Matching Task Predicting Static Inattentional Blindness
Adam Barnas
Last compiled at 1:55 PM on August 05, 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/Predicting_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] 1273 Matching task
3.1 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_matching <- tbl_all_blank_removed %>%
filter(grepl('matching', stim1))
tbl_all_matching[tbl_all_matching== "matching_prompt" ] <- NA
tbl_all_matching[tbl_all_matching== "timeout" ] <- NA
tbl_all_matching <- tbl_all_matching %>%
group_by(ID) %>%
fill(stim1) %>% #default direction down
fill(stim1, .direction = "up")
tbl_all_matching_condensed <- tbl_all_matching %>%
group_by(ID, stim1) %>%
dplyr::summarise(RT = sum(rt), response = first(na.omit(response)))Split up stim1 nomenclature to task, answer, and number.
tbl_all_matching_condensed <- tbl_all_matching_condensed %>%
separate(stim1,into=c('task', 'answer', 'number'))3.2 Accuracy
3.2.1 Analyze
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).
tbl_all_matching_condensed$acc = "filler"
for (i in 1:length(tbl_all_matching_condensed$ID)){
if (tbl_all_matching_condensed$task[i] == "matching"){
if (tbl_all_matching_condensed$answer[i] == "same"){
if (tbl_all_matching_condensed$response[i] == "f"){
tbl_all_matching_condensed$acc[i] = 1
} else {
tbl_all_matching_condensed$acc[i] = 0
}
} else {
if (tbl_all_matching_condensed$response[i] == "j"){
tbl_all_matching_condensed$acc[i] = 1
} else {
tbl_all_matching_condensed$acc[i] = 0
}
}
}
}
write.csv(tbl_all_matching_condensed,'matching_v3.csv', row.names=FALSE)3.2.2 Plot
tbl_all_matching_condensed_acc <- tbl_all_matching_condensed %>%
group_by(ID,task,acc) %>%
dplyr::summarize(counts = n()) %>%
spread(acc,counts) %>%
mutate(total = rowSums(.[3:4], na.rm = TRUE))
colnames(tbl_all_matching_condensed_acc) <- c("ID", "task", "inacc", "acc", "total")
tbl_all_matching_condensed_acc[is.na(tbl_all_matching_condensed_acc)] <- 0
tbl_all_matching_condensed_acc$rate <- tbl_all_matching_condensed_acc$acc / tbl_all_matching_condensed_acc$total
tbl_all_matching_condensed_acc %>%
ggbarplot("ID", "rate", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2) + theme(legend.position = "none")
tbl_all_matching_condensed_acc %>%
ggbarplot("task", "rate", add = "mean_se",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8), label = TRUE, lab.vjust = -1, lab.nb.digits = 2) + geom_hline(yintercept = .5, linetype = 2) + theme(legend.position = "none")
matching_chance <- tbl_all_matching_condensed_acc %>%
filter(task =="matching")
matching_chance <-t.test(matching_chance$rate, mu = .50, alternative="greater")
matching_chance##
## One Sample t-test
##
## data: matching_chance$rate
## t = 19.425, df = 126, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.7209316 Inf
## sample estimates:
## mean of x
## 0.74153543.2.3 Remove subjects below chance and replot
tbl_all_matching_condensed_acc <- subset(tbl_all_matching_condensed_acc, select = c(ID, task, rate))
tbl_all_matching_condensed_acc_wide <- tbl_all_matching_condensed_acc %>%
spread(task,rate)
tbl_all_matching_condensed_acc <- tbl_all_matching_condensed_acc_wide %>%
filter(matching > 0.5)
tbl_all_matching_condensed_acc <- gather(tbl_all_matching_condensed_acc, task, rate, matching, factor_key=TRUE)
tbl_all_matching_condensed_acc %>%
ggbarplot("ID", "rate", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2) + theme(legend.position = "none")
tbl_all_matching_condensed_acc %>%
ggbarplot("task", "rate", add = "mean_se",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8), label = TRUE, lab.vjust = -1, lab.nb.digits = 2) + geom_hline(yintercept = .5, linetype = 2) + theme(legend.position = "none")
matching_chance <- tbl_all_matching_condensed_acc %>%
filter(task =="matching")
matching_chance <-t.test(matching_chance$rate, mu = .50, alternative="greater")
matching_chance##
## One Sample t-test
##
## data: matching_chance$rate
## t = 24.103, df = 117, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.7454295 Inf
## sample estimates:
## mean of x
## 0.76355933.2.4 Count subjects
nrow(tbl_all_matching_condensed_acc %>% distinct(ID,.keep_all = FALSE))## [1] 1183.3 RTs
3.3.1 Plot
tbl_all_matching_condensed_rts <- tbl_all_matching_condensed[(tbl_all_matching_condensed$ID %in% tbl_all_matching_condensed_acc$ID),] %>%
filter(acc == 1)
tbl_all_matching_condensed_rts %>%
ggbarplot("ID", "RT", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, add = "median", position = position_dodge(0.8), ylab = "Median RT (ms)", ylim = c(0,7000)) + rotate_x_text() + theme(legend.position = "none") + geom_hline(yintercept = 200, linetype = 2)
tbl_all_matching_condensed_rts %>%
ggbarplot("task", "RT", add = "median",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), position = position_dodge(0.8), order = c("embedded", "matching"), ylab = "Median RT (ms)", ylim = c(0,4000), label = TRUE, lab.vjust = -1, lab.nb.digits = 2) + theme(legend.position = "none")
tbl_all_matching_condensed_rts_median <- tbl_all_matching_condensed_rts %>%
group_by(ID,task) %>%
dplyr::summarize(median_rt = median(RT, na.rm=TRUE))3.3.2 Remove subjects with median RT below 200 ms and replot
tbl_all_matching_condensed_rts_median_200ms_removed <- tbl_all_matching_condensed_rts_median %>%
filter(median_rt > 200)
tbl_all_matching_condensed_rts_200ms_removed <- tbl_all_matching_condensed_rts[(tbl_all_matching_condensed_rts$ID %in% tbl_all_matching_condensed_rts_median_200ms_removed$ID),]
tbl_all_matching_condensed_rts_200ms_removed %>%
ggbarplot("ID", "RT", fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), font.xtickslab = 4, add = "median", position = position_dodge(0.8), ylab = "Median RT (ms)", ylim = c(0,7000)) + rotate_x_text() + theme(legend.position = "none") + geom_hline(yintercept = 200, linetype = 2)
tbl_all_matching_condensed_rts_200ms_removed %>%
ggbarplot("task", "RT", add = "median",fill = "task", color = "task", palette = c("#0d2240", "#00a8e1"), position = position_dodge(0.8), order = c("embedded", "matching"), ylab = "Median RT (ms)", ylim = c(0,4000), label = TRUE, lab.vjust = -1, lab.nb.digits = 2) + theme(legend.position = "none")
3.3.3 Count subjects
nrow(tbl_all_matching_condensed_rts_200ms_removed %>% distinct(ID,.keep_all = FALSE))## [1] 1134 IB performance
tbl_all <- tbl_all[(tbl_all$ID %in% tbl_all_matching_condensed_rts_median_200ms_removed$ID),]
tbl_all_IB <- tbl_all %>%
filter(grepl('Did you notice|item', head))
tbl_all_IB = subset(tbl_all_IB, select = -c(stim1,stim2,stim3,stim4,stim5,stim6,stim7,ITI,ISI,presTime,if.,then,presTime_ms,presTime_f,presTime_fDuration,RTkeys,correct,stimFormat,button1,keyboard,key,responseWindow,randomBlock,responseType,randomPick,responseOptions,pageBreak,required,ITI_ms,ITI_f,ITI_fDuration,responseCode))
tbl_all_IB <- tbl_all_IB[(tbl_all_IB$ID %in% tbl_all_matching_condensed_rts_median$ID),]
nrow(tbl_all_IB %>% distinct(ID,.keep_all = FALSE))## [1] 1134.1 Line judgment task
tbl_all_line <- tbl_all %>%
filter(rowNo >= 205 & rowNo <= 220)
tbl_all_line = subset(tbl_all_line, select = -c(rowNo,type,stim1,stim2,stim3,stim4,stim6,stim7,timestamp,RT,correct,ISI,ITI,stimFormat,button1,keyboard,key,responseWindow,randomBlock,presTime,head,responseType,randomPick,responseOptions,pageBreak,required,if.,then,ITI_ms,ITI_f,ITI_fDuration,presTime_ms,presTime_f,presTime_fDuration,RTkeys,responseCode,responseOptionsRandom))
tbl_all_line <- tbl_all_line[(tbl_all_line$ID %in% tbl_all_matching_condensed_rts_median$ID),]
tbl_all_line <- tbl_all_line %>%
separate(stim5,into=c('longer_line', 'type', 'per_similar'),sep = "([\\_])")
tbl_all_line$acc = "filler"
for (i in 1:length(tbl_all_line$ID)){
if (tbl_all_line$longer_line[i] == "vertical"){
if (tbl_all_line$response[i] == "v"){
tbl_all_line$acc[i] = 1
} else {
tbl_all_line$acc[i] = 0
}
} else {
if (tbl_all_line$response[i] == "h"){
tbl_all_line$acc[i] = 1
} else {
tbl_all_line$acc[i] = 0
}
}
}
tbl_all_line_acc <- tbl_all_line %>%
group_by(ID,acc) %>%
dplyr::summarize(counts = n()) %>%
spread(acc,counts) %>%
mutate(total = rowSums(.[2:3], na.rm = TRUE))
colnames(tbl_all_line_acc) <- c("ID", "inacc", "acc", "total")
tbl_all_line_acc[is.na(tbl_all_line_acc)] <- 0
tbl_all_line_acc$rate <- tbl_all_line_acc$acc / tbl_all_line_acc$total
tbl_all_line_acc %>%
ggbarplot("ID", "rate", fill = "#0d2240", color = "#0d2240", font.xtickslab = 4, ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8)) + rotate_x_text() + geom_hline(yintercept = .5, linetype = 2)
tbl_all_line_acc %>%
ggbarplot(y = "rate", add = "mean_se",fill = "#0d2240", color = "#0d2240", ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8), label = TRUE, lab.vjust = -1.5, lab.nb.digits = 2) + geom_hline(yintercept = .5, linetype = 2)
all_line_chance <-t.test(tbl_all_line_acc$rate, mu = .50, alternative="greater")
all_line_chance##
## One Sample t-test
##
## data: tbl_all_line_acc$rate
## t = 7.9518, df = 112, p-value = 8.018e-13
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.6505804 Inf
## sample estimates:
## mean of x
## 0.6902655write.csv(tbl_all_line,'line_judgment_v3.csv', row.names=FALSE)4.2 Remove subjects who missed catch trials
tbl_all_line_good_catch <- tbl_all_line %>%
group_by(ID) %>%
filter(!any(type == "catch" & acc == 0))
tbl_all_line_acc_good_catch <- tbl_all_line_good_catch %>%
group_by(ID,acc) %>%
dplyr::summarize(counts = n()) %>%
spread(acc,counts) %>%
mutate(total = rowSums(.[2:3], na.rm = TRUE))
colnames(tbl_all_line_acc_good_catch) <- c("ID", "inacc", "acc", "total")
tbl_all_line_acc_good_catch[is.na(tbl_all_line_acc_good_catch)] <- 0
tbl_all_line_acc_good_catch$rate <- tbl_all_line_acc_good_catch$acc / tbl_all_line_acc_good_catch$total
tbl_all_line_acc_good_catch %>%
ggbarplot("ID", "rate", fill = "#0d2240", color = "#0d2240", 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_line_acc_good_catch %>%
ggbarplot(y = "rate", add = "mean_se",fill = "#0d2240", color = "#0d2240", ylab = "Accuracy", ylim = c(0, 1), position = position_dodge(0.8), label = TRUE, lab.vjust = -1.5, lab.nb.digits = 2) + geom_hline(yintercept = .5, linetype = 2)
all_line_chance <-t.test(tbl_all_line_acc_good_catch$rate, mu = .50, alternative="greater")
all_line_chance##
## One Sample t-test
##
## data: tbl_all_line_acc_good_catch$rate
## t = 18.028, df = 64, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 0.5
## 95 percent confidence interval:
## 0.8110053 Inf
## sample estimates:
## mean of x
## 0.8427354.2.1 Count subjects
nrow(tbl_all_line_acc_good_catch %>% distinct(ID,.keep_all = FALSE))## [1] 654.3 Noticers vs. Non-noticers
tbl_all_IB <- tbl_all_IB[(tbl_all_IB$ID %in% tbl_all_line_acc_good_catch$ID),]
tbl_all_notice <- tbl_all_IB %>%
filter(grepl('shapes', head))
tbl_all_notice = subset(tbl_all_notice, select = -c(rowNo,type,head,timestamp,RT,responseOptionsRandom))
tbl_all_notice$notice_shape = "filler"
for (i in 1:length(tbl_all_notice$ID)){
if (tbl_all_notice$response[i] == "Square" | tbl_all_notice$response[i] == "Circle" | tbl_all_notice$response[i] == "Triangle"){
tbl_all_notice$notice_shape[i] = 1
}
else {
tbl_all_notice$notice_shape[i] = 0
}
}
tbl_all_notice$notice_square = "filler"
for (i in 1:length(tbl_all_notice$ID)){
if (tbl_all_notice$response[i] == "Square"){
tbl_all_notice$notice_square[i] = 1
}
else {
tbl_all_notice$notice_square[i] = 0
}
}
table(tbl_all_notice$response)##
## Circle I did not notice any of these shapes
## 7 42
## Square Triangle
## 10 6table(tbl_all_notice$notice_shape)##
## 0 1
## 42 23table(tbl_all_notice$notice_square)##
## 0 1
## 55 104.4 Unexpected event location
tbl_all_line_unexpected <- tbl_all_line %>%
filter(type == "UR" | type == "UL" | type == "LL" | type == "LR")
tbl_all_line_unexpected <- tbl_all_line_unexpected[(tbl_all_line_unexpected$ID %in% tbl_all_notice$ID),]
tbl_all_notice<- cbind.data.frame(tbl_all_notice, tbl_all_line_unexpected[3])
tbl_all <- tbl_all[(tbl_all$ID %in% tbl_all_notice$ID),]
tbl_all_location <- tbl_all %>%
filter(grepl('located', head))
tbl_all_location = subset(tbl_all_location, select = c(ID,response))
tbl_all_notice <- merge(tbl_all_notice, tbl_all_location, by = "ID", all.x = TRUE, all.y = TRUE)
colnames(tbl_all_notice) <- c("ID", "notice_response", "notice_shape", "notice_square", "square_location", "location_response")
tbl_all_notice <- tbl_all_notice %>% mutate(location_response = recode_factor(location_response, `Upper left`="UL", `Upper right`="UR", `Lower left`="LL", `Lower right`="LR"))
tbl_all_notice$notice_square_and_location = "filler"
for (i in 1:length(tbl_all_notice$ID)){
if (tbl_all_notice$notice_response[i] == "Square"){
if (tbl_all_notice$square_location[i] == "UL"){
if (tbl_all_notice$location_response[i] == "UL"){
tbl_all_notice$notice_square_and_location[i] = 1
} else {
tbl_all_notice$notice_square_and_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "UR"){
if (tbl_all_notice$location_response[i] == "UR"){
tbl_all_notice$notice_square_and_location[i] = 1
} else {
tbl_all_notice$notice_square_and_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "LL"){
if (tbl_all_notice$location_response[i] == "LL"){
tbl_all_notice$notice_square_and_location[i] = 1
} else {
tbl_all_notice$notice_square_and_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "LR"){
if (tbl_all_notice$location_response[i] == "LR"){
tbl_all_notice$notice_square_and_location[i] = 1
} else {
tbl_all_notice$notice_square_and_location[i] = 0
}
}
}
}
tbl_all_notice$notice_location = "filler"
for (i in 1:length(tbl_all_notice$ID)){
if (tbl_all_notice$notice_response[i] == "Square" | tbl_all_notice$notice_response[i] == "Circle" | tbl_all_notice$notice_response[i] == "Triangle"){
if (tbl_all_notice$square_location[i] == "UL"){
if (tbl_all_notice$location_response[i] == "UL"){
tbl_all_notice$notice_location[i] = 1
} else {
tbl_all_notice$notice_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "UR"){
if (tbl_all_notice$location_response[i] == "UR"){
tbl_all_notice$notice_location[i] = 1
} else {
tbl_all_notice$notice_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "LL"){
if (tbl_all_notice$location_response[i] == "LL"){
tbl_all_notice$notice_location[i] = 1
} else {
tbl_all_notice$notice_location[i] = 0
}
} else if (tbl_all_notice$square_location[i] == "LR"){
if (tbl_all_notice$location_response[i] == "LR"){
tbl_all_notice$notice_location[i] = 1
} else {
tbl_all_notice$notice_location[i] = 0
}
}
}
}
tbl_all_notice[tbl_all_notice == "filler"] <- 0
write.csv(tbl_all_notice,'IB_v3.csv', row.names=FALSE)5 Post IB questions
5.1 Expecting an unexpected event
tbl_all <- tbl_all[(tbl_all$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_matching_condensed_rts_median$ID),]
table(tbl_all_expecting$response)##
## No Yes
## 58 75.2 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$ID %in% tbl_all_matching_condensed_rts_median$ID),]
table(tbl_all_familiarity$response)##
## No Yes
## 44 216 Predicting IB
tbl_median_rts <- tbl_all_matching_condensed_rts_median_200ms_removed[(tbl_all_matching_condensed_rts_median_200ms_removed$ID %in% tbl_all_notice$ID),]
tbl_acc <- tbl_all_matching_condensed_acc[(tbl_all_matching_condensed_acc$ID %in% tbl_all_notice$ID),]
tbl_log_reg <- cbind.data.frame(tbl_all_notice, tbl_median_rts[3], tbl_acc[3])
tbl_log_reg$notice_shape <- as.numeric(tbl_log_reg$notice_shape)
tbl_log_reg$notice_square <- as.numeric(tbl_log_reg$notice_square)
tbl_log_reg$notice_square_and_location <- as.numeric(tbl_log_reg$notice_square_and_location)
tbl_log_reg$notice_location <- as.numeric(tbl_log_reg$notice_location)6.1 Matching median RT
6.1.1 Notice shape
log_reg_matching_median_rt <- glm(notice_shape ~ median_rt, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt)##
## Call:
## glm(formula = notice_shape ~ median_rt, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9622 -0.9434 -0.9212 1.4272 1.5151
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.257e-01 8.555e-01 -0.498 0.619
## median_rt -5.622e-05 2.605e-04 -0.216 0.829
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 84.473 on 64 degrees of freedom
## Residual deviance: 84.427 on 63 degrees of freedom
## AIC: 88.427
##
## Number of Fisher Scoring iterations: 4plot_matching_median_rt <- ggplot(tbl_log_reg, aes(x=median_rt, y=notice_shape)) + 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_rt))
6.1.2 Notice correct location
log_reg_matching_median_rt <- glm(notice_location ~ median_rt, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt)##
## Call:
## glm(formula = notice_location ~ median_rt, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5965 -0.5809 -0.5744 -0.5705 1.9598
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.805e+00 1.132e+00 -1.595 0.111
## median_rt 3.172e-05 3.402e-04 0.093 0.926
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 55.803 on 63 degrees of freedom
## AIC: 59.803
##
## Number of Fisher Scoring iterations: 3plot_matching_median_rt <- ggplot(tbl_log_reg, aes(x=median_rt, y=notice_square)) + 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_rt))
6.1.3 Notice square
log_reg_matching_median_rt <- glm(notice_square ~ median_rt, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt)##
## Call:
## glm(formula = notice_square ~ median_rt, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7995 -0.6311 -0.5445 -0.3956 2.2781
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3744064 1.1900836 -0.315 0.753
## median_rt -0.0004434 0.0003972 -1.116 0.264
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 54.420 on 63 degrees of freedom
## AIC: 58.42
##
## Number of Fisher Scoring iterations: 5plot_matching_median_rt <- ggplot(tbl_log_reg, aes(x=median_rt, y=notice_square)) + 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_rt))
6.1.4 Notice square in correct location
log_reg_matching_median_rt <- glm(notice_square_and_location ~ median_rt, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt)##
## Call:
## glm(formula = notice_square_and_location ~ median_rt, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5733 -0.4080 -0.3419 -0.2591 2.4428
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.8372659 1.8262761 -0.458 0.647
## median_rt -0.0006518 0.0006529 -0.998 0.318
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 30.053 on 64 degrees of freedom
## Residual deviance: 28.872 on 63 degrees of freedom
## AIC: 32.872
##
## Number of Fisher Scoring iterations: 6plot_matching_median_rt <- ggplot(tbl_log_reg, aes(x=median_rt, y=notice_square_and_location)) + 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_rt))
6.2 Matching accuracy
6.2.1 Notice shape
log_reg_matching_acc <- glm(notice_shape ~ rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_acc)##
## Call:
## glm(formula = notice_shape ~ rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4521 -0.8798 -0.7051 1.2781 1.8168
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.924 2.532 1.945 0.0518 .
## rate -6.877 3.149 -2.184 0.0290 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 84.473 on 64 degrees of freedom
## Residual deviance: 79.285 on 63 degrees of freedom
## AIC: 83.285
##
## Number of Fisher Scoring iterations: 4plot_matching_acc<- ggplot(tbl_log_reg, aes(x=rate, y=notice_shape)) + 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))
6.2.2 Notice correct location
log_reg_matching_acc <- glm(notice_location ~ rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_acc)##
## Call:
## glm(formula = notice_location ~ rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7716 -0.6006 -0.5373 -0.4984 2.0723
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.868 2.986 0.291 0.771
## rate -3.212 3.744 -0.858 0.391
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 55.091 on 63 degrees of freedom
## AIC: 59.091
##
## Number of Fisher Scoring iterations: 4plot_matching_acc <- ggplot(tbl_log_reg, aes(x=rate, y=notice_square)) + 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))
6.2.3 Notice square
log_reg_matching_acc <- glm(notice_square ~ rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_acc)##
## Call:
## glm(formula = notice_square ~ rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8629 -0.6058 -0.5163 -0.4632 2.1861
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.968 2.949 0.667 0.505
## rate -4.607 3.731 -1.235 0.217
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 54.309 on 63 degrees of freedom
## AIC: 58.309
##
## Number of Fisher Scoring iterations: 4plot_matching_acc<- ggplot(tbl_log_reg, aes(x=rate, y=notice_square)) + 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 Notice square in correct location
log_reg_matching_acc <- glm(notice_square_and_location ~ rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_acc)##
## Call:
## glm(formula = notice_square_and_location ~ rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5005 -0.3703 -0.3247 -0.2973 2.5065
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.1522 4.3162 0.035 0.972
## rate -3.6103 5.4685 -0.660 0.509
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 30.053 on 64 degrees of freedom
## Residual deviance: 29.634 on 63 degrees of freedom
## AIC: 33.634
##
## Number of Fisher Scoring iterations: 5plot_matching_acc<- ggplot(tbl_log_reg, aes(x=rate, y=notice_square_and_location)) + 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))
6.3 Matching median RT and accuracy
6.3.1 Notice shape
log_reg_matching_median_rt_and_acc <- glm(notice_shape ~ median_rt + rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt_and_acc)##
## Call:
## glm(formula = notice_shape ~ median_rt + rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4724 -0.8851 -0.6796 1.2357 1.8580
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.3076534 2.5839397 2.054 0.0400 *
## median_rt 0.0002977 0.0003087 0.965 0.3348
## rate -8.5237011 3.6533366 -2.333 0.0196 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 84.473 on 64 degrees of freedom
## Residual deviance: 78.350 on 62 degrees of freedom
## AIC: 84.35
##
## Number of Fisher Scoring iterations: 46.3.2 Notice correct location
log_reg_matching_median_rt_and_acc <- glm(notice_location ~ median_rt + rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt_and_acc)##
## Call:
## glm(formula = notice_location ~ median_rt + rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.8565 -0.5998 -0.5407 -0.4624 2.1560
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.1363911 3.0425684 0.373 0.709
## median_rt 0.0002190 0.0003877 0.565 0.572
## rate -4.4118210 4.3700393 -1.010 0.313
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 54.778 on 62 degrees of freedom
## AIC: 60.778
##
## Number of Fisher Scoring iterations: 46.3.3 Notice square
log_reg_matching_median_rt_and_acc <- glm(notice_square ~ median_rt + rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt_and_acc)##
## Call:
## glm(formula = notice_square ~ median_rt + rate, family = binomial(link = "logit"),
## data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.9384 -0.6133 -0.5416 -0.4101 2.3304
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7260064 2.9732169 0.581 0.562
## median_rt -0.0002917 0.0004383 -0.665 0.506
## rate -3.2034088 4.2223005 -0.759 0.448
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 55.812 on 64 degrees of freedom
## Residual deviance: 53.843 on 62 degrees of freedom
## AIC: 59.843
##
## Number of Fisher Scoring iterations: 46.3.4 Notice square in correct location
log_reg_matching_median_rt_and_acc <- glm(notice_square_and_location ~ median_rt + rate, data = tbl_log_reg, family = binomial(link = "logit"))
summary(log_reg_matching_median_rt_and_acc)##
## Call:
## glm(formula = notice_square_and_location ~ median_rt + rate,
## family = binomial(link = "logit"), data = tbl_log_reg)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.5995 -0.4079 -0.3475 -0.2596 2.4625
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2870951 4.2998483 -0.067 0.947
## median_rt -0.0006036 0.0007315 -0.825 0.409
## rate -0.8643979 6.1654753 -0.140 0.889
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 30.053 on 64 degrees of freedom
## Residual deviance: 28.853 on 62 degrees of freedom
## AIC: 34.853
##
## Number of Fisher Scoring iterations: 6