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"))
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)) Joining with `by = join_by(Trials.trialID, Trials.targetImage,
Trials.distractorImage, Trials.imagePair, section)`
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(image_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(image_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: 10932.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.86417 -0.63785 -0.02227 0.67964 2.90826
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 2.363e-02 1.537e-01
scale(text_similarity) 1.307e-04 1.143e-02 1.00
Trials.targetImage (Intercept) 6.008e-03 7.751e-02
Trials.imagePair (Intercept) 2.797e-10 1.672e-05
Residual 9.585e-01 9.790e-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) -0.006982 0.023580 64.528890 -0.296 0.768115
scale(image_similarity) -0.030247 0.017924 178.258959 -1.687 0.093261 .
scale(age_in_months) 0.068651 0.020305 144.179092 3.381 0.000930 ***
scale(AoA_Est_target) -0.081919 0.019985 48.593526 -4.099 0.000157 ***
scale(MeanSaliencyDiff) 0.006468 0.019028 65.523913 0.340 0.734988
---
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) 0.006
scl(g_n_mn) 0.002 -0.013
scl(AA_Es_) 0.016 -0.155 0.016
scl(MnSlnD) 0.008 -0.011 -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 | section)
+ (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 | section) + (1 | Trials.targetImage) +
+(1 | Trials.imagePair)
Data: trials_with_effect_vars
REML criterion at convergence: 10930.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.88345 -0.63506 -0.02781 0.68075 2.91511
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0240227 0.15499
scale(image_similarity) 0.0009329 0.03054 1.00
Trials.targetImage (Intercept) 0.0060650 0.07788
Trials.imagePair (Intercept) 0.0000000 0.00000
section (Intercept) 0.0000000 0.00000
Residual 0.9572932 0.97841
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.007540 0.023693 64.703779 -0.318 0.751317
scale(image_similarity) -0.030601 0.018136 170.965905 -1.687 0.093376 .
scale(age_in_months) 0.069097 0.020261 148.158902 3.410 0.000836 ***
scale(AoA_Est_target) -0.081316 0.020030 48.722653 -4.060 0.000177 ***
scale(MeanSaliencyDiff) 0.006805 0.019047 65.529605 0.357 0.722024
---
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 | section)
+ (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 | section)
+ (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 | section) +
(1 | Trials.imagePair) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10943.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.93471 -0.63471 -0.02957 0.67000 2.86224
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0238941 0.15458
scale(text_similarity) 0.0001475 0.01215 1.00
Trials.targetImage (Intercept) 0.0122063 0.11048
Trials.imagePair (Intercept) 0.0019774 0.04447
section (Intercept) 0.0000000 0.00000
Residual 0.9578812 0.97871
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -8.513e-03 2.758e-02 5.246e+01
scale(text_similarity) -2.328e-02 2.064e-02 3.216e+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 3.715e+03
t value Pr(>|t|)
(Intercept) -0.309 0.758799
scale(text_similarity) -1.128 0.267851
scale(age_in_months) 3.373 0.000957 ***
scale(text_similarity):scale(age_in_months) -0.695 0.486953
---
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 | section) +
(1 | Trials.imagePair) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10939.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.93619 -0.63040 -0.02968 0.67129 2.87363
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) 0.001025 0.03202
section (Intercept) 0.000000 0.00000
Residual 0.956826 0.97818
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -0.00736 0.02671 51.96271
scale(image_similarity) -0.04052 0.01994 27.53117
scale(age_in_months) 0.06874 0.02046 144.28741
scale(image_similarity):scale(age_in_months) -0.01010 0.01596 927.71712
t value Pr(>|t|)
(Intercept) -0.276 0.784013
scale(image_similarity) -2.033 0.051813 .
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
Now for the fun stuff: adding in our covariates using our original model – only doing this for text similarity for now because of the singular effects
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 | section)
+ (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 | section) + (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) 0.0238983 0.15459
Trials.targetImage (Intercept) 0.0063105 0.07944
Trials.imagePair (Intercept) 0.0004333 0.02082
section (Intercept) 0.0000000 0.00000
Residual 0.9574901 0.97851
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
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 0.762686
scale(image_similarity_bv) 3.410e+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 0.762828
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 0.005
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')r.squaredGLMM(prereg_image_effect) R2m R2c
[1,] 0.0132299 0.04420174r.squaredGLMM(prereg_text_effect) R2m R2c
[1,] 0.01324029 0.04296428r.squaredGLMM(bv_image_effect) R2m R2c
[1,] 0.01370518 0.04429036Image 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)
+ (1 | section),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
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) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10947.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.92167 -0.63595 -0.02846 0.66930 2.86011
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.0238468 0.15442
Trials.targetImage (Intercept) 0.0117906 0.10858
Trials.imagePair (Intercept) 0.0009043 0.03007
section (Intercept) 0.0000000 0.00000
Residual 0.9584418 0.97900
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -6.861e-03 2.678e-02 5.105e+01
scale(image_similarity) -3.985e-02 1.973e-02 2.690e+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 0.798836
scale(image_similarity) -2.020 0.053457 .
scale(age_in_months) 3.385 0.000918 ***
scale(MeanSaliencyDiff) 0.408 0.684999
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 -0.010
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(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) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10949.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.9367 -0.6336 -0.0293 0.6711 2.8625
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0239003 0.15460
scale(text_similarity) 0.0001482 0.01217 1.00
Trials.targetImage (Intercept) 0.0127850 0.11307
Trials.imagePair (Intercept) 0.0018435 0.04294
section (Intercept) 0.0000000 0.00000
Residual 0.9579065 0.97873
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -8.566e-03 2.773e-02 5.193e+01
scale(text_similarity) -2.355e-02 2.065e-02 3.214e+01
scale(age_in_months) 6.867e-02 2.037e-02 1.432e+02
scale(MeanSaliencyDiff) 8.989e-03 2.176e-02 5.866e+01
scale(text_similarity):scale(age_in_months) -1.114e-02 1.594e-02 3.712e+03
t value Pr(>|t|)
(Intercept) -0.309 0.758659
scale(text_similarity) -1.141 0.262473
scale(age_in_months) 3.371 0.000964 ***
scale(MeanSaliencyDiff) 0.413 0.681099
scale(text_similarity):scale(age_in_months) -0.699 0.484758
---
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)
+ (1 | section),
data = first_instance_target)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
data = second_instance_target)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
data = first_instance_primary_target)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
data = primary_targets)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
data = first_instance_image_pair)boundary (singular) fit: see help('isSingular')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) + (1 | section)
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) 0.0268061 0.16373
Trials.targetImage (Intercept) 0.0128112 0.11319
Trials.ordinal (Intercept) 0.0006618 0.02573
section (Intercept) 0.0000000 0.00000
Residual 0.9541238 0.97679
Number of obs: 3050, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.ordinal, 47; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -1.483e-04 2.834e-02 4.732e+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) 4.298e-05 1.767e-02 2.956e+03
t value Pr(>|t|)
(Intercept) -0.005 0.99585
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 -0.006
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(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) + (1 | section)
Data: second_instance_target
REML criterion at convergence: 2340.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.87045 -0.61735 -0.06732 0.63564 2.67477
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.0274798 0.16577
Trials.ordinal (Intercept) 0.0003388 0.01841
Trials.targetImage (Intercept) 0.0047334 0.06880
section (Intercept) 0.0000000 0.00000
Residual 0.9666993 0.98321
Number of obs: 819, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 34; Trials.targetImage, 16; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -0.003067 0.041564 11.906740
scale(image_similarity) -0.003673 0.040452 302.729612
scale(age_in_months) 0.062243 0.037291 128.166555
scale(MeanSaliencyDiff) 0.025945 0.042532 38.851725
scale(image_similarity):scale(age_in_months) -0.046945 0.035139 804.229065
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 -0.003
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(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) + (1 | section)
Data: first_instance_primary_target
REML criterion at convergence: 3164
Scaled residuals:
Min 1Q Median 3Q Max
-2.78542 -0.64758 -0.03046 0.68587 2.86506
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.04141 0.2035
Trials.ordinal (Intercept) 0.01023 0.1011
Trials.targetImage (Intercept) 0.01419 0.1191
section (Intercept) 0.00000 0.0000
Residual 0.92620 0.9624
Number of obs: 1116, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 35; Trials.targetImage, 16; section, 2
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 -0.011
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(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) + (1 | section)
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) 3.288e-02 1.813e-01
Trials.ordinal (Intercept) 2.900e-03 5.386e-02
Trials.targetImage (Intercept) 1.192e-02 1.092e-01
section (Intercept) 2.737e-10 1.654e-05
Residual 9.485e-01 9.739e-01
Number of obs: 1935, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 44; Trials.targetImage, 16; section, 2
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 -0.006
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(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) + (1 | section)
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 2.85407
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.02131 0.14599
Trials.targetImage (Intercept) 0.01030 0.10151
Trials.ordinal (Intercept) 0.00137 0.03701
section (Intercept) 0.00000 0.00000
Residual 0.96325 0.98145
Number of obs: 2219, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.ordinal, 33; section, 2
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 0.011
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Random 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)
+ (1 | section),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')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) + (1 | section)
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) 0.023599 0.15362
Trials.ordinal (Intercept) 0.001485 0.03853
Trials.targetImage (Intercept) 0.005992 0.07741
section (Intercept) 0.000000 0.00000
Residual 0.957317 0.97843
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.ordinal, 52; Trials.targetImage, 48; section, 2
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 0.791045
scale(image_similarity) -1.692 0.092424 .
scale(age_in_months) 3.341 0.001065 **
scale(AoA_Est_target) -4.138 0.000139 ***
scale(MeanSaliencyDiff) 0.338 0.736562
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 -0.010
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Order 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)
+ (1 | section),
data = ranked_trials)boundary (singular) fit: see help('isSingular')# |> 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) + (1 | section)
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
section (Intercept) 0.000000 0.00000
Residual 0.959048 0.97931
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
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) 0.815303
scale(image_similarity) 0.089393 .
scale(order) 0.196778
scale(AoA_Est_target) 0.000142 ***
scale(age_in_months) 0.000990 ***
scale(image_similarity):scale(order) 0.630980
---
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 0.014
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Z-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 | section)
+ (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 | section)
+ (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 | section) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10409.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.82326 -0.70271 0.05869 0.75332 2.49828
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0387777 0.19692
scale(text_similarity) 0.0002968 0.01723 1.00
Trials.targetImage (Intercept) 0.0200830 0.14171
Trials.imagePair (Intercept) 0.0010531 0.03245
section (Intercept) 0.0016236 0.04029
Residual 0.8185343 0.90473
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -4.578e-03 4.233e-02 9.047e-01
scale(age_in_months) 8.853e-02 2.196e-02 1.405e+02
scale(text_similarity) -5.406e-02 2.031e-02 2.804e+01
scale(AoA_Est_target) -1.207e-01 2.615e-02 4.038e+01
scale(MeanSaliencyDiff) 6.246e-02 2.311e-02 7.446e+01
scale(mean_target_looking_baseline_window) 2.814e-01 1.495e-02 3.798e+03
scale(age_in_months):scale(text_similarity) -1.397e-02 1.482e-02 3.169e+03
t value Pr(>|t|)
(Intercept) -0.108 0.93278
scale(age_in_months) 4.031 9.07e-05 ***
scale(text_similarity) -2.661 0.01274 *
scale(AoA_Est_target) -4.617 3.91e-05 ***
scale(MeanSaliencyDiff) 2.702 0.00853 **
scale(mean_target_looking_baseline_window) 18.829 < 2e-16 ***
scale(age_in_months):scale(text_similarity) -0.943 0.34575
---
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) 0.056 -0.005
scl(AA_Es_) 0.035 0.006 0.050
scl(MnSlnD) 0.009 -0.004 0.024 0.034
scl(mn____) -0.007 -0.007 0.018 0.013 -0.046
scl(__):(_) 0.001 0.077 -0.015 0.003 -0.007 0.001
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 | section) + (1 | Trials.imagePair) +
(1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10409.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.80455 -0.70681 0.05695 0.75384 2.51748
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 3.934e-02 1.983e-01
scale(image_similarity) 8.197e-04 2.863e-02 1.00
Trials.targetImage (Intercept) 1.857e-02 1.363e-01
Trials.imagePair (Intercept) 2.931e-03 5.414e-02
section (Intercept) 2.584e-10 1.607e-05
Residual 8.179e-01 9.044e-01
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 1.325e-03 3.166e-02 5.181e+01
scale(age_in_months) 8.745e-02 2.204e-02 1.410e+02
scale(image_similarity) -4.267e-02 2.168e-02 2.705e+01
scale(AoA_Est_target) -1.086e-01 2.607e-02 4.234e+01
scale(MeanSaliencyDiff) 6.475e-02 2.282e-02 6.763e+01
scale(mean_target_looking_baseline_window) 2.822e-01 1.494e-02 3.795e+03
scale(age_in_months):scale(image_similarity) -1.965e-02 1.475e-02 1.151e+03
t value Pr(>|t|)
(Intercept) 0.042 0.966772
scale(age_in_months) 3.968 0.000115 ***
scale(image_similarity) -1.968 0.059366 .
scale(AoA_Est_target) -4.165 0.000150 ***
scale(MeanSaliencyDiff) 2.837 0.006001 **
scale(mean_target_looking_baseline_window) 18.886 < 2e-16 ***
scale(age_in_months):scale(image_similarity) -1.333 0.182902
---
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) 0.046 -0.009
scl(AA_Es_) 0.033 0.011 -0.120
scl(MnSlnD) 0.011 -0.004 0.018 0.024
scl(mn____) -0.011 -0.007 0.006 0.012 -0.049
scl(__):(_) -0.006 0.129 -0.007 0.007 -0.010 0.009
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Now we’re starting to see effects. although these models are still very ## Just predicting critical window looking
critical_looking_text <- lmer(scale(mean_target_looking_critical_window) ~ scale(age_in_months)*scale(text_similarity)
+ scale(AoA_Est_target)
+ scale(MeanSaliencyDiff)
+ (scale(text_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair)
+ (1 | section),
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 | section)
+ (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 | section)
+ (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(AoA_Est_target) + scale(MeanSaliencyDiff) +
(scale(text_similarity) | SubjectInfo.subjID) + (1 | Trials.targetImage) +
(1 | Trials.imagePair) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10739.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.42450 -0.67727 0.07311 0.75722 2.41030
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0389379 0.19733
scale(text_similarity) 0.0003709 0.01926 1.00
Trials.targetImage (Intercept) 0.0357476 0.18907
Trials.imagePair (Intercept) 0.0036441 0.06037
section (Intercept) 0.0014580 0.03818
Residual 0.8899007 0.94335
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 3.336e-03 4.631e-02 8.920e-01
scale(age_in_months) 9.161e-02 2.241e-02 1.397e+02
scale(text_similarity) -6.372e-02 2.429e-02 2.654e+01
scale(AoA_Est_target) -1.242e-01 3.278e-02 3.879e+01
scale(MeanSaliencyDiff) 7.861e-02 2.765e-02 6.713e+01
scale(age_in_months):scale(text_similarity) -1.447e-02 1.545e-02 2.994e+03
t value Pr(>|t|)
(Intercept) 0.072 0.955259
scale(age_in_months) 4.087 7.32e-05 ***
scale(text_similarity) -2.624 0.014238 *
scale(AoA_Est_target) -3.789 0.000515 ***
scale(MeanSaliencyDiff) 2.843 0.005911 **
scale(age_in_months):scale(text_similarity) -0.936 0.349103
---
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) -0.002
scl(txt_sm) 0.056 -0.005
scl(AA_Es_) 0.042 0.007 0.054
scl(MnSlnD) 0.010 -0.004 0.043 0.037
scl(__):(_) 0.001 0.081 -0.012 0.003 -0.006
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 | section) +
(1 | Trials.imagePair) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10740.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.42108 -0.68108 0.06944 0.75401 2.37688
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 3.950e-02 1.988e-01
scale(image_similarity) 6.128e-04 2.476e-02 1.00
Trials.targetImage (Intercept) 3.452e-02 1.858e-01
Trials.imagePair (Intercept) 6.754e-03 8.218e-02
section (Intercept) 1.430e-10 1.196e-05
Residual 8.893e-01 9.430e-01
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 9.595e-03 3.857e-02 4.492e+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 3.296e-02 4.066e+01
scale(MeanSaliencyDiff) 8.219e-02 2.769e-02 6.232e+01
scale(age_in_months):scale(image_similarity) -2.167e-02 1.531e-02 1.425e+03
t value Pr(>|t|)
(Intercept) 0.249 0.80466
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 0.00425 **
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 | section) +
(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) 0.0395903 0.19897
scale(image_similarity) 0.0005542 0.02354 1.00
Trials.targetImage (Intercept) 0.0369188 0.19214
Trials.imagePair (Intercept) 0.0082629 0.09090
section (Intercept) 0.0000000 0.00000
Residual 0.8890892 0.94292
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
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) 45.93532 0.268 0.78986
scale(age_in_months) 141.30814 4.125 6.3e-05 ***
scale(image_similarity_bv) 30.56142 -0.402 0.69070
scale(AoA_Est_target) 42.52317 -3.329 0.00181 **
scale(MeanSaliencyDiff) 62.10526 2.920 0.00488 **
scale(age_in_months):scale(image_similarity_bv) 3715.23482 -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,] 0.03151521 0.1127237r.squaredGLMM(critical_looking_text) R2m R2c
[1,] 0.03398413 0.1138084Similar 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) + (1|section),
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) + (1|section),
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) + (1 | section)
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 2.09637
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0123811 0.11127
scale(image_similarity) 0.0001165 0.01079 1.00
Trials.targetImage (Intercept) 0.0412618 0.20313
Trials.imagePair (Intercept) 0.0099066 0.09953
section (Intercept) 0.0000000 0.00000
Residual 0.9246844 0.96161
Number of obs: 7738, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
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) 5.676e+01 2.572
scale(age_in_months):trial_window_c 7.509e+03 3.749
scale(age_in_months):scale(image_similarity) 2.702e+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) 0.567086
scale(age_in_months) 0.000595 ***
trial_window_c < 2e-16 ***
scale(image_similarity) 0.333415
scale(AoA_Est_target) 0.059757 .
scale(MeanSaliencyDiff) 0.012760 *
scale(age_in_months):trial_window_c 0.000179 ***
scale(age_in_months):scale(image_similarity) 0.209279
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) + (1 | section)
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 2.12925
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.012216 0.11053
scale(text_similarity) 0.000280 0.01673 1.00
Trials.targetImage (Intercept) 0.041533 0.20380
Trials.imagePair (Intercept) 0.007538 0.08682
section (Intercept) 0.000000 0.00000
Residual 0.925248 0.96190
Number of obs: 7738, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; Trials.imagePair, 32; section, 2
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) 4.117e+01 0.480
scale(age_in_months) 1.390e+02 3.564
trial_window_c 7.509e+03 8.509
scale(text_similarity) 2.640e+01 -2.008
scale(AoA_Est_target) 4.091e+01 -2.115
scale(MeanSaliencyDiff) 5.930e+01 2.464
scale(age_in_months):trial_window_c 7.509e+03 3.717
scale(age_in_months):scale(text_similarity) 2.969e+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) 0.633554
scale(age_in_months) 0.000502 ***
trial_window_c < 2e-16 ***
scale(text_similarity) 0.054978 .
scale(AoA_Est_target) 0.040603 *
scale(MeanSaliencyDiff) 0.016637 *
scale(age_in_months):trial_window_c 0.000203 ***
scale(age_in_months):scale(text_similarity) 0.427277
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 | section) + (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 |
section) + (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) 3.141e-10 1.772e-05
Trials.targetImage (Intercept) 3.549e-02 1.884e-01
section (Intercept) 0.000e+00 0.000e+00
Residual 9.614e-01 9.805e-01
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.02734 0.03206 41.85561 0.853 0.3987
scale(AoA_Est_target) -0.01732 0.03148 42.28672 -0.550 0.5851
scale(MeanSaliencyDiff) 0.06451 0.02749 89.85197 2.346 0.0211 *
---
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)
+ (1 | section),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')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)
+ (1 | section),
data = trials_with_effect_vars)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.00247669 (tol = 0.002, component 1)
See ?lme4::convergence and ?lme4::troubleshooting.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) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10934.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.9371 -0.6663 -0.0099 0.6616 2.8001
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.020616 0.14358
Trials.targetImage (Intercept) 0.006441 0.08025
section (Intercept) 0.000259 0.01609
Residual 0.960550 0.98008
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -1.015e-02 2.609e-02 7.784e-01
scale(text_similarity) -1.915e-02 1.800e-02 1.749e+02
scale(age_in_months) 7.431e-02 1.982e-02 1.410e+02
scale(AoA_Est_target) -9.025e-02 2.005e-02 4.955e+01
scale(text_similarity):scale(age_in_months) 8.922e-04 1.595e-02 3.840e+03
t value Pr(>|t|)
(Intercept) -0.389 0.777195
scale(text_similarity) -1.064 0.288825
scale(age_in_months) 3.750 0.000258 ***
scale(AoA_Est_target) -4.502 4.1e-05 ***
scale(text_similarity):scale(age_in_months) 0.056 0.955409
---
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) 0.020
scl(g_n_mn) 0.000 -0.002
scl(AA_Es_) 0.025 0.041 0.012
scl(_):(__) 0.001 -0.021 0.004 0.005
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00247669 (tol = 0.002, component 1)
See ?lme4::convergence and ?lme4::troubleshooting.summary(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) + (1 | section)
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
section (Intercept) 0.000000 0.00000
Residual 0.960488 0.98005
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
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 -0.010
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')Our effects are not significant with the shorter window.