Set up R environment.
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(plyr)
library(magick)
library(png)
library(EBImage)
library(lme4)
library(lmerTest)
Set the R working drectory to the main experiment directory.
setwd("/Users/adambarnas/Box/MetaAwareness/data/")
Read in the individual subject files.
tbl_all <- list.files(path = "./Wolfe2_Recontact", pattern = "*.csv", full.names = T, ignore.case = F) %>%
map_df(~read.csv(., colClasses=c("gender..m.f."="character", "a"="character", "tp_a"="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 = "([\\_])", extra = "merge")
tbl_all$image <- lapply(tbl_all$image, gsub, pattern='-a_w_outline.jpg', replacement='')
tbl_all <- tbl_all %>%
mutate(image = as.character(image))
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] 2.915882
tbl_all_img_avg <- data.frame(img_avg = colMeans(tbl_all_subj_avg[,2:255], na.rm = TRUE))
tbl_all_img_avg <- tibble::rownames_to_column(tbl_all_img_avg, "image")
Add change_type to dataframe.
wolfe2_change_type<- read_csv("wolfe2_change_type.csv")
wolfe2_RTs_likelihood <- left_join(wolfe2_RTs_likelihood, wolfe2_change_type, by = "image")
Compute likelihood rating for each image.
wolfe2_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 = 2, sort.val = c("asc")) + rotate_x_text() + theme(legend.position = "none")
wolfe2_RTs_likelihood_no_NA <- wolfe2_RTs_likelihood %>%
drop_na()
wolfe2_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 = 2, sort.val = c("asc")) + rotate_x_text() + theme(legend.position = "none")
write.csv(wolfe2_RTs_likelihood,'Wolfe2_RTs_likelihood.csv', row.names=FALSE)
write.csv(wolfe2_RTs_likelihood_no_NA,'Wolfe2_RTs_likelihood_no_NA.csv', row.names=FALSE)
Count number of ratings.
wolfe2_RTs_likelihood_count <- wolfe2_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)
wolfe2_RTs_likelihood_count <- data.frame(count = colSums(wolfe2_RTs_likelihood_count[,2:253], na.rm = TRUE))
wolfe2_RTs_likelihood_count <- tibble::rownames_to_column(wolfe2_RTs_likelihood_count, "image")
wolfe2_RTs_likelihood_count
## image count
## 1 001_L_mirror 6
## 2 001_R_mirror 9
## 3 002_L_napkin 8
## 4 002_R_napkin 5
## 5 003_L_ducks 4
## 6 003_R_ducks 8
## 7 004_L_electricalgrid 3
## 8 004_R_electricalgrid 13
## 9 005_L_mirror 6
## 10 005_R_mirror 10
## 11 006_L_vase 7
## 12 006_R_vase 9
## 13 007_L_soap 4
## 14 007_R_soap 11
## 15 008_L_bottle 6
## 16 008_R_bottle 13
## 17 009_L_carpet 7
## 18 009_R_carpet 11
## 19 010_L_candle 2
## 20 010_R_candle 2
## 21 011_L_parfum 5
## 22 011_R_parfum 9
## 23 012_L_tubaccesory 4
## 24 012_R_tubaccesory 7
## 25 013_L_lamp 5
## 26 013_R_lamp 8
## 27 014_L_lamp 6
## 28 014_R_lamp 14
## 29 015_L_ceilinglight 2
## 30 015_R_ceilinglight 4
## 31 016_L_art 6
## 32 016_R_art 15
## 33 017_L_wood 3
## 34 017_R_wood 4
## 35 018_L_plant 7
## 36 018_R_plant 16
## 37 019_L_lamp_sized 8
## 38 019_R_lamp_sized 8
## 39 020_L_walldeco 7
## 40 020_R_walldeco 8
## 41 021_L_keyboard 9
## 42 021_R_keyboard 8
## 43 022_L_lamp 6
## 44 022_R_lamp 9
## 45 023_L_remote 4
## 46 023_R_remote 9
## 47 024_L_towels 7
## 48 024_R_towels 10
## 49 025_L_vase 3
## 50 025_R_vase 9
## 51 026_L_rack 7
## 52 026_R_rack 8
## 53 027_L_soapdish 4
## 54 027_R_soapdish 7
## 55 028_L_vase 7
## 56 028_R_vase 7
## 57 029_L_glass 7
## 58 029_R_glass 10
## 59 030_L_drawer 5
## 60 030_R_drawer 12
## 61 031_L_log 3
## 62 031_R_log 6
## 63 032_L_bottle 7
## 64 032_R_bottle 3
## 65 033_L_vase 4
## 66 033_R_vase 13
## 67 034_L_handle 2
## 68 034_R_handle 5
## 69 035_L_fruit 4
## 70 035_R_fruit 6
## 71 036_L_bowl 5
## 72 036_R_bowl 7
## 73 037_R_towel 5
## 74 038_L_art 5
## 75 038_R_art 15
## 76 039_L_ventilator 1
## 77 039_R_ventilator 7
## 78 040_L_painting 8
## 79 040_R_painting 9
## 80 041_L_fruit 2
## 81 041_R_fruit 3
## 82 042_L_tap 4
## 83 042_R_tap 6
## 84 043_L_clock 9
## 85 043_R_clock 11
## 86 044_L_light 8
## 87 044_R_light 9
## 88 045_L_musicdock 7
## 89 045_R_musicdock 10
## 90 046_L_remote 5
## 91 046_R_remote 14
## 92 047_L_handle 5
## 93 047_R_handle 9
## 94 048_L_art 5
## 95 048_R_art 10
## 96 049_L_painting 7
## 97 049_R_painting 14
## 98 050_L_book 7
## 99 050_R_book 7
## 100 051_L_owl 8
## 101 051_R_owl 10
## 102 052_L_speaker 2
## 103 052_R_speaker 8
## 104 053_L_handle 1
## 105 053_R_handle 4
## 106 054_L_firewood 7
## 107 054_R_firewood 10
## 108 055_R_carpet 1
## 109 056_L_comforter 7
## 110 056_R_comforter 12
## 111 057_L_bin 5
## 112 057_R_bin 10
## 113 058_L_clutch 8
## 114 058_R_clutch 8
## 115 059_L_car 6
## 116 059_R_car 3
## 117 060_L_pillow 11
## 118 060_R_pillow 10
## 119 061_L_glass 7
## 120 061_R_glass 9
## 121 062_L_flowers 7
## 122 062_R_flowers 14
## 123 063_L_laptop 2
## 124 063_R_laptop 3
## 125 064_L_bottles 8
## 126 064_R_bottles 8
## 127 065_L_cd 6
## 128 065_R_cd 12
## 129 066_L_vase 7
## 130 066_R_vase 8
## 131 067_L_painting 4
## 132 067_R_painting 7
## 133 068_L_faucet 8
## 134 068_R_faucet 11
## 135 069_L_things 8
## 136 069_R_things 6
## 137 070_L_lamp 7
## 138 070_R_lamp 12
## 139 071_L_art 5
## 140 071_R_art 22
## 141 072_L_handle 4
## 142 072_R_handle 7
## 143 073_L_coffeemug 5
## 144 073_R_coffeemug 11
## 145 074_L_book 8
## 146 074_R_book 14
## 147 075_L_light 6
## 148 075_R_light 6
## 149 076_L_hook 6
## 150 076_R_hook 6
## 151 077_L_footrest 1
## 152 077_R_footrest 8
## 153 078_L_faucet 2
## 154 078_R_faucet 11
## 155 079_L_bowl 4
## 156 079_R_bowl 7
## 157 080_L_plant 4
## 158 080_R_plant 15
## 159 081_L_football 5
## 160 081_R_football 5
## 161 082_L_bowl 6
## 162 082_R_bowl 11
## 163 083_L_knob 2
## 164 083_R_knob 5
## 165 084_L_light 6
## 166 084_R_light 12
## 167 085_L_switchboard 3
## 168 085_R_switchboard 8
## 169 086_L_book 6
## 170 086_R_book 9
## 171 087_L_towelhandle 6
## 172 087_R_towelhandle 9
## 173 088_L_clock 5
## 174 088_R_clock 3
## 175 089_L_poster 5
## 176 089_R_poster 8
## 177 090_L_lamp 5
## 178 090_R_lamp 6
## 179 091_L_airfreshener 4
## 180 091_R_airfreshener 4
## 181 092_L_candles 7
## 182 092_R_candles 13
## 183 093_L_switch 5
## 184 093_R_switch 9
## 185 095_L_candle 8
## 186 095_R_candle 9
## 187 096_L_painting 6
## 188 096_R_painting 13
## 189 097_L_light 7
## 190 097_R_light 8
## 191 098_L_slippers 7
## 192 098_R_slippers 13
## 193 099_L_sconce 9
## 194 099_R_sconce 10
## 195 100_L_mirror 7
## 196 100_R_mirror 16
## 197 101_L_cup 7
## 198 101_R_cup 12
## 199 102_L_shoppingbag 9
## 200 102_R_shoppingbag 13
## 201 103_L_hook 2
## 202 103_R_hook 4
## 203 104_L_bottle 6
## 204 104_R_bottle 12
## 205 105_L_hat 9
## 206 105_R_hat 9
## 207 106_L_toweldispenser 9
## 208 106_R_toweldispenser 9
## 209 107_L_shirts 7
## 210 107_R_shirts 13
## 211 108_L_boa 7
## 212 108_R_boa 9
## 213 109_L_pillow 6
## 214 109_R_pillow 10
## 215 110_L_plant 2
## 216 110_R_plant 12
## 217 111_L_pot 5
## 218 111_R_pot 12
## 219 112_L_basket 7
## 220 112_R_basket 11
## 221 113_L_plant 5
## 222 113_R_plant 8
## 223 114_L_bird 6
## 224 114_R_bird 11
## 225 115_L_pot 7
## 226 115_R_pot 8
## 227 116_L_basket 5
## 228 116_R_basket 10
## 229 117_L_plant_sized 5
## 230 117_R_plant_sized 5
## 231 118_L_shoes 7
## 232 118_R_shoes 12
## 233 119_L_painting 6
## 234 119_R_painting 12
## 235 120_L_art 6
## 236 120_R_art 8
## 237 121_L_car 6
## 238 121_R_car 12
## 239 122_L_chair 3
## 240 122_R_chair 5
## 241 123_L_pot 5
## 242 123_R_pot 10
## 243 124_L_vase 4
## 244 124_R_vase 9
## 245 125_L_sofa 8
## 246 125_R_sofa 10
## 247 126_L_candles 4
## 248 126_R_candles 12
## 249 127_L_light 5
## 250 127_R_light 8
## 251 128_L_pot 6
## 252 128_R_pot 4
Mixed effects model and correlation.
Raw RT
fit_raw <- lmer(detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image), data=wolfe2_RTs_likelihood_no_NA)
summary(fit_raw)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating + (1 | workerId) + (1 | image)
## Data: wolfe2_RTs_likelihood_no_NA
##
## REML criterion at convergence: 11005.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5397 -0.4535 -0.1316 0.1687 8.0509
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 1.789 1.337
## workerId (Intercept) 8.783 2.964
## Residual 18.436 4.294
## Number of obs: 1856, groups: image, 252; workerId, 85
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.0773 0.4652 248.7069 23.812 < 2e-16 ***
## likelihood_rating -0.4184 0.1020 940.3462 -4.103 4.43e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.654
corr <- wolfe2_RTs_likelihood_no_NA %>%
group_by(image) %>%
dplyr::summarize(detection_rt = mean(detection_rt), likelihood_rating = mean(likelihood_rating))
corr <- left_join(corr, wolfe2_change_type, by = "image")
corr %>%
ggscatter(y = "detection_rt", x = "likelihood_rating", ylab = "Raw Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", title = "N = 85", add = "reg.line", cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 1, label.y = 25), conf.int = TRUE, xlim = c(1, 5), ylim = c(5, 30), label = "image", font.label = c(5, "plain", "black"))
Log RT
fit_log <- lmer(log10(detection_rt) ~ likelihood_rating + (1 | workerId) + (1 | image), data=wolfe2_RTs_likelihood_no_NA)
summary(fit_log)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log10(detection_rt) ~ likelihood_rating + (1 | workerId) + (1 |
## image)
## Data: wolfe2_RTs_likelihood_no_NA
##
## REML criterion at convergence: -2094.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6203 -0.5805 -0.1284 0.3469 4.4287
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 0.001855 0.04307
## workerId (Intercept) 0.012554 0.11205
## Residual 0.015189 0.12324
## Number of obs: 1856, groups: image, 252; workerId, 85
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.98894 0.01575 191.03118 62.781 < 2e-16 ***
## likelihood_rating -0.01229 0.00302 1000.13027 -4.071 5.06e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.572
corr <- wolfe2_RTs_likelihood_no_NA %>%
group_by(image) %>%
dplyr::summarize(detection_rt = mean(detection_rt), likelihood_rating = mean(likelihood_rating))
corr <- left_join(corr, wolfe2_change_type, by = "image")
corr$log <- log10(corr$detection_rt)
corr %>%
ggscatter(y = "log", x = "likelihood_rating", ylab = "Log Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", title = "N = 85", add = "reg.line", cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 1, label.y = 1.4), conf.int = TRUE, xlim = c(1, 5), ylim = c(0.75, 1.5), label = "image", font.label = c(5, "plain", "black"))
