1 Set up R environment

library(tidyverse)
library(ggplot2)
library(ggpubr)
library(plyr)
library(magick)
library(png)
library(EBImage)
library(lme4)
library(lmerTest)

2 Set the R working drectory to the main experiment directory.

setwd("/Users/adambarnas/Box/MetaAwareness/data/")

3 Read in the individual subject files.

tbl_all <- list.files(path = "./Wolfe1_Recontact", pattern = "*.csv", full.names = T, ignore.case = F) %>%
  map_df(~read.csv(., colClasses=c("gender..m.f."="character")))
tbl_all = subset(tbl_all, select = c(user_resp.keys,user_resp.rt,workerId,image_a))
col_idx <- grep("workerId", names(tbl_all))
tbl_all <- tbl_all[, c(col_idx, (1:ncol(tbl_all))[-col_idx])]
tbl_all <- data.frame(na.omit(tbl_all))
tbl_all <- tbl_all %>%
separate(image_a,into=c('database', 'image'), sep = "([\\_])")
tbl_all$image <- lapply(tbl_all$image, gsub, pattern='-a', replacement='')
tbl_all <- tbl_all %>%  
    mutate(image = as.character(image))

4 Compute average likelihood rating.

tbl_all_subj_avg <- tbl_all %>%
  group_by(workerId,image) %>%
  dplyr::summarize(average = mean(user_resp.keys)) %>%
  spread(image,average) %>% 
  mutate(subj_avg = rowMeans(.[-1], na.rm = TRUE))
mean(tbl_all_subj_avg$subj_avg)
## [1] 3.017315
tbl_all_img_avg <- data.frame(img_avg = colMeans(tbl_all_subj_avg[,2:112], na.rm = TRUE))
tbl_all_img_avg <- tibble::rownames_to_column(tbl_all_img_avg, "image")

5 Merge Mudsplash and Meta-Awareness data files.

wolfe1_RTs_raw <- read_csv("wolfe1_RTs_raw.csv")
wolfe1_RTs_raw <- wolfe1_RTs_raw[, -c(2,3,6:16,18,19)]
wolfe1_RTs_raw <- wolfe1_RTs_raw[(wolfe1_RTs_raw$workerId %in% tbl_all_subj_avg$workerId),]

tbl_all <- tbl_all[order(tbl_all$workerId, tbl_all$image), , drop = FALSE]
wolfe1_RTs_raw <- wolfe1_RTs_raw[order(wolfe1_RTs_raw$workerId, wolfe1_RTs_raw$image), , drop = FALSE]
wolfe1_RTs_raw <- wolfe1_RTs_raw %>%  
    mutate(image = as.character(image))

wolfe1_RTs_likelihood <- left_join(tbl_all, wolfe1_RTs_raw, by = c("workerId", "image"))
colnames(wolfe1_RTs_likelihood)[2] <- "likelihood_rating"
colnames(wolfe1_RTs_likelihood)[3] <- "likelihood_rt"
colnames(wolfe1_RTs_likelihood)[7] <- "detection_rt"
wolfe1_RTs_likelihood <- wolfe1_RTs_likelihood[, c(-4,-6)]
wolfe1_RTs_likelihood <- wolfe1_RTs_likelihood[,c(1,4,5,2,3)]

6 Compute likelihood rating for each image.

wolfe1_RTs_likelihood %>%
  ggbarplot(x = "image", y = "likelihood_rating", ylab = "Likelihood of Detecting Change", title = "All images (30 per subject)", fill = "#f7a800", add = "mean_se", font.xtickslab = 4, sort.val = c("asc")) + rotate_x_text() + theme(legend.position = "none")

wolfe1_RTs_likelihood_no_NA <- wolfe1_RTs_likelihood %>%
  drop_na()
wolfe1_RTs_likelihood_no_NA %>% 
  ggbarplot(x = "image", y = "likelihood_rating", ylab = "Likelihood of Detecting Change", title = "'Correct' images", fill = "#f7a800", add = "mean_se", font.xtickslab = 4, sort.val = c("asc")) + rotate_x_text() + theme(legend.position = "none")

write.csv(wolfe1_RTs_likelihood,'wolfe1_RTs_likelihood.csv', row.names=FALSE)
write.csv(wolfe1_RTs_likelihood_no_NA,'wolfe1_RTs_likelihood_no_NA.csv', row.names=FALSE)

7 Count number of ratings

wolfe1_RTs_likelihood_count <- wolfe1_RTs_likelihood_no_NA %>% 
  group_by(workerId,image) %>% 
  dplyr::summarize(counts = n()) %>%
  spread(image,counts) %>%
  mutate(sum = rowSums(.[-1], na.rm = TRUE))
#head(tbl_all_counts,10)

wolfe1_RTs_likelihood_count <- data.frame(count = colSums(wolfe1_RTs_likelihood_count[,2:109], na.rm = TRUE))
wolfe1_RTs_likelihood_count <- tibble::rownames_to_column(wolfe1_RTs_likelihood_count, "image")
wolfe1_RTs_likelihood_count
##         image count
## 1   image-001     6
## 2   image-002     7
## 3   image-003    11
## 4   image-004    14
## 5   image-005     5
## 6   image-006     8
## 7   image-007    12
## 8   image-008     6
## 9   image-009     7
## 10  image-010     9
## 11  image-011     6
## 12  image-012     7
## 13  image-013     6
## 14  image-014     8
## 15  image-015     5
## 16  image-016     4
## 17  image-017     2
## 18  image-018     6
## 19  image-019     6
## 20  image-020     8
## 21  image-021     2
## 22  image-023     4
## 23  image-024     3
## 24  image-025     9
## 25  image-026    11
## 26  image-028     6
## 27  image-029     6
## 28  image-030     4
## 29  image-031     5
## 30  image-032     5
## 31  image-033     6
## 32  image-034     6
## 33  image-037     6
## 34  image-038     2
## 35  image-039     5
## 36  image-040     8
## 37  image-041     1
## 38  image-042     4
## 39  image-043     8
## 40  image-044     2
## 41  image-045     5
## 42  image-046     5
## 43  image-047     4
## 44  image-048     1
## 45  image-049     5
## 46  image-050     8
## 47  image-076     4
## 48  image-077     7
## 49  image-078     6
## 50  image-079     9
## 51  image-080     6
## 52  image-081     6
## 53  image-082     7
## 54  image-083     5
## 55  image-084     3
## 56  image-085     4
## 57  image-086     6
## 58  image-087     6
## 59  image-088     9
## 60  image-089     7
## 61  image-090     4
## 62  image-091     2
## 63  image-092     8
## 64  image-093     4
## 65  image-094    10
## 66  image-095    10
## 67  image-096     3
## 68  image-097     7
## 69  image-098     6
## 70  image-099    11
## 71  image-100     2
## 72  image-101    11
## 73  image-102     2
## 74  image-103     4
## 75  image-104     2
## 76  image-105     5
## 77  image-106     6
## 78  image-107     8
## 79  image-108     8
## 80  image-109     3
## 81  image-110     4
## 82  image-111     8
## 83  image-112     5
## 84  image-113     5
## 85  image-114     4
## 86  image-115     8
## 87  image-117    12
## 88  image-118     5
## 89  image-119    13
## 90  image-120     8
## 91  image-121     8
## 92  image-122    10
## 93  image-123     3
## 94  image-124     3
## 95  image-125     7
## 96  image-126     5
## 97  image-127     6
## 98  image-128     5
## 99  image-129     3
## 100 image-130     3
## 101 image-131     2
## 102 image-132     3
## 103 image-133     9
## 104 image-134     5
## 105 image-135    10
## 106 image-136     7
## 107 image-137     7
## 108       sum   643

8 Mixed effects model and correlation.

fit0 <- lmer(detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image), data=wolfe1_RTs_likelihood_no_NA)
summary(fit0)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image)
##    Data: wolfe1_RTs_likelihood_no_NA
## 
## REML criterion at convergence: 3884.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.0053 -0.4845 -0.1682  0.1371  7.1142 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  image    (Intercept)  1.197   1.094   
##  workerId (Intercept)  4.209   2.052   
##  Residual             21.937   4.684   
## Number of obs: 643, groups:  image, 107; workerId, 28
## 
## Fixed effects:
##                   Estimate Std. Error       df t value Pr(>|t|)    
## (Intercept)        11.5243     0.7052 136.6586  16.343  < 2e-16 ***
## likelihood_rating  -0.5193     0.1747 435.0807  -2.972  0.00312 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## liklhd_rtng -0.771
corr <- wolfe1_RTs_likelihood_no_NA %>% 
  group_by(image) %>% 
  dplyr::summarize(detection_rt = mean(detection_rt), likelihood_rating = mean(likelihood_rating))
corr %>%
  ggscatter(y = "detection_rt", x = "likelihood_rating", ylab = "Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", title = "N = 28", fill = "#f7a800", color = "#f7a800", add = "reg.line", cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 1, label.y = 5, label.sep = "\n"), xlim = c(1, 5), ylim = c(0, 25))