library(tidyverse)
source("helpers.R")
library(ggrepel)
library(cowplot)
library(here)Peekbank similarity analyses
Import data
usable_trials_summarized_with_sims <- read.csv(here("data/usable_trials_with_similarities.csv"))
aoa_ratings <- read.csv(here("data/metadata/level-aoaratings_type-kuperman_data.csv"))
saliency_values <- read.csv(here("data/metadata/level-imagepair_added-saliency_data.csv"))
usable_trials_summarized_with_sims <- usable_trials_summarized_with_sims |>
left_join(aoa_ratings |> transmute(text1=Word, aoa=AoA_Kup_lem)) |>
left_join(saliency_values, by=c("unique_pair"="ImagePair"))
# rounding each participant to the closest 5
age_based_trials <- usable_trials_summarized_with_sims |> mutate(
rounded_age = round_to_nearest(age, round_to=5)
)
clip_data_summarized <- summarize_similarity_data_collapsed(usable_trials_summarized_with_sims, extra_fields = c("dataset_name", "vanilla_trial", "aoa", "MeanSaliencyDiff")) |> mutate(
sim_logit = qlogis(pmin(pmax(image_similarity, 1e-6), 1 - 1e-6))
) clip_data_summarized |> mutate(
sim_bucket = cut(
image_similarity,
breaks = seq(0, 1, by = 0.05),
include.lowest = TRUE,
right = FALSE
)
) |>
count(sim_bucket) |>
ggplot(aes(x = n, y = sim_bucket)) +
geom_col() +
labs(
x = "Count",
y = "Image similarity bucket",
title = "Distribution of image similarity"
) +
theme_minimal()
Main similarity plots
CLIP analysis: current similarity effects are dubious with lots of dataset level variance of course that will have to be accounted for in a mixed effects model; Garrison Bergelson dataset has individualized trials so difficult to make use of as well.
# N = the number of participants in a single trial here
adams_marchman_data_summarized <- summarize_similarity_data_collapsed(usable_trials_summarized_with_sims, extra_fields = c("dataset_name", "vanilla_trial")) |> filter(N > 10 & dataset_name == "adams_marchman_2018" & vanilla_trial==1)
am_plots <- generate_multimodal_plots(adams_marchman_data_summarized, "CLIP", title="Adams & Marchman, 2018")
am_plots
weaver_zettersten_data_summarized <- summarize_similarity_data_collapsed(usable_trials_summarized_with_sims, extra_fields = c("dataset_name", "vanilla_trial")) |> filter(N > 10 & dataset_name == "weaver_zettersten_2024" & vanilla_trial==1)
wz_plots <- generate_multimodal_plots(weaver_zettersten_data_summarized, "CLIP", title="Weaver et al., 2024")
wz_plots
clip_plots <- generate_multimodal_plots(clip_data_summarized |> filter(vanilla_trial==1), "CLIP", title="all vanilla trials")
clip_plots
comparing all IVs
library(GGally)
confusability_vars <- clip_data_summarized |>
filter(vanilla_trial==1) |>
transmute(
mean_value=scale(mean_value)[, 1],
image_similarity=scale(image_similarity)[, 1],
text_similarity=scale(text_similarity)[, 1],
multimodal_similarity=scale(multimodal_similarity)[, 1],
ooo_similarity=scale(ooo_similarity)[, 1],
aoa=scale(aoa)[, 1],
MeanSaliencyDiff=scale(MeanSaliencyDiff)[, 1]
)
p <- ggpairs(
confusability_vars,
upper = list(continuous = wrap("cor", method = "spearman",
use = "pairwise.complete.obs", size = 3)),
lower = list(continuous = wrap("smooth", method = "lm",
alpha = 0.4, size = 0.8)),
diag = list(continuous = wrap("densityDiag", alpha = 0.5))
) +
theme_bw()
p
lots of interesting colinear effects here with AoA and mean saliency..
Analysis across age
calculate_correlations <- function(data, x_var, y_var, group_var = c("rounded_age"), conf_level = 0.95) {
data |>
group_by(across(all_of(group_var))) |>
summarize(
{
cor_test <- cor.test(.data[[x_var]], .data[[y_var]], method = "pearson", conf.level = conf_level)
tibble(
pearson_cor = cor_test$estimate,
p_value = cor_test$p.value,
ci_lower = cor_test$conf.int[1],
ci_upper = cor_test$conf.int[2]
)
},
.groups = "drop"
)
}
sim_age_plot <- function(data) {
ggplot(data, aes(x = rounded_age, y = pearson_cor, color = similarity_type)) +
geom_hline(yintercept = 0, linetype = "dashed") +
geom_point(size = 3, position = position_dodge(width=0.5)) + # Apply jitter to points only
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
width = 0.3, alpha = 0.2,
position=position_dodge(width=0.5)) + # No jitter on error bars
geom_smooth(span = 2, alpha=0.1, se=FALSE) +
labs(title = paste("Similarity correlations across age"),
x = "Age",
y = "Coefficient of similarity") +
theme_minimal() +
guides(shape = "none") +
scale_x_continuous(breaks=seq(5,70,5)) +
scale_color_brewer(palette = "Set1", name = "Similarity type")
}
# can't figure out what values to filter to here.
clip_data_age_summarized <- summarize_similarity_data_collapsed(age_based_trials |> filter(vanilla_trial == 1), extra_fields = c("rounded_age", "dataset_name")) |> filter(N >= 5) |> group_by(rounded_age) |>
filter(n() >= 5) |>
ungroup()
clip_age_image_cors <- calculate_correlations(clip_data_age_summarized, "image_similarity", "mean_value") |> mutate(similarity_type = "image")
clip_age_text_cors <- calculate_correlations(clip_data_age_summarized, "text_similarity", "mean_value") |> mutate(similarity_type = "text")
clip_age_multimodal_cors <- calculate_correlations(clip_data_age_summarized, "multimodal_similarity", "mean_value") |> mutate(similarity_type = "multimodal")
clip_age_ooo_cors <- calculate_correlations(clip_data_age_summarized, "ooo_similarity", "mean_value") |> mutate(similarity_type = "ooo")
clip_age_cors <- bind_rows(clip_age_image_cors, clip_age_text_cors, clip_age_multimodal_cors, clip_age_ooo_cors)
sim_age_plot(clip_age_cors)
ggplot(clip_age_cors, aes(x = rounded_age, y = pearson_cor)) +
geom_point(aes(color = p_value < 0.05), size = 3) +
geom_smooth(span = 2) +
labs(title = "Image similarity correlation across age",
x = "Age in months",
y = "Pearson Correlation") +
scale_color_manual(values = c("TRUE" = "black", "FALSE" = "gray")) + # Set color for significance
theme_minimal() +
theme(legend.position = "none")
stats
pre-registered models
library(lmerTest)
library(glmmTMB)
library(MuMIn)
library(broom.mixed)
model_data <- usable_trials_summarized_with_sims |> filter(vanilla_trial == 1)
sims <- c("image_similarity","text_similarity","multimodal_similarity","ooo_similarity")
fit_main <- function(sim,
data = model_data,
response = "mean_target_looking_critical_window",
added_structure = NULL, pruned_model=FALSE) {
terms <- c(
sprintf("scale(%s)*scale(age)", sim),
"(1 | dataset_id)"
)
if (pruned_model) {
terms <- c(terms, "(1 | subject_id)")
} else {
terms <- c(terms, sprintf("(1 + scale(%s) | subject_id)", sim))
}
if (!is.null(added_structure)) {
terms <- c(terms, added_structure)
}
f <- reformulate(
terms,
response = sprintf("scale(%s)", response)
)
lmer(f, data = data)
}
mods <- lapply(sims, fit_main); names(mods) <- sims
lapply(mods, function(m) m@optinfo$conv$lme4$messages)$image_similarity
NULL
$text_similarity
[1] "boundary (singular) fit: see help('isSingular')"
$multimodal_similarity
[1] "boundary (singular) fit: see help('isSingular')"
$ooo_similarity
NULLlapply(mods, function(m) summary(m))$image_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60829
Scaled residuals:
Min 1Q Median 3Q Max
-3.0642 -0.6366 0.1137 0.7406 2.1038
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.04257 0.20632
scale(image_similarity) 0.00136 0.03688 -0.29
dataset_id (Intercept) 0.03365 0.18344
Residual 0.89770 0.94747
Number of obs: 22017, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.309e-03 4.010e-02 2.217e+01 -0.033
scale(image_similarity) -4.736e-02 8.831e-03 2.779e+02 -5.363
scale(age) 2.017e-01 1.490e-02 1.085e+03 13.542
scale(image_similarity):scale(age) -7.891e-03 9.312e-03 3.580e+03 -0.847
Pr(>|t|)
(Intercept) 0.974
scale(image_similarity) 1.72e-07 ***
scale(age) < 2e-16 ***
scale(image_similarity):scale(age) 0.397
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mg_sml) -0.037
scale(age) -0.113 -0.073
scl(mg_):() -0.015 0.263 -0.263
$text_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60857.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1336 -0.6395 0.1162 0.7379 2.0764
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.0422339 0.20551
scale(text_similarity) 0.0003814 0.01953 -1.00
dataset_id (Intercept) 0.0352938 0.18787
Residual 0.8989005 0.94810
Number of obs: 22017, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.143e-03 4.118e-02 2.229e+01 -0.028
scale(text_similarity) 5.776e-03 1.012e-02 7.097e+03 0.571
scale(age) 2.044e-01 1.486e-02 1.167e+03 13.757
scale(text_similarity):scale(age) 1.402e-02 7.878e-03 1.307e+04 1.780
Pr(>|t|)
(Intercept) 0.9781
scale(text_similarity) 0.5680
scale(age) <2e-16 ***
scale(text_similarity):scale(age) 0.0751 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(txt_sm) 0.080
scale(age) -0.109 -0.038
scl(tx_):() 0.026 -0.298 0.206
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
$multimodal_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60863.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.0807 -0.6388 0.1150 0.7393 2.1008
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 4.267e-02 0.206562
scale(multimodal_similarity) 5.351e-05 0.007315 -1.00
dataset_id (Intercept) 3.363e-02 0.183384
Residual 8.997e-01 0.948517
Number of obs: 22017, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -7.347e-03 4.008e-02 2.242e+01
scale(multimodal_similarity) -3.780e-03 6.733e-03 1.167e+04
scale(age) 1.992e-01 1.443e-02 1.002e+03
scale(multimodal_similarity):scale(age) 9.351e-03 7.065e-03 1.813e+04
t value Pr(>|t|)
(Intercept) -0.183 0.856
scale(multimodal_similarity) -0.561 0.575
scale(age) 13.803 <2e-16 ***
scale(multimodal_similarity):scale(age) 1.324 0.186
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mltmd_) 0.012
scale(age) -0.121 0.005
scl(ml_):() 0.016 0.033 0.063
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
$ooo_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 57832.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.9802 -0.6355 0.1163 0.7383 2.1239
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.044738 0.2115
scale(ooo_similarity) 0.002612 0.0511 -0.26
dataset_id (Intercept) 0.033070 0.1819
Residual 0.887520 0.9421
Number of obs: 20996, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -2.196e-02 4.002e-02 2.201e+01 -0.549
scale(ooo_similarity) -5.704e-02 9.620e-03 8.145e+02 -5.930
scale(age) 2.080e-01 1.481e-02 9.216e+02 14.048
scale(ooo_similarity):scale(age) 2.435e-02 7.107e-03 2.964e+03 3.426
Pr(>|t|)
(Intercept) 0.58863
scale(ooo_similarity) 4.48e-09 ***
scale(age) < 2e-16 ***
scale(ooo_similarity):scale(age) 0.00062 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(_smlrt) 0.051
scale(age) -0.125 -0.024
scl(_sm):() -0.023 -0.206 0.175r.squaredGLMM(mods$image_similarity) R2m R2c
[1,] 0.03681259 0.1134435Trying to see why text and multimodal similarity models were singular.
vars <- c("image_similarity", "text_similarity", "multimodal_similarity",
"ooo_similarity", "mean_target_looking_critical_window",
"age", "aoa", "MeanSaliencyDiff")
colSums(is.na(model_data[vars])) image_similarity text_similarity
0 0
multimodal_similarity ooo_similarity
0 1044
mean_target_looking_critical_window age
0 131
aoa MeanSaliencyDiff
2044 0 # total rows, and rows complete across all vars
nrow(model_data)[1] 22148sum(complete.cases(model_data[vars]))[1] 18982# rows complete without requiring ooo
sum(complete.cases(model_data[setdiff(vars, "ooo_similarity")]))[1] 19973getting rid of singular effects
getting rid of singular effects
pruned_text_model <- lmer(scale(mean_target_looking_critical_window) ~ scale(text_similarity)*scale(age)
+ scale(aoa) + scale(MeanSaliencyDiff)
+ (1 | subject_id)
+ (1 | text1:unique_pair)
+ (1 | dataset_id), data = model_data)
pruned_multimodal_model <- lmer(scale(mean_target_looking_critical_window) ~ scale(multimodal_similarity)*scale(age)
+ scale(aoa) + scale(MeanSaliencyDiff)
+ (1 | subject_id)
+ (1 | text1:unique_pair)
+ (1|dataset_id), data = model_data)
summary(pruned_text_model)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(text_similarity) *
scale(age) + scale(aoa) + scale(MeanSaliencyDiff) + (1 |
subject_id) + (1 | text1:unique_pair) + (1 | dataset_id)
Data: model_data
REML criterion at convergence: 54999.6
Scaled residuals:
Min 1Q Median 3Q Max
-3.2204 -0.6251 0.1108 0.7313 2.1902
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04338 0.2083
text1:unique_pair (Intercept) 0.01716 0.1310
dataset_id (Intercept) 0.03262 0.1806
Residual 0.87852 0.9373
Number of obs: 19973, groups:
subject_id, 1316; text1:unique_pair, 481; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -2.669e-03 4.119e-02 2.253e+01 -0.065
scale(text_similarity) 8.740e-04 1.493e-02 2.893e+02 0.059
scale(age) 2.065e-01 1.516e-02 1.183e+03 13.626
scale(aoa) -1.845e-02 1.050e-02 3.744e+02 -1.757
scale(MeanSaliencyDiff) 1.366e-02 1.060e-02 2.093e+02 1.288
scale(text_similarity):scale(age) 5.074e-03 9.955e-03 1.145e+03 0.510
Pr(>|t|)
(Intercept) 0.9489
scale(text_similarity) 0.9533
scale(age) <2e-16 ***
scale(aoa) 0.0798 .
scale(MeanSaliencyDiff) 0.1990
scale(text_similarity):scale(age) 0.6104
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) scal() s(MSD)
scl(txt_sm) 0.136
scale(age) -0.099 -0.024
scale(aoa) -0.030 0.142 -0.007
scl(MnSlnD) -0.003 0.001 0.002 0.039
scl(tx_):() 0.033 -0.198 0.272 0.052 -0.011summary(pruned_multimodal_model)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(mean_target_looking_critical_window) ~ scale(multimodal_similarity) *
scale(age) + scale(aoa) + scale(MeanSaliencyDiff) + (1 |
subject_id) + (1 | text1:unique_pair) + (1 | dataset_id)
Data: model_data
REML criterion at convergence: 54999.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.2077 -0.6243 0.1115 0.7322 2.1975
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04336 0.2082
text1:unique_pair (Intercept) 0.01741 0.1320
dataset_id (Intercept) 0.03177 0.1782
Residual 0.87844 0.9373
Number of obs: 19973, groups:
subject_id, 1316; text1:unique_pair, 481; dataset_id, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -5.285e-03 4.032e-02 2.222e+01
scale(multimodal_similarity) -9.961e-03 1.174e-02 1.682e+02
scale(age) 2.048e-01 1.464e-02 9.706e+02
scale(aoa) -1.942e-02 1.042e-02 3.838e+02
scale(MeanSaliencyDiff) 1.327e-02 1.067e-02 2.152e+02
scale(multimodal_similarity):scale(age) 2.578e-03 9.341e-03 1.007e+03
t value Pr(>|t|)
(Intercept) -0.131 0.8969
scale(multimodal_similarity) -0.848 0.3975
scale(age) 13.994 <2e-16 ***
scale(aoa) -1.863 0.0632 .
scale(MeanSaliencyDiff) 1.244 0.2147
scale(multimodal_similarity):scale(age) 0.276 0.7826
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) scal() s(MSD)
scl(mltmd_) 0.027
scale(age) -0.117 0.002
scale(aoa) -0.054 0.065 -0.022
scl(MnSlnD) -0.003 0.022 -0.002 0.038
scl(ml_):() 0.012 -0.041 0.104 0.049 -0.074model comparison
Effects persist in the pruned models. Now trying to compare to see which one has the best fit.
dat_cc <- model_data |>
tidyr::drop_na(image_similarity, text_similarity, multimodal_similarity, ooo_similarity,
mean_target_looking_critical_window,
age, aoa, MeanSaliencyDiff)
fit_common <- function(sim, data=dat_cc) {
f <- reformulate(
c(sprintf("scale(%s)*scale(age)", sim),
"scale(aoa)", "scale(MeanSaliencyDiff)",
"(1 | administration_id)",
"(1 | text1:unique_pair)",
"(1 | dataset_id)"),
response = "scale(mean_target_looking_critical_window)")
lmer(f, data = dat_cc, REML = FALSE)
}
mods_common <- lapply(sims, fit_common); names(mods_common) <- sims
sel <- model.sel(mods_common$image_similarity, mods_common$text_similarity,
mods_common$multimodal_similarity, mods_common$ooo_similarity)
selModel selection table
(Int) scl(age) scl(aoa) scl(img_sml)
mods_common$image_similarity -0.005985 0.2199 -0.01819 -0.03991
mods_common$ooo_similarity -0.017390 0.2191 -0.01735
mods_common$multimodal_similarity -0.012780 0.2163 -0.01927
mods_common$text_similarity -0.008183 0.2175 -0.01821
scl(MSD) scl(age):scl(img_sml) scl(txt_sml)
mods_common$image_similarity 0.01752 -0.007845
mods_common$ooo_similarity 0.01708
mods_common$multimodal_similarity 0.01726
mods_common$text_similarity 0.01786 0.007615
scl(age):scl(txt_sml) scl(mlt_sml)
mods_common$image_similarity
mods_common$ooo_similarity
mods_common$multimodal_similarity -0.007351
mods_common$text_similarity 0.004104
scl(age):scl(mlt_sml) scl(ooo_sml)
mods_common$image_similarity
mods_common$ooo_similarity -0.02402
mods_common$multimodal_similarity 0.005516
mods_common$text_similarity
scl(age):scl(ooo_sml) df logLik AICc
mods_common$image_similarity 10 -26158.44 52336.9
mods_common$ooo_similarity 0.01331 10 -26159.62 52339.3
mods_common$multimodal_similarity 10 -26161.13 52342.3
mods_common$text_similarity 10 -26161.21 52342.4
delta weight
mods_common$image_similarity 0.00 0.696
mods_common$ooo_similarity 2.36 0.214
mods_common$multimodal_similarity 5.38 0.047
mods_common$text_similarity 5.54 0.044
Models ranked by AICc(x)
Random terms (all models):
1 | administration_id, 1 | text1:unique_pair, 1 | dataset_idsummary(mods_common$image_similarity)Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: f
Data: dat_cc
AIC BIC logLik -2*log(L) df.resid
52336.9 52415.4 -26158.4 52316.9 18972
Scaled residuals:
Min 1Q Median 3Q Max
-3.2490 -0.6214 0.1149 0.7279 2.2022
Random effects:
Groups Name Variance Std.Dev.
administration_id (Intercept) 0.05496 0.2344
text1:unique_pair (Intercept) 0.01502 0.1226
dataset_id (Intercept) 0.02692 0.1641
Residual 0.87186 0.9337
Number of obs: 18982, groups:
administration_id, 1632; text1:unique_pair, 432; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -5.985e-03 3.784e-02 2.254e+01 -0.158
scale(image_similarity) -3.991e-02 1.636e-02 1.785e+02 -2.440
scale(age) 2.199e-01 1.617e-02 8.877e+02 13.597
scale(aoa) -1.818e-02 1.068e-02 3.238e+02 -1.702
scale(MeanSaliencyDiff) 1.751e-02 1.090e-02 1.877e+02 1.607
scale(image_similarity):scale(age) -7.845e-03 1.192e-02 1.686e+03 -0.658
Pr(>|t|)
(Intercept) 0.8757
scale(image_similarity) 0.0157 *
scale(age) <2e-16 ***
scale(aoa) 0.0897 .
scale(MeanSaliencyDiff) 0.1098
scale(image_similarity):scale(age) 0.5105
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) scal() s(MSD)
scl(mg_sml) -0.066
scale(age) -0.095 -0.051
scale(aoa) -0.065 -0.048 -0.046
scl(MnSlnD) 0.015 0.034 0.030 0.036
scl(mg_):() -0.034 0.098 -0.310 0.041 -0.071Image similarity model comes out on top.
including non-vanilla trials
mods_all <- lapply(sims, fit_main, usable_trials_summarized_with_sims); names(mods_all) <- sims
lapply(mods_all, function(m) m@optinfo$conv$lme4$messages)$image_similarity
NULL
$text_similarity
NULL
$multimodal_similarity
NULL
$ooo_similarity
NULLlapply(mods_all, function(m) summary(m))$image_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 81924.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.0857 -0.6429 0.1131 0.7491 2.1076
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.042931 0.20720
scale(image_similarity) 0.002058 0.04536 -0.29
dataset_id (Intercept) 0.031225 0.17671
Residual 0.897199 0.94721
Number of obs: 29647, groups: subject_id, 1549; dataset_id, 26
Fixed effects:
Estimate Std. Error df t value
(Intercept) 7.916e-03 3.657e-02 2.400e+01 0.216
scale(image_similarity) -7.951e-02 8.154e-03 5.042e+02 -9.751
scale(age) 2.416e-01 1.400e-02 1.444e+03 17.254
scale(image_similarity):scale(age) -4.720e-02 8.057e-03 2.926e+03 -5.859
Pr(>|t|)
(Intercept) 0.83
scale(image_similarity) < 2e-16 ***
scale(age) < 2e-16 ***
scale(image_similarity):scale(age) 5.18e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mg_sml) -0.022
scale(age) 0.004 -0.121
scl(mg_):() -0.037 0.197 -0.330
$text_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 80129
Scaled residuals:
Min 1Q Median 3Q Max
-2.9602 -0.6376 0.1150 0.7431 2.0996
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.041618 0.20401
scale(text_similarity) 0.001999 0.04471 -0.32
dataset_id (Intercept) 0.029641 0.17217
Residual 0.896872 0.94703
Number of obs: 29001, groups: subject_id, 1549; dataset_id, 26
Fixed effects:
Estimate Std. Error df t value
(Intercept) 1.207e-03 3.576e-02 2.372e+01 0.034
scale(text_similarity) -1.436e-02 7.998e-03 1.242e+03 -1.796
scale(age) 2.150e-01 1.359e-02 1.279e+03 15.821
scale(text_similarity):scale(age) -2.004e-02 7.196e-03 2.562e+03 -2.785
Pr(>|t|)
(Intercept) 0.9734
scale(text_similarity) 0.0728 .
scale(age) <2e-16 ***
scale(text_similarity):scale(age) 0.0054 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(txt_sm) 0.056
scale(age) -0.012 -0.039
scl(tx_):() -0.011 -0.238 -0.021
$multimodal_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 81850.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.9543 -0.6447 0.1115 0.7454 2.1158
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.042843 0.20699
scale(multimodal_similarity) 0.004891 0.06993 -0.13
dataset_id (Intercept) 0.029172 0.17080
Residual 0.892008 0.94446
Number of obs: 29647, groups: subject_id, 1549; dataset_id, 26
Fixed effects:
Estimate Std. Error df
(Intercept) 3.504e-03 3.544e-02 2.396e+01
scale(multimodal_similarity) -6.545e-02 6.737e-03 6.387e+02
scale(age) 2.301e-01 1.339e-02 1.304e+03
scale(multimodal_similarity):scale(age) -4.139e-02 6.234e-03 1.090e+03
t value Pr(>|t|)
(Intercept) 0.099 0.922
scale(multimodal_similarity) -9.715 < 2e-16 ***
scale(age) 17.189 < 2e-16 ***
scale(multimodal_similarity):scale(age) -6.639 4.97e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mltmd_) 0.005
scale(age) -0.005 -0.004
scl(ml_):() -0.025 -0.200 -0.181
$ooo_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 64644.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.9774 -0.6299 0.1140 0.7346 2.1192
Random effects:
Groups Name Variance Std.Dev. Corr
subject_id (Intercept) 0.044046 0.20987
scale(ooo_similarity) 0.003655 0.06046 -0.21
dataset_id (Intercept) 0.034263 0.18510
Residual 0.887298 0.94196
Number of obs: 23462, groups: subject_id, 1513; dataset_id, 26
Fixed effects:
Estimate Std. Error df t value
(Intercept) 3.529e-02 3.869e-02 2.231e+01 0.912
scale(ooo_similarity) -4.416e-02 9.286e-03 1.205e+03 -4.756
scale(age) 2.222e-01 1.581e-02 1.078e+03 14.052
scale(ooo_similarity):scale(age) 2.427e-02 7.637e-03 2.502e+03 3.178
Pr(>|t|)
(Intercept) 0.3715
scale(ooo_similarity) 2.21e-06 ***
scale(age) < 2e-16 ***
scale(ooo_similarity):scale(age) 0.0015 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(_smlrt) 0.063
scale(age) -0.014 -0.054
scl(_sm):() -0.022 -0.115 0.096multimodal similarity and image similarity ar the only significant ones here…pruning
pruned_multimodal_model_all <- lmer(scale(mean_target_looking_critical_window) ~ scale(multimodal_similarity)*scale(age)
+ scale(aoa) + scale(MeanSaliencyDiff)
+ (1 | subject_id)
+ (1|dataset_id), data = usable_trials_summarized_with_sims)
summary(pruned_multimodal_model_all)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(mean_target_looking_critical_window) ~ scale(multimodal_similarity) *
scale(age) + scale(aoa) + scale(MeanSaliencyDiff) + (1 |
subject_id) + (1 | dataset_id)
Data: usable_trials_summarized_with_sims
REML criterion at convergence: 67340
Scaled residuals:
Min 1Q Median 3Q Max
-2.9724 -0.6349 0.1181 0.7349 2.1338
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04312 0.2076
dataset_id (Intercept) 0.03651 0.1911
Residual 0.88036 0.9383
Number of obs: 24519, groups: subject_id, 1547; dataset_id, 26
Fixed effects:
Estimate Std. Error df
(Intercept) 3.762e-02 3.940e-02 2.387e+01
scale(multimodal_similarity) -2.945e-02 7.434e-03 2.383e+04
scale(age) 2.023e-01 1.490e-02 1.360e+03
scale(aoa) -1.633e-02 6.786e-03 2.362e+04
scale(MeanSaliencyDiff) 2.708e-02 6.144e-03 2.432e+04
scale(multimodal_similarity):scale(age) -3.904e-02 7.765e-03 2.321e+04
t value Pr(>|t|)
(Intercept) 0.955 0.3492
scale(multimodal_similarity) -3.962 7.45e-05 ***
scale(age) 13.580 < 2e-16 ***
scale(aoa) -2.406 0.0161 *
scale(MeanSaliencyDiff) 4.408 1.05e-05 ***
scale(multimodal_similarity):scale(age) -5.028 4.99e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) scal() s(MSD)
scl(mltmd_) 0.046
scale(age) -0.010 -0.049
scale(aoa) -0.023 -0.041 -0.008
scl(MnSlnD) 0.010 0.049 0.019 -0.063
scl(ml_):() -0.009 0.054 0.013 0.151 -0.096effect stays and with a pretty robust random effects structure and with an interaction with age. feels like this is a really interesting effect, especially given that the other models across vanilla and non-vanilla are not similarly predictive.
including baseline window as covariate
baseline_data <- model_data |> filter(min_time <= -500)
mods_baseline_covariate <- lapply(sims, fit_main, data=baseline_data, added_structure="mean_target_looking_baseline_window", pruned_model=TRUE); names(mods_baseline_covariate) <- sims
lapply(mods_baseline_covariate, function(m) summary(m))$image_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60453.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.1653 -0.6412 0.1131 0.7542 2.3233
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04419 0.2102
dataset_id (Intercept) 0.03314 0.1820
Residual 0.88258 0.9395
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.755e-01 4.083e-02 2.451e+01 -4.297
scale(image_similarity) -4.861e-02 8.322e-03 1.340e+04 -5.841
scale(age) 1.995e-01 1.481e-02 1.074e+03 13.466
mean_target_looking_baseline_window 3.519e-01 1.820e-02 2.156e+04 19.341
scale(image_similarity):scale(age) -7.801e-03 9.204e-03 5.485e+03 -0.848
Pr(>|t|)
(Intercept) 0.000239 ***
scale(image_similarity) 5.3e-09 ***
scale(age) < 2e-16 ***
mean_target_looking_baseline_window < 2e-16 ***
scale(image_similarity):scale(age) 0.396705
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) mn____
scl(mg_sml) -0.028
scale(age) -0.109 -0.081
mn_trgt_l__ -0.220 -0.004 -0.007
scl(mg_):() -0.016 0.306 -0.251 -0.001
$text_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60485.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.1746 -0.6429 0.1142 0.7522 2.2993
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04405 0.2099
dataset_id (Intercept) 0.03472 0.1863
Residual 0.88390 0.9402
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.759e-01 4.186e-02 2.481e+01 -4.203
scale(text_similarity) 4.271e-03 9.950e-03 1.342e+04 0.429
scale(age) 2.013e-01 1.476e-02 1.178e+03 13.637
mean_target_looking_baseline_window 3.511e-01 1.821e-02 2.156e+04 19.276
scale(text_similarity):scale(age) 1.256e-02 7.783e-03 1.630e+04 1.613
Pr(>|t|)
(Intercept) 0.000297 ***
scale(text_similarity) 0.667764
scale(age) < 2e-16 ***
mean_target_looking_baseline_window < 2e-16 ***
scale(text_similarity):scale(age) 0.106704
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) mn____
scl(txt_sm) 0.091
scale(age) -0.105 -0.044
mn_trgt_l__ -0.217 -0.009 -0.010
scl(tx_):() 0.023 -0.294 0.224 -0.009
$multimodal_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 60488.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1724 -0.6426 0.1140 0.7546 2.3107
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04405 0.2099
dataset_id (Intercept) 0.03298 0.1816
Residual 0.88402 0.9402
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -1.816e-01 4.075e-02 2.483e+01
scale(multimodal_similarity) -4.864e-03 6.660e-03 2.179e+04
scale(age) 1.969e-01 1.437e-02 9.868e+02
mean_target_looking_baseline_window 3.513e-01 1.821e-02 2.156e+04
scale(multimodal_similarity):scale(age) 6.905e-03 6.998e-03 2.132e+04
t value Pr(>|t|)
(Intercept) -4.456 0.000155 ***
scale(multimodal_similarity) -0.730 0.465181
scale(age) 13.701 < 2e-16 ***
mean_target_looking_baseline_window 19.285 < 2e-16 ***
scale(multimodal_similarity):scale(age) 0.987 0.323761
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) mn____
scl(mltmd_) 0.018
scale(age) -0.116 0.004
mn_trgt_l__ -0.221 -0.006 -0.009
scl(ml_):() 0.018 0.034 0.070 -0.018
$ooo_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 57513.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1394 -0.6400 0.1144 0.7464 2.3319
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.04693 0.2166
dataset_id (Intercept) 0.03314 0.1820
Residual 0.87538 0.9356
Number of obs: 20991, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.855e-01 4.105e-02 2.460e+01 -4.518
scale(ooo_similarity) -5.735e-02 9.256e-03 9.813e+03 -6.195
scale(age) 2.070e-01 1.471e-02 9.567e+02 14.068
mean_target_looking_baseline_window 3.294e-01 1.849e-02 2.053e+04 17.816
scale(ooo_similarity):scale(age) 2.567e-02 6.964e-03 1.451e+04 3.685
Pr(>|t|)
(Intercept) 0.000134 ***
scale(ooo_similarity) 6.05e-10 ***
scale(age) < 2e-16 ***
mean_target_looking_baseline_window < 2e-16 ***
scale(ooo_similarity):scale(age) 0.000229 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g) mn____
scl(_smlrt) 0.053
scale(age) -0.120 -0.026
mn_trgt_l__ -0.222 0.005 -0.007
scl(_sm):() -0.025 -0.191 0.191 0.006predicting baseline corrected looking
mods_baseline_corrected <- lapply(sims, fit_main, data=baseline_data, response="corrected_target_looking", pruned_model=TRUE); names(mods_baseline_corrected) <- sims
lapply(mods_baseline_corrected, function(m) summary(m))$image_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 61949.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.1666 -0.6387 -0.0278 0.7128 2.4607
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.01687 0.1299
dataset_id (Intercept) 0.01344 0.1159
Residual 0.96077 0.9802
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -1.349e-02 2.683e-02 2.165e+01 -0.503
scale(image_similarity) -3.574e-02 8.484e-03 4.976e+03 -4.213
scale(age) 1.274e-01 1.334e-02 4.456e+02 9.545
scale(image_similarity):scale(age) -5.589e-03 8.982e-03 1.539e+03 -0.622
Pr(>|t|)
(Intercept) 0.620
scale(image_similarity) 2.57e-05 ***
scale(age) < 2e-16 ***
scale(image_similarity):scale(age) 0.534
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mg_sml) -0.042
scale(age) -0.141 -0.087
scl(mg_):() -0.026 0.268 -0.270
$text_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 61966.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.1738 -0.6369 -0.0275 0.7110 2.4526
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.01689 0.1299
dataset_id (Intercept) 0.01278 0.1131
Residual 0.96157 0.9806
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -2.158e-02 2.658e-02 2.241e+01 -0.812
scale(text_similarity) -8.847e-03 9.997e-03 4.063e+03 -0.885
scale(age) 1.242e-01 1.327e-02 4.302e+02 9.358
scale(text_similarity):scale(age) 6.897e-04 7.909e-03 6.942e+03 0.087
Pr(>|t|)
(Intercept) 0.425
scale(text_similarity) 0.376
scale(age) <2e-16 ***
scale(text_similarity):scale(age) 0.931
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(txt_sm) 0.133
scale(age) -0.133 -0.029
scl(tx_):() 0.034 -0.288 0.263
$multimodal_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 61964.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.1850 -0.6374 -0.0276 0.7117 2.4529
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.01690 0.1300
dataset_id (Intercept) 0.01294 0.1138
Residual 0.96143 0.9805
Number of obs: 22012, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -1.965e-02 2.642e-02 2.186e+01
scale(multimodal_similarity) -8.468e-03 6.893e-03 2.127e+04
scale(age) 1.230e-01 1.284e-02 3.767e+02
scale(multimodal_similarity):scale(age) -9.959e-03 7.210e-03 1.765e+04
t value Pr(>|t|)
(Intercept) -0.744 0.465
scale(multimodal_similarity) -1.229 0.219
scale(age) 9.577 <2e-16 ***
scale(multimodal_similarity):scale(age) -1.381 0.167
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mltmd_) 0.025
scale(age) -0.153 0.010
scl(ml_):() 0.022 0.030 0.075
$ooo_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 59295.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.11432 -0.63684 -0.02658 0.71501 2.44713
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.01703 0.1305
dataset_id (Intercept) 0.01265 0.1125
Residual 0.97088 0.9853
Number of obs: 20991, groups: subject_id, 1316; dataset_id, 24
Fixed effects:
Estimate Std. Error df t value
(Intercept) -2.466e-02 2.643e-02 2.156e+01 -0.933
scale(ooo_similarity) -3.177e-02 9.445e-03 2.692e+03 -3.364
scale(age) 1.327e-01 1.318e-02 3.468e+02 10.072
scale(ooo_similarity):scale(age) 2.081e-02 7.094e-03 6.502e+03 2.934
Pr(>|t|)
(Intercept) 0.361046
scale(ooo_similarity) 0.000779 ***
scale(age) < 2e-16 ***
scale(ooo_similarity):scale(age) 0.003357 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(_smlrt) 0.081
scale(age) -0.156 -0.024
scl(_sm):() -0.025 -0.189 0.200Under 3 years old
LWL specific ages and AoA?
mods_younger <- lapply(sims, fit_main, data=model_data |> filter(age < 36), response="corrected_target_looking", pruned_model=TRUE); names(mods_younger) <- sims
lapply(mods_younger, function(m) summary(m))$image_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 53811
Scaled residuals:
Min 1Q Median 3Q Max
-2.91976 -0.64564 -0.04135 0.72710 2.40348
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.016781 0.12954
dataset_id (Intercept) 0.009405 0.09698
Residual 0.969924 0.98485
Number of obs: 19062, groups: subject_id, 821; dataset_id, 19
Fixed effects:
Estimate Std. Error df t value
(Intercept) -4.376e-02 2.601e-02 1.880e+01 -1.682
scale(image_similarity) -3.323e-02 8.322e-03 4.209e+03 -3.994
scale(age) 5.403e-02 1.234e-02 2.548e+02 4.377
scale(image_similarity):scale(age) -3.714e-04 9.120e-03 5.073e+02 -0.041
Pr(>|t|)
(Intercept) 0.109
scale(image_similarity) 6.62e-05 ***
scale(age) 1.76e-05 ***
scale(image_similarity):scale(age) 0.968
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mg_sml) -0.066
scale(age) -0.168 -0.040
scl(mg_):() -0.072 0.176 -0.114
$text_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 53823.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.93015 -0.64502 -0.03793 0.72771 2.39862
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.016643 0.12901
dataset_id (Intercept) 0.009712 0.09855
Residual 0.970658 0.98522
Number of obs: 19062, groups: subject_id, 821; dataset_id, 19
Fixed effects:
Estimate Std. Error df t value
(Intercept) -5.167e-02 2.654e-02 1.967e+01 -1.947
scale(text_similarity) -1.117e-02 1.083e-02 2.295e+03 -1.032
scale(age) 5.851e-02 1.276e-02 2.542e+02 4.584
scale(text_similarity):scale(age) 1.385e-02 8.081e-03 6.076e+03 1.714
Pr(>|t|)
(Intercept) 0.0660 .
scale(text_similarity) 0.3023
scale(age) 7.15e-06 ***
scale(text_similarity):scale(age) 0.0866 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(txt_sm) 0.146
scale(age) -0.162 -0.048
scl(tx_):() 0.006 -0.298 0.265
$multimodal_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 53827.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.92997 -0.64486 -0.03764 0.72640 2.39552
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.016756 0.12944
dataset_id (Intercept) 0.009582 0.09789
Residual 0.970745 0.98526
Number of obs: 19062, groups: subject_id, 821; dataset_id, 19
Fixed effects:
Estimate Std. Error df
(Intercept) -4.961e-02 2.611e-02 1.921e+01
scale(multimodal_similarity) -3.892e-03 7.345e-03 1.866e+04
scale(age) 5.325e-02 1.232e-02 2.274e+02
scale(multimodal_similarity):scale(age) 3.495e-03 7.270e-03 1.283e+04
t value Pr(>|t|)
(Intercept) -1.900 0.0725 .
scale(multimodal_similarity) -0.530 0.5962
scale(age) 4.323 2.3e-05 ***
scale(multimodal_similarity):scale(age) 0.481 0.6307
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(mltmd_) 0.023
scale(age) -0.175 0.007
scl(ml_):() 0.028 -0.082 0.066
$ooo_similarity
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: f
Data: data
REML criterion at convergence: 51519.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.9060 -0.6450 -0.0389 0.7339 2.3890
Random effects:
Groups Name Variance Std.Dev.
subject_id (Intercept) 0.01693 0.1301
dataset_id (Intercept) 0.01092 0.1045
Residual 0.98336 0.9916
Number of obs: 18161, groups: subject_id, 821; dataset_id, 19
Fixed effects:
Estimate Std. Error df t value
(Intercept) -6.194e-02 2.781e-02 1.833e+01 -2.228
scale(ooo_similarity) -4.206e-02 1.005e-02 1.780e+03 -4.185
scale(age) 5.984e-02 1.320e-02 2.302e+02 4.535
scale(ooo_similarity):scale(age) 1.647e-02 7.784e-03 2.277e+03 2.115
Pr(>|t|)
(Intercept) 0.0387 *
scale(ooo_similarity) 2.99e-05 ***
scale(age) 9.27e-06 ***
scale(ooo_similarity):scale(age) 0.0345 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(g)
scl(_smlrt) 0.107
scale(age) -0.176 -0.040
scl(_sm):() -0.034 -0.177 0.268AoA effect is significant here which makes some intuitive sense.