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] 2.939906
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[,2:252], 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 5
## 2 001_R_mirror 8
## 3 002_L_napkin 7
## 4 002_R_napkin 5
## 5 003_L_ducks 4
## 6 003_R_ducks 7
## 7 004_L_electricalgrid 3
## 8 004_R_electricalgrid 11
## 9 005_L_mirror 6
## 10 005_R_mirror 9
## 11 006_L_vase 5
## 12 006_R_vase 6
## 13 007_L_soap 4
## 14 007_R_soap 10
## 15 008_L_bottle 5
## 16 008_R_bottle 10
## 17 009_L_carpet 5
## 18 009_R_carpet 9
## 19 010_L_candle 2
## 20 010_R_candle 2
## 21 011_L_parfum 5
## 22 011_R_parfum 9
## 23 012_L_tubaccesory 3
## 24 012_R_tubaccesory 6
## 25 013_L_lamp 5
## 26 013_R_lamp 6
## 27 014_L_lamp 5
## 28 014_R_lamp 12
## 29 015_L_ceilinglight 1
## 30 015_R_ceilinglight 2
## 31 016_L_art 6
## 32 016_R_art 11
## 33 017_L_wood 3
## 34 017_R_wood 3
## 35 018_L_plant 7
## 36 018_R_plant 11
## 37 019_L_lamp_sized 8
## 38 019_R_lamp_sized 6
## 39 020_L_walldeco 7
## 40 020_R_walldeco 7
## 41 021_L_keyboard 7
## 42 021_R_keyboard 7
## 43 022_L_lamp 5
## 44 022_R_lamp 5
## 45 023_L_remote 3
## 46 023_R_remote 7
## 47 024_L_towels 6
## 48 024_R_towels 8
## 49 025_L_vase 3
## 50 025_R_vase 8
## 51 026_L_rack 5
## 52 026_R_rack 7
## 53 027_L_soapdish 4
## 54 027_R_soapdish 6
## 55 028_L_vase 6
## 56 028_R_vase 4
## 57 029_L_glass 4
## 58 029_R_glass 8
## 59 030_L_drawer 5
## 60 030_R_drawer 8
## 61 031_L_log 3
## 62 031_R_log 5
## 63 032_L_bottle 7
## 64 032_R_bottle 3
## 65 033_L_vase 4
## 66 033_R_vase 11
## 67 034_L_handle 2
## 68 034_R_handle 5
## 69 035_L_fruit 3
## 70 035_R_fruit 3
## 71 036_L_bowl 3
## 72 036_R_bowl 6
## 73 037_R_towel 4
## 74 038_L_art 5
## 75 038_R_art 9
## 76 039_L_ventilator 1
## 77 039_R_ventilator 7
## 78 040_L_painting 6
## 79 040_R_painting 8
## 80 041_L_fruit 2
## 81 041_R_fruit 3
## 82 042_L_tap 3
## 83 042_R_tap 5
## 84 043_L_clock 6
## 85 043_R_clock 10
## 86 044_L_light 8
## 87 044_R_light 9
## 88 045_L_musicdock 5
## 89 045_R_musicdock 8
## 90 046_L_remote 3
## 91 046_R_remote 12
## 92 047_L_handle 5
## 93 047_R_handle 8
## 94 048_L_art 4
## 95 048_R_art 7
## 96 049_L_painting 7
## 97 049_R_painting 12
## 98 050_L_book 6
## 99 050_R_book 7
## 100 051_L_owl 8
## 101 051_R_owl 10
## 102 052_L_speaker 2
## 103 052_R_speaker 6
## 104 053_L_handle 1
## 105 053_R_handle 3
## 106 054_L_firewood 5
## 107 054_R_firewood 10
## 108 055_R_carpet 1
## 109 056_L_comforter 7
## 110 056_R_comforter 9
## 111 057_L_bin 4
## 112 057_R_bin 10
## 113 058_L_clutch 6
## 114 058_R_clutch 8
## 115 059_L_car 6
## 116 059_R_car 2
## 117 060_L_pillow 8
## 118 060_R_pillow 9
## 119 061_L_glass 6
## 120 061_R_glass 6
## 121 062_L_flowers 6
## 122 062_R_flowers 13
## 123 063_L_laptop 2
## 124 063_R_laptop 3
## 125 064_L_bottles 8
## 126 064_R_bottles 7
## 127 065_L_cd 5
## 128 065_R_cd 11
## 129 066_L_vase 5
## 130 066_R_vase 7
## 131 067_L_painting 4
## 132 067_R_painting 6
## 133 068_L_faucet 7
## 134 068_R_faucet 9
## 135 069_L_things 7
## 136 069_R_things 5
## 137 070_L_lamp 7
## 138 070_R_lamp 11
## 139 071_L_art 5
## 140 071_R_art 19
## 141 072_L_handle 4
## 142 072_R_handle 6
## 143 073_L_coffeemug 4
## 144 073_R_coffeemug 8
## 145 074_L_book 7
## 146 074_R_book 9
## 147 075_L_light 4
## 148 075_R_light 5
## 149 076_L_hook 5
## 150 076_R_hook 4
## 151 077_R_footrest 8
## 152 078_L_faucet 1
## 153 078_R_faucet 10
## 154 079_L_bowl 3
## 155 079_R_bowl 6
## 156 080_L_plant 4
## 157 080_R_plant 13
## 158 081_L_football 3
## 159 081_R_football 4
## 160 082_L_bowl 6
## 161 082_R_bowl 11
## 162 083_L_knob 2
## 163 083_R_knob 4
## 164 084_L_light 5
## 165 084_R_light 9
## 166 085_L_switchboard 3
## 167 085_R_switchboard 7
## 168 086_L_book 4
## 169 086_R_book 8
## 170 087_L_towelhandle 5
## 171 087_R_towelhandle 8
## 172 088_L_clock 4
## 173 088_R_clock 2
## 174 089_L_poster 5
## 175 089_R_poster 7
## 176 090_L_lamp 4
## 177 090_R_lamp 5
## 178 091_L_airfreshener 4
## 179 091_R_airfreshener 3
## 180 092_L_candles 7
## 181 092_R_candles 9
## 182 093_L_switch 5
## 183 093_R_switch 7
## 184 095_L_candle 7
## 185 095_R_candle 6
## 186 096_L_painting 4
## 187 096_R_painting 10
## 188 097_L_light 6
## 189 097_R_light 7
## 190 098_L_slippers 7
## 191 098_R_slippers 12
## 192 099_L_sconce 8
## 193 099_R_sconce 9
## 194 100_L_mirror 4
## 195 100_R_mirror 13
## 196 101_L_cup 6
## 197 101_R_cup 11
## 198 102_L_shoppingbag 8
## 199 102_R_shoppingbag 12
## 200 103_L_hook 2
## 201 103_R_hook 2
## 202 104_L_bottle 5
## 203 104_R_bottle 9
## 204 105_L_hat 6
## 205 105_R_hat 6
## 206 106_L_toweldispenser 9
## 207 106_R_toweldispenser 8
## 208 107_L_shirts 4
## 209 107_R_shirts 13
## 210 108_L_boa 7
## 211 108_R_boa 9
## 212 109_L_pillow 5
## 213 109_R_pillow 7
## 214 110_L_plant 2
## 215 110_R_plant 10
## 216 111_L_pot 5
## 217 111_R_pot 11
## 218 112_L_basket 7
## 219 112_R_basket 9
## 220 113_L_plant 5
## 221 113_R_plant 6
## 222 114_L_bird 5
## 223 114_R_bird 9
## 224 115_L_pot 6
## 225 115_R_pot 6
## 226 116_L_basket 4
## 227 116_R_basket 7
## 228 117_L_plant_sized 4
## 229 117_R_plant_sized 4
## 230 118_L_shoes 6
## 231 118_R_shoes 10
## 232 119_L_painting 6
## 233 119_R_painting 11
## 234 120_L_art 4
## 235 120_R_art 7
## 236 121_L_car 5
## 237 121_R_car 10
## 238 122_L_chair 3
## 239 122_R_chair 5
## 240 123_L_pot 5
## 241 123_R_pot 6
## 242 124_L_vase 3
## 243 124_R_vase 7
## 244 125_L_sofa 6
## 245 125_R_sofa 10
## 246 126_L_candles 2
## 247 126_R_candles 10
## 248 127_L_light 4
## 249 127_R_light 7
## 250 128_L_pot 5
## 251 128_R_pot 3
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: 9304.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5060 -0.4500 -0.1301 0.1555 7.8443
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 1.729 1.315
## workerId (Intercept) 9.621 3.102
## Residual 19.063 4.366
## Number of obs: 1560, groups: image, 251; workerId, 71
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.2254 0.5175 196.0359 21.694 < 2e-16 ***
## likelihood_rating -0.4365 0.1103 847.2097 -3.957 8.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.640
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 = 71", 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: -1736.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5938 -0.5823 -0.1239 0.3517 4.5944
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 0.00190 0.04359
## workerId (Intercept) 0.01368 0.11696
## Residual 0.01528 0.12361
## Number of obs: 1560, groups: image, 251; workerId, 71
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.991814 0.017557 149.904302 56.492 < 2e-16 ***
## likelihood_rating -0.012454 0.003245 900.959318 -3.838 0.000132 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## liklhd_rtng -0.555
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 = 47", 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"))
