl_all_cog_style_condensed$ID)){
  if (tbl_all_cog_style_condensed$task[i] == "matching"){
    if (tbl_all_cog_style_condensed$answer[i] == "same"){
      if (tbl_all_cog_style_condensed$response[i] == "f"){
        tbl_all_cog_style_condensed$acc[i] = 1
    } else {
        tbl_all_cog_style_condensed$acc[i] = 0
    }
    } else {
      if (tbl_all_cog_style_condensed$response[i] == "j"){
        tbl_all_cog_style_condensed$acc[i] = 1
    } else {
        tbl_all_cog_style_condensed$acc[i] = 0
    }
  }
  }
  if (tbl_all_cog_style_condensed$task[i] == "embedded"){
    if (tbl_all_cog_style_condensed$answer[i] == "yes"){
      if (tbl_all_cog_style_condensed$response[i] == "f"){
        tbl_all_cog_style_condensed$acc[i] = 1
    } else {
        tbl_all_cog_style_condensed$acc[i] = 0
    }
    } else {
      if (tbl_all_cog_style_condensed$response[i] == "j"){
        tbl_all_cog_style_condensed$acc[i] = 1
    } else {
        tbl_all_cog_style_condensed$acc[i] = 0
    }
  }
  }
}

write.csv(tbl_all_cog_style_condensed,'embedded_matching_data.csv', row.names=FALSE)

3.2 Plot accuracy

tbl_all_cog_style_condensed_acc <- tbl_all_cog_style_condensed %>%
  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_condensed_acc) <- c("ID", "task", "inacc", "acc", "total")
tbl_all_cog_style_condensed_acc[is.na(tbl_all_cog_style_condensed_acc)] <- 0
tbl_all_cog_style_condensed_acc$rate <- tbl_all_cog_style_condensed_acc$acc / tbl_all_cog_style_condensed_acc$total

tbl_all_cog_style_condensed_acc %>%
  ggbarplot("ID", "rate", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2)

tbl_all_cog_style_condensed_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_sona <- tbl_all_cog_style_condensed_acc %>% 
  filter(task =="embedded")
embedded_chance_sona <-t.test(embedded_chance_sona$rate, mu = .50, alternative="greater")
embedded_chance_sona

## 
##  One Sample t-test
## 
## data:  embedded_chance_sona$rate
## t = 17.767, df = 126, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
##  0.7479248       Inf
## sample estimates:
## mean of x 
## 0.7734252

matching_chance_sona <- tbl_all_cog_style_condensed_acc %>% 
  filter(task =="matching")
matching_chance_sona <-t.test(matching_chance_sona$rate, mu = .50, alternative="greater")
matching_chance_sona

## 
##  One Sample t-test
## 
## data:  matching_chance_sona$rate
## t = 17.153, df = 126, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
##  0.7023753       Inf
## sample estimates:
## mean of x 
## 0.7240157

tbl_all_cog_style_condensed_acc %>% 
  with(t.test(rate~task,paired=TRUE))

## 
##  Paired t-test
## 
## data:  rate by task
## t = 5.1607, df = 126, p-value = 9.306e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.03046237 0.06835653
## sample estimates:
## mean of the differences 
##              0.04940945

3.3 Remove subjects below chance

tbl_all_cog_style_condensed_acc <- subset(tbl_all_cog_style_condensed_acc, select = c(ID, task, rate))
tbl_all_cog_style_condensed_acc_wide <- tbl_all_cog_style_condensed_acc %>%
  spread(task,rate)

tbl_all_cog_style_condensed_acc <- tbl_all_cog_style_condensed_acc_wide %>%
  filter(embedded > 0.5 & matching > 0.5)

tbl_all_cog_style_condensed_acc <- gather(tbl_all_cog_style_condensed_acc, task, rate, embedded:matching, factor_key=TRUE)

tbl_all_cog_style_condensed_acc %>%
  ggbarplot("ID", "rate", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2)

tbl_all_cog_style_condensed_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_sona <- tbl_all_cog_style_condensed_acc %>% 
  filter(task =="embedded")
embedded_chance_sona <-t.test(embedded_chance_sona$rate, mu = .50, alternative="greater")
embedded_chance_sona

## 
##  One Sample t-test
## 
## data:  embedded_chance_sona$rate
## t = 29.934, df = 100, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
##  0.8233401       Inf
## sample estimates:
## mean of x 
## 0.8423267

matching_chance_sona <- tbl_all_cog_style_condensed_acc %>% 
  filter(task =="matching")
matching_chance_sona <-t.test(matching_chance_sona$rate, mu = .50, alternative="greater")
matching_chance_sona

## 
##  One Sample t-test
## 
## data:  matching_chance_sona$rate
## t = 26.602, df = 100, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
##  0.7617828       Inf
## sample estimates:
## mean of x 
## 0.7792079

tbl_all_cog_style_condensed_acc %>% 
  with(t.test(rate~task,paired=TRUE))

## 
##  Paired t-test
## 
## data:  rate by task
## t = 6.0773, df = 100, p-value = 2.234e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.04251314 0.08372449
## sample estimates:
## mean of the differences 
##              0.06311881

3.4 Plot RTs

tbl_all_cog_style_condensed_rts <- tbl_all_cog_style_condensed[(tbl_all_cog_style_condensed$ID %in% tbl_all_cog_style_condensed_acc$ID),] %>% 
  filter(acc == 1)

tbl_all_cog_style_condensed_rts %>%
  ggbarplot("ID", "RT", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, add = "median", position = position_dodge(0.8), ylab = "Median RT (ms)", ylim = c(0,6000)) + rotate_x_text()

tbl_all_cog_style_condensed_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_condensed_rts_median <- tbl_all_cog_style_condensed_rts %>%
  group_by(ID,task) %>%
  dplyr::summarize(median_rt = median(RT, na.rm=TRUE))

tbl_all_cog_style_condensed_rts_median %>% 
  with(t.test(median_rt~task,paired=TRUE))

## 
##  Paired t-test
## 
## data:  median_rt by task
## t = -20.432, df = 100, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -1448.303 -1191.935
## sample estimates:
## mean of the differences 
##               -1320.119

3.5 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_condensed_rts_median <- tbl_all_cog_style_condensed_rts_median %>% 
  spread(task, median_rt)
tbl_all_cog_style_condensed_rts_median$ratio <- tbl_all_cog_style_condensed_rts_median$matching / tbl_all_cog_style_condensed_rts_median$embedded

tbl_all_cog_style_condensed_rts_median$style = "filler"
for (i in 1:length(tbl_all_cog_style_condensed_rts_median$ID)){
  if (tbl_all_cog_style_condensed_rts_median$ratio[i] > 1){
    tbl_all_cog_style_condensed_rts_median$style[i] = "analytic"
  } else {
    tbl_all_cog_style_condensed_rts_median$style[i] = "wholist"
  }
}

tbl_all_cog_style_condensed_rts_median %>%
  ggbarplot("ID", "ratio", fill = "#f7a800", color = "#f7a800", ylim = c(0,4), font.xtickslab = 4, ylab = "Median wholist-analytic ratio") + rotate_x_text() + geom_hline(yintercept = 1, linetype = 2)

table(tbl_all_cog_style_condensed_rts_median$style)

## 
## analytic 
##      101

4 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,ISI,presTime,if.,then,presTime_ms,presTime_f,presTime_fDuration,RTkeys,correct,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))

tbl_all_IB <- tbl_all_IB[(tbl_all_IB$ID %in% tbl_all_cog_style_condensed_rts_median$ID),]

4.1 Counting task

tbl_all_counting <- tbl_all %>% 
  filter(rowNo >= 406 & rowNo <= 433)
tbl_all_counting = subset(tbl_all_counting, select = -c(rowNo,type,timestamp,RT,correct,ISI,ITI,stimFormat,button1,keyboard,key,responseWindow,randomBlock,presTime,head,responseType,randomPick,responseOptions,pageBreak,required,if.,then,ITI_ms,ITI_f,ITI_fDuration,presTime_ms,presTime_f,presTime_fDuration,RTkeys,responseCode))

tbl_all_counting <- tbl_all_counting[(tbl_all_counting$ID %in% tbl_all_cog_style_condensed_rts_median$ID),]

tbl_all_counting <- cbind(tbl_all_counting[c(TRUE, FALSE), ],tbl_all_counting[c(FALSE, TRUE), ])

tbl_all_counting = subset(tbl_all_counting, select = -c(3:5))

tbl_counts <- read_csv("./IB_stims/IB_counting.csv")

## Parsed with column specification:
## cols(
##   stim1 = col_character(),
##   count = col_double()
## )

tbl_counts_comparison <- full_join(tbl_all_counting, tbl_counts, by = "stim1")
tbl_counts_comparison <- tbl_counts_comparison[complete.cases(tbl_counts_comparison), ]

tbl_counts_comparison[is.na(tbl_counts_comparison)] <- 0

tbl_counts_comparison$response  <- as.numeric(tbl_counts_comparison$response)
tbl_counts_comparison$count  <- as.numeric(tbl_counts_comparison$count)
tbl_counts_comparison$error <- (abs(tbl_counts_comparison$response - tbl_counts_comparison$count) / tbl_counts_comparison$count) * 100

tbl_counts_comparison %>%
  ggbarplot("ID", "error", fill = "#f7a800", color = "#f7a800", ylim = c(0,150), font.xtickslab = 4, add = "mean_se", ylab = "Mean Percent Error on Midline-crossings Task") + rotate_x_text() + geom_hline(yintercept = 20, linetype = 2)

tbl_counts_comparison_average <- tbl_counts_comparison %>% 
  group_by(ID) %>% 
  dplyr::summarize(mean_error = mean(error, na.rm=TRUE))

tbl_counts_comparison_average %>%
  ggbarplot(y = "mean_error", fill = "#f7a800", color = "#f7a800", ylim = c(0,100), sort.val = c("asc") ,xlab = "Group", add = "mean_se", ylab = "Mean Percent Error on Midline-crossings Task")

error <-t.test(tbl_counts_comparison_average$mean_error, mu = 0, alternative="greater")
error

## 
##  One Sample t-test
## 
## data:  tbl_counts_comparison_average$mean_error
## t = 7.4469, df = 100, p-value = 1.721e-11
## alternative hypothesis: true mean is greater than 0
## 95 percent confidence interval:
##  14.74232      Inf
## sample estimates:
## mean of x 
##  18.97197

write.csv(tbl_counts_comparison,'IB_counting.csv', row.names=FALSE)

4.2 Remove subjects greater than 50%

tbl_counts_comparison_average_good <- tbl_counts_comparison_average %>%
  filter(mean_error < 20)

tbl_counts_comparison_good <- tbl_counts_comparison[(tbl_counts_comparison$ID %in% tbl_counts_comparison_average_good$ID),]

tbl_counts_comparison_good %>%
  ggbarplot("ID", "error", fill = "#f7a800", color = "#f7a800", ylim = c(0,30), font.xtickslab = 6, add = "mean_se", ylab = "Mean Percent Error on Midline-crossings Task") + rotate_x_text()

tbl_counts_comparison_average_good %>%
  ggbarplot(y = "mean_error", fill = "#f7a800", color = "#f7a800", ylim = c(0,100), sort.val = c("asc") ,xlab = "Group", add = "mean_se", ylab = "Mean Percent Error on Midline-crossings Task")

error_good <-t.test(tbl_counts_comparison_average_good$mean_error, mu = 0, alternative="greater")
error_good

## 
##  One Sample t-test
## 
## data:  tbl_counts_comparison_average_good$mean_error
## t = 10.202, df = 73, p-value = 5.39e-16
## alternative hypothesis: true mean is greater than 0
## 95 percent confidence interval:
##  4.700469      Inf
## sample estimates:
## mean of x 
##  5.617839

4.3 Noticers vs. Non-noticers

tbl_all_IB <- tbl_all_IB[(tbl_all_IB$ID %in% tbl_counts_comparison_average_good$ID),]

tbl_all_notice <- tbl_all_IB %>% 
  filter(grepl('items', head))
table(tbl_all_notice$response)

## 
##       No Not sure      Yes 
##       31       21       22

tbl_all_notice = subset(tbl_all_notice, select = -c(rowNo,type,head,timestamp,RT))

write.csv(tbl_all_notice,'IB_notice.csv', row.names=FALSE)

4.4 Unexpected event description

tbl_all_event <- tbl_all_IB %>% 
  filter(grepl('moved', head))
tbl_all_different <- tbl_all_IB %>% 
  filter(grepl('different', head))
tbl_all_event <- cbind.data.frame(tbl_all_event[1], tbl_all_event[6])
tbl_all_different <- cbind.data.frame(tbl_all_different[1], tbl_all_different[6])
tbl_all_event <- cbind.data.frame(tbl_all_different, tbl_all_notice[2], tbl_all_event[2])
colnames(tbl_all_event) <- c("ID", "notice_anything_different", "unexpected_item", "item_description")
tbl_all_event <- arrange(tbl_all_event, ID)
tbl_all_event$noticer_or_nonnoticer <- c(0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,1,1,0,0)
knitr::kable(tbl_all_event)

ID	notice_anything_different	unexpected_item	item_description	noticer_or_nonnoticer
A11PIARI7WALAX	No	Not sure	I did not see anything. I was purely focused on white shapes.	0
A12BPNWOK3NZT9	The trial itself was much different. I get the feeling that something was going on in the background but I was too busy counting the letters.	Not sure	I am not sure. I know I saw letters inside the box but I’m wondering if there were letters anywhere else.	0
A15XIKX3PI7SLK	On the first two trials, I had to compare two shapes, while on the last trial, I had to count the number of times letters cross a line.	No	I did not notice any of the items.	0
A17CTPL658MHXG	a plus sign moved across the screen	Yes	a plus sign moved from the right to the left of the screen	1
A18E16UFE351U6	I didn’t notice anything different on the last trial.	Not sure	I don’t think noticed any of those symbols in the last trial.	0
A1969Q0R4Y0E3J	No.	Not sure	I am guessing one of those items were present and moved at a slower rate than others.	0
A198MSVO1VTAT5	No, I didn’t notice anything different.	No	I didn’t notice it at all. I was watching the L T.	0
A1AF25FCVKC87X	The cross was moving right to left.	Yes	It was a cross and it moved on the line from right to left.	1
A1BH6H9WRK49I	No	No	Didn’t see the item	0
A1IQUHRI7G63YF	it involved letters	Yes	The plus sign appeared on the line	1
A1JJYY622DGE5L	There were letters rather than shapes to deal with.	Yes	There was a + sign in the middle of the purple line. It never moved.	0
A1JR35HATOEME9	No	Yes	They moved slower	0
A1M8HPA2YRBSEN	Nothing different.	Not sure	I’m not sure about that.	0
A1N0Q3QP4OMTZ2	I noticed a grey cross moving across the center line at one point.	Yes	It moved right on the center line, and it was the third image (grey cross).	1
A1O0BGHFTMPQM0	NO	No	DIDN’T NOTICE IT	0
A1P3HHEXWNLJMP	No I failed to notice anything different. I am guessing that maybe the shapes changed color.	No	I did not notice it at all.	0
A1Q4ZOXZ0Y7K6I	There was a gray plus/cross shape that moved on the line	Yes	I saw a gray plus shape moving from right to left on the line	1
A1RQLE4ZMTU22A	I did not notice anything different.	Not sure	I’d say it was the X if I was to guess. I have no idea how it moved.	0
A1SHLWKA0UH1IS	It had moving objects. Also I had to type in a number instead of clicking a letter.	No	I did not notice it.	0
A1U4DNYKF6YUY3	A dark gray cross/plus sign/+ traveled along the center line.	Yes	+, moved along the center horizontal line from the right to the left.	1
A1W7I6FN183I8F	No, I did not notice anything different. Then again, my attention was focused on the white shapes crossing.	No	I did not see those letters/equation appear at all while I was counting.	0
A248QG4DPULP46	Yes. The last trial involved counting moving shapes while the shapes in the two other trials did not move.	Not sure	I don’t think I saw any of the shapes. I was concentrating on counting the white figures that crossed the line.	0
A258ZJM0HU1OR3	Yes entirely different. The main different is to count the white shape letters in the last task. first and second task to say same or different.	Yes	X and + I can see to travel left to right and bottom to top.	0
A270HX8LH9LJ8W	no	Not sure	I didn’t consciously see one of those items.	0
A279YSE7NB16M0	no	No	no	0
A297OTX4PW0XS3	Just that it was a completely different task from the first two. I was counting the number of times white letters crossed a line instead of comparing shapes.	Yes	The plus symbol moved along the line from one side to the other.	1
A2APG8MSLJ6G2K	no, sorry. there was a different letter i bet.	No	i didnt see it, sorry.	0
A2BBFG0J0TJI78	No, I did not notice any difference.	Not sure	I did not see them	0
A2DFN1612YMN00	I noticed a grey cross slide across the horizontal line.	Yes	I think I saw a grey cross go across the blue horizontal line on the last test.	1
A2FOYHZ7HOFKBI	No difference	Not sure	Didn’t see any of the items, I was too focused on the white letters	0
A2GOS6NF74E2ST	No	No	No, I didn’t see anything	0
A2H9ZRBC0JYKVJ	i saw a plus sign crossing from right to left.	Yes	Plus sign	1
A2HDDHOOCUCN9P	There was an animation rather than still shapes, and the shapes were letters T and L.	No	I did not see X, +, or E.	0
A2HJDLSU95MH8Q	no	Not sure	no idea, sorry	0
A2HRUFTA09371Y	there was a + moving from right to left on the screen	Yes	#NAME?	1
A2JW59JRBWUMR2	NO	Not sure	X	0
A2K287FPB9YFIE	counting white crosses.	No	I didn’t pay attention to the dark symbols.	0
A2OPYRV3GLAPS1	I did not!	No	I didn’t notice anything.	0
A2OVMMDZQ524IL	none	Yes	+	1
A2OX8TSRCU6NKD	no	No	I did not see anything different but if there was my guess was it didn’t move	0
A2POU9TTW177VH	No	No	I did not see it	0
A2R75YFKVALBXE	Didn’t notice anything different.	No	Didn’t see it.	0
A2UYZFH5VT5R3H	no	Yes	A cross shape moved through the center	1
A2VNSNAN1LZBAM	Maybe the shapes moved faster? I’m not really sure.	No	I didn’t see that.	0
A2VO8C41JJIQY9	There was less number of black characters in the animation.	Not sure	I think I saw the black faded character “E” and it was moving up and down.	0
A2XQ3CFB5HT2ZQ	I did not notice.	Not sure	no idea	0
A2ZDEERVRN5AMC	nope.	No	I was too focused on the purple line.	0
A31JM9RECQGYEX	Different goal	Yes	none of them were present	0
A3APKUC67F9IMW	They all started too quick. I was caught off guard most of the time. I guess that’s what the study is or my internet is slow? No clue.	Not sure	I’m sure I saw crosses but not in the dimensions or rotation that you have there. You got me worried when you hit me right in the gut immediately.	0
A3BHRFFG75X3GO	no	Not sure	I did not notice any of that.	0
A3EC3OP6U52JYC	In the last trial, I noticed a moving gray cross. The gray cross ran across the middle from right to left.	Yes	I saw a gray cross move from right to left across the middle of the screen.	1
A3EZ3BRM1C5WKV	There more crossings by white letters.	No	X moved up and down	0
A3IF13XHP5UPO2	Maybe required the most attention.	No	I didn’t see any of the previous letters.	0
A3IXWFXJC2YKU7	No.	Not sure	The large X perhaps	0
A3L2FPKRD46FRW	no not really	No	L and T	0
A3LT7W355XOAKF	no	No	x, only a guess, moved the same as the others	0
A3N0QZ9ZKUCTCQ	There was a moving cross from the right to the left in the center.	Yes	Plus sign, moving slowly right to left.	1
A3OP24TYKA619W	i did not notice anything	No	i did not see it. too busy concentrating	0
A3RHJEMZ4EGY2U	No, I was too busy trying to count.	Not sure	It seemed that something was moving faster but I have no idea what it was because I was busy trying to count.	0
A3SJVR4BROAIJO	They included moving letters rather than static shapes.	No	I did not see any of those items.	0
A3V2XCDF45VN9X	no	Not sure	I did not notice.	0
A9ZCY6FLUCIU1	different type of measurement, no yes or no	No	not sure saw	0
AABCSDU3TZLNG	No. This last L-and-T trial? No.	No	N/A. I didn’t see it.	0
AD1WGUMVD6KED	I didn’t notice anything different.	Not sure	I think there was a plus sign somewhere.	1
AD21ES5UMD77U	The movement and the focusing on one color.	No	I did not see any of the previous items	0
AFIK3VBMMX6G6	gray plus sign went from right to left on horizontal bisector	Yes	plus sign, right to left	1
AI5RMOS8R652G	No, I didn’t notice anything different.	No	I didn’t see anything different I was paying too much attention to white passing through the line.	0
ALKQPW0O9C98N	No	No	I don’t think I saw any of them	0
AM8OWAW9TUVLN	the shapes moved differently each round	No	I just watched the white ones and forgot what they were	0
AMPMTF5IAAMK8	N/A	Yes	an x moved on a horizontal line	0
AOJPRTHGMEPFX	A cross traveled across the screen from right to left.	Yes	Middle, from right to left	1
AONN1W54VC8YD	There was a moving + across the screen.	Yes	The + symbol moved to left, along the line.	1
AQN3WMCEA96DQ	I didn’t notice anything.	Not sure	I was only paying attention to the white figures. Maybe an X crossed the screen though. just a guess	0
AYFDUTHXE54I	The letters are moving in the last task not matching drawings.	No	I saw letters T L crossing a center line where as L is in white color.	0

table(tbl_all_event$noticer_or_nonnoticer)

## 
##  0  1 
## 56 18

5 Post IB questions

5.1 Expecting an unexpected event

tbl_all <- tbl_all[(tbl_all$ID %in% tbl_all_IB$ID),]

tbl_all_expecting <- tbl_all %>% 
  filter(grepl('Before', head))
tbl_all_expecting = subset(tbl_all_expecting, select = -c(stim1,ITI,ISI,presTime,if.,then,presTime_ms,presTime_f,presTime_fDuration,RTkeys,correct,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))

tbl_all_expecting <- tbl_all_expecting[(tbl_all_expecting$ID %in% tbl_all_cog_style_condensed_rts_median$ID),]

table(tbl_all_expecting$response)

## 
##  No Yes 
##  71   3

5.2 Familiarity with IB experiments

tbl_all_familiarity <- tbl_all %>% 
  filter(grepl('gorilla', head))
tbl_all_familiarity = subset(tbl_all_familiarity, select = -c(stim1,ITI,ISI,presTime,if.,then,presTime_ms,presTime_f,presTime_fDuration,RTkeys,correct,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))

tbl_all_familiarity <- tbl_all_familiarity[(tbl_all_familiarity$ID %in% tbl_all_cog_style_condensed_rts_median$ID),]

table(tbl_all_familiarity$response)

## 
##  No Yes 
##  47  27

6 Cognitive style ratio predicting IB

tbl_all_cog_style_condensed_rts_median <- tbl_all_cog_style_condensed_rts_median[(tbl_all_cog_style_condensed_rts_median$ID %in% tbl_all_event$ID),]

tbl_log_reg_median <- cbind.data.frame(tbl_all_cog_style_condensed_rts_median, tbl_all_event[5])

#tbl_log_reg_median[tbl_log_reg_median == "Yes"] <- 1
#tbl_log_reg_median[tbl_log_reg_median == "No"] <- 0
#tbl_log_reg_median[tbl_log_reg_median == "Not sure"] <- 0
names(tbl_log_reg_median)[names(tbl_log_reg_median)=="noticer_or_nonnoticer"] <- "notice"
tbl_log_reg_median$notice <- as.numeric(tbl_log_reg_median$notice)

6.1 Median RTs

6.1.1 Logistic regression w/ median RT ratio

log_reg_median_ratio <- glm(notice ~ ratio, data = tbl_log_reg_median, family = binomial(link = "logit"))
summary(log_reg_median_ratio)

## 
## Call:
## glm(formula = notice ~ ratio, family = binomial(link = "logit"), 
##     data = tbl_log_reg_median)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7817  -0.7559  -0.7393  -0.6972   1.7663  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -0.8748     1.1680  -0.749    0.454
## ratio        -0.1360     0.5962  -0.228    0.820
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 82.109  on 73  degrees of freedom
## Residual deviance: 82.056  on 72  degrees of freedom
## AIC: 86.056
## 
## Number of Fisher Scoring iterations: 4

plot_median_ratio <- ggplot(tbl_log_reg_median, aes(x=ratio, y=notice)) + xlim(1,4) + geom_point() + stat_smooth(method="glm", method.args=list(family="binomial"), se=TRUE, color="#f7a800") + theme_classic((base_size = 15))
suppressMessages(print(plot_median_ratio))

6.1.2 Logistic regression w/ embedded median RT

log_reg_embedded_median <- glm(notice ~ embedded, data = tbl_log_reg_median, family = binomial(link = "logit"))
summary(log_reg_embedded_median)

## 
## Call:
## glm(formula = notice ~ embedded, family = binomial(link = "logit"), 
##     data = tbl_log_reg_median)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.1260  -0.7846  -0.6744  -0.3514   1.9827  
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)  1.506551   1.653167   0.911    0.362
## embedded    -0.001718   0.001085  -1.584    0.113
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 82.109  on 73  degrees of freedom
## Residual deviance: 79.248  on 72  degrees of freedom
## AIC: 83.248
## 
## Number of Fisher Scoring iterations: 4

plot_embedded_median <- ggplot(tbl_log_reg_median, aes(x=embedded, 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_embedded_median))

6.1.3 Logistic regression w/ matching median RT

log_reg_matching_median <- glm(notice ~ matching, data = tbl_log_reg_median, family = binomial(link = "logit"))
summary(log_reg_matching_median)

## 
## Call:
## glm(formula = notice ~ matching, family = binomial(link = "logit"), 
##     data = tbl_log_reg_median)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.9592  -0.7831  -0.6790  -0.4584   2.1084  
## 
## Coefficients:
##               Estimate Std. Error z value Pr(>|z|)
## (Intercept)  0.4319417  1.1983444    0.36    0.719
## matching    -0.0005392  0.0004117   -1.31    0.190
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 82.109  on 73  degrees of freedom
## Residual deviance: 80.223  on 72  degrees of freedom
## AIC: 84.223
## 
## Number of Fisher Scoring iterations: 4

plot_matching_median <- ggplot(tbl_log_reg_median, aes(x=matching, 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_matching_median))

6.1.4 Logistic regression (Dichotomous predictor, dichotomous outcome)

Does not work because there are no ‘wholists’

#log_reg_style <- glm(notice ~ style, data = tbl_log_reg, family = binomial)
#summary(log_reg_style)

6.2 Accuracy

tbl_all_cog_style_condensed_acc_wide <- tbl_all_cog_style_condensed_acc_wide[(tbl_all_cog_style_condensed_acc_wide$ID %in% tbl_log_reg_median$ID),]

tbl_log_reg_acc <- merge(tbl_all_cog_style_condensed_acc_wide, tbl_all_notice, by = "ID")
#tbl_log_reg_acc = subset(tbl_log_reg_acc, select = -c(rowNo,type,head,timestamp,RT))
tbl_log_reg_acc[tbl_log_reg_acc == "Yes"] <- 1
tbl_log_reg_acc[tbl_log_reg_acc == "No"] <- 0
tbl_log_reg_acc[tbl_log_reg_acc == "Not sure"] <- 0
names(tbl_log_reg_acc)[names(tbl_log_reg_acc)=="response"] <- "notice"
tbl_log_reg_acc$notice <- as.numeric(tbl_log_reg_acc$notice)
tbl_log_reg_acc$ratio <- tbl_log_reg_acc$matching / tbl_log_reg_acc$embedded

6.2.1 Logistic regression w/ accuracy ratio

log_reg_acc_ratio <- glm(notice ~ ratio, data = tbl_log_reg_acc, family = binomial(link = "logit"))
summary(log_reg_acc_ratio)

## 
## Call:
## glm(formula = notice ~ ratio, family = binomial(link = "logit"), 
##     data = tbl_log_reg_acc)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.8977  -0.8473  -0.8273   1.4786   1.6810  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)
## (Intercept)  -1.7246     2.1940  -0.786    0.432
## ratio         0.9214     2.3176   0.398    0.691
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 90.066  on 73  degrees of freedom
## Residual deviance: 89.909  on 72  degrees of freedom
## AIC: 93.909
## 
## Number of Fisher Scoring iterations: 4

plot_acc_ratio <- ggplot(tbl_log_reg_acc, 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_acc_ratio))

6.2.2 Logistic regression w/ embedded accuracy

log_reg_embedded_acc <- glm(notice ~ embedded, data = tbl_log_reg_acc, family = binomial(link = "logit"))
summary(log_reg_embedded_acc)

## 
## Call:
## glm(formula = notice ~ embedded, family = binomial(link = "logit"), 
##     data = tbl_log_reg_acc)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.2676  -0.8428  -0.6834   1.2124   1.8288  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)    3.557      2.224   1.600   0.1097  
## embedded      -5.151      2.594  -1.985   0.0471 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 90.066  on 73  degrees of freedom
## Residual deviance: 85.929  on 72  degrees of freedom
## AIC: 89.929
## 
## Number of Fisher Scoring iterations: 4

plot_embedded_acc <- ggplot(tbl_log_reg_acc, aes(x=embedded, 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_embedded_acc))

6.2.3 Logistic regression w/ matching accuracy

log_reg_matching_acc <- glm(notice ~ matching, data = tbl_log_reg_acc, family = binomial(link = "logit"))
summary(log_reg_matching_acc)

## 
## Call:
## glm(formula = notice ~ matching, family = binomial(link = "logit"), 
##     data = tbl_log_reg_acc)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.4089  -0.8295  -0.6750   1.2722   1.8540  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)  
## (Intercept)    4.157      2.268   1.833   0.0668 .
## matching      -6.309      2.861  -2.205   0.0275 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 90.066  on 73  degrees of freedom
## Residual deviance: 84.871  on 72  degrees of freedom
## AIC: 88.871
## 
## Number of Fisher Scoring iterations: 4

plot_matching_acc <- ggplot(tbl_log_reg_acc, aes(x=matching, 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_matching_acc))

6.2.4 Logistic regression (Dichotomous predictor, dichotomous outcome)