Raw RT
fit_raw1 <- lmer(detection_rt ~ likelihood_rating * change_type + (1 | workerId) + (1 | image) + (1 | stim_set), data=tbl_all)
summary(fit_raw1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating * change_type + (1 | workerId) +
## (1 | image) + (1 | stim_set)
## Data: tbl_all
##
## REML criterion at convergence: 25961.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5965 -0.4283 -0.1249 0.1458 8.8315
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 1.4857 1.2189
## workerId (Intercept) 8.1531 2.8554
## stim_set (Intercept) 0.1343 0.3665
## Residual 20.9854 4.5810
## Number of obs: 4305, groups: image, 480; workerId, 188; stim_set, 4
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 12.1880 0.5900 10.9872 20.656
## likelihood_rating -0.5421 0.1368 2874.6672 -3.962
## change_typedisappear -1.3041 0.5918 843.4201 -2.204
## change_typemovement -2.7111 0.8450 1409.1071 -3.208
## change_typereplacement -3.4837 2.2188 2530.8259 -1.570
## change_typesize -3.0626 1.1418 1239.0035 -2.682
## likelihood_rating:change_typedisappear 0.1151 0.1554 2844.3487 0.741
## likelihood_rating:change_typemovement 0.3911 0.2586 4041.5107 1.513
## likelihood_rating:change_typereplacement 0.7147 0.6374 3976.6076 1.121
## likelihood_rating:change_typesize 0.3401 0.3462 2975.2569 0.982
## Pr(>|t|)
## (Intercept) 3.85e-10 ***
## likelihood_rating 7.63e-05 ***
## change_typedisappear 0.02782 *
## change_typemovement 0.00137 **
## change_typereplacement 0.11653
## change_typesize 0.00741 **
## likelihood_rating:change_typedisappear 0.45875
## likelihood_rating:change_typemovement 0.13044
## likelihood_rating:change_typereplacement 0.26228
## likelihood_rating:change_typesize 0.32601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lklhd_ chng_typd chng_typm chng_typr chng_typs
## liklhd_rtng -0.780
## chng_typdsp -0.747 0.743
## chng_typmvm -0.531 0.556 0.504
## chng_typrpl -0.209 0.215 0.197 0.176
## chang_typsz -0.396 0.407 0.372 0.345 0.144
## lklhd_rtng:chng_typd 0.666 -0.846 -0.874 -0.475 -0.184 -0.348
## lklhd_rtng:chng_typm 0.391 -0.505 -0.384 -0.841 -0.119 -0.241
## lklhd_rtng:chng_typr 0.170 -0.216 -0.164 -0.130 -0.878 -0.100
## lklhd_rtng:chng_typs 0.299 -0.381 -0.289 -0.243 -0.091 -0.841
## lklhd_rtng:chng_typd lklhd_rtng:chng_typm
## liklhd_rtng
## chng_typdsp
## chng_typmvm
## chng_typrpl
## chang_typsz
## lklhd_rtng:chng_typd
## lklhd_rtng:chng_typm 0.440
## lklhd_rtng:chng_typr 0.185 0.120
## lklhd_rtng:chng_typs 0.327 0.229
## lklhd_rtng:chng_typr
## liklhd_rtng
## chng_typdsp
## chng_typmvm
## chng_typrpl
## chang_typsz
## lklhd_rtng:chng_typd
## lklhd_rtng:chng_typm
## lklhd_rtng:chng_typr
## lklhd_rtng:chng_typs 0.095
fit_raw2 <- lmer(detection_rt ~ likelihood_rating + change_type + box_percent + change_percent + eccentricity + (1 | workerId) + (1 | image) + (1 | stim_set), data=tbl_all)
summary(fit_raw2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: detection_rt ~ likelihood_rating + change_type + box_percent +
## change_percent + eccentricity + (1 | workerId) + (1 | image) +
## (1 | stim_set)
## Data: tbl_all
##
## REML criterion at convergence: 25976.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5690 -0.4305 -0.1278 0.1476 8.8366
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 1.515e+00 1.231e+00
## workerId (Intercept) 8.165e+00 2.857e+00
## stim_set (Intercept) 3.895e-09 6.241e-05
## Residual 2.095e+01 4.578e+00
## Number of obs: 4305, groups: image, 480; workerId, 188; stim_set, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.155e+01 4.378e-01 7.111e+02 26.395 < 2e-16 ***
## likelihood_rating -4.054e-01 6.879e-02 2.500e+03 -5.893 4.3e-09 ***
## change_typedisappear -9.788e-01 2.841e-01 4.091e+02 -3.445 0.00063 ***
## change_typemovement -1.377e+00 4.684e-01 4.449e+02 -2.940 0.00345 **
## change_typereplacement -1.112e+00 1.068e+00 4.400e+02 -1.041 0.29826
## change_typesize -1.958e+00 6.172e-01 4.210e+02 -3.173 0.00162 **
## box_percent -1.154e-02 6.949e-02 3.946e+02 -0.166 0.86818
## change_percent -6.242e-02 1.044e-01 3.937e+02 -0.598 0.55039
## eccentricity 2.087e-03 1.099e-03 3.792e+02 1.900 0.05818 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lklhd_ chng_typd chng_typm chng_typr chng_typs bx_prc chng_p
## liklhd_rtng -0.497
## chng_typdsp -0.477 0.006
## chng_typmvm -0.310 0.195 0.305
## chng_typrpl -0.156 0.029 0.145 0.187
## chang_typsz -0.210 0.104 0.243 0.355 0.183
## box_percent -0.088 -0.011 0.092 0.050 0.032 0.071
## chang_prcnt 0.039 -0.032 -0.038 -0.188 -0.041 -0.132 -0.894
## eccentricty -0.468 0.010 -0.038 0.072 0.084 0.004 0.064 -0.084
## convergence code: 0
## boundary (singular) fit: see ?isSingular
corr <- tbl_all %>%
group_by(image) %>%
dplyr::summarize(detection_rt = mean(detection_rt), likelihood_rating = mean(likelihood_rating), change_type = unique(change_type))
corr %>%
ggscatter(y = "detection_rt", x = "likelihood_rating", ylab = "Raw Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", add = "reg.line", cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 1, label.y = 35), conf.int = TRUE, xlim = c(1, 5), ylim = c(5, 40))
corr %>%
ggscatter(y = "detection_rt", x = "likelihood_rating", color = "change_type", palette = c("#0d2240", "#00a8e1", "#f7a800", "#E31818", "#dfdddc"), ylab = "Raw Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", add = "reg.line", conf.int = TRUE, xlim = c(1, 5), ylim = c(5, 40)) + stat_cor(aes(color = change_type), label.x = c(1, 1, 2.5, 2.5, 4), label.y = c(37.5, 35, 37.5, 35, 37.5), method = "pearson")
Log RT
fit_log1 <- lmer(log10(detection_rt) ~ likelihood_rating * change_type + (1 | workerId) + (1 | image) + (1 | stim_set), data=tbl_all)
summary(fit_log1)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log10(detection_rt) ~ likelihood_rating * change_type + (1 |
## workerId) + (1 | image) + (1 | stim_set)
## Data: tbl_all
##
## REML criterion at convergence: -4668.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7767 -0.5563 -0.1306 0.3372 5.2042
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 0.0015940 0.03992
## workerId (Intercept) 0.0119031 0.10910
## stim_set (Intercept) 0.0003578 0.01892
## Residual 0.0161143 0.12694
## Number of obs: 4305, groups: image, 480; workerId, 188; stim_set, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 1.014e+00 1.969e-02 6.828e+00
## likelihood_rating -1.673e-02 3.892e-03 3.251e+03
## change_typedisappear -3.645e-02 1.712e-02 1.455e+03
## change_typemovement -7.353e-02 2.431e-02 1.839e+03
## change_typereplacement -9.898e-02 6.313e-02 2.494e+03
## change_typesize -8.479e-02 3.297e-02 1.461e+03
## likelihood_rating:change_typedisappear 4.569e-03 4.417e-03 3.141e+03
## likelihood_rating:change_typemovement 1.119e-02 7.292e-03 4.109e+03
## likelihood_rating:change_typereplacement 2.339e-02 1.789e-02 4.043e+03
## likelihood_rating:change_typesize 8.801e-03 9.811e-03 3.249e+03
## t value Pr(>|t|)
## (Intercept) 51.479 4.24e-10 ***
## likelihood_rating -4.299 1.77e-05 ***
## change_typedisappear -2.129 0.03340 *
## change_typemovement -3.024 0.00253 **
## change_typereplacement -1.568 0.11702
## change_typesize -2.571 0.01023 *
## likelihood_rating:change_typedisappear 1.034 0.30108
## likelihood_rating:change_typemovement 1.535 0.12487
## likelihood_rating:change_typereplacement 1.308 0.19109
## likelihood_rating:change_typesize 0.897 0.36974
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lklhd_ chng_typd chng_typm chng_typr chng_typs
## liklhd_rtng -0.667
## chng_typdsp -0.639 0.727
## chng_typmvm -0.462 0.551 0.498
## chng_typrpl -0.185 0.216 0.197 0.182
## chang_typsz -0.343 0.402 0.366 0.352 0.150
## lklhd_rtng:chng_typd 0.567 -0.843 -0.860 -0.469 -0.185 -0.343
## lklhd_rtng:chng_typm 0.335 -0.506 -0.379 -0.826 -0.121 -0.240
## lklhd_rtng:chng_typr 0.147 -0.219 -0.164 -0.131 -0.865 -0.101
## lklhd_rtng:chng_typs 0.256 -0.380 -0.284 -0.242 -0.093 -0.826
## lklhd_rtng:chng_typd lklhd_rtng:chng_typm
## liklhd_rtng
## chng_typdsp
## chng_typmvm
## chng_typrpl
## chang_typsz
## lklhd_rtng:chng_typd
## lklhd_rtng:chng_typm 0.441
## lklhd_rtng:chng_typr 0.188 0.123
## lklhd_rtng:chng_typs 0.327 0.232
## lklhd_rtng:chng_typr
## liklhd_rtng
## chng_typdsp
## chng_typmvm
## chng_typrpl
## chang_typsz
## lklhd_rtng:chng_typd
## lklhd_rtng:chng_typm
## lklhd_rtng:chng_typr
## lklhd_rtng:chng_typs 0.098
fit_log2 <- lmer(log10(detection_rt) ~ likelihood_rating + change_type + box_percent + change_percent + eccentricity + (1 | workerId) + (1 | image) + (1 | stim_set), data=tbl_all)
summary(fit_log2)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log10(detection_rt) ~ likelihood_rating + change_type + box_percent +
## change_percent + eccentricity + (1 | workerId) + (1 | image) +
## (1 | stim_set)
## Data: tbl_all
##
## REML criterion at convergence: -4669.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7366 -0.5542 -0.1304 0.3434 5.2512
##
## Random effects:
## Groups Name Variance Std.Dev.
## image (Intercept) 0.0015548 0.03943
## workerId (Intercept) 0.0119264 0.10921
## stim_set (Intercept) 0.0001138 0.01067
## Residual 0.0160895 0.12684
## Number of obs: 4305, groups: image, 480; workerId, 188; stim_set, 4
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.841e-01 1.509e-02 3.378e+00 65.210 2.34e-06 ***
## likelihood_rating -1.201e-02 1.968e-03 2.682e+03 -6.104 1.18e-09 ***
## change_typedisappear -2.332e-02 8.673e-03 1.682e+02 -2.688 0.007901 **
## change_typemovement -3.160e-02 1.397e-02 3.749e+02 -2.262 0.024258 *
## change_typereplacement -1.659e-02 3.157e-02 4.209e+02 -0.525 0.599519
## change_typesize -5.347e-02 1.848e-02 3.433e+02 -2.893 0.004064 **
## box_percent 1.967e-04 2.051e-03 3.912e+02 0.096 0.923642
## change_percent -2.979e-03 3.089e-03 3.890e+02 -0.965 0.335373
## eccentricity 1.137e-04 3.243e-05 3.846e+02 3.505 0.000511 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) lklhd_ chng_typd chng_typm chng_typr chng_typs bx_prc chng_p
## liklhd_rtng -0.416
## chng_typdsp -0.397 0.001
## chng_typmvm -0.269 0.190 0.297
## chng_typrpl -0.139 0.031 0.140 0.199
## chang_typsz -0.183 0.102 0.232 0.369 0.195
## box_percent -0.078 -0.011 0.092 0.049 0.031 0.068
## chang_prcnt 0.027 -0.028 -0.041 -0.186 -0.042 -0.133 -0.889
## eccentricty -0.400 0.010 -0.038 0.071 0.084 0.003 0.065 -0.085
corr <- tbl_all %>%
group_by(image) %>%
dplyr::summarize(detection_rt = mean(detection_rt), likelihood_rating = mean(likelihood_rating), change_type = unique(change_type))
corr$log <- log10(corr$detection_rt)
corr %>%
ggscatter(y = "log", x = "likelihood_rating", ylab = "LogChange Detection RT (sec)", xlab = "Likelihood of Detecting Change", add = "reg.line", cor.coef = TRUE, cor.coeff.args = list(method = "pearson", label.x = 1, label.y = 1.75), conf.int = TRUE, xlim = c(1, 5), ylim = c(0.75, 1.75))
corr %>%
ggscatter(y = "log", x = "likelihood_rating", color = "change_type", palette = c("#0d2240", "#00a8e1", "#f7a800", "#E31818", "#dfdddc"), ylab = "Log Change Detection RT (sec)", xlab = "Likelihood of Detecting Change", add = "reg.line", conf.int = TRUE, xlim = c(1, 5), ylim = c(0.75, 1.75)) + stat_cor(aes(color = change_type), label.x = c(1, 1, 2.5, 2.5, 4), label.y = c(1.7, 1.6, 1.7, 1.6, 1.7), method = "pearson")
