WordKnowers-Adults
07 May, 2026
Adults Data
Number of participants
df.response_exp1 <- read.csv("../../data/adults/PROCESSED_DATA/response.csv")
#check number of participants
df.response_exp1 %>%
group_by(PID) %>%
slice(1) %>%
nrow()## [1] 36df.demog_age <- df.response_exp1 %>%
group_by(PID) %>%
slice(1) %>%
ungroup()
df.demog_age_summary <- df.demog_age %>%
summarise(mean_age = mean(age, na.rm = T),
sd_age = sd(age, na.rm = T),
min_age = min(age, na.rm = T),
max_age = max(age, na.rm = T))
df.demog_age_summary_by_cond <- df.demog_age %>%
group_by(condition) %>%
summarise(mean_age = mean(age, na.rm = T),
sd_age = sd(age, na.rm = T),
min_age = min(age, na.rm = T),
max_age = max(age, na.rm = T))
df.demog_gender <- df.response_exp1 %>%
group_by(PID) %>%
slice(1) %>%
ungroup()
df.demog_gender_summary <- df.demog_gender %>%
group_by(gender) %>%
count()
df.demog_gender_summary_by_cond <- df.demog_gender %>%
group_by(condition, gender) %>%
count()Plots
Speaker Choice
ggplot(df.response_exp1 %>%
group_by(PID, phase, trial_type) %>%
summarise(mean_select_many_same = mean(coded_resp, na.rm = T)),
aes(x = trial_type, y = mean_select_many_same, fill = phase)) +
geom_violin(alpha = 0.5) +
geom_jitter(alpha = 0.3,
height = 0) +
# geom_dotplot(binaxis = "y", stackdir = "center",
# alpha = 0.5,
# dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
geom_hline(yintercept = 1/2, linetype = 'dashed') +
ylim(0, 1) +
labs(y = "Prop. Selecting Speaker with\nDifferent Objects or One Object") +
facet_grid(~phase) +
guides(fill="none") +
theme(text=element_text(size=18))## `summarise()` has grouped output by 'PID', 'phase'. You can override using the
## `.groups` argument.## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_ydensity()`).## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_summary()`).## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
Descriptives
Speaker Choice
df.response_exp1_sum <- df.response_exp1 %>%
filter(block %in% c(1, 2)) %>%
mutate(condition = factor(condition, levels = c('2-OB', 'VP', '3-OB'))) %>%
group_by(PID, condition) %>%
summarise(mean_correct_resp_by_ppt = mean(correct_resp)) %>%
ungroup() %>%
group_by(condition) %>%
summarise(mean_correct_resp = mean(mean_correct_resp_by_ppt),
sd_correct_resp = sd(mean_correct_resp_by_ppt),
n = n()) %>%
mutate(se_correct_resp = sd_correct_resp / sqrt(n),
lower.ci.correct_resp = mean_correct_resp - qt(1 - (0.05 / 2), n - 1) * se_correct_resp,
upper.ci.correct_resp = mean_correct_resp + qt(1 - (0.05 / 2), n - 1) * se_correct_resp)Regression
Random structure: by-participant and by-target-object random intercepts.
Choice of speaker on Test trials on each task
Model 1: choice of speaker (0/1) ~ 1 + (1|participant) + (1|object)
fit.knowledge_chance <- glmer(coded_resp ~ 1 + (1|PID) + (1|taught_cat),
data = df.response_exp1 %>%
filter(phase == "knowledge_judgment" & trial_type == "test"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluessummary(fit.knowledge_chance) # did not converge## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: coded_resp ~ 1 + (1 | PID) + (1 | taught_cat)
## Data:
## df.response_exp1 %>% filter(phase == "knowledge_judgment" & trial_type ==
## "test")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 17.2 24.0 -5.6 11.2 69
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.071611 0.000000 0.000000 0.000012 0.004208
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 4939 70.28
## taught_cat (Intercept) 3167 56.28
## Number of obs: 72, groups: PID, 36; taught_cat, 8
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 35.997540 0.009528 3778 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## unable to evaluate scaled gradient
## Model failed to converge: degenerate Hessian with 1 negative eigenvaluesfit.label_chance <- glmer(coded_resp ~ 1 + (1|PID) + (1|taught_cat),
data = df.response_exp1 %>%
filter(phase == "label_endorsement" & trial_type == "test"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.label_chance)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: coded_resp ~ 1 + (1 | PID) + (1 | taught_cat)
## Data:
## df.response_exp1 %>% filter(phase == "label_endorsement" & trial_type ==
## "test")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 88.7 95.2 -41.3 82.7 62
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4306 -1.1446 0.5667 0.6393 0.7951
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 0.53557 0.7318
## taught_cat (Intercept) 0.06401 0.2530
## Number of obs: 65, groups: PID, 34; taught_cat, 4
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.7949 0.3893 2.042 0.0412 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Comparing choice of speaker between control and test trials on each task
Model 2: choice of speaker (0/1) ~ Trial Type (Control / Test) + (1|participant) + (1|object)
fit.knowledge_cond_compare <- glmer(coded_resp ~ trial_type + (1|PID) + (1|taught_cat),
data = df.response_exp1 %>%
filter(phase == "knowledge_judgment"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.knowledge_cond_compare) # did not converge## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: coded_resp ~ trial_type + (1 | PID) + (1 | taught_cat)
## Data: df.response_exp1 %>% filter(phase == "knowledge_judgment")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 127.6 139.5 -59.8 119.6 140
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5873 -0.6553 0.1792 0.2137 1.3809
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 0.2217 0.4709
## taught_cat (Intercept) 0.2053 0.4531
## Number of obs: 144, groups: PID, 36; taught_cat, 16
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.5731 0.3273 -1.751 0.0799 .
## trial_typetest 3.8954 0.8045 4.842 1.29e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tril_typtst -0.503fit.label_cond_compare <- glmer(coded_resp ~ trial_type + (1|PID) + (1|taught_cat),
data = df.response_exp1 %>%
filter(phase == "label_endorsement"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))## boundary (singular) fit: see help('isSingular')summary(fit.label_cond_compare) # did not converge## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: coded_resp ~ trial_type + (1 | PID) + (1 | taught_cat)
## Data: df.response_exp1 %>% filter(phase == "label_endorsement")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik deviance df.resid
## 180.6 192.0 -86.3 172.6 126
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3981 -0.8975 0.7153 0.7153 1.1142
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 0 0
## taught_cat (Intercept) 0 0
## Number of obs: 130, groups: PID, 34; taught_cat, 4
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.2162 0.2495 -0.867 0.3862
## trial_typetest 0.8864 0.3619 2.449 0.0143 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## tril_typtst -0.689
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Session Info
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.4.1 (2024-06-14)
## os macOS 26.3.1
## system aarch64, darwin20
## ui X11
## language (EN)
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## ctype en_US.UTF-8
## tz America/Los_Angeles
## date 2026-05-07
## pandoc 3.8.3 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/aarch64/ (via rmarkdown)
## quarto 1.5.57 @ /usr/local/bin/quarto
##
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