pkbb_secondary-analysis
Qiong Cao
2025-01-14
rm(list = ls())
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorssource("process_pairs.R")
source("process_ABBA.R")load data
exp2_adult <- read_csv("adult_stimuli_type.csv")## Rows: 44775 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): prolific_id, trial_type, block_type, background_stimulus, deviant_s...
## dbl (4): total_rt, block_number, trial_number, total_trial_number
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# log LT
exp2_adult$total_rt_log <- log(exp2_adult$total_rt)
# create bg_image, dv_image (contain all four info: pose, identity id, number, animacy)
exp2_adult <- exp2_adult %>%
mutate(
bg_image = paste(background_type, str_extract(background_stimulus, "\\d+"), sep = "_"),
dv_image = paste(deviant_type, str_extract(deviant_stimulus, "\\d+"), sep = "_")
) %>%
mutate(dv_image = ifelse(violation_type == "background", bg_image, dv_image))# calculate dishab
exp2_test_only <- exp2_adult %>% filter(trial_number == total_trial_number) %>% mutate(my_trial_type = "test")
exp2_lastfam_only <- exp2_adult %>% filter(trial_number == total_trial_number - 1) %>% mutate(my_trial_type = "lastfam")
exp2_firstfam_only <- exp2_adult %>% filter(trial_number == 1) %>% mutate(my_trial_type = "firstfam")
exp2_core <- pivot_wider(bind_rows(exp2_firstfam_only, exp2_lastfam_only, exp2_test_only),
id_cols = c(prolific_id, block_number, violation_type, total_trial_number, bg_image, dv_image),
names_from = my_trial_type,
values_from = total_rt_log) %>%
mutate(dishab = test - lastfam)data quality check
raw data
skimr::skim(exp2_adult)| Name | exp2_adult |
| Number of rows | 44775 |
| Number of columns | 15 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| prolific_id | 0 | 1.00 | 24 | 24 | 0 | 468 | 0 |
| trial_type | 0 | 1.00 | 7 | 10 | 0 | 2 | 0 |
| block_type | 0 | 1.00 | 13 | 16 | 0 | 2 | 0 |
| background_stimulus | 0 | 1.00 | 39 | 42 | 0 | 64 | 0 |
| deviant_stimulus | 14931 | 0.67 | 39 | 42 | 0 | 64 | 0 |
| background_type | 0 | 1.00 | 17 | 22 | 0 | 8 | 0 |
| deviant_type | 14931 | 0.67 | 17 | 22 | 0 | 8 | 0 |
| violation_type | 0 | 1.00 | 4 | 10 | 0 | 5 | 0 |
| bg_image | 0 | 1.00 | 21 | 26 | 0 | 128 | 0 |
| dv_image | 0 | 1.00 | 21 | 26 | 0 | 128 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_rt | 0 | 1 | 2347.60 | 2161.48 | 500.00 | 949.00 | 1568.00 | 2981.5 | 20526.00 | ▇▁▁▁▁ |
| block_number | 0 | 1 | 12.49 | 6.92 | 1.00 | 6.00 | 13.00 | 18.0 | 24.00 | ▇▇▆▇▇ |
| trial_number | 0 | 1 | 2.84 | 1.57 | 1.00 | 2.00 | 3.00 | 4.0 | 6.00 | ▇▂▂▂▂ |
| total_trial_number | 0 | 1 | 4.67 | 1.49 | 2.00 | 4.00 | 6.00 | 6.0 | 6.00 | ▃▁▅▁▇ |
| total_rt_log | 0 | 1 | 7.46 | 0.74 | 6.21 | 6.86 | 7.36 | 8.0 | 9.93 | ▇▇▆▂▁ |
naniar::vis_miss(exp2_adult)
janitor::get_dupes(exp2_adult)## No variable names specified - using all columns.## No duplicate combinations found of: prolific_id, trial_type, total_rt, block_type, block_number, background_stimulus, deviant_stimulus, background_type, deviant_type, ... and 6 other variables## # A tibble: 0 × 16
## # ℹ 16 variables: prolific_id <chr>, trial_type <chr>, total_rt <dbl>,
## # block_type <chr>, block_number <dbl>, background_stimulus <chr>,
## # deviant_stimulus <chr>, background_type <chr>, deviant_type <chr>,
## # violation_type <chr>, trial_number <dbl>, total_trial_number <dbl>,
## # total_rt_log <dbl>, bg_image <chr>, dv_image <chr>, dupe_count <int>dishab data
skimr::skim(exp2_core)| Name | exp2_core |
| Number of rows | 11232 |
| Number of columns | 10 |
| _______________________ | |
| Column type frequency: | |
| character | 4 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| prolific_id | 0 | 1 | 24 | 24 | 0 | 468 | 0 |
| violation_type | 0 | 1 | 4 | 10 | 0 | 5 | 0 |
| bg_image | 0 | 1 | 21 | 26 | 0 | 128 | 0 |
| dv_image | 0 | 1 | 21 | 26 | 0 | 128 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| block_number | 0 | 1.00 | 12.50 | 6.92 | 1.00 | 6.75 | 12.50 | 18.25 | 24.00 | ▇▇▆▇▇ |
| total_trial_number | 0 | 1.00 | 4.00 | 1.64 | 2.00 | 2.00 | 4.00 | 6.00 | 6.00 | ▇▁▇▁▇ |
| firstfam | 62 | 0.99 | 7.72 | 0.73 | 6.22 | 7.15 | 7.68 | 8.24 | 9.92 | ▃▇▇▃▁ |
| lastfam | 39 | 1.00 | 7.46 | 0.74 | 6.22 | 6.84 | 7.35 | 8.00 | 9.92 | ▇▇▆▂▁ |
| test | 25 | 1.00 | 7.44 | 0.72 | 6.21 | 6.85 | 7.33 | 7.96 | 9.93 | ▇▇▆▂▁ |
| dishab | 62 | 0.99 | -0.02 | 0.50 | -3.20 | -0.23 | 0.00 | 0.21 | 2.95 | ▁▁▇▁▁ |
naniar::vis_miss(exp2_core)
janitor::get_dupes(exp2_core)## No variable names specified - using all columns.## No duplicate combinations found of: prolific_id, block_number, violation_type, total_trial_number, bg_image, dv_image, firstfam, lastfam, test, dishab## # A tibble: 0 × 11
## # ℹ 11 variables: prolific_id <chr>, block_number <dbl>, violation_type <chr>,
## # total_trial_number <dbl>, bg_image <chr>, dv_image <chr>, firstfam <dbl>,
## # lastfam <dbl>, test <dbl>, dishab <dbl>, dupe_count <int># dlookr::plot_outlier(exp2_core)unique pairs
## unique combinations of stimuli pairs in test trials
exp2_unique_stimuli_pairs <- exp2_test_only %>%
group_by(bg_image, dv_image, violation_type) %>%
summarise(count = n(), .groups = "drop") %>%
mutate(pairID = row_number())### number of unique combinations of deviant trials used in study:
nrow(filter(exp2_unique_stimuli_pairs, violation_type != "background")) # 2674 (in theory there are 128*2+128*2+128*15+128*2=2688 unique pairs)## [1] 2674### number of background trials used in study
nrow(filter(exp2_unique_stimuli_pairs, violation_type == "background")) # 128 (in theory 128)## [1] 128pose
exp2_pairs_pose <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "pose")) # withinpairID: 1: left to right, 2: right to leftnumber
exp2_pairs_number <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "number")) # withinpairID: 1: pair to single, 2: single to pairidentity
## no ignored types
exp2_pairs_identity <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "identity"))
check_single_within_PoP(exp2_pairs_identity) # 440## [1] 440## combing across pose
exp2_pairs_identity_nopose <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "identity"), ignore_violation_types = c("pose"))
check_single_within_PoP(exp2_pairs_identity_nopose) # 116## [1] 116## combing across pose and number
exp2_pairs_identity_nopose_nonumber <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "identity"), ignore_violation_types = c("pose", "number"))
check_single_within_PoP(exp2_pairs_identity_nopose_nonumber) # 6## [1] 6animacy
## no ignored types
exp2_pairs_animacy <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "animacy"))
check_single_within_PoP(exp2_pairs_animacy) # 484## [1] 484## combing across pose
exp2_pairs_animacy_nopose <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "animacy"), ignore_violation_types = c("pose"))
check_single_within_PoP(exp2_pairs_animacy_nopose) # 126## [1] 126## combing across identity
exp2_pairs_animacy_noidentity <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "animacy"), ignore_violation_types = c("identity"))
check_single_within_PoP(exp2_pairs_animacy_noidentity) # 0, but there are only 4 pair_of_pair_IDs. ## [1] 0## combining across pose and number
exp2_pairs_animacy_nopose_nonumber <- process_pairs(filter(exp2_unique_stimuli_pairs, violation_type == "animacy"), ignore_violation_types = c("pose", "number"))
check_single_within_PoP(exp2_pairs_animacy_nopose_nonumber) # 9## [1] 9ABBA: pose
ABBA_pose <- exp2_core %>% filter(violation_type == "pose") %>%
left_join(exp2_pairs_pose, by = c("violation_type", "bg_image", "dv_image"))# data quality check
ABBA_pose[which(is.na(ABBA_pose$dishab)),]## # A tibble: 8 × 12
## prolific_id block_number violation_type total_trial_number bg_image dv_image
## <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 6025d40ede4e… 3 pose 4 inanima… inanima…
## 2 6140d7cdf9bc… 16 pose 6 animate… animate…
## 3 63d9cfec2060… 7 pose 6 inanima… inanima…
## 4 6526ba4ac53a… 16 pose 2 animate… animate…
## 5 657767d604c0… 9 pose 2 inanima… inanima…
## 6 657a7e142c5f… 5 pose 6 inanima… inanima…
## 7 659aaea375ee… 7 pose 2 inanima… inanima…
## 8 600959b17473… 3 pose 2 inanima… inanima…
## # ℹ 6 more variables: firstfam <dbl>, lastfam <dbl>, test <dbl>, dishab <dbl>,
## # pair_of_pair_ID <int>, within_PoP_ID <int># e.g.
exp2_adult %>% filter(prolific_id == "600959b17473895801425313", block_number == 3)## # A tibble: 1 × 15
## prolific_id trial_type total_rt block_type block_number background_stimulus
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 600959b174738… deviant 13567 deviant_b… 3 media/padded_stims…
## # ℹ 9 more variables: deviant_stimulus <chr>, background_type <chr>,
## # deviant_type <chr>, violation_type <chr>, trial_number <dbl>,
## # total_trial_number <dbl>, total_rt_log <dbl>, bg_image <chr>,
## # dv_image <chr>exp2_adult %>% filter(prolific_id == "6025d40ede4e061f3c7ca877", block_number == 3)## # A tibble: 3 × 15
## prolific_id trial_type total_rt block_type block_number background_stimulus
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 6025d40ede4e0… background 14334 deviant_b… 3 media/padded_stims…
## 2 6025d40ede4e0… background 1539 deviant_b… 3 media/padded_stims…
## 3 6025d40ede4e0… background 2364 deviant_b… 3 media/padded_stims…
## # ℹ 9 more variables: deviant_stimulus <chr>, background_type <chr>,
## # deviant_type <chr>, violation_type <chr>, trial_number <dbl>,
## # total_trial_number <dbl>, total_rt_log <dbl>, bg_image <chr>,
## # dv_image <chr>process_ABBA(ABBA_pose, exp2_pairs_pose)## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 8 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 8 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number + block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2436.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8670 -0.4447 0.0321 0.5054 6.0991
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.006312 0.07945
## Residual 0.207064 0.45504
## Number of obs: 1864, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.500e-01 4.658e-02 1.852e+03 -9.662 <2e-16 ***
## total_trial_number 1.027e-01 6.485e-03 1.858e+03 15.840 <2e-16 ***
## block_number -2.783e-03 1.520e-03 1.431e+03 -1.830 0.0674 .
## within_PoP_ID 2.221e-04 2.133e-02 1.831e+03 0.010 0.9917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n
## ttl_trl_nmb -0.560
## block_numbr -0.402 0.000
## withn_PP_ID -0.685 0.009 -0.010
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2442.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9197 -0.4422 0.0202 0.5038 6.0444
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.006358 0.07973
## Residual 0.206471 0.45439
## Number of obs: 1864, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.391e-01 6.538e-02 1.856e+03 -5.187
## total_trial_number 7.483e-02 1.324e-02 1.859e+03 5.652
## block_number -1.174e-02 4.010e-03 1.850e+03 -2.929
## within_PoP_ID 3.128e-04 2.131e-02 1.830e+03 0.015
## total_trial_number:block_number 2.250e-03 9.318e-04 1.859e+03 2.414
## Pr(>|t|)
## (Intercept) 2.37e-07 ***
## total_trial_number 1.83e-08 ***
## block_number 0.00345 **
## within_PoP_ID 0.98829
## total_trial_number:block_number 0.01586 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I
## ttl_trl_nmb -0.808
## block_numbr -0.759 0.807
## withn_PP_ID -0.486 0.003 -0.005
## ttl_trl_n:_ 0.703 -0.872 -0.926 0.002
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number * within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2467.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8860 -0.4348 0.0244 0.5039 6.0385
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.006432 0.0802
## Residual 0.206463 0.4544
## Number of obs: 1864, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.916e-01 1.799e-01 1.852e+03
## total_trial_number 2.649e-02 4.158e-02 1.853e+03
## block_number -1.684e-02 1.271e-02 1.856e+03
## within_PoP_ID -9.889e-02 1.142e-01 1.853e+03
## total_trial_number:block_number 4.431e-03 2.940e-03 1.856e+03
## total_trial_number:within_PoP_ID 3.259e-02 2.646e-02 1.854e+03
## block_number:within_PoP_ID 3.453e-03 8.037e-03 1.856e+03
## total_trial_number:block_number:within_PoP_ID -1.477e-03 1.864e-03 1.856e+03
## t value Pr(>|t|)
## (Intercept) -1.065 0.287
## total_trial_number 0.637 0.524
## block_number -1.325 0.185
## within_PoP_ID -0.866 0.387
## total_trial_number:block_number 1.507 0.132
## total_trial_number:within_PoP_ID 1.232 0.218
## block_number:within_PoP_ID 0.430 0.667
## total_trial_number:block_number:within_PoP_ID -0.792 0.428
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I tt__:_ t__:_P b_:_PP
## ttl_trl_nmb -0.923
## block_numbr -0.872 0.806
## withn_PP_ID -0.948 0.876 0.826
## ttl_trl_n:_ 0.805 -0.871 -0.925 -0.763
## tt__:_PP_ID 0.874 -0.948 -0.762 -0.923 0.825
## blc_:_PP_ID 0.829 -0.767 -0.949 -0.873 0.878 0.806
## t__:_:_PP_I -0.764 0.828 0.876 0.805 -0.948 -0.873 -0.924## refitting model(s) with ML (instead of REML)
## Data: dataset
## Models:
## model1: dishab ~ total_trial_number + block_number + within_PoP_ID + (1 | prolific_id)
## model2: dishab ~ total_trial_number * block_number + within_PoP_ID + (1 | prolific_id)
## model3: dishab ~ total_trial_number * block_number * within_PoP_ID + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model1 6 2416.1 2449.3 -1202.1 2404.1
## model2 7 2412.3 2451.0 -1199.2 2398.3 5.8350 1 0.01571 *
## model3 10 2415.8 2471.1 -1197.9 2395.8 2.4856 3 0.47790
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## dishab ~ total_trial_number * block_number + firstfam + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2357
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5503 -0.5053 0.0156 0.5069 6.2232
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.00647 0.08043
## Residual 0.19728 0.44416
## Number of obs: 1856, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 6.489e-01 1.358e-01 1.183e+03 4.777
## total_trial_number 7.746e-02 1.300e-02 1.850e+03 5.959
## block_number -1.363e-02 3.936e-03 1.842e+03 -3.462
## firstfam -1.241e-01 1.509e-02 9.751e+02 -8.225
## within_PoP_ID -1.747e-03 2.089e-02 1.820e+03 -0.084
## total_trial_number:block_number 2.004e-03 9.154e-04 1.850e+03 2.189
## Pr(>|t|)
## (Intercept) 2.01e-06 ***
## total_trial_number 3.04e-09 ***
## block_number 0.000547 ***
## firstfam 6.22e-16 ***
## within_PoP_ID 0.933359
## total_trial_number:block_number 0.028715 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n frstfm w_PP_I
## ttl_trl_nmb -0.360
## block_numbr -0.413 0.804
## firstfam -0.882 -0.024 0.063
## withn_PP_ID -0.230 0.003 -0.004 0.001
## ttl_trl_n:_ 0.306 -0.873 -0.921 0.029 0.000
ABBA: number
ABBA_number <- exp2_core %>% filter(violation_type == "number") %>%
left_join(exp2_pairs_number, by = c("violation_type", "bg_image", "dv_image"))
ABBA_number[which(is.na(ABBA_number$dishab)),]## # A tibble: 14 × 12
## prolific_id block_number violation_type total_trial_number bg_image dv_image
## <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 5b9aa47491b… 3 number 4 inanima… inanima…
## 2 5e743544be8… 17 number 4 animate… animate…
## 3 606cddc3cc6… 22 number 4 animate… animate…
## 4 62eed9e3e4c… 12 number 4 inanima… inanima…
## 5 63d7a79e260… 9 number 4 inanima… inanima…
## 6 659aaea375e… 5 number 4 inanima… inanima…
## 7 659c0682d10… 2 number 6 inanima… inanima…
## 8 57a1e03b8ec… 6 number 2 inanima… inanima…
## 9 60511bc8689… 10 number 2 inanima… inanima…
## 10 60d20236448… 8 number 4 inanima… inanima…
## 11 60f18127a0b… 6 number 2 inanima… inanima…
## 12 6164692cd6e… 5 number 2 inanima… inanima…
## 13 650ba232969… 3 number 2 inanima… inanima…
## 14 659c0682d10… 14 number 2 animate… animate…
## # ℹ 6 more variables: firstfam <dbl>, lastfam <dbl>, test <dbl>, dishab <dbl>,
## # pair_of_pair_ID <int>, within_PoP_ID <int># e.g.
exp2_adult %>% filter(prolific_id == "57a1e03b8eccbc0001f39c13", block_number == 6)## # A tibble: 1 × 15
## prolific_id trial_type total_rt block_type block_number background_stimulus
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 57a1e03b8eccb… deviant 786 deviant_b… 6 media/padded_stims…
## # ℹ 9 more variables: deviant_stimulus <chr>, background_type <chr>,
## # deviant_type <chr>, violation_type <chr>, trial_number <dbl>,
## # total_trial_number <dbl>, total_rt_log <dbl>, bg_image <chr>,
## # dv_image <chr>process_ABBA(ABBA_number, exp2_pairs_number)## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 14 rows containing missing values or values outside the scale range
## (`geom_point()`).## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number + block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2151.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.6642 -0.4552 0.0210 0.5095 5.7176
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.0000 0.0000
## Residual 0.1835 0.4283
## Number of obs: 1858, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.700e-01 4.351e-02 1.854e+03 -13.102 < 2e-16 ***
## total_trial_number 9.991e-02 6.041e-03 1.854e+03 16.538 < 2e-16 ***
## block_number -2.773e-03 1.432e-03 1.854e+03 -1.937 0.0529 .
## within_PoP_ID 8.639e-02 1.988e-02 1.854e+03 4.346 1.46e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n
## ttl_trl_nmb -0.565
## block_numbr -0.409 0.006
## withn_PP_ID -0.682 0.008 -0.011
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2156.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.5788 -0.4606 0.0218 0.5110 5.7247
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.0000 0.0000
## Residual 0.1829 0.4277
## Number of obs: 1858, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -4.582e-01 6.158e-02 1.853e+03 -7.441
## total_trial_number 7.189e-02 1.249e-02 1.853e+03 5.755
## block_number -1.160e-02 3.729e-03 1.853e+03 -3.110
## within_PoP_ID 8.615e-02 1.985e-02 1.853e+03 4.340
## total_trial_number:block_number 2.222e-03 8.673e-04 1.853e+03 2.562
## Pr(>|t|)
## (Intercept) 1.52e-13 ***
## total_trial_number 1.01e-08 ***
## block_number 0.0019 **
## within_PoP_ID 1.50e-05 ***
## total_trial_number:block_number 0.0105 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I
## ttl_trl_nmb -0.813
## block_numbr -0.765 0.810
## withn_PP_ID -0.484 0.008 0.000
## ttl_trl_n:_ 0.709 -0.876 -0.924 -0.005
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number * within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2180.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.4931 -0.4694 0.0153 0.5122 5.6811
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.0000 0.0000
## Residual 0.1828 0.4276
## Number of obs: 1858, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -1.862e-01 1.702e-01 1.850e+03
## total_trial_number 2.323e-02 3.926e-02 1.850e+03
## block_number -3.134e-02 1.186e-02 1.850e+03
## within_PoP_ID -9.468e-02 1.078e-01 1.850e+03
## total_trial_number:block_number 5.620e-03 2.743e-03 1.850e+03
## total_trial_number:within_PoP_ID 3.228e-02 2.500e-02 1.850e+03
## block_number:within_PoP_ID 1.308e-02 7.460e-03 1.850e+03
## total_trial_number:block_number:within_PoP_ID -2.244e-03 1.735e-03 1.850e+03
## t value Pr(>|t|)
## (Intercept) -1.094 0.27429
## total_trial_number 0.592 0.55406
## block_number -2.643 0.00828 **
## within_PoP_ID -0.879 0.37978
## total_trial_number:block_number 2.048 0.04066 *
## total_trial_number:within_PoP_ID 1.291 0.19679
## block_number:within_PoP_ID 1.753 0.07973 .
## total_trial_number:block_number:within_PoP_ID -1.293 0.19614
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I tt__:_ t__:_P b_:_PP
## ttl_trl_nmb -0.926
## block_numbr -0.874 0.811
## withn_PP_ID -0.949 0.880 0.827
## ttl_trl_n:_ 0.808 -0.874 -0.926 -0.766
## tt__:_PP_ID 0.875 -0.948 -0.764 -0.925 0.827
## blc_:_PP_ID 0.832 -0.773 -0.949 -0.875 0.880 0.810
## t__:_:_PP_I -0.767 0.833 0.875 0.807 -0.949 -0.876 -0.924
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')## refitting model(s) with ML (instead of REML)
## Data: dataset
## Models:
## model1: dishab ~ total_trial_number + block_number + within_PoP_ID + (1 | prolific_id)
## model2: dishab ~ total_trial_number * block_number + within_PoP_ID + (1 | prolific_id)
## model3: dishab ~ total_trial_number * block_number * within_PoP_ID + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model1 6 2130.2 2163.3 -1059.1 2118.2
## model2 7 2125.6 2164.3 -1055.8 2111.6 6.5680 1 0.01038 *
## model3 10 2127.7 2182.9 -1053.8 2107.7 3.9124 3 0.27107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## dishab ~ total_trial_number * block_number + firstfam + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2097.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9969 -0.4944 0.0032 0.5288 5.5937
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.0048 0.06928
## Residual 0.1726 0.41548
## Number of obs: 1852, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.053e-01 1.285e-01 1.239e+03 3.153
## total_trial_number 7.220e-02 1.231e-02 1.841e+03 5.864
## block_number -1.382e-02 3.673e-03 1.815e+03 -3.762
## firstfam -1.061e-01 1.402e-02 1.000e+03 -7.566
## within_PoP_ID 7.657e-02 1.955e-02 1.815e+03 3.916
## total_trial_number:block_number 2.129e-03 8.541e-04 1.837e+03 2.493
## Pr(>|t|)
## (Intercept) 0.001653 **
## total_trial_number 5.34e-09 ***
## block_number 0.000174 ***
## firstfam 8.68e-14 ***
## within_PoP_ID 9.34e-05 ***
## total_trial_number:block_number 0.012755 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n frstfm w_PP_I
## ttl_trl_nmb -0.366
## block_numbr -0.423 0.808
## firstfam -0.882 -0.020 0.072
## withn_PP_ID -0.275 0.007 0.003 0.054
## ttl_trl_n:_ 0.310 -0.876 -0.920 0.027 -0.003
ABBA: animacy
combining across identity
ABBA_animacy_noidentity <- exp2_core %>% filter(violation_type == "animacy") %>%
mutate(
bg_image = str_replace_all(bg_image, "[0-9]+", ""),
dv_image = str_replace_all(dv_image, "[0-9]+", "")
) %>% left_join(exp2_pairs_animacy_noidentity, by = c("violation_type", "bg_image", "dv_image"))
ABBA_animacy_noidentity[which(is.na(ABBA_animacy_noidentity$dishab)),]## # A tibble: 9 × 12
## prolific_id block_number violation_type total_trial_number bg_image dv_image
## <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 599ec8c1617c… 17 animacy 4 animate… inanima…
## 2 5eb48e67752e… 3 animacy 4 inanima… animate…
## 3 64dd27e89ef2… 5 animacy 4 inanima… animate…
## 4 6501bf8cd968… 21 animacy 6 animate… inanima…
## 5 657a7e142c5f… 21 animacy 4 animate… inanima…
## 6 659aaea375ee… 10 animacy 4 inanima… animate…
## 7 5b9aa47491b2… 4 animacy 6 inanima… animate…
## 8 5be9d1cb271b… 1 animacy 2 inanima… animate…
## 9 60a1b5fe1f7f… 3 animacy 2 inanima… animate…
## # ℹ 6 more variables: firstfam <dbl>, lastfam <dbl>, test <dbl>, dishab <dbl>,
## # pair_of_pair_ID <int>, within_PoP_ID <int>process_ABBA(ABBA_animacy_noidentity, exp2_pairs_animacy_noidentity)## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number + block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2712.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0821 -0.5017 -0.0486 0.4614 4.7432
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01439 0.1199
## Residual 0.23438 0.4841
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.742e-01 1.100e-01 1.574e+03 -5.218 2.05e-07 ***
## total_trial_number 1.017e-01 7.086e-03 1.832e+03 14.357 < 2e-16 ***
## block_number 7.312e-03 3.273e-03 1.530e+03 2.234 0.02564 *
## within_PoP_ID 1.534e-01 4.495e-02 1.493e+03 3.413 0.00066 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n
## ttl_trl_nmb -0.284
## block_numbr -0.911 0.034
## withn_PP_ID -0.938 0.015 0.866
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2708.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0730 -0.4985 -0.0517 0.4736 4.7393
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01523 0.1234
## Residual 0.23165 0.4813
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.407e-01 1.236e-01 1.647e+03 -2.756
## total_trial_number 4.917e-02 1.475e-02 1.837e+03 3.334
## block_number -1.060e-02 5.486e-03 1.765e+03 -1.932
## within_PoP_ID 1.474e-01 4.473e-02 1.491e+03 3.296
## total_trial_number:block_number 4.232e-03 1.044e-03 1.841e+03 4.055
## Pr(>|t|)
## (Intercept) 0.005922 **
## total_trial_number 0.000874 ***
## block_number 0.053542 .
## within_PoP_ID 0.001005 **
## total_trial_number:block_number 5.21e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I
## ttl_trl_nmb -0.529
## block_numbr -0.853 0.716
## withn_PP_ID -0.845 0.036 0.541
## ttl_trl_n:_ 0.465 -0.878 -0.805 -0.033
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number * within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2727
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0229 -0.5005 -0.0617 0.4759 4.7737
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01522 0.1234
## Residual 0.23153 0.4812
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -4.516e-01 5.005e-01 1.825e+03
## total_trial_number 4.256e-02 1.136e-01 1.847e+03
## block_number -1.787e-02 2.936e-02 1.832e+03
## within_PoP_ID 1.623e-01 2.638e-01 1.829e+03
## total_trial_number:block_number 8.340e-03 6.665e-03 1.850e+03
## total_trial_number:within_PoP_ID 1.540e-02 5.975e-02 1.849e+03
## block_number:within_PoP_ID 1.140e-02 1.835e-02 1.836e+03
## total_trial_number:block_number:within_PoP_ID -4.317e-03 4.153e-03 1.852e+03
## t value Pr(>|t|)
## (Intercept) -0.902 0.367
## total_trial_number 0.375 0.708
## block_number -0.609 0.543
## within_PoP_ID 0.615 0.538
## total_trial_number:block_number 1.251 0.211
## total_trial_number:within_PoP_ID 0.258 0.797
## block_number:within_PoP_ID 0.621 0.535
## total_trial_number:block_number:within_PoP_ID -1.039 0.299
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I tt__:_ t__:_P b_:_PP
## ttl_trl_nmb -0.934
## block_numbr -0.945 0.883
## withn_PP_ID -0.984 0.919 0.967
## ttl_trl_n:_ 0.883 -0.944 -0.935 -0.902
## tt__:_PP_ID 0.921 -0.985 -0.904 -0.935 0.965
## blc_:_PP_ID 0.816 -0.762 -0.950 -0.886 0.888 0.827
## t__:_:_PP_I -0.764 0.816 0.890 0.828 -0.951 -0.884 -0.935## refitting model(s) with ML (instead of REML)
## Data: dataset
## Models:
## model1: dishab ~ total_trial_number + block_number + within_PoP_ID + (1 | prolific_id)
## model2: dishab ~ total_trial_number * block_number + within_PoP_ID + (1 | prolific_id)
## model3: dishab ~ total_trial_number * block_number * within_PoP_ID + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model1 6 2694.4 2727.6 -1341.2 2682.4
## model2 7 2680.0 2718.8 -1333.0 2666.0 16.3814 1 5.179e-05 ***
## model3 10 2682.0 2737.3 -1331.0 2662.0 4.0349 3 0.2577
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## dishab ~ total_trial_number * block_number + firstfam + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2680.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7409 -0.4985 -0.0748 0.4622 4.8525
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.02222 0.1491
## Residual 0.22307 0.4723
## Number of obs: 1856, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.245e-01 1.918e-01 1.702e+03 2.213
## total_trial_number 4.939e-02 1.466e-02 1.809e+03 3.370
## block_number -1.461e-02 5.475e-03 1.737e+03 -2.669
## firstfam -8.850e-02 1.732e-02 1.159e+03 -5.110
## within_PoP_ID 1.259e-01 4.415e-02 1.456e+03 2.850
## total_trial_number:block_number 4.229e-03 1.036e-03 1.814e+03 4.081
## Pr(>|t|)
## (Intercept) 0.027030 *
## total_trial_number 0.000767 ***
## block_number 0.007668 **
## firstfam 3.76e-07 ***
## within_PoP_ID 0.004430 **
## total_trial_number:block_number 4.67e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n frstfm w_PP_I
## ttl_trl_nmb -0.337
## block_numbr -0.639 0.712
## firstfam -0.772 -0.001 0.131
## withn_PP_ID -0.597 0.036 0.543 0.081
## ttl_trl_n:_ 0.297 -0.879 -0.800 0.000 -0.033
combining across pose and number
ABBA_animacy_nopose_nonumber <- exp2_core %>% filter(violation_type == "animacy") %>%
mutate(
bg_image = str_replace_all(bg_image, "_pair|_single", ""),
dv_image = str_replace_all(dv_image, "_pair|_single", "")
) %>%
mutate(
bg_image = str_replace_all(bg_image, "_left|_right", ""),
dv_image = str_replace_all(dv_image, "_left|_right", "")
) %>%
left_join(exp2_pairs_animacy_nopose_nonumber, by = c("violation_type", "bg_image", "dv_image"))
ABBA_animacy_nopose_nonumber[which(is.na(ABBA_animacy_nopose_nonumber$dishab)),]## # A tibble: 9 × 12
## prolific_id block_number violation_type total_trial_number bg_image dv_image
## <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 599ec8c1617c… 17 animacy 4 animate… inanima…
## 2 5eb48e67752e… 3 animacy 4 inanima… animate…
## 3 64dd27e89ef2… 5 animacy 4 inanima… animate…
## 4 6501bf8cd968… 21 animacy 6 animate… inanima…
## 5 657a7e142c5f… 21 animacy 4 animate… inanima…
## 6 659aaea375ee… 10 animacy 4 inanima… animate…
## 7 5b9aa47491b2… 4 animacy 6 inanima… animate…
## 8 5be9d1cb271b… 1 animacy 2 inanima… animate…
## 9 60a1b5fe1f7f… 3 animacy 2 inanima… animate…
## # ℹ 6 more variables: firstfam <dbl>, lastfam <dbl>, test <dbl>, dishab <dbl>,
## # pair_of_pair_ID <int>, within_PoP_ID <int>process_ABBA(ABBA_animacy_nopose_nonumber, exp2_pairs_animacy_nopose_nonumber)## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 9 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 9 rows containing missing values or values outside the scale range
## (`geom_point()`).
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number + block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2711.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0873 -0.5015 -0.0519 0.4614 4.7476
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01403 0.1185
## Residual 0.23452 0.4843
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -5.741e-01 1.052e-01 1.620e+03 -5.455 5.67e-08 ***
## total_trial_number 1.019e-01 7.086e-03 1.833e+03 14.385 < 2e-16 ***
## block_number 7.216e-03 3.130e-03 1.567e+03 2.305 0.021283 *
## within_PoP_ID 1.544e-01 4.302e-02 1.547e+03 3.589 0.000342 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n
## ttl_trl_nmb -0.302
## block_numbr -0.902 0.040
## withn_PP_ID -0.932 0.021 0.853
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2707.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0791 -0.4983 -0.0494 0.4732 4.7421
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01488 0.1220
## Residual 0.23182 0.4815
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) -3.396e-01 1.198e-01 1.684e+03 -2.834
## total_trial_number 4.985e-02 1.476e-02 1.837e+03 3.378
## block_number -1.061e-02 5.419e-03 1.779e+03 -1.958
## within_PoP_ID 1.470e-01 4.283e-02 1.544e+03 3.432
## total_trial_number:block_number 4.192e-03 1.044e-03 1.841e+03 4.016
## Pr(>|t|)
## (Intercept) 0.004652 **
## total_trial_number 0.000745 ***
## block_number 0.050443 .
## within_PoP_ID 0.000615 ***
## total_trial_number:block_number 6.16e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I
## ttl_trl_nmb -0.553
## block_numbr -0.851 0.730
## withn_PP_ID -0.834 0.047 0.524
## ttl_trl_n:_ 0.486 -0.878 -0.818 -0.042
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dishab ~ total_trial_number * block_number * within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2726.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0561 -0.4976 -0.0623 0.4724 4.7672
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.01477 0.1215
## Residual 0.23182 0.4815
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) -2.794e-01 4.557e-01 1.818e+03
## total_trial_number 8.877e-03 1.004e-01 1.846e+03
## block_number -2.669e-02 2.731e-02 1.823e+03
## within_PoP_ID 7.308e-02 2.428e-01 1.821e+03
## total_trial_number:block_number 1.005e-02 6.062e-03 1.848e+03
## total_trial_number:within_PoP_ID 3.332e-02 5.355e-02 1.848e+03
## block_number:within_PoP_ID 1.595e-02 1.758e-02 1.828e+03
## total_trial_number:block_number:within_PoP_ID -5.213e-03 3.931e-03 1.849e+03
## t value Pr(>|t|)
## (Intercept) -0.613 0.5398
## total_trial_number 0.088 0.9295
## block_number -0.977 0.3286
## within_PoP_ID 0.301 0.7635
## total_trial_number:block_number 1.657 0.0976 .
## total_trial_number:within_PoP_ID 0.622 0.5339
## block_number:within_PoP_ID 0.907 0.3646
## total_trial_number:block_number:within_PoP_ID -1.326 0.1850
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n w_PP_I tt__:_ t__:_P b_:_PP
## ttl_trl_nmb -0.935
## block_numbr -0.936 0.876
## withn_PP_ID -0.981 0.917 0.961
## ttl_trl_n:_ 0.870 -0.931 -0.935 -0.895
## tt__:_PP_ID 0.916 -0.981 -0.900 -0.935 0.958
## blc_:_PP_ID 0.796 -0.746 -0.947 -0.875 0.889 0.821
## t__:_:_PP_I -0.736 0.788 0.882 0.811 -0.947 -0.869 -0.935## refitting model(s) with ML (instead of REML)
## Data: dataset
## Models:
## model1: dishab ~ total_trial_number + block_number + within_PoP_ID + (1 | prolific_id)
## model2: dishab ~ total_trial_number * block_number + within_PoP_ID + (1 | prolific_id)
## model3: dishab ~ total_trial_number * block_number * within_PoP_ID + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## model1 6 2693.2 2726.4 -1340.6 2681.2
## model2 7 2679.1 2717.8 -1332.6 2665.1 16.0642 1 6.123e-05 ***
## model3 10 2681.4 2736.7 -1330.7 2661.4 3.6977 3 0.296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## dishab ~ total_trial_number * block_number + firstfam + within_PoP_ID +
## (1 | prolific_id)
## Data: dataset
##
## REML criterion at convergence: 2680.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7451 -0.5023 -0.0755 0.4648 4.8558
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.02192 0.1480
## Residual 0.22320 0.4724
## Number of obs: 1856, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.212e-01 1.887e-01 1.675e+03 2.232
## total_trial_number 4.998e-02 1.466e-02 1.809e+03 3.409
## block_number -1.454e-02 5.409e-03 1.751e+03 -2.689
## firstfam -8.829e-02 1.730e-02 1.157e+03 -5.104
## within_PoP_ID 1.266e-01 4.230e-02 1.505e+03 2.994
## total_trial_number:block_number 4.193e-03 1.036e-03 1.814e+03 4.046
## Pr(>|t|)
## (Intercept) 0.025774 *
## total_trial_number 0.000666 ***
## block_number 0.007239 **
## firstfam 3.88e-07 ***
## within_PoP_ID 0.002800 **
## total_trial_number:block_number 5.43e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) ttl_t_ blck_n frstfm w_PP_I
## ttl_trl_nmb -0.348
## block_numbr -0.629 0.725
## firstfam -0.779 0.000 0.128
## withn_PP_ID -0.580 0.047 0.527 0.076
## ttl_trl_n:_ 0.307 -0.879 -0.813 0.000 -0.042## Warning: Returning more (or less) than 1 row per `summarise()` group was deprecated in
## dplyr 1.1.0.
## ℹ Please use `reframe()` instead.
## ℹ When switching from `summarise()` to `reframe()`, remember that `reframe()`
## always returns an ungrouped data frame and adjust accordingly.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
check baseline looking
animacy
exp2_core_animacy <- exp2_core %>%
filter(c(violation_type == "animacy")) %>%
mutate(bg_image_feature = ifelse(str_detect(bg_image, "inanimate") == TRUE, "inanimate", "animate"))
exp2_core_animacy %>% group_by(bg_image_feature) %>% rstatix::get_summary_stats(firstfam, type = "common")## # A tibble: 2 × 11
## bg_image_feature variable n min max median iqr mean sd se
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 animate firstfam 934 6.24 9.92 7.48 1.01 7.58 0.711 0.023
## 2 inanimate firstfam 928 6.22 9.82 7.92 1.10 7.87 0.723 0.024
## # ℹ 1 more variable: ci <dbl>m1 <- lmerTest::lmer(firstfam ~ block_number + bg_image_feature + (1|prolific_id), data = exp2_core_animacy)
m2 <- lmerTest::lmer(firstfam ~ block_number * bg_image_feature + (1|prolific_id), data = exp2_core_animacy)
anova(m1, m2)## refitting model(s) with ML (instead of REML)## Data: exp2_core_animacy
## Models:
## m1: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## m2: firstfam ~ block_number * bg_image_feature + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1 5 3370.8 3398.5 -1680.4 3360.8
## m2 6 3287.2 3320.4 -1637.6 3275.2 85.643 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## Data: exp2_core_animacy
##
## REML criterion at convergence: 3381.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5167 -0.6027 -0.0763 0.5205 3.5992
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.2625 0.5124
## Residual 0.2324 0.4820
## Number of obs: 1862, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.332e+00 6.758e-02 1.781e+03 123.284 < 2e-16 ***
## block_number -4.071e-02 3.327e-03 1.426e+03 -12.236 < 2e-16 ***
## bg_image_featureinanimate -1.953e-01 4.545e-02 1.417e+03 -4.298 1.84e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) blck_n
## block_numbr -0.907
## bg_mg_ftrnn -0.871 0.871summary(m2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: firstfam ~ block_number * bg_image_feature + (1 | prolific_id)
## Data: exp2_core_animacy
##
## REML criterion at convergence: 3304.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7756 -0.5734 -0.0848 0.5158 3.7700
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.2664 0.5162
## Residual 0.2186 0.4675
## Number of obs: 1862, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.756e+00 9.004e-02 1.643e+03 86.134
## block_number -9.422e-03 4.638e-03 1.421e+03 -2.031
## bg_image_featureinanimate 5.731e-01 9.294e-02 1.420e+03 6.166
## block_number:bg_image_featureinanimate -6.064e-02 6.458e-03 1.422e+03 -9.390
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## block_number 0.0424 *
## bg_image_featureinanimate 9.11e-10 ***
## block_number:bg_image_featureinanimate < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) blck_n bg_mg_
## block_numbr -0.949
## bg_mg_ftrnn -0.901 0.920
## blck_nmb:__ 0.682 -0.718 -0.880number
exp2_core_number <- exp2_core %>%
filter(c(violation_type == "number")) %>%
mutate(bg_image_feature = ifelse(str_detect(bg_image, "pair") == TRUE, "pair", "single"))
exp2_core_number %>% group_by(bg_image_feature) %>% rstatix::get_summary_stats(firstfam, type = "common")## # A tibble: 2 × 11
## bg_image_feature variable n min max median iqr mean sd se
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 pair firstfam 946 6.26 9.90 7.71 1.11 7.76 0.751 0.024
## 2 single firstfam 913 6.27 9.84 7.65 1.03 7.69 0.72 0.024
## # ℹ 1 more variable: ci <dbl>m1 <- lmerTest::lmer(firstfam ~ block_number + bg_image_feature + (1|prolific_id), data = exp2_core_number)
m2 <- lmerTest::lmer(firstfam ~ block_number * bg_image_feature + (1|prolific_id), data = exp2_core_number)
anova(m1, m2)## refitting model(s) with ML (instead of REML)## Data: exp2_core_number
## Models:
## m1: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## m2: firstfam ~ block_number * bg_image_feature + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1 5 3407.2 3434.8 -1698.6 3397.2
## m2 6 3408.9 3442.0 -1698.4 3396.9 0.346 1 0.5564summary(m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## Data: exp2_core_number
##
## REML criterion at convergence: 3419.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8719 -0.5939 -0.1119 0.5037 4.3813
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.2768 0.5261
## Residual 0.2357 0.4855
## Number of obs: 1859, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.081e+00 3.579e-02 1.214e+03 225.771 <2e-16 ***
## block_number -2.601e-02 1.633e-03 1.396e+03 -15.925 <2e-16 ***
## bg_image_featuresingle -5.414e-02 2.480e-02 1.513e+03 -2.183 0.0292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) blck_n
## block_numbr -0.569
## bg_mg_ftrsn -0.332 -0.015pose
exp2_core_pose <- exp2_core %>%
filter(c(violation_type == "pose")) %>%
mutate(bg_image_feature = ifelse(str_detect(bg_image, "left") == TRUE, "left", "right"))
exp2_core_pose %>% group_by(bg_image_feature) %>% rstatix::get_summary_stats(firstfam, type = "common")## # A tibble: 2 × 11
## bg_image_feature variable n min max median iqr mean sd se
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 left firstfam 942 6.25 9.88 7.69 1.12 7.73 0.737 0.024
## 2 right firstfam 921 6.24 9.89 7.68 1.04 7.72 0.729 0.024
## # ℹ 1 more variable: ci <dbl>m1 <- lmerTest::lmer(firstfam ~ block_number + bg_image_feature + (1|prolific_id), data = exp2_core_pose)
m2 <- lmerTest::lmer(firstfam ~ block_number * bg_image_feature + (1|prolific_id), data = exp2_core_pose)
anova(m1, m2)## refitting model(s) with ML (instead of REML)## Data: exp2_core_pose
## Models:
## m1: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## m2: firstfam ~ block_number * bg_image_feature + (1 | prolific_id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1 5 3332 3359.6 -1661 3322
## m2 6 3332 3365.2 -1660 3320 1.9557 1 0.162summary(m1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: firstfam ~ block_number + bg_image_feature + (1 | prolific_id)
## Data: exp2_core_pose
##
## REML criterion at convergence: 3344
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9244 -0.5771 -0.0920 0.4948 3.8651
##
## Random effects:
## Groups Name Variance Std.Dev.
## prolific_id (Intercept) 0.2916 0.540
## Residual 0.2199 0.469
## Number of obs: 1863, groups: prolific_id, 468
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.018e+00 3.556e-02 1.151e+03 225.484 <2e-16 ***
## block_number -2.340e-02 1.578e-03 1.400e+03 -14.831 <2e-16 ***
## bg_image_featureright 8.011e-03 2.404e-02 1.510e+03 0.333 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) blck_n
## block_numbr -0.549
## bg_mg_ftrrg -0.325 -0.017exp2_core_baseline_check <- bind_rows(exp2_core_pose, exp2_core_number, exp2_core_animacy)
exp2_core_baseline_check$bg_image_feature <- as.factor(exp2_core_baseline_check$bg_image_feature)
exp2_core_baseline_check$bg_image_feature <- fct_relevel(exp2_core_baseline_check$bg_image_feature, "left", "right", "single", "pair", "inanimate", "animate")
exp2_core_baseline_check %>%
ggplot(aes(x = as.factor(bg_image_feature), y = dishab, fill = violation_type)) +
geom_boxplot(outlier.color = "red", outlier.shape = 1, outlier.alpha = 0.05) +
geom_point(alpha = 0.01) +
labs(x = "Feature of Familiarization Image",
y = "Looking Time at First Familiarization (s)",
title = "Baseline Looking for Different Stimuli") +
theme_classic()## Warning: Removed 31 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 31 rows containing missing values or values outside the scale range
## (`geom_point()`).