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 data files.

Rensink_RTs_likelihood_no_NA <- read_csv("Rensink_RTs_likelihood_no_NA.csv")
Ma_RTs_likelihood_no_NA <- read_csv("Ma_RTs_likelihood_no_NA.csv")
Wolfe1_RTs_likelihood_no_NA <- read_csv("Wolfe1_RTs_likelihood_no_NA.csv")

tbl_all <- rbind(Rensink_RTs_likelihood_no_NA, Ma_RTs_likelihood_no_NA, Wolfe1_RTs_likelihood_no_NA)

4 Compute average likelihood rating.

tbl_all_subj_avg <- tbl_all %>%
  group_by(workerId,image) %>%
  dplyr::summarize(average = mean(likelihood_rating)) %>%
  spread(image,average) %>% 
  mutate(subj_avg = rowMeans(.[-1], na.rm = TRUE))
mean(tbl_all_subj_avg$subj_avg)
## [1] 3.242547

5 Mixed effects model and correlation.

fit0 <- lmer(detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image) + (1 | stim_set), data=tbl_all)
summary(fit0)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image) +  
##     (1 | stim_set)
##    Data: tbl_all
## 
## REML criterion at convergence: 7185.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.5470 -0.4288 -0.1369  0.1354  8.9576 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  image    (Intercept)  0.884   0.9402  
##  workerId (Intercept)  4.115   2.0285  
##  stim_set (Intercept)  0.588   0.7668  
##  Residual             13.641   3.6934  
## Number of obs: 1285, groups:  image, 223; workerId, 54; stim_set, 3
## 
## Fixed effects:
##                    Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)        10.13469    0.63045   3.87594  16.075 0.000108 ***
## likelihood_rating  -0.36836    0.09514 791.31672  -3.872 0.000117 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr)
## liklhd_rtng -0.491
corr <- tbl_all %>% 
  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", 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))