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")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)
}Run mixed-effects model
Model 1: This is the model we said we’d run in our pre-reg
prereg_main_effect <- lmer(scale(corrected_target_looking) ~ scale(text_similarity)*scale(age_in_months)
+ (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(prereg_main_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.targetImage) +
(1 | Trials.imagePair) + (1 | section)
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')Singular fit debugs for image similarity
Swapping text similarity with image similarity:
image_similarity_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ (scale(image_similarity) | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1|Trials.imagePair)
+ (1|section),
data = trials_with_effect_vars)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.targetImage) +
(1 | Trials.imagePair) + (1 | section)
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')running into a singular fit, I tried removing each of the random effects but only removing the random slope of image_similarity and the random intercept for image pair fixed this. This is okay for now since we see a similar effect with the singular model and this model. (although the effect below is stronger)
image_similarity_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (1 | section),
data = trials_with_effect_vars)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) +
(1 | SubjectInfo.subjID) + (1 | Trials.targetImage) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10941.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.9306 -0.6336 -0.0282 0.6690 2.8633
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.02381 0.1543
Trials.targetImage (Intercept) 0.01191 0.1092
section (Intercept) 0.00000 0.0000
Residual 0.95874 0.9792
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -7.047e-03 2.628e-02 6.559e+01
scale(image_similarity) -4.038e-02 1.899e-02 2.120e+02
scale(age_in_months) 6.907e-02 2.036e-02 1.433e+02
scale(image_similarity):scale(age_in_months) -9.679e-03 1.572e-02 3.740e+03
t value Pr(>|t|)
(Intercept) -0.268 0.789439
scale(image_similarity) -2.127 0.034575 *
scale(age_in_months) 3.393 0.000895 ***
scale(image_similarity):scale(age_in_months) -0.616 0.538067
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) scl(_) sc(__)
scl(mg_sml) -0.008
scl(g_n_mn) 0.000 -0.011
scl(_):(__) -0.009 -0.007 0.008
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 0.009Adding in covariates
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
main_image_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity) * scale(age_in_months) + 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')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.targetImage),
data = trials_with_effect_vars)boundary (singular) fit: see help('isSingular')main_text_effect <- lmer(scale(corrected_target_looking) ~ scale(text_similarity) + scale(age_in_months) + 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(main_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) + (1 | SubjectInfo.subjID) +
(1 | section) + (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
section (Intercept) 0.000000 0.00000
Residual 0.958808 0.97919
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
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 4.847e+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 -0.010
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')summary(main_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) + (1 | SubjectInfo.subjID) +
(1 | section) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10933.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.87019 -0.63207 -0.02072 0.67053 2.89757
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023646 0.15377
Trials.targetImage (Intercept) 0.006501 0.08063
section (Intercept) 0.000000 0.00000
Residual 0.958490 0.97902
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) -0.008405 0.023849 64.786573 -0.352 0.72565
scale(text_similarity) -0.025846 0.017948 182.218929 -1.440 0.15158
scale(age_in_months) 0.067508 0.020324 143.283766 3.322 0.00114 **
scale(AoA_Est_target) -0.088129 0.020002 48.435588 -4.406 5.81e-05 ***
scale(MeanSaliencyDiff) 0.006237 0.019248 66.311662 0.324 0.74695
---
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) 0.014
scl(g_n_mn) 0.002 0.000
scl(AA_Es_) 0.019 0.035 0.014
scl(MnSlnD) 0.008 -0.006 -0.005 0.016
optimizer (nloptwrap) convergence code: 0 (OK)
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.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10936.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.85158 -0.63075 -0.02342 0.67780 2.90671
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.023887 0.15455
Trials.targetImage (Intercept) 0.006565 0.08102
section (Intercept) 0.000000 0.00000
Residual 0.957648 0.97860
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error
(Intercept) -7.404e-03 2.391e-02
scale(image_similarity_bv) -1.054e-02 1.931e-02
scale(age_in_months) 6.749e-02 2.036e-02
scale(AoA_Est_target) -8.438e-02 2.046e-02
scale(MeanSaliencyDiff) 5.694e-03 1.927e-02
scale(image_similarity_bv):scale(age_in_months) -3.702e-02 1.597e-02
df t value Pr(>|t|)
(Intercept) 6.477e+01 -0.310 0.757823
scale(image_similarity_bv) 9.069e+01 -0.546 0.586575
scale(age_in_months) 1.433e+02 3.315 0.001162 **
scale(AoA_Est_target) 4.848e+01 -4.124 0.000145 ***
scale(MeanSaliencyDiff) 6.615e+01 0.295 0.768582
scale(image_similarity_bv):scale(age_in_months) 3.817e+03 -2.319 0.020471 *
---
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 -0.206 0.016
scl(MnSlnD) 0.008 0.016 -0.006 0.013
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(main_image_effect) R2m R2c
[1,] 0.01325033 0.04273696r.squaredGLMM(main_text_effect) R2m R2c
[1,] 0.01310146 0.04319586r.squaredGLMM(bv_image_effect) R2m R2c
[1,] 0.0137672 0.04416151Image 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 Well image similarity has a higher r but both are still insignificant.
vs_image_effect <- lmer(scale(corrected_target_looking) ~ scale(image_similarity)*scale(age_in_months)
+ scale(MeanSaliencyDiff)
+ (1 | SubjectInfo.subjID)
+ (1|Trials.targetImage)
+ (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 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10947.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.93087 -0.63658 -0.02981 0.66719 2.86315
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.02382 0.1543
Trials.targetImage (Intercept) 0.01233 0.1111
section (Intercept) 0.00000 0.0000
Residual 0.95875 0.9792
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -7.031e-03 2.646e-02 6.449e+01
scale(image_similarity) -4.027e-02 1.906e-02 2.098e+02
scale(age_in_months) 6.902e-02 2.036e-02 1.433e+02
scale(MeanSaliencyDiff) 8.787e-03 2.153e-02 7.311e+01
scale(image_similarity):scale(age_in_months) -9.755e-03 1.572e-02 3.739e+03
t value Pr(>|t|)
(Intercept) -0.266 0.791301
scale(image_similarity) -2.113 0.035824 *
scale(age_in_months) 3.389 0.000905 ***
scale(MeanSaliencyDiff) 0.408 0.684349
scale(image_similarity):scale(age_in_months) -0.621 0.534910
---
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 0.007 -0.006
scl(_):(__) -0.009 -0.007 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()
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')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(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 leads to significant image similarity even with AoA as a covariate – todo!
#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.targetImage)
+ (1 | section),
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)
+ (1 | SubjectInfo.subjID)
+ (1 | section)
+ (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.targetImage) + (1 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10410.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.80666 -0.70874 0.05715 0.75175 2.49124
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.038508 0.19623
SubjectInfo.subjID.1 scale(text_similarity) 0.000000 0.00000
Trials.targetImage (Intercept) 0.020075 0.14169
section (Intercept) 0.001696 0.04118
Residual 0.819481 0.90525
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) -4.432e-03 4.237e-02 9.074e-01
scale(age_in_months) 8.821e-02 2.192e-02 1.404e+02
scale(text_similarity) -5.475e-02 1.943e-02 2.592e+02
scale(AoA_Est_target) -1.213e-01 2.575e-02 4.669e+01
scale(MeanSaliencyDiff) 6.133e-02 2.300e-02 8.258e+01
scale(mean_target_looking_baseline_window) 2.817e-01 1.495e-02 3.799e+03
scale(age_in_months):scale(text_similarity) -1.335e-02 1.482e-02 3.813e+03
t value Pr(>|t|)
(Intercept) -0.105 0.93493
scale(age_in_months) 4.023 9.33e-05 ***
scale(text_similarity) -2.818 0.00521 **
scale(AoA_Est_target) -4.711 2.25e-05 ***
scale(MeanSaliencyDiff) 2.666 0.00923 **
scale(mean_target_looking_baseline_window) 18.843 < 2e-16 ***
scale(age_in_months):scale(text_similarity) -0.901 0.36774
---
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 -0.006
scl(AA_Es_) 0.035 0.006 0.046
scl(MnSlnD) 0.009 -0.004 0.028 0.038
scl(mn____) -0.007 -0.007 0.019 0.012 -0.046
scl(__):(_) 0.000 0.004 -0.016 0.004 -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) + (1 | SubjectInfo.subjID) +
(1 | section) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10413
Scaled residuals:
Min 1Q Median 3Q Max
-2.79602 -0.71069 0.06101 0.74992 2.51699
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 3.883e-02 1.970e-01
Trials.targetImage (Intercept) 1.857e-02 1.363e-01
section (Intercept) 2.521e-14 1.588e-07
Residual 8.204e-01 9.058e-01
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 1.574e-03 3.009e-02 7.718e+01
scale(age_in_months) 8.786e-02 2.197e-02 1.409e+02
scale(image_similarity) -4.160e-02 1.912e-02 2.742e+02
scale(AoA_Est_target) -1.086e-01 2.511e-02 4.884e+01
scale(MeanSaliencyDiff) 6.208e-02 2.259e-02 8.182e+01
scale(mean_target_looking_baseline_window) 2.825e-01 1.495e-02 3.798e+03
scale(age_in_months):scale(image_similarity) -1.892e-02 1.456e-02 3.723e+03
t value Pr(>|t|)
(Intercept) 0.052 0.958413
scale(age_in_months) 3.999 0.000102 ***
scale(image_similarity) -2.175 0.030475 *
scale(AoA_Est_target) -4.327 7.47e-05 ***
scale(MeanSaliencyDiff) 2.748 0.007376 **
scale(mean_target_looking_baseline_window) 18.897 < 2e-16 ***
scale(age_in_months):scale(image_similarity) -1.300 0.193809
---
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) 0.002
scl(mg_sml) -0.013 -0.011
scl(AA_Es_) 0.032 0.012 -0.139
scl(MnSlnD) 0.011 -0.005 0.029 0.032
scl(mn____) -0.011 -0.007 0.005 0.011 -0.048
scl(__):(_) -0.007 0.007 -0.006 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 | 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)
+ (1 | SubjectInfo.subjID)
+ (1 | section)
+ (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 | section)
Data: trials_with_effect_vars
REML criterion at convergence: 10741.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.42260 -0.67586 0.07834 0.75387 2.39815
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.038580 0.1964
SubjectInfo.subjID.1 scale(text_similarity) 0.000000 0.0000
Trials.targetImage (Intercept) 0.034511 0.1858
section (Intercept) 0.001875 0.0433
Residual 0.892267 0.9446
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 3.002e-03 4.705e-02 9.069e-01
scale(age_in_months) 9.134e-02 2.237e-02 1.396e+02
scale(text_similarity) -6.539e-02 2.154e-02 3.211e+02
scale(AoA_Est_target) -1.253e-01 3.139e-02 4.497e+01
scale(MeanSaliencyDiff) 7.448e-02 2.686e-02 9.255e+01
scale(age_in_months):scale(text_similarity) -1.349e-02 1.546e-02 3.813e+03
t value Pr(>|t|)
(Intercept) 0.064 0.96022
scale(age_in_months) 4.084 7.44e-05 ***
scale(text_similarity) -3.036 0.00259 **
scale(AoA_Est_target) -3.991 0.00024 ***
scale(MeanSaliencyDiff) 2.773 0.00671 **
scale(age_in_months):scale(text_similarity) -0.873 0.38292
---
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) 0.026 -0.006
scl(AA_Es_) 0.040 0.006 0.041
scl(MnSlnD) 0.010 -0.005 0.055 0.045
scl(__):(_) 0.000 0.004 -0.013 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) +
(1 | SubjectInfo.subjID) + (1 | section) + (1 | Trials.targetImage)
Data: trials_with_effect_vars
REML criterion at convergence: 10745.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.42582 -0.68146 0.07695 0.75389 2.38223
Random effects:
Groups Name Variance Std.Dev.
SubjectInfo.subjID (Intercept) 0.03897 0.1974
Trials.targetImage (Intercept) 0.03228 0.1797
section (Intercept) 0.00000 0.0000
Residual 0.89368 0.9453
Number of obs: 3869, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate Std. Error df
(Intercept) 9.512e-03 3.495e-02 6.798e+01
scale(age_in_months) 9.093e-02 2.243e-02 1.400e+02
scale(image_similarity) -4.174e-02 2.119e-02 3.470e+02
scale(AoA_Est_target) -1.120e-01 3.057e-02 4.747e+01
scale(MeanSaliencyDiff) 7.645e-02 2.644e-02 9.126e+01
scale(age_in_months):scale(image_similarity) -2.108e-02 1.520e-02 3.722e+03
t value Pr(>|t|)
(Intercept) 0.272 0.786341
scale(age_in_months) 4.054 8.31e-05 ***
scale(image_similarity) -1.969 0.049725 *
scale(AoA_Est_target) -3.663 0.000626 ***
scale(MeanSaliencyDiff) 2.892 0.004786 **
scale(age_in_months):scale(image_similarity) -1.387 0.165412
---
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) 0.000
scl(mg_sml) -0.015 -0.011
scl(AA_Es_) 0.041 0.011 -0.125
scl(MnSlnD) 0.011 -0.005 0.065 0.035
scl(__):(_) -0.007 0.007 -0.004 0.006 -0.010
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')r.squaredGLMM(critical_looking_image) R2m R2c
[1,] 0.03051747 0.1020998r.squaredGLMM(critical_looking_text) R2m R2c
[1,] 0.03385116 0.1087337Similar 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.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.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.targetImage) +
(1 | section)
Data: trials_window_type_separated
REML criterion at convergence: 21606.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.39523 -0.71505 0.03621 0.75575 2.08991
Random effects:
Groups Name Variance Std.Dev. Corr
SubjectInfo.subjID (Intercept) 0.0122129 0.11051
scale(image_similarity) 0.0001329 0.01153 1.00
Trials.targetImage (Intercept) 0.0344450 0.18559
section (Intercept) 0.0000000 0.00000
Residual 0.9291984 0.96395
Number of obs: 7738, groups:
SubjectInfo.subjID, 144; Trials.targetImage, 48; section, 2
Fixed effects:
Estimate
(Intercept) 2.114e-02
scale(age_in_months) 5.038e-02
trial_window_c 1.862e-01
scale(image_similarity) -2.096e-02
scale(AoA_Est_target) -6.308e-02
scale(MeanSaliencyDiff) 5.579e-02
scale(age_in_months):trial_window_c 8.199e-02
scale(age_in_months):scale(image_similarity) -1.421e-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) 3.085e-02
scale(age_in_months) 1.435e-02
trial_window_c 2.192e-02
scale(image_similarity) 1.671e-02
scale(AoA_Est_target) 2.903e-02
scale(MeanSaliencyDiff) 2.291e-02
scale(age_in_months):trial_window_c 2.192e-02
scale(age_in_months):scale(image_similarity) 1.100e-02
trial_window_c:scale(image_similarity) 2.192e-02
scale(age_in_months):trial_window_c:scale(image_similarity) 2.181e-02
df t value
(Intercept) 5.066e+01 0.685
scale(age_in_months) 1.385e+02 3.511
trial_window_c 7.537e+03 8.497
scale(image_similarity) 5.462e+02 -1.254
scale(AoA_Est_target) 4.398e+01 -2.173
scale(MeanSaliencyDiff) 1.195e+02 2.435
scale(age_in_months):trial_window_c 7.537e+03 3.740
scale(age_in_months):scale(image_similarity) 2.448e+03 -1.292
trial_window_c:scale(image_similarity) 7.537e+03 -2.283
scale(age_in_months):trial_window_c:scale(image_similarity) 7.537e+03 -0.449
Pr(>|t|)
(Intercept) 0.496466
scale(age_in_months) 0.000604 ***
trial_window_c < 2e-16 ***
scale(image_similarity) 0.210337
scale(AoA_Est_target) 0.035190 *
scale(MeanSaliencyDiff) 0.016365 *
scale(age_in_months):trial_window_c 0.000185 ***
scale(age_in_months):scale(image_similarity) 0.196399
trial_window_c:scale(image_similarity) 0.022432 *
scale(age_in_months):trial_window_c:scale(image_similarity) 0.653735
---
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) 0.001 -0.011 0.000
scl(AA_Es_) 0.056 0.009 0.000 -0.098
scl(MnSlnD) 0.010 -0.007 0.000 0.137 0.034
scl(g__):__ 0.000 0.000 0.000 0.000 0.000 0.000
scl(__):(_) -0.006 0.065 0.000 -0.001 0.005 -0.009 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')tidy_model(window_type_looking_text)# A tibble: 10 × 8
effect Predictor b SE t df p.full p
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 fixed Intercept 0.02 0.03 0.55 48.4 5.88e- 1 0.588
2 fixed Age (scaled) 0.05 0.01 3.57 139. 4.90e- 4 <.001
3 fixed trial_window_c 0.19 0.02 8.49 7535. 2.39e-17 <.001
4 fixed scale(text_similarity) -0.05 0.02 -2.97 496. 3.14e- 3 <.01
5 fixed scale(AoA_Est_target) -0.07 0.03 -2.3 41.8 2.65e- 2 <.05
6 fixed scale(MeanSaliencyDiff) 0.05 0.02 2.2 127. 3.00e- 2 <.05
7 fixed scale(age_in_months):trial_wi… 0.08 0.02 3.71 7535. 2.08e- 4 <.001
8 fixed scale(age_in_months):scale(te… -0.01 0.01 -0.79 2791. 4.31e- 1 0.431
9 fixed trial_window_c:scale(text_sim… -0.02 0.02 -0.95 7535. 3.44e- 1 0.344
10 fixed scale(age_in_months):trial_wi… -0.01 0.02 -0.63 7535. 5.32e- 1 0.532The 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.