Wolfe1 Meta-Awareness
Adam Barnas
Last compiled at 11:51 AM on October 28, 2020
- 1 Set up R environment
- 2 Set the R working drectory to the main experiment directory.
- 3 Read in the individual subject files.
- 4 Compute average likelihood rating.
- 5 Merge Mudsplash and Meta-Awareness data files.
- 6 Add change_type to dataframe.
- 7 Compute likelihood rating for each image.
- 8 Count number of ratings
- 9 Mixed effects model and correlation.
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$subj_avg)## [1] 2.999677tbl_all_img_avg <- data.frame(img_avg = colMeans(tbl_all_subj_avg[,2:112], na.rm = TRUE))
tbl_all_img_avg <- tibble::rownames_to_column(tbl_all_img_avg, "image")5 Merge Mudsplash and Meta-Awareness data files.
wolfe1_RTs_raw <- read_csv("wolfe1_RTs_raw.csv")
wolfe1_RTs_raw <- wolfe1_RTs_raw[, -c(2,3,6:16,18,19)]
wolfe1_RTs_raw <- wolfe1_RTs_raw[(wolfe1_RTs_raw$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)]
colnames(wolfe1_RTs_likelihood)[5] <- "stim_set"
wolfe1_RTs_likelihood <- wolfe1_RTs_likelihood[,c(1,4,5,6,2,3)]6 Add change_type to dataframe.
wolfe1_change_type<- read_csv("Wolfe1_change_type.csv")
wolfe1_RTs_likelihood <- left_join(wolfe1_RTs_likelihood, wolfe1_change_type, by = "image")7 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)8 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 15
## 2 image-002 9
## 3 image-003 12
## 4 image-004 15
## 5 image-005 7
## 6 image-006 10
## 7 image-007 15
## 8 image-008 11
## 9 image-009 11
## 10 image-010 16
## 11 image-011 10
## 12 image-012 9
## 13 image-013 8
## 14 image-014 13
## 15 image-015 10
## 16 image-016 8
## 17 image-017 4
## 18 image-018 8
## 19 image-019 15
## 20 image-020 12
## 21 image-021 9
## 22 image-022 5
## 23 image-023 6
## 24 image-024 7
## 25 image-025 11
## 26 image-026 16
## 27 image-027 2
## 28 image-028 12
## 29 image-029 11
## 30 image-030 7
## 31 image-031 7
## 32 image-032 9
## 33 image-033 10
## 34 image-034 11
## 35 image-035 1
## 36 image-037 11
## 37 image-038 4
## 38 image-039 11
## 39 image-040 13
## 40 image-041 8
## 41 image-042 7
## 42 image-043 12
## 43 image-044 5
## 44 image-045 9
## 45 image-046 8
## 46 image-047 8
## 47 image-048 3
## 48 image-049 10
## 49 image-050 12
## 50 image-076 7
## 51 image-077 9
## 52 image-078 10
## 53 image-079 11
## 54 image-080 10
## 55 image-081 8
## 56 image-082 12
## 57 image-083 9
## 58 image-084 7
## 59 image-085 6
## 60 image-086 7
## 61 image-087 10
## 62 image-088 12
## 63 image-089 10
## 64 image-090 9
## 65 image-091 5
## 66 image-092 14
## 67 image-093 9
## 68 image-094 13
## 69 image-095 14
## 70 image-096 7
## 71 image-097 10
## 72 image-098 8
## 73 image-099 16
## 74 image-100 7
## 75 image-101 14
## 76 image-102 3
## 77 image-103 9
## 78 image-104 5
## 79 image-105 9
## 80 image-106 12
## 81 image-107 13
## 82 image-108 13
## 83 image-109 8
## 84 image-110 11
## 85 image-111 12
## 86 image-112 9
## 87 image-113 12
## 88 image-114 10
## 89 image-115 11
## 90 image-116 2
## 91 image-117 17
## 92 image-118 6
## 93 image-119 19
## 94 image-120 14
## 95 image-121 9
## 96 image-122 17
## 97 image-123 8
## 98 image-124 7
## 99 image-125 11
## 100 image-126 11
## 101 image-127 8
## 102 image-128 8
## 103 image-129 5
## 104 image-130 9
## 105 image-131 7
## 106 image-132 8
## 107 image-133 11
## 108 image-134 109 Mixed effects model and correlation.
fit0 <- lmer(detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image) + (1 | change_type), 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) +
## (1 | change_type)
## Data: wolfe1_RTs_likelihood_no_NA
##
## REML criterion at convergence: 7065
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9559 -0.4648 -0.1640 0.1439 6.6972
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 3.0303 1.7408
## workerId (Intercept) 8.5664 2.9268
## change_type (Intercept) 0.7592 0.8713
## Residual 35.2588 5.9379
## Number of obs: 1080, groups: image, 111; workerId, 47; change_type, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.2270 0.9630 4.2979 12.697 0.000143 ***
## likelihood_rating -0.4735 0.1755 735.8717 -2.699 0.007124 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.568corr <- 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 = 47", 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, 50), label = "image", font.label = c(5, "plain", "black"))
Here, I am dropping image-116 from the analyses. It is clearly an outlier and has only one meta-awareness rating.
wolfe1_RTs_likelihood_no_NA_no_outlier <- wolfe1_RTs_likelihood_no_NA %>%
filter(image != "image-116")
fit1 <- lmer(detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image) + (1 | change_type), data=wolfe1_RTs_likelihood_no_NA_no_outlier)
summary(fit1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image) +
## (1 | change_type)
## Data: wolfe1_RTs_likelihood_no_NA_no_outlier
##
## REML criterion at convergence: 7019.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0054 -0.4670 -0.1655 0.1408 6.8007
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 2.4015 1.5497
## workerId (Intercept) 8.2192 2.8669
## change_type (Intercept) 0.5611 0.7491
## Residual 34.5400 5.8771
## Number of obs: 1078, groups: image, 110; workerId, 47; change_type, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.3799 0.8951 5.4951 13.830 1.77e-05 ***
## likelihood_rating -0.5447 0.1717 725.0270 -3.173 0.00157 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.598corr %>%
filter(image!="image-116") %>%
ggscatter(y = "detection_rt", x = "likelihood_rating", ylab = "Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", title = "N = 37", 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, 40))