library(tidyverse)
library(here)
library(lmerTest)
library(MuMIn)
library(lme4)
library(dotenv)
library(broom)
library(broom.mixed)
library(effects)
library(emmeans)
env_file = here(".env")
if (file.exists(env_file)) {
load_dot_env(file = env_file)
PROJECT_VERSION <- Sys.getenv("PROJECT_VERSION")
if (PROJECT_VERSION == "") PROJECT_VERSION <- "main"
} else {
PROJECT_VERSION <- "main"
}
VALID_SECTIONS <- c("sample1", "sample2")
DATA_FOLDER <- if (PROJECT_VERSION %in% c(VALID_SECTIONS, "main")) "main" else PROJECT_VERSION
SECTION_FILTER <- if (PROJECT_VERSION %in% VALID_SECTIONS) PROJECT_VERSION else NULL
source("lmer_helpers.R")Main visual precision statistics
Load data
trial_metadata <- read.csv(here("data","metadata","level-trialtype_data.csv"))
trial_summary_data <- read.csv(here("data", DATA_FOLDER, "processed_data", "level-trials_data.csv"))
bv_dino_similarities <- read.csv(here("data", "embeddings", "similarities-dinobv_data.csv"))
dinov3_similarities <- read.csv(here("data", "embeddings", "similarities-dino_data.csv"))
usable_trials <- trial_summary_data |>
# excluding possible scam participant
filter(exclude_participant_insufficient_data == 0 & trial_exclusion == 0 & exclude_participant == 0 & SubjectInfo.subjID != "PH2RNZ")
# Merging with similarity information and mean-centering main effects
trials_with_effect_vars <- usable_trials |>
left_join(trial_metadata) |>
mutate(age_in_months = SubjectInfo.testAge/30) |>
left_join(bv_dino_similarities |> transmute(Trials.imagePair = paste0(word1, "-", word2), image_similarity_bv = image_similarity)) |>
left_join(dinov3_similarities |> transmute(Trials.imagePair = paste0(word1, "-", word2), image_similarity_dinov3 = image_similarity))Joining with `by = join_by(Trials.trialID, Trials.targetImage,
Trials.distractorImage, Trials.imagePair, section)`
Joining with `by = join_by(Trials.imagePair)`
Joining with `by = join_by(Trials.imagePair)`# Filter by section if PROJECT_VERSION specifies a particular sample
if (!is.null(SECTION_FILTER)) {
trials_with_effect_vars <- trials_with_effect_vars |> filter(section == SECTION_FILTER)
}Sanity check - making sure all participants have at least 16 trials and that we have 83 participants
low_trial_count <- trials_with_effect_vars |> distinct(SubjectInfo.subjID,Trials.trialID) |> summarize(n=n(),.by=SubjectInfo.subjID) |> filter(n < 25)
nrow(trials_with_effect_vars |> distinct(SubjectInfo.subjID))[1] 144tidy_model <- function(main_effect){
table_data <- tidy(main_effect, effects = "fixed") %>%
mutate(
#p.value = 2 * (1 - pt(abs(statistic),df)), # Calculate p-values for lmer - just using default calculated ones
p.value.condensed = case_when(
p.value < .001 ~ "<.001",
p.value < .01 ~ "<.01",
p.value < .05 ~ "<.05",
TRUE ~ sprintf("%.3f", p.value)),
term = case_when(
term == "(Intercept)" ~ "Intercept",
term == "scale(age_in_months)" ~ "Age (scaled)",
#term == "scale(image_similarity)" ~ "Target-distractor image embedding similarity (scaled)",
TRUE ~ term
)
) %>%
rename(
Predictor = term,
"b" = estimate,
"SE" = std.error,
"t" = statistic, # Note: changed from z to t for lmer
"p" = p.value.condensed,
"p.full" = p.value
) %>%
mutate(across(c("b", "SE", "t"), ~round(., 2)))
return(table_data)
}Main mixed-effects models
Model 1 and 2: These are the models we said we’d run in our pre-reg
prereg_text_effect <- lmer(scale(corrected_target_looking) ~ scale(text_similarity) + scale(age_in_months) + scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(prereg_text_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(text_similarity) + scale(age_in_months) +
scale(AoA_Est_target) + scale(MeanSaliencyDiff) + (scale(text_similarity) |
SubjectInfo.subjID) + (1 | Trials.targetImage) + (1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10933.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.87299 -0.63059 -0.02228 0.67425 2.90341
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 2.377e-02 1.542e-01
scale(text_similarity) 1.088e-04 1.043e-02 1.00
Trials.targetImage (Intercept) 6.530e-03 8.081e-02
Trials.imagePair (Intercept) 5.415e-10 2.327e-05
Residual 9.583e-01 9.789e-01
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.008588 0.023874 64.963316 -0.360 0.72023
scale(text_similarity) -0.025581 0.017934 181.133924 -1.426 0.15547
scale(age_in_months) 0.068140 0.020329 143.836246 3.352 0.00103 **
scale(AoA_Est_target) -0.088014 0.020036 48.459152 -4.393 6.07e-05 ***
scale(MeanSaliencyDiff) 0.006286 0.019259 66.323964 0.326 0.74517
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E
scl(txt_sm) 0.042
scl(g_n_mn) 0.002 0.001
scl(AA_Es_) 0.020 0.033 0.014
scl(MnSlnD) 0.008 -0.006 -0.005 0.016
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')#Swapping text similarity with image similarity:
prereg_image_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity) + scale(age_in_months) + scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ + (1|Trials.imagePair),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(prereg_image_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) + scale(age_in_months) +
scale(AoA_Est_target) + scale(MeanSaliencyDiff) + (scale(image_similarity) |
SubjectInfo.subjID) + (1 | Trials.targetImage) + +(1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10930.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.88344 -0.63506 -0.02781 0.68074 2.91510
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0240220 0.15499
scale(image_similarity) 0.0009326 0.03054 1.00
Trials.targetImage (Intercept) 0.0060646 0.07788
Trials.imagePair (Intercept) 0.0000000 0.00000
Residual 0.9572939 0.97841
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.007540 0.023692 64.703974 -0.318 0.751322
scale(image_similarity) -0.030601 0.018136 170.968478 -1.687 0.093374 .
scale(age_in_months) 0.069096 0.020261 148.159697 3.410 0.000836 ***
scale(AoA_Est_target) -0.081317 0.020029 48.723311 -4.060 0.000177 ***
scale(MeanSaliencyDiff) 0.006805 0.019047 65.530224 0.357 0.722025
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E
scl(mg_sml) 0.071
scl(g_n_mn) 0.003 -0.011
scl(AA_Es_) 0.017 -0.157 0.015
scl(MnSlnD) 0.008 -0.010 -0.004 0.016
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')running into a singular fit, I tried removing each of the random effects but can’t fix it yet unless I removed the random effect for ‘section’. This could just be because we don’t have enough data in section 2 yet.
image_similarity_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) + scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)
summary(image_similarity_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(AoA_Est_target) + scale(MeanSaliencyDiff) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10938.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.86694 -0.63442 -0.02347 0.67461 2.90320
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023538 0.15342
Trials.targetImage (Intercept) 0.005996 0.07744
Residual 0.958808 0.97919
Number of obs: 3869, groups: SubjectInfo.subjID, 144; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df
(Intercept) -7.129e-03 2.359e-02 6.410e+01
scale(image_similarity) -3.010e-02 1.792e-02 1.784e+02
scale(age_in_months) 6.783e-02 2.031e-02 1.435e+02
scale(AoA_Est_target) -8.210e-02 1.996e-02 4.846e+01
scale(MeanSaliencyDiff) 6.539e-03 1.903e-02 6.542e+01
scale(image_similarity):scale(age_in_months) -1.021e-02 1.571e-02 3.743e+03
t value Pr(>|t|)
(Intercept) -0.302 0.76350
scale(image_similarity) -1.679 0.09484 .
scale(age_in_months) 3.340 0.00107 **
scale(AoA_Est_target) -4.114 0.00015 ***
scale(MeanSaliencyDiff) 0.344 0.73220
scale(image_similarity):scale(age_in_months) -0.650 0.51603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E s(MSD)
scl(mg_sml) -0.008
scl(g_n_mn) 0.002 -0.013
scl(AA_Es_) 0.019 -0.155 0.016
scl(MnSlnD) 0.008 -0.011 -0.005 0.016
scl(_):(__) -0.009 -0.010 0.008 0.010 -0.010Models from 1st prereg
text_similarity_effect <- lmer(scale(corrected_target_looking) ~ scale(text_similarity) * scale(age_in_months) +
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1 | Trials.imagePair)
+ (1 | Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')image_similarity_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1 | Trials.imagePair)
+ (1 | Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(text_similarity_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(text_similarity) * scale(age_in_months) +
+(scale(text_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10943.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.93466 -0.63477 -0.02959 0.66999 2.86223
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0238958 0.15458
scale(text_similarity) 0.0001477 0.01215 1.00
Trials.targetImage (Intercept) 0.0122031 0.11047
Trials.imagePair (Intercept) 0.0019834 0.04454
Residual 0.9578784 0.97871
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) -8.512e-03 2.758e-02 5.246e+01
scale(text_similarity) -2.327e-02 2.065e-02 3.218e+01
scale(age_in_months) 6.871e-02 2.037e-02 1.432e+02
scale(text_similarity):scale(age_in_months) -1.108e-02 1.594e-02 3.714e+03
t value Pr(>|t|)
(Intercept) -0.309 0.758843
scale(text_similarity) -1.127 0.268039
scale(age_in_months) 3.373 0.000957 ***
scale(text_similarity):scale(age_in_months) -0.695 0.486950
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(txt_sm) 0.048
scl(g_n_mn) -0.001 -0.001
scl(_):(__) 0.001 -0.018 0.045
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(image_similarity_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
+(scale(image_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10939.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.93621 -0.63039 -0.02968 0.67128 2.87364
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.024477 0.15645
scale(image_similarity) 0.001102 0.03320 1.00
Trials.targetImage (Intercept) 0.011243 0.10603
Trials.imagePair (Intercept) 0.001024 0.03201
Residual 0.956826 0.97818
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) -0.00736 0.02671 51.96119
scale(image_similarity) -0.04052 0.01993 27.52460
scale(age_in_months) 0.06874 0.02046 144.28633
scale(image_similarity):scale(age_in_months) -0.01010 0.01596 927.42684
t value Pr(>|t|)
(Intercept) -0.276 0.784005
scale(image_similarity) -2.033 0.051807 .
scale(age_in_months) 3.359 0.000999 ***
scale(image_similarity):scale(age_in_months) -0.633 0.527215
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(mg_sml) 0.062
scl(g_n_mn) 0.000 -0.009
scl(_):(__) -0.007 -0.007 0.119
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')What about this similarity effect per section?
image_similarity_effect_sample1 <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage),
data = trials_with_effect_vars |> filter(section=="sample1"))
summary(image_similarity_effect_sample1)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
(1 | SubjectInfo.subjID) + (1 | Trials.targetImage)
Data: filter(trials_with_effect_vars, section == "sample1")
REML criterion at convergence: 6983
Scaled residuals:
Min 1Q Median 3Q Max
-2.79001 -0.62653 -0.03021 0.66446 2.78185
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.01750 0.1323
Trials.targetImage (Intercept) 0.01556 0.1247
Residual 0.96109 0.9804
Number of obs: 2468, groups: SubjectInfo.subjID, 91; Trials.targetImage, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -8.395e-03 3.555e-02 2.977e+01
scale(image_similarity) -6.167e-02 2.474e-02 1.576e+02
scale(age_in_months) 5.875e-02 2.416e-02 9.031e+01
scale(image_similarity):scale(age_in_months) -2.584e-02 1.969e-02 2.379e+03
t value Pr(>|t|)
(Intercept) -0.236 0.8149
scale(image_similarity) -2.493 0.0137 *
scale(age_in_months) 2.432 0.0170 *
scale(image_similarity):scale(age_in_months) -1.313 0.1895
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(mg_sml) -0.002
scl(g_n_mn) 0.002 -0.003
scl(_):(__) 0.001 -0.007 0.007image_similarity_effect_sample2 <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage),
data = trials_with_effect_vars |> filter(section=="sample2"))
summary(image_similarity_effect_sample2)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
(1 | SubjectInfo.subjID) + (1 | Trials.targetImage)
Data: filter(trials_with_effect_vars, section == "sample2")
REML criterion at convergence: 3968.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.01344 -0.65087 -0.03933 0.69452 2.82763
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.038361 0.19586
Trials.targetImage (Intercept) 0.005349 0.07314
Residual 0.952483 0.97595
Number of obs: 1401, groups: SubjectInfo.subjID, 53; Trials.targetImage, 24
Fixed effects:
Estimate Std. Error df
(Intercept) -5.472e-03 4.069e-02 2.977e+01
scale(image_similarity) -1.320e-02 2.872e-02 4.550e+01
scale(age_in_months) 8.755e-02 3.743e-02 5.142e+01
scale(image_similarity):scale(age_in_months) 1.236e-02 2.584e-02 1.334e+03
t value Pr(>|t|)
(Intercept) -0.134 0.8939
scale(image_similarity) -0.460 0.6479
scale(age_in_months) 2.339 0.0233 *
scale(image_similarity):scale(age_in_months) 0.478 0.6324
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(mg_sml) -0.003
scl(g_n_mn) 0.012 -0.016
scl(_):(__) -0.016 0.017 0.009BV-image model
Trying out image only models
bv_image_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity_bv) * scale(age_in_months) + scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)
dinov3_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity_dinov3) * scale(age_in_months)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(bv_image_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(corrected_target_looking) ~ scale(image_similarity_bv) *
scale(age_in_months) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(1 | SubjectInfo.subjID) + (1 | Trials.imagePair) + (1 |
Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10936.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.84864 -0.63279 -0.02404 0.67947 2.90483
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.0238986 0.15459
Trials.targetImage (Intercept) 0.0063106 0.07944
Trials.imagePair (Intercept) 0.0004334 0.02082
Residual 0.9574898 0.97851
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error
(Intercept) -7.313e-03 2.408e-02
scale(image_similarity_bv) -1.068e-02 1.958e-02
scale(age_in_months) 6.748e-02 2.036e-02
scale(AoA_Est_target) -8.400e-02 2.051e-02
scale(MeanSaliencyDiff) 5.813e-03 1.917e-02
scale(image_similarity_bv):scale(age_in_months) -3.698e-02 1.597e-02
df t value Pr(>|t|)
(Intercept) 4.949e+01 -0.304 0.762685
scale(image_similarity_bv) 3.411e+01 -0.546 0.588932
scale(age_in_months) 1.432e+02 3.314 0.001166 **
scale(AoA_Est_target) 4.707e+01 -4.096 0.000164 ***
scale(MeanSaliencyDiff) 5.451e+01 0.303 0.762827
scale(image_similarity_bv):scale(age_in_months) 3.816e+03 -2.316 0.020594 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(m__) scl(g__) s(AA_E s(MSD)
scl(mg_sm_) -0.012
scl(g_n_mn) 0.002 -0.012
scl(AA_Es_) 0.021 -0.204 0.016
scl(MnSlnD) 0.007 0.015 -0.006 0.011
sc(__):(__) -0.007 -0.008 0.003 -0.007 0.005summary(dinov3_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(corrected_target_looking) ~ scale(image_similarity_dinov3) *
scale(age_in_months) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(1 | SubjectInfo.subjID) + (1 | Trials.imagePair) + (1 |
Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10939.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.87950 -0.62755 -0.02442 0.67409 2.90733
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023583 0.15357
Trials.targetImage (Intercept) 0.006537 0.08085
Trials.imagePair (Intercept) 0.000000 0.00000
Residual 0.958549 0.97906
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error
(Intercept) -7.946e-03 2.386e-02
scale(image_similarity_dinov3) -2.378e-02 1.849e-02
scale(age_in_months) 6.767e-02 2.031e-02
scale(AoA_Est_target) -8.337e-02 2.023e-02
scale(MeanSaliencyDiff) 4.664e-03 1.929e-02
scale(image_similarity_dinov3):scale(age_in_months) -1.751e-02 1.575e-02
df t value Pr(>|t|)
(Intercept) 6.385e+01 -0.333 0.740135
scale(image_similarity_dinov3) 1.283e+02 -1.286 0.200759
scale(age_in_months) 1.434e+02 3.331 0.001101
scale(AoA_Est_target) 4.823e+01 -4.121 0.000147
scale(MeanSaliencyDiff) 6.356e+01 0.242 0.809753
scale(image_similarity_dinov3):scale(age_in_months) 3.739e+03 -1.111 0.266508
(Intercept)
scale(image_similarity_dinov3)
scale(age_in_months) **
scale(AoA_Est_target) ***
scale(MeanSaliencyDiff)
scale(image_similarity_dinov3):scale(age_in_months)
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__3) sc(__) s(AA_E s(MSD)
scl(mg_s_3) 0.002
scl(g_n_mn) 0.002 -0.005
scl(AA_Es_) 0.018 -0.147 0.014
scl(MnSlnD) 0.008 0.055 -0.006 0.008
s(__3):(__) -0.001 -0.006 -0.002 0.004 0.002
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')r.squaredGLMM(prereg_image_effect) R2m R2c
[1,] 0.0132299 0.04420046r.squaredGLMM(prereg_text_effect) R2m R2c
[1,] 0.01315592 0.04350641r.squaredGLMM(bv_image_effect) R2m R2c
[1,] 0.01370516 0.04429087Image similarity is trending towards significance.
Checking if text similarity and image similarity are differently correlated with AoA
trials_with_effect_vars_metadata <- trials_with_effect_vars |> distinct(Trials.imagePair, text_similarity, image_similarity, image_similarity_bv)
cor.test(trial_metadata$AoA_Est_target, trial_metadata$text_similarity)
Pearson's product-moment correlation
data: trial_metadata$AoA_Est_target and trial_metadata$text_similarity
t = -0.47957, df = 62, p-value = 0.6332
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.3020875 0.1878236
sample estimates:
cor
-0.0607924 cor.test(trial_metadata$AoA_Est_target, trial_metadata$image_similarity)
Pearson's product-moment correlation
data: trial_metadata$AoA_Est_target and trial_metadata$image_similarity
t = 0.88711, df = 62, p-value = 0.3784
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.1376426 0.3481820
sample estimates:
cor
0.1119545 cor.test(trials_with_effect_vars_metadata$image_similarity, trials_with_effect_vars_metadata$image_similarity_bv)
Pearson's product-moment correlation
data: trials_with_effect_vars_metadata$image_similarity and trials_with_effect_vars_metadata$image_similarity_bv
t = 2.3117, df = 30, p-value = 0.02784
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.0464438 0.6494747
sample estimates:
cor
0.3888406 cor.test(trials_with_effect_vars_metadata$text_similarity, trials_with_effect_vars_metadata$image_similarity_bv)
Pearson's product-moment correlation
data: trials_with_effect_vars_metadata$text_similarity and trials_with_effect_vars_metadata$image_similarity_bv
t = 1.3913, df = 30, p-value = 0.1744
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.1121168 0.5478627
sample estimates:
cor
0.2462023 cor.test(trial_metadata$MeanSaliencyDiff, trial_metadata$image_similarity, method="pearson")
Pearson's product-moment correlation
data: trial_metadata$MeanSaliencyDiff and trial_metadata$image_similarity
t = -3.3454e-16, df = 62, p-value = 1
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.2458093 0.2458093
sample estimates:
cor
-4.248667e-17 Well image similarity has a higher r but both are still insignificant.
visual saliency without AoA
vs_image_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair)
,
data = trials_with_effect_vars)
vs_text_effect <- lmer(scale(corrected_target_looking) ~ scale(text_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair)
,
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(vs_image_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | SubjectInfo.subjID) + (1 |
Trials.targetImage) + (1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10947.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.92168 -0.63596 -0.02847 0.66929 2.86012
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.0238477 0.15443
Trials.targetImage (Intercept) 0.0117913 0.10859
Trials.imagePair (Intercept) 0.0009026 0.03004
Residual 0.9584418 0.97900
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) -6.862e-03 2.678e-02 5.105e+01
scale(image_similarity) -3.986e-02 1.973e-02 2.688e+01
scale(age_in_months) 6.894e-02 2.037e-02 1.433e+02
scale(MeanSaliencyDiff) 8.712e-03 2.138e-02 6.257e+01
scale(image_similarity):scale(age_in_months) -9.774e-03 1.572e-02 3.736e+03
t value Pr(>|t|)
(Intercept) -0.256 0.798823
scale(image_similarity) -2.020 0.053445 .
scale(age_in_months) 3.385 0.000919 ***
scale(MeanSaliencyDiff) 0.408 0.684998
scale(image_similarity):scale(age_in_months) -0.622 0.534072
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(mg_sml) -0.009
scl(g_n_mn) 0.000 -0.011
scl(MnSlnD) 0.009 0.003 -0.006
scl(_):(__) -0.009 -0.008 0.008 -0.010summary(vs_text_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(text_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (scale(text_similarity) | SubjectInfo.subjID) +
(1 | Trials.targetImage) + (1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10949.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.93668 -0.63361 -0.02931 0.67112 2.86251
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0239010 0.15460
scale(text_similarity) 0.0001479 0.01216 1.00
Trials.targetImage (Intercept) 0.0127812 0.11305
Trials.imagePair (Intercept) 0.0018467 0.04297
Residual 0.9579062 0.97873
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) -8.565e-03 2.773e-02 5.194e+01
scale(text_similarity) -2.355e-02 2.065e-02 3.215e+01
scale(age_in_months) 6.867e-02 2.037e-02 1.432e+02
scale(MeanSaliencyDiff) 8.988e-03 2.176e-02 5.865e+01
scale(text_similarity):scale(age_in_months) -1.114e-02 1.594e-02 3.713e+03
t value Pr(>|t|)
(Intercept) -0.309 0.758692
scale(text_similarity) -1.140 0.262575
scale(age_in_months) 3.371 0.000964 ***
scale(MeanSaliencyDiff) 0.413 0.681116
scale(text_similarity):scale(age_in_months) -0.699 0.484755
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(txt_sm) 0.048
scl(g_n_mn) -0.001 -0.001
scl(MnSlnD) 0.009 0.002 -0.005
scl(_):(__) 0.001 -0.018 0.045 -0.008
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Just a sanity check that adding our saliency metric as a covariate does not affect our similarity effects.
Alternate window analyses
Only using first instance of an item
first_instance_target <- trials_with_effect_vars |>
group_by(SubjectInfo.subjID, Trials.targetImage) |>
arrange(Trials.ordinal, .by_group = TRUE) |>
slice(1) |>
ungroup()
second_instance_target <- trials_with_effect_vars |>
group_by(SubjectInfo.subjID, Trials.targetImage) |>
arrange(Trials.ordinal, .by_group = TRUE) |>
slice(2) |>
ungroup()
primary_targets <- trials_with_effect_vars |>
filter(Trials.trialType %in% c("easy", "hard"))
first_instance_primary_target <- first_instance_target |>
filter(Trials.trialType %in% c("easy", "hard"))
first_instance_image_pair <- trials_with_effect_vars |>
group_by(SubjectInfo.subjID, Trials.imagePair) |>
arrange(Trials.ordinal, .by_group = TRUE) |>
slice(1) |>
ungroup()
first_instance_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = first_instance_target)
second_instance_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = second_instance_target)
first_instance_pt_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = first_instance_primary_target)
pt_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity) + scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = primary_targets)
first_instance_image_pair_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = first_instance_image_pair)
summary(first_instance_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | Trials.ordinal) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: first_instance_target
REML criterion at convergence: 8633.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.94959 -0.64112 -0.02766 0.66632 2.83295
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.0268054 0.16372
Trials.targetImage (Intercept) 0.0128114 0.11319
Trials.ordinal (Intercept) 0.0006614 0.02572
Residual 0.9541246 0.97679
Number of obs: 3050, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.ordinal, 47
Fixed effects:
Estimate Std. Error df
(Intercept) -1.486e-04 2.834e-02 4.731e+01
scale(image_similarity) -4.876e-02 2.197e-02 1.276e+02
scale(age_in_months) 7.247e-02 2.237e-02 1.458e+02
scale(MeanSaliencyDiff) 5.100e-03 2.357e-02 6.291e+01
scale(image_similarity):scale(age_in_months) 4.293e-05 1.767e-02 2.956e+03
t value Pr(>|t|)
(Intercept) -0.005 0.99584
scale(image_similarity) -2.220 0.02819 *
scale(age_in_months) 3.240 0.00148 **
scale(MeanSaliencyDiff) 0.216 0.82941
scale(image_similarity):scale(age_in_months) 0.002 0.99806
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(mg_sml) -0.007
scl(g_n_mn) 0.001 -0.017
scl(MnSlnD) 0.000 -0.081 -0.009
scl(_):(__) -0.014 -0.021 0.013 -0.006summary(second_instance_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | Trials.ordinal) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: second_instance_target
REML criterion at convergence: 2340.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.87044 -0.61734 -0.06732 0.63563 2.67477
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.027475 0.16576
Trials.ordinal (Intercept) 0.000338 0.01839
Trials.targetImage (Intercept) 0.004733 0.06880
Residual 0.966704 0.98321
Number of obs: 819, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 34; Trials.targetImage, 16
Fixed effects:
Estimate Std. Error df
(Intercept) -0.003067 0.041564 11.901191
scale(image_similarity) -0.003672 0.040452 302.728420
scale(age_in_months) 0.062243 0.037291 128.162467
scale(MeanSaliencyDiff) 0.025945 0.042532 38.851355
scale(image_similarity):scale(age_in_months) -0.046945 0.035139 804.228357
t value Pr(>|t|)
(Intercept) -0.074 0.9424
scale(image_similarity) -0.091 0.9277
scale(age_in_months) 1.669 0.0975 .
scale(MeanSaliencyDiff) 0.610 0.5454
scale(image_similarity):scale(age_in_months) -1.336 0.1819
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(mg_sml) 0.000
scl(g_n_mn) 0.000 0.024
scl(MnSlnD) 0.006 0.456 0.028
scl(_):(__) 0.012 0.032 -0.009 -0.003summary(first_instance_pt_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | Trials.ordinal) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: first_instance_primary_target
REML criterion at convergence: 3164
Scaled residuals:
Min 1Q Median 3Q Max
-2.78542 -0.64758 -0.03047 0.68587 2.86506
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.04141 0.2035
Trials.ordinal (Intercept) 0.01023 0.1012
Trials.targetImage (Intercept) 0.01419 0.1191
Residual 0.92620 0.9624
Number of obs: 1116, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 35; Trials.targetImage, 16
Fixed effects:
Estimate Std. Error df
(Intercept) 9.529e-03 4.950e-02 2.057e+01
scale(image_similarity) -6.371e-02 3.558e-02 4.530e+02
scale(age_in_months) 8.107e-02 3.355e-02 1.435e+02
scale(MeanSaliencyDiff) -1.742e-02 4.179e-02 4.156e+01
scale(image_similarity):scale(age_in_months) 6.563e-02 2.928e-02 1.086e+03
t value Pr(>|t|)
(Intercept) 0.193 0.8492
scale(image_similarity) -1.791 0.0740 .
scale(age_in_months) 2.416 0.0169 *
scale(MeanSaliencyDiff) -0.417 0.6790
scale(image_similarity):scale(age_in_months) 2.241 0.0252 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(mg_sml) -0.006
scl(g_n_mn) -0.003 -0.041
scl(MnSlnD) 0.003 0.427 -0.026
scl(_):(__) -0.023 -0.044 0.006 -0.011summary(pt_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) + scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | Trials.ordinal) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: primary_targets
REML criterion at convergence: 5479.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.03560 -0.63184 -0.03267 0.67059 2.86648
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.03288 0.18132
Trials.ordinal (Intercept) 0.00290 0.05385
Trials.targetImage (Intercept) 0.01192 0.10916
Residual 0.94854 0.97393
Number of obs: 1935, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 44; Trials.targetImage, 16
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.585e-04 3.987e-02 2.137e+01 0.022 0.98302
scale(image_similarity) -3.630e-02 2.723e-02 7.162e+02 -1.333 0.18289
scale(age_in_months) 7.128e-02 2.689e-02 1.356e+02 2.651 0.00898 **
scale(MeanSaliencyDiff) 8.121e-03 3.378e-02 5.237e+01 0.240 0.81098
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(mg_sml) -0.005
scl(g_n_mn) -0.004 -0.014
scl(MnSlnD) 0.010 0.419 -0.006summary(first_instance_image_pair_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(MeanSaliencyDiff) + (1 | Trials.ordinal) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: first_instance_image_pair
REML criterion at convergence: 6300.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.84270 -0.63429 -0.03589 0.66606 2.85406
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.021312 0.1460
Trials.targetImage (Intercept) 0.010303 0.1015
Trials.ordinal (Intercept) 0.001369 0.0370
Residual 0.963252 0.9815
Number of obs: 2219, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.ordinal, 33
Fixed effects:
Estimate Std. Error df
(Intercept) -1.662e-03 2.991e-02 3.025e+01
scale(image_similarity) -3.785e-02 2.359e-02 1.612e+02
scale(age_in_months) 5.783e-02 2.419e-02 1.436e+02
scale(MeanSaliencyDiff) -3.014e-02 2.524e-02 7.175e+01
scale(image_similarity):scale(age_in_months) 9.082e-03 2.092e-02 2.091e+03
t value Pr(>|t|)
(Intercept) -0.056 0.9560
scale(image_similarity) -1.605 0.1105
scale(age_in_months) 2.391 0.0181 *
scale(MeanSaliencyDiff) -1.194 0.2362
scale(image_similarity):scale(age_in_months) 0.434 0.6643
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(MSD)
scl(mg_sml) -0.008
scl(g_n_mn) 0.000 -0.008
scl(MnSlnD) 0.007 -0.030 -0.004
scl(_):(__) -0.003 -0.010 0.005 0.011Random order effects
main_image_effect_ordinal <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | Trials.ordinal)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = trials_with_effect_vars)
summary(main_image_effect_ordinal)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) +
scale(AoA_Est_target) + scale(MeanSaliencyDiff) + (1 | Trials.ordinal) +
(1 | SubjectInfo.subjID) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10938.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.85788 -0.64014 -0.02222 0.67477 2.88444
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023600 0.15362
Trials.ordinal (Intercept) 0.001485 0.03853
Trials.targetImage (Intercept) 0.005991 0.07740
Residual 0.957316 0.97843
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 52; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df
(Intercept) -6.538e-03 2.454e-02 4.785e+01
scale(image_similarity) -3.031e-02 1.792e-02 1.785e+02
scale(age_in_months) 6.785e-02 2.031e-02 1.435e+02
scale(AoA_Est_target) -8.260e-02 1.996e-02 4.860e+01
scale(MeanSaliencyDiff) 6.428e-03 1.903e-02 6.555e+01
scale(image_similarity):scale(age_in_months) -1.011e-02 1.571e-02 3.741e+03
t value Pr(>|t|)
(Intercept) -0.266 0.791051
scale(image_similarity) -1.692 0.092423 .
scale(age_in_months) 3.341 0.001066 **
scale(AoA_Est_target) -4.138 0.000139 ***
scale(MeanSaliencyDiff) 0.338 0.736561
scale(image_similarity):scale(age_in_months) -0.644 0.519823
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E s(MSD)
scl(mg_sml) -0.008
scl(g_n_mn) 0.002 -0.013
scl(AA_Es_) 0.017 -0.155 0.016
scl(MnSlnD) 0.007 -0.011 -0.005 0.016
scl(_):(__) -0.009 -0.010 0.008 0.009 -0.010Order does not explain much variance.
Target image order effects
ranked_trials <- trials_with_effect_vars |>
group_by(SubjectInfo.subjID, Trials.targetImage) |>
arrange(Trials.ordinal, .by_group = TRUE) |>
mutate(
slice_num = row_number(),
order = case_when(
slice_num == 1 ~ -0.5,
slice_num == 2 ~ 0.5,
TRUE ~ NA
)
)
main_image_effect_ranked <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*(scale(order))
+ scale(AoA_Est_target)
+ scale(age_in_months)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = ranked_trials)
# |> filter(Trials.trialType %in% c("easy", "hard")
summary(main_image_effect_ranked)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(corrected_target_looking) ~ scale(image_similarity) * (scale(order)) +
scale(AoA_Est_target) + scale(age_in_months) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: ranked_trials
REML criterion at convergence: 10937.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.88325 -0.63247 -0.02264 0.67221 2.86456
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023205 0.15233
Trials.targetImage (Intercept) 0.005383 0.07337
Residual 0.959048 0.97931
Number of obs: 3869, groups: SubjectInfo.subjID, 144; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df t value
(Intercept) -5.457e-03 2.327e-02 6.351e+01 -0.235
scale(image_similarity) -3.038e-02 1.778e-02 1.698e+02 -1.708
scale(order) 2.144e-02 1.660e-02 8.945e+02 1.292
scale(AoA_Est_target) -8.137e-02 1.974e-02 4.977e+01 -4.122
scale(age_in_months) 6.811e-02 2.025e-02 1.435e+02 3.363
scale(image_similarity):scale(order) 7.930e-03 1.651e-02 1.854e+03 0.480
Pr(>|t|)
(Intercept) 0.815301
scale(image_similarity) 0.089394 .
scale(order) 0.196780
scale(AoA_Est_target) 0.000142 ***
scale(age_in_months) 0.000990 ***
scale(image_similarity):scale(order) 0.630978
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) scl(r) s(AA_E sc(__)
scl(mg_sml) -0.008
scale(ordr) 0.047 0.008
scl(AA_Es_) 0.018 -0.159 -0.008
scl(g_n_mn) 0.003 -0.014 0.001 0.017
scl(mg_):() 0.010 -0.049 0.010 0.112 0.014Z-scoring embeddings
Z-scoring embeddings led to significant image similarity even with AoA as a covariate in section 1 – todo to include section 2!
#main_image_effect_zscored <- lmer(scale(corrected_target_looking) ~ scale(image_sim_zscore)*scale(age_in_months)
# + scale(AoA_Est_target)
# + scale(MeanSaliencyDiff)
# + (1 | SubjectInfo.subjID)
# + (1|Trials.targetImage)
# + ( 1 | section),
# data = trials_with_effect_vars)
#summary(main_image_effect_zscored)Baseline window as a covariate
baseline_covariate_looking_text <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)*scale(text_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ scale(mean_target_looking_baseline_window)
+ (scale(text_similarity) || SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')baseline_covariate_looking_image <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)*scale(image_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ scale(mean_target_looking_baseline_window)
+ (scale(image_similarity) || SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(baseline_covariate_looking_text)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(age_in_months) *
scale(text_similarity) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
scale(mean_target_looking_baseline_window) + (scale(text_similarity) ||
SubjectInfo.subjID) + (1 | Trials.imagePair) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10410.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.80169 -0.70781 0.05646 0.75168 2.48896
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID scale(text_similarity) 0.00000 0.00000
SubjectInfo.subjID.1 (Intercept) 0.03876 0.19687
Trials.targetImage (Intercept) 0.02044 0.14297
Trials.imagePair (Intercept) 0.00120 0.03464
Residual 0.81886 0.90491
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) -1.478e-03 3.136e-02 5.388e+01
scale(age_in_months) 8.762e-02 2.195e-02 1.403e+02
scale(text_similarity) -5.234e-02 2.033e-02 2.791e+01
scale(AoA_Est_target) -1.176e-01 2.607e-02 4.053e+01
scale(MeanSaliencyDiff) 6.230e-02 2.323e-02 7.472e+01
scale(mean_target_looking_baseline_window) 2.815e-01 1.495e-02 3.799e+03
scale(age_in_months):scale(text_similarity) -1.336e-02 1.482e-02 3.802e+03
t value Pr(>|t|)
(Intercept) -0.047 0.962580
scale(age_in_months) 3.991 0.000105 ***
scale(text_similarity) -2.574 0.015658 *
scale(AoA_Est_target) -4.509 5.45e-05 ***
scale(MeanSaliencyDiff) 2.682 0.009004 **
scale(mean_target_looking_baseline_window) 18.831 < 2e-16 ***
scale(age_in_months):scale(text_similarity) -0.901 0.367526
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) scl(_) s(AA_E s(MSD) s(____
scl(g_n_mn) 0.001
scl(txt_sm) 0.024 -0.002
scl(AA_Es_) 0.034 0.010 0.039
scl(MnSlnD) 0.012 -0.004 0.025 0.035
scl(mn____) -0.010 -0.007 0.017 0.012 -0.046
scl(__):(_) 0.000 0.004 -0.016 0.003 -0.007 0.000
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(baseline_covariate_looking_image)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(age_in_months) *
scale(image_similarity) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
scale(mean_target_looking_baseline_window) + (scale(image_similarity) ||
SubjectInfo.subjID) + (1 | Trials.imagePair) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10412.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.79380 -0.70901 0.06044 0.75141 2.49638
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID scale(image_similarity) 0.000000 0.00000
SubjectInfo.subjID.1 (Intercept) 0.038928 0.19730
Trials.targetImage (Intercept) 0.018701 0.13675
Trials.imagePair (Intercept) 0.002896 0.05381
Residual 0.818879 0.90492
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) 1.421e-03 3.164e-02 5.160e+01
scale(age_in_months) 8.783e-02 2.198e-02 1.409e+02
scale(image_similarity) -4.224e-02 2.151e-02 2.655e+01
scale(AoA_Est_target) -1.086e-01 2.608e-02 4.208e+01
scale(MeanSaliencyDiff) 6.435e-02 2.286e-02 6.782e+01
scale(mean_target_looking_baseline_window) 2.820e-01 1.495e-02 3.795e+03
scale(age_in_months):scale(image_similarity) -1.883e-02 1.455e-02 3.707e+03
t value Pr(>|t|)
(Intercept) 0.045 0.964362
scale(age_in_months) 3.996 0.000103 ***
scale(image_similarity) -1.963 0.060165 .
scale(AoA_Est_target) -4.164 0.000152 ***
scale(MeanSaliencyDiff) 2.814 0.006389 **
scale(mean_target_looking_baseline_window) 18.863 < 2e-16 ***
scale(age_in_months):scale(image_similarity) -1.294 0.195719
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) scl(_) s(AA_E s(MSD) s(____
scl(g_n_mn) 0.001
scl(mg_sml) -0.015 -0.010
scl(AA_Es_) 0.034 0.011 -0.118
scl(MnSlnD) 0.010 -0.004 0.018 0.025
scl(mn____) -0.011 -0.007 0.006 0.012 -0.048
scl(__):(_) -0.007 0.007 -0.007 0.006 -0.010 0.009
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')We’re seeing some interesting effects. although these models are still singular fits.
Predicting critical window looking
critical_looking_text <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)
+ scale(text_similarity)
+ scale(image_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair)
,
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')critical_looking_image <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)*scale(image_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')critical_looking_bvimage <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)*scale(image_similarity_bv)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(critical_looking_text)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(age_in_months) +
scale(text_similarity) + scale(image_similarity) + scale(AoA_Est_target) +
scale(MeanSaliencyDiff) + (scale(text_similarity) | SubjectInfo.subjID) +
(1 | Trials.targetImage) + (1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10738.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.42343 -0.67925 0.06841 0.75564 2.41202
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0392784 0.19819
scale(text_similarity) 0.0003304 0.01818 1.00
Trials.targetImage (Intercept) 0.0361532 0.19014
Trials.imagePair (Intercept) 0.0045609 0.06753
Residual 0.8896729 0.94322
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.005773 0.038108 45.067173 0.151 0.880273
scale(age_in_months) 0.092963 0.022392 141.061836 4.152 5.69e-05 ***
scale(text_similarity) -0.055314 0.029682 31.550003 -1.864 0.071719 .
scale(image_similarity) -0.012276 0.029852 28.954692 -0.411 0.683924
scale(AoA_Est_target) -0.119237 0.033238 39.874407 -3.587 0.000903 ***
scale(MeanSaliencyDiff) 0.078704 0.027850 64.511135 2.826 0.006267 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) scl(t_) scl(m_) s(AA_E
scl(g_n_mn) 0.000
scl(txt_sm) 0.074 0.004
scl(mg_sml) -0.043 -0.010 -0.550
scl(AA_Es_) 0.050 0.010 0.118 -0.147
scl(MnSlnD) 0.011 -0.004 0.016 0.033 0.030
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(critical_looking_image)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(age_in_months) *
scale(image_similarity) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(scale(image_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10740.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.42107 -0.68108 0.06944 0.75401 2.37689
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0395040 0.19876
scale(image_similarity) 0.0006125 0.02475 1.00
Trials.targetImage (Intercept) 0.0345184 0.18579
Trials.imagePair (Intercept) 0.0067548 0.08219
Residual 0.8892894 0.94302
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error df
(Intercept) 9.594e-03 3.857e-02 4.493e+01
scale(age_in_months) 9.056e-02 2.249e-02 1.400e+02
scale(image_similarity) -4.377e-02 2.633e-02 2.546e+01
scale(AoA_Est_target) -1.125e-01 3.295e-02 4.066e+01
scale(MeanSaliencyDiff) 8.219e-02 2.769e-02 6.233e+01
scale(age_in_months):scale(image_similarity) -2.167e-02 1.531e-02 1.426e+03
t value Pr(>|t|)
(Intercept) 0.249 0.80467
scale(age_in_months) 4.026 9.24e-05 ***
scale(image_similarity) -1.662 0.10870
scale(AoA_Est_target) -3.414 0.00146 **
scale(MeanSaliencyDiff) 2.968 0.00424 **
scale(age_in_months):scale(image_similarity) -1.415 0.15723
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) scl(_) s(AA_E s(MSD)
scl(g_n_mn) -0.001
scl(mg_sml) 0.018 -0.008
scl(AA_Es_) 0.043 0.010 -0.094
scl(MnSlnD) 0.010 -0.005 0.040 0.025
scl(__):(_) -0.005 0.107 -0.005 0.005 -0.009
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(critical_looking_bvimage)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_critical_window) ~ scale(age_in_months) *
scale(image_similarity_bv) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(scale(image_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10743.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.43431 -0.68341 0.07012 0.74891 2.38340
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0395912 0.19898
scale(image_similarity) 0.0005543 0.02354 1.00
Trials.targetImage (Intercept) 0.0369193 0.19214
Trials.imagePair (Intercept) 0.0082635 0.09090
Residual 0.8890886 0.94291
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate Std. Error
(Intercept) 0.01068 0.03983
scale(age_in_months) 0.09243 0.02241
scale(image_similarity_bv) -0.01240 0.03088
scale(AoA_Est_target) -0.11434 0.03435
scale(MeanSaliencyDiff) 0.08287 0.02838
scale(age_in_months):scale(image_similarity_bv) -0.01926 0.01546
df t value Pr(>|t|)
(Intercept) 45.93476 0.268 0.78986
scale(age_in_months) 141.30651 4.125 6.3e-05 ***
scale(image_similarity_bv) 30.56083 -0.402 0.69070
scale(AoA_Est_target) 42.52277 -3.329 0.00181 **
scale(MeanSaliencyDiff) 62.10401 2.920 0.00488 **
scale(age_in_months):scale(image_similarity_bv) 3715.22664 -1.246 0.21286
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(g__) scl(m__) s(AA_E s(MSD)
scl(g_n_mn) -0.001
scl(mg_sm_) -0.008 -0.009
scl(AA_Es_) 0.046 0.010 -0.168
scl(MnSlnD) 0.009 -0.004 0.067 0.016
sc(__):(__) -0.004 0.043 -0.007 -0.004 0.003
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')r.squaredGLMM(critical_looking_image) R2m R2c
[1,] 0.03151588 0.1127214r.squaredGLMM(critical_looking_text) R2m R2c
[1,] 0.03309068 0.1131578Similar predictions to our original model.
Adding window type as a covariate
trials_window_type_separated <- trials_with_effect_vars |>
pivot_longer(cols=c(mean_target_looking_critical_window, mean_target_looking_baseline_window), names_to="window_type", values_to="target_looking") |>
mutate(window_type = str_replace(window_type, "mean_target_looking_", "")) |>
mutate(trial_window_c = case_when(
window_type=="critical_window" ~ 0.5,
window_type=="baseline_window" ~ -0.5))
window_type_looking_text <- lmer(scale(target_looking) ~ scale(age_in_months)*trial_window_c*scale(text_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1|Trials.imagePair)
+ (1|Trials.targetImage) ,
data = trials_window_type_separated)boundary (singular) fit: see help('isSingular')window_type_looking_image <- lmer(scale(target_looking) ~ scale(age_in_months)*trial_window_c*scale(image_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1 | Trials.imagePair)
+ (1|Trials.targetImage) ,
data = trials_window_type_separated)boundary (singular) fit: see help('isSingular')summary(window_type_looking_image)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(target_looking) ~ scale(age_in_months) * trial_window_c *
scale(image_similarity) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(scale(image_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_window_type_separated
REML criterion at convergence: 21594.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.42295 -0.70604 0.03112 0.75352 2.09636
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0123803 0.11127
scale(image_similarity) 0.0001164 0.01079 1.00
Trials.targetImage (Intercept) 0.0412580 0.20312
Trials.imagePair (Intercept) 0.0099064 0.09953
Residual 0.9246853 0.96161
Number of obs: 7738, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate
(Intercept) 2.165e-02
scale(age_in_months) 5.053e-02
trial_window_c 1.862e-01
scale(image_similarity) -2.508e-02
scale(AoA_Est_target) -6.477e-02
scale(MeanSaliencyDiff) 6.838e-02
scale(age_in_months):trial_window_c 8.199e-02
scale(age_in_months):scale(image_similarity) -1.378e-02
trial_window_c:scale(image_similarity) -5.006e-02
scale(age_in_months):trial_window_c:scale(image_similarity) -9.786e-03
Std. Error
(Intercept) 3.752e-02
scale(age_in_months) 1.438e-02
trial_window_c 2.186e-02
scale(image_similarity) 2.544e-02
scale(AoA_Est_target) 3.348e-02
scale(MeanSaliencyDiff) 2.659e-02
scale(age_in_months):trial_window_c 2.187e-02
scale(age_in_months):scale(image_similarity) 1.097e-02
trial_window_c:scale(image_similarity) 2.187e-02
scale(age_in_months):trial_window_c:scale(image_similarity) 2.176e-02
df t value
(Intercept) 4.132e+01 0.577
scale(age_in_months) 1.384e+02 3.515
trial_window_c 7.509e+03 8.518
scale(image_similarity) 2.555e+01 -0.986
scale(AoA_Est_target) 4.216e+01 -1.935
scale(MeanSaliencyDiff) 5.677e+01 2.572
scale(age_in_months):trial_window_c 7.509e+03 3.749
scale(age_in_months):scale(image_similarity) 2.703e+03 -1.256
trial_window_c:scale(image_similarity) 7.509e+03 -2.289
scale(age_in_months):trial_window_c:scale(image_similarity) 7.509e+03 -0.450
Pr(>|t|)
(Intercept) 0.567077
scale(age_in_months) 0.000595 ***
trial_window_c < 2e-16 ***
scale(image_similarity) 0.333409
scale(AoA_Est_target) 0.059746 .
scale(MeanSaliencyDiff) 0.012755 *
scale(age_in_months):trial_window_c 0.000179 ***
scale(age_in_months):scale(image_similarity) 0.209282
trial_window_c:scale(image_similarity) 0.022106 *
scale(age_in_months):trial_window_c:scale(image_similarity) 0.652946
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) trl_w_ scl(_) s(AA_E s(MSD) sc(__):__ s(__):( t__:(_
scl(g_n_mn) -0.003
tril_wndw_c 0.000 0.000
scl(mg_sml) -0.010 -0.007 0.000
scl(AA_Es_) 0.058 0.007 0.000 -0.052
scl(MnSlnD) 0.009 -0.004 0.000 0.072 0.024
scl(g__):__ 0.000 0.000 0.000 0.000 0.000 0.000
scl(__):(_) -0.005 0.062 0.000 -0.004 0.003 -0.007 0.000
trl_wn_:(_) 0.000 0.000 0.000 0.000 0.000 0.000 -0.014 0.000
s(__):__:(_ 0.000 0.000 -0.013 0.000 0.000 0.000 0.009 0.000 -0.011
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(window_type_looking_text)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(target_looking) ~ scale(age_in_months) * trial_window_c *
scale(text_similarity) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(scale(text_similarity) | SubjectInfo.subjID) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_window_type_separated
REML criterion at convergence: 21596.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.41677 -0.70605 0.03487 0.75453 2.12926
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0122173 0.11053
scale(text_similarity) 0.0002801 0.01674 1.00
Trials.targetImage (Intercept) 0.0415307 0.20379
Trials.imagePair (Intercept) 0.0075376 0.08682
Residual 0.9252478 0.96190
Number of obs: 7738, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32
Fixed effects:
Estimate
(Intercept) 1.757e-02
scale(age_in_months) 5.110e-02
trial_window_c 1.861e-01
scale(text_similarity) -4.733e-02
scale(AoA_Est_target) -7.010e-02
scale(MeanSaliencyDiff) 6.475e-02
scale(age_in_months):trial_window_c 8.130e-02
scale(age_in_months):scale(text_similarity) -8.835e-03
trial_window_c:scale(text_similarity) -2.073e-02
scale(age_in_months):trial_window_c:scale(text_similarity) -1.373e-02
Std. Error
(Intercept) 3.658e-02
scale(age_in_months) 1.434e-02
trial_window_c 2.187e-02
scale(text_similarity) 2.357e-02
scale(AoA_Est_target) 3.315e-02
scale(MeanSaliencyDiff) 2.627e-02
scale(age_in_months):trial_window_c 2.187e-02
scale(age_in_months):scale(text_similarity) 1.113e-02
trial_window_c:scale(text_similarity) 2.188e-02
scale(age_in_months):trial_window_c:scale(text_similarity) 2.191e-02
df t value
(Intercept) 4.118e+01 0.480
scale(age_in_months) 1.390e+02 3.563
trial_window_c 7.509e+03 8.509
scale(text_similarity) 2.640e+01 -2.008
scale(AoA_Est_target) 4.092e+01 -2.115
scale(MeanSaliencyDiff) 5.931e+01 2.465
scale(age_in_months):trial_window_c 7.509e+03 3.717
scale(age_in_months):scale(text_similarity) 2.968e+03 -0.794
trial_window_c:scale(text_similarity) 7.509e+03 -0.948
scale(age_in_months):trial_window_c:scale(text_similarity) 7.509e+03 -0.626
Pr(>|t|)
(Intercept) 0.633553
scale(age_in_months) 0.000502 ***
trial_window_c < 2e-16 ***
scale(text_similarity) 0.054979 .
scale(AoA_Est_target) 0.040600 *
scale(MeanSaliencyDiff) 0.016634 *
scale(age_in_months):trial_window_c 0.000203 ***
scale(age_in_months):scale(text_similarity) 0.427273
trial_window_c:scale(text_similarity) 0.343383
scale(age_in_months):trial_window_c:scale(text_similarity) 0.531090
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sc(__) trl_w_ scl(_) s(AA_E s(MSD) sc(__):__ s(__):( t__:(_
scl(g_n_mn) -0.003
tril_wndw_c 0.000 0.000
scl(txt_sm) 0.058 -0.005 0.000
scl(AA_Es_) 0.060 0.007 0.000 0.059
scl(MnSlnD) 0.013 -0.004 0.000 0.067 0.036
scl(g__):__ 0.000 0.000 0.000 0.000 0.000 0.000
scl(__):(_) 0.001 0.086 0.000 -0.008 0.001 -0.005 0.000
trl_wn_:(_) 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.000
s(__):__:(_ 0.000 0.000 0.002 0.000 0.000 0.000 0.004 0.000 -0.022
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')The interaction makes sense, plotting the predictions here to try to understand what’s going on
trials_window_type_separated$predicted <- predict(window_type_looking_image)
# Plot interaction effect
ggplot(trials_window_type_separated, aes(x = image_similarity, y = predicted, color = factor(window_type))) +
geom_point(alpha = 0.5) + # Add points for raw data
geom_smooth(method = "lm", se = TRUE) + # Add regression lines
labs(title = "Interaction Between Trial Window & Image Similarity",
x = "Scaled Image Similarity",
y = "Predicted Target Looking",
color = "Trial Window") +
theme_minimal()`geom_smooth()` using formula = 'y ~ x'
This is also making me wonder whether we see any signal in how infants look at images in the baseline window. ## Baseline window looking
baseline_looking_image <- lmer(scale(mean_target_looking_baseline_window) ~ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID) + (1 | Trials.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')summary(baseline_looking_image)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(mean_target_looking_baseline_window) ~ scale(AoA_Est_target) +
scale(MeanSaliencyDiff) + (1 | SubjectInfo.subjID) + (1 |
Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10903.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.05141 -0.73467 0.00675 0.74517 1.96078
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.00000 0.0000
Trials.targetImage (Intercept) 0.03549 0.1884
Residual 0.96144 0.9805
Number of obs: 3869, groups: SubjectInfo.subjID, 144; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.02734 0.03206 41.85517 0.853 0.3987
scale(AoA_Est_target) -0.01732 0.03148 42.28627 -0.550 0.5851
scale(MeanSaliencyDiff) 0.06451 0.02749 89.85148 2.346 0.0212 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) s(AA_E
scl(AA_Es_) 0.056
scl(MnSlnD) 0.013 0.045
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Can’t get this to not be not singular but still fun to see that saliency is predictive.
Using a shorter critical window
short_image_effect <- lmer(scale(corrected_target_looking_short) ~ scale(image_similarity)*scale(age_in_months)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
,
data = trials_with_effect_vars)
short_text_effect <- lmer(scale(corrected_target_looking_short) ~ scale(text_similarity)*scale(age_in_months)
+ scale(AoA_Est_target)
+ (1 | SubjectInfo.subjID)
+ (1 | Trials.targetImage)
,
data = trials_with_effect_vars)
summary(short_text_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(corrected_target_looking_short) ~ scale(text_similarity) *
scale(age_in_months) + scale(AoA_Est_target) + (1 | SubjectInfo.subjID) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10934.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.93862 -0.66595 -0.01107 0.66115 2.80103
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.020688 0.14383
Trials.targetImage (Intercept) 0.006489 0.08055
Residual 0.960529 0.98007
Number of obs: 3869, groups: SubjectInfo.subjID, 144; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df
(Intercept) -9.195e-03 2.341e-02 6.462e+01
scale(text_similarity) -1.851e-02 1.795e-02 1.927e+02
scale(age_in_months) 7.412e-02 1.983e-02 1.413e+02
scale(AoA_Est_target) -8.940e-02 1.999e-02 5.164e+01
scale(text_similarity):scale(age_in_months) 8.881e-04 1.596e-02 3.841e+03
t value Pr(>|t|)
(Intercept) -0.393 0.695825
scale(text_similarity) -1.031 0.303702
scale(age_in_months) 3.739 0.000268 ***
scale(AoA_Est_target) -4.473 4.27e-05 ***
scale(text_similarity):scale(age_in_months) 0.056 0.955616
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E
scl(txt_sm) 0.015
scl(g_n_mn) 0.001 -0.001
scl(AA_Es_) 0.020 0.034 0.014
scl(_):(__) 0.001 -0.021 0.004 0.005summary(short_image_effect)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(corrected_target_looking_short) ~ scale(image_similarity) *
scale(age_in_months) + scale(AoA_Est_target) + scale(MeanSaliencyDiff) +
(1 | SubjectInfo.subjID) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10937.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.85576 -0.66644 -0.01379 0.65922 2.82003
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.020583 0.14347
Trials.targetImage (Intercept) 0.005591 0.07478
Residual 0.960488 0.98005
Number of obs: 3869, groups: SubjectInfo.subjID, 144; Trials.targetImage, 48
Fixed effects:
Estimate Std. Error df
(Intercept) -7.555e-03 2.295e-02 6.222e+01
scale(image_similarity) -2.979e-02 1.783e-02 1.811e+02
scale(age_in_months) 7.426e-02 1.981e-02 1.415e+02
scale(AoA_Est_target) -8.344e-02 1.973e-02 4.983e+01
scale(MeanSaliencyDiff) 2.274e-02 1.885e-02 6.690e+01
scale(image_similarity):scale(age_in_months) -1.107e-02 1.573e-02 3.745e+03
t value Pr(>|t|)
(Intercept) -0.329 0.743156
scale(image_similarity) -1.671 0.096445 .
scale(age_in_months) 3.749 0.000258 ***
scale(AoA_Est_target) -4.230 9.99e-05 ***
scale(MeanSaliencyDiff) 1.206 0.232020
scale(image_similarity):scale(age_in_months) -0.704 0.481659
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__) s(AA_E s(MSD)
scl(mg_sml) -0.008
scl(g_n_mn) 0.002 -0.014
scl(AA_Es_) 0.019 -0.155 0.016
scl(MnSlnD) 0.007 -0.011 -0.006 0.015
scl(_):(__) -0.010 -0.010 0.008 0.010 -0.010Our effects are not significant with the shorter window.