Material Polysemy Results

knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(cache=TRUE)

library(brms)
library(tidyverse)
library(cowplot)
library(wesanderson)
library(knitr)
library(lme4)
## for bootstrapping 95% confidence intervals -- from Mike Frank https://github.com/langcog/KTE/blob/master/mcf.useful.R
library(bootstrap)
theta <- function(x,xdata,na.rm=T) {mean(xdata[x],na.rm=na.rm)}
ci.low <- function(x,na.rm=T) {
  quantile(bootstrap(1:length(x),1000,theta,x,na.rm=na.rm)$thetastar,.025,na.rm=na.rm)} #  mean(x,na.rm=na.rm) -
ci.high <- function(x,na.rm=T) {
  quantile(bootstrap(1:length(x),1000,theta,x,na.rm=na.rm)$thetastar,.975,na.rm=na.rm) } #- mean(x,na.rm=na.rm)}

 convert_stan_to_dataframe <- function(stan_object){
   sum.df <- data.frame(summary(stan_object)$fixed)
   sum.df$Diff_from_zero <- ifelse((sum.df$l.95..CI * sum.df$u.95..CI) > 0, "*","-")
   return(sum.df)
 }

1 Experiment 1 - Choice Task

Unambiguous Task

Polysemy Task

Vague Control Task

Homophony Task

1.1  Child data

a=1

child.SB.expt1 <- read_csv("./Expt1_ChoiceTask/Data/Children2.csv") %>%
  mutate(Item = ifelse(order <=2 ,
                       seq, abs((seq - 11))))

# Add readable condition labels

labels = tibble(condition = c(1:6), 
                condition_label = factor(x = c("Polysemy",
                                    "Unambiguous",
                                    "Material versus Object",
                                    "Word Extension (Some/A)",
                                    "Expt.4 Homophone",
                                    "Material vs. Object (Replication)"), 
                                    levels = c("Unambiguous",
                                    "Polysemy",
                                    "Material versus Object",
                                    "Word Extension (Some/A)",
                                    "Material vs. Object (Replication)",
                                    "Expt.4 Homophone")),
                homophone_comparison_label = factor(x = c( "Polysemy",
                                    "Unambiguous",
                                    "Material versus Object",
                                    "Word Extension (Some/A)",
                                    "Material vs. Object (Replication)",
                                    "Expt.4 Homophone"
                                    ), 
                                    levels = c("Expt.4 Homophone",
                                    "Material vs. Object (Replication)",
                                    "Word Extension (Some/A)",
                                    "Material versus Object",
                                    "Unambiguous",
                                    "Polysemy")))


kable(child.SB.expt1 %>%
        left_join(labels, by = "condition") %>%
        filter(Exclude == "no",type == "test", condition != 4) %>%
        mutate(age_group = floor(age.yrs)) %>%
        group_by(age_group,condition_label)%>%
        summarise(N = length(unique(subject))),
      caption = "Number of participants per condition")

Number of participants per condition

age_group	condition_label	N
3	Unambiguous	16
3	Polysemy	17
3	Material versus Object	16
3	Expt.4 Homophone	17
4	Unambiguous	18
4	Polysemy	16
4	Material versus Object	17
4	Expt.4 Homophone	16

child.SB.expt1.sum <- child.SB.expt1 %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4) %>%
  select(condition_label,subject,age.yrs,response) %>%
  mutate(age_group = floor(age.yrs)) %>%   #, response = -1*(response-1)) %>%
  group_by(condition_label,subject)%>%
  summarise(response.m = mean(response,na.rm = T)) %>%
  group_by(condition_label) %>%
  summarise(response.mean = mean(response.m,na.rm = T), response.ci.low = ci.low(response.m),response.ci.high = ci.high(response.m))
  
dodge <- position_dodge(width=0.9)

# 
# ggplot(child.SB.expt1.sum, aes(condition_label, response.mean, fill = condition_label))+
#   geom_bar(stat="identity")+
#   theme_cowplot()+
#   geom_text(aes(y = response.mean,label = condition_label), angle = 30,nudge_y = -child.SB.expt1.sum$response.mean/1.8, colour = "white",fontface="bold",size = 5)+
#   geom_errorbar(aes(ymax = ifelse(child.SB.expt1.sum$response.mean > 0.5,child.SB.expt1.sum$response.ci.low,child.SB.expt1.sum$response.ci.high), 
#                     ymin = child.SB.expt1.sum$response.mean
#                       ), width=0, position = dodge, size = 1.1) +
#   ylim(c(0,1))+
#   ylab("Proportion showing shape bias")+
#   geom_hline(yintercept = 0.5, lty = 2)+
#   theme(axis.title.x=element_blank(),
#         axis.text.x=element_blank(),
#         axis.ticks.x=element_blank())+
#   guides(fill=FALSE)+
#   scale_fill_brewer(palette="Set1")
    
a = 1

child.SB.expt1.analysis <- child.SB.expt1 %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4) %>%
  dplyr::mutate(age_group = as.character(floor(age.yrs)), 
                #response = -1*(response-1),
                contrasts_polysemy = ifelse(condition %in% c("1"),-1,1))

1.2  Adult data (Substance first)

a=0
adult.SB.expt1 <- read_csv("./Expt1_ChoiceTask/Data/Adults_SubstanceFirst.csv")

adult.SB.expt1_rep <- read_csv("./Expt1_ChoiceTask/Data/Adults_SubstanceFirst_Cond3Rep.csv")
adult.SB.expt1_rep$condition = 6

adult.SB.expt1 = bind_rows(adult.SB.expt1, adult.SB.expt1_rep)  %>%
  mutate(Item = ifelse(order <=2 ,
                       seq, abs((seq - 11))))

adult.SB.expt1.sum <- adult.SB.expt1 %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4) %>%
  select(condition_label,subject,response) %>%
  #mutate(response = -1*(response-1)) %>%
  group_by(condition_label,subject)%>%
  summarise(response.m = mean(response,na.rm = T)) %>%
  group_by(condition_label) %>%
  summarise(response.mean = mean(response.m,na.rm = T), response.ci.low = ci.low(response.m),response.ci.high = ci.high(response.m))
  

adult.SB.expt1.analysis <- adult.SB.expt1 %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4) %>%
  dplyr::mutate(age_group = as.character(floor(age.yrs)), 
                contrasts_polysemy = ifelse(condition %in% c("1"),-1,1))


# dodge <- position_dodge(width=0.9)

# 
# ggplot(adult.SB.expt1.sum, aes(condition_label, response.mean, fill = condition_label))+
#   geom_bar(stat="identity")+
#   geom_text(aes(y = response.mean,label = condition_label), angle = 30,nudge_y = -adult.SB.expt1.sum$response.mean/1.8, colour = "white",fontface="bold",size = 5)+
#   geom_errorbar(aes(ymax = ifelse(adult.SB.expt1.sum$response.mean > 0.5,adult.SB.expt1.sum$response.ci.low,adult.SB.expt1.sum$response.ci.high), 
#                     ymin = adult.SB.expt1.sum$response.mean
#                       ), width=0, position = dodge, size = 1.1) +
#   ylim(c(0,1))+
#   ylab("Proportion showing shape bias")+
#   geom_hline(yintercept = 0.5, lty = 2)+
#   theme_cowplot()+
#   theme(axis.title.x=element_blank(),
#         axis.text.x=element_blank(),
#         axis.ticks.x=element_blank())+
#   guides(fill=FALSE)+
#   scale_fill_brewer(palette="Set1")

1.3 Combine Adults and Kids for graphs

a=1
adult.SB.expt1.sum$AgeGroup = "Adults"
child.SB.expt1.sum$AgeGroup = "Children"

combine_graph = bind_rows(adult.SB.expt1.sum,child.SB.expt1.sum)

combine_graph$AgeGroup = ordered(combine_graph$AgeGroup, levels = c("Children","Adults"))

kable(combine_graph)

condition_label	response.mean	response.ci.low	response.ci.high	AgeGroup
Unambiguous	0.0156250	0.0000000	0.0472656	Adults
Polysemy	0.8906250	0.7500000	1.0000000	Adults
Material versus Object	0.3906250	0.1875000	0.6250000	Adults
Material vs. Object (Replication)	0.5625000	0.3750000	0.7347656	Adults
Expt.4 Homophone	0.2031250	0.0781250	0.3437500	Adults
Unambiguous	0.2720588	0.1468750	0.4044118	Children
Polysemy	0.7045455	0.5681818	0.8257576	Children
Material versus Object	0.8333333	0.7121212	0.9393939	Children
Expt.4 Homophone	0.3560606	0.2196970	0.4850379	Children

combine_graph$condition_label = factor(combine_graph$condition_label, levels = levels(combine_graph$condition_label), labels = gsub(" ", "\n", levels(combine_graph$condition_label), fixed = TRUE))


ggplot(combine_graph, aes(condition_label, response.mean, fill = condition_label))+
  geom_bar(stat="identity")+
  facet_grid(AgeGroup~.)+
 #geom_text(data = combine_graph, aes(y = response.mean,label = condition_label), angle = 30, colour = "white",fontface="bold",size = 5)+ #,nudge_y = -combine_graph$response.mean/1.8
  geom_errorbar(data= combine_graph, aes(ymax = ifelse(response.mean > 0.5,response.ci.low,response.ci.high), 
                    ymin = response.mean
                      ), width=0, position = dodge, size = 1.1) +
  ylim(c(0,1))+
  ylab("Proportion choosing material-match object")+
  geom_hline(yintercept = 0.5, lty = 2)+
  theme_cowplot()+
  theme(axis.title.x=element_blank(),
        axis.ticks.x=element_blank())+ #axis.text.x=element_blank(),
  guides(fill=FALSE)+
  scale_fill_brewer(palette="Set1")

ggplot(combine_graph, aes(condition_label, response.mean, fill = condition_label))+
  geom_bar(stat="identity")+
  facet_grid(AgeGroup~.)+
  #geom_text(data = combine_graph, aes(y = response.mean,label = condition_label), angle = 30, colour = "white",fontface="bold",size = 5)+ #,nudge_y = -combine_graph$response.mean/1.8
  geom_errorbar(data= combine_graph, aes(ymax = ifelse(response.mean > 0.5,response.ci.low,response.ci.high), 
                    ymin = response.mean
                      ), width=0, position = dodge, size = 1.1,color = c("black", "white","white","black",
                                                   "black","white","black","black","white")) +
  ylim(c(0,1))+
  ylab("Proportion choosing material-match object")+
  geom_hline(yintercept = 0.5, lty = 2)+
  theme_cowplot()+
  theme(axis.title.x=element_blank(),
        
        axis.ticks.x=element_blank())+ #axis.text.x=element_blank(),
  guides(fill=FALSE)+
  scale_fill_grey()

child.SB.expt1.sum <- child.SB.expt1 %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4) %>%
  select(condition_label,subject,age.yrs,response) %>%
  mutate(age_group = as.character(floor(age.yrs))) %>%  #, response = -1*(response-1)) %>%
  group_by(age_group,condition_label,subject)%>%
  summarise(response.m = mean(response,na.rm = T)) %>%
  group_by(age_group,condition_label) %>%
  summarise(response.mean = mean(response.m,na.rm = T), response.ci.low = ci.low(response.m),response.ci.high = ci.high(response.m))
  

adult.SB.expt1.sum$age_group = "Adults"
combine_graph = bind_rows(adult.SB.expt1.sum,child.SB.expt1.sum)

combine_graph$AgeGroup = ordered(combine_graph$AgeGroup, levels = c("Children","Adults"))



ggplot(combine_graph, aes(condition_label, response.mean, fill = condition_label))+
  geom_bar(stat="identity")+
  facet_grid(age_group~.)+
  geom_errorbar(data= combine_graph, aes(,ymax = ifelse(response.mean > 0.5,response.ci.low,response.ci.high), 
                    ymin = response.mean
                      ), width=0, position = dodge, size = 1.1,color = c("black", "white","white","black",
                                                   "black","white","white","black",
                                                   "black","white","black","black","white")) +
  ylim(c(0,1))+
  ylab("Proportion choosing material-match object")+
  xlab("Condition") +
  geom_hline(yintercept = 0.5, lty = 2)+
  theme_cowplot()+
  theme(axis.title.x=element_blank(),
        axis.ticks.x=element_blank())+ #axis.text.x=element_blank(),
  guides(fill=FALSE)+
  scale_fill_grey()

polysemy_effect <- glmer(response ~  condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "binomial", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

polysemy_effect_bayes_continuous_age <- brm(response ~  scale(age.mos)*condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "bernoulli", cores = 4, prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

polysemy_effect_bayes_continuous_age_order <- brm(response ~  scale(age.mos)*condition_label*scale(seq) + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "bernoulli", cores = 4, prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

polysemy_effect_bayes_continuous_age_list <- brm(response ~  scale(age.mos)*condition_label*as.factor(order) + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "bernoulli", cores = 4, prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

polysemy_effect_bayes <- brm(response ~  age_group*condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "bernoulli", cores = 4, prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

simpler_polysemy_effect_bayes <- brm(response ~  condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3)), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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simpler_polysemy_effect_bayes_three <- brm(response ~  condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3) & age_group == "3"), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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simpler_polysemy_effect_bayes_four <- brm(response ~  condition_label + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c(1,2,3) & age_group == "4"), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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adult_polysemy_effect_bayes <- brm(response ~  condition_label + (1|subject), 
                         data = subset(adult.SB.expt1.analysis,condition %in% c(1,2,3)), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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adult_polysemy_effect_bayes_seq <- brm(response ~  condition_label*scale(seq) + (1|subject), 
                         data = subset(adult.SB.expt1.analysis,condition %in% c(1,2,3)), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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adult_polysemy_effect_bayes_list <- brm(response ~  condition_label*as.factor(order) + (1|subject), 
                         data = subset(adult.SB.expt1.analysis,condition %in% c(1,2,3)), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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unambiguous_kids <- brm(response ~  1 + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition_label %in% c("Unambiguous") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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polysemy_kids <- brm(response ~  1 + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition_label %in% c("Polysemy") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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material_kids <- brm(response ~  1 + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c("3") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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unambiguous_adults <- brm(response ~  1 + (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition_label %in% c("Unambiguous") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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polysemy_adults <- brm(response ~  1 + (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition_label %in% c("Polysemy") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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material_adults <- brm(response ~  1 + (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition %in% c("3") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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a = 1

material_adults_rep <- brm(response ~  1 + (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition %in% c("6") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_kids <- brm(response ~  1 + (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_adults <- brm(response ~  1 + (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_kids_seq <- brm(response ~  1 + scale(seq)+ (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_adults_seq <- brm(response ~  1 + scale(seq)+ (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_kids_list <- brm(response ~  1 + as.factor(order)+ (1|subject), 
                         data = subset(child.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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homophone_adults_list <- brm(response ~  1 +as.factor(order)+ scale(seq)+ (1|subject), 
                         data = subset(adult.SB.expt1.analysis, condition %in% c("5") ), 
                                       family = "bernoulli", prior= c(prior(normal(0, 1), class = sd),prior(normal(0, 2.5), class = b)))

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a=1
kable(summary(polysemy_effect)$coef,
     caption = "GLMER model")

GLMER model

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	-7.00439	0.0007121	-9836.576	0
condition_labelPolysemy	13.75911	0.0007121	19322.621	0
condition_labelMaterial versus Object	15.01090	0.0007121	21080.465	0

kable(convert_stan_to_dataframe(polysemy_effect_bayes_continuous_age),
     caption = "Continuous Age Bayesian model")

Continuous Age Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.6998709	0.6469575	-3.0059335	-0.4610000	1757.460	1.000891	*
scaleage.mos	-0.2612191	0.6201771	-1.4911206	0.9479904	1597.518	1.001659	-
condition_labelPolysemy	3.5114542	0.9024642	1.7221345	5.3222225	1819.649	1.000481	*
condition_labelMaterialversusObject	5.0811228	0.9905836	3.2078876	7.0907123	1989.226	1.000463	*
scaleage.mos:condition_labelPolysemy	1.2613814	0.8557599	-0.4291301	2.9629697	1659.341	1.000986	-
scaleage.mos:condition_labelMaterialversusObject	0.5552364	0.9558591	-1.3029083	2.4353371	1667.134	1.002386	-

kable(convert_stan_to_dataframe(polysemy_effect_bayes),
     caption = "Categorical Age Bayesian model")

Categorical Age Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.3947931	0.7839043	-2.9537064	0.1216951	1301.738	1.004244	-
age_group4	-0.7785081	1.0093151	-2.7522025	1.1466221	1156.688	1.005525	-
condition_labelPolysemy	2.2788982	1.0716646	0.1895725	4.4288276	1125.985	1.004302	*
condition_labelMaterialversusObject	4.2302808	1.0956463	2.1067321	6.4080651	1445.581	1.003857	*
age_group4:condition_labelPolysemy	3.0078022	1.4396845	0.2484581	5.8243139	1430.583	1.003305	*
age_group4:condition_labelMaterialversusObject	2.0208103	1.4581765	-0.8320497	4.9207661	1330.140	1.003582	-

kable(convert_stan_to_dataframe(polysemy_effect_bayes_continuous_age_order),
     caption = "Continuous Age plus effect of item order Bayesian model")

Continuous Age plus effect of item order Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.7175983	0.6759117	-3.0480584	-0.3771064	1758.784	1.0025077	*
scaleage.mos	-0.2800654	0.6515267	-1.5261737	1.0277616	1703.326	1.0019588	-
condition_labelPolysemy	3.6460990	0.9529432	1.8062331	5.5978533	1852.984	1.0028809	*
condition_labelMaterialversusObject	5.2963171	1.0014131	3.3746439	7.3011070	2353.708	0.9996053	*
scaleseq	-0.4160170	0.3192030	-1.0448547	0.2052267	2495.381	0.9998088	-
scaleage.mos:condition_labelPolysemy	1.3874551	0.9091499	-0.3631011	3.1478393	1724.682	1.0013477	-
scaleage.mos:condition_labelMaterialversusObject	0.6174986	0.9928104	-1.3230234	2.5650410	1985.724	1.0008726	-
scaleage.mos:scaleseq	0.2680796	0.3344675	-0.3697947	0.9407333	2833.816	1.0008771	-
condition_labelPolysemy:scaleseq	0.7752601	0.4523064	-0.0970650	1.6706994	2907.862	1.0002956	-
condition_labelMaterialversusObject:scaleseq	0.0290829	0.5217127	-1.0108081	1.0643365	3159.125	1.0003019	-
scaleage.mos:condition_labelPolysemy:scaleseq	0.0156892	0.4424374	-0.8729618	0.9019794	2936.119	1.0027406	-
scaleage.mos:condition_labelMaterialversusObject:scaleseq	-0.7079933	0.5225072	-1.7543376	0.2948526	3373.595	0.9996036	-

kable(convert_stan_to_dataframe(polysemy_effect_bayes_continuous_age_list),
     caption = "Continuous Age plus effect of List Bayesian model")

Continuous Age plus effect of List Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.1013612	0.9306761	-2.9592635	0.7092786	1583.841	1.0029905	-
scaleage.mos	-0.1288249	0.8535806	-1.8057609	1.5371031	1574.579	1.0015831	-
condition_labelPolysemy	3.8960288	1.1844296	1.5986466	6.2066365	1978.910	1.0015370	*
condition_labelMaterialversusObject	4.1714771	1.2613243	1.7979790	6.7164369	1907.291	1.0030591	*
as.factororder2	0.2663181	1.2501293	-2.2635858	2.7482218	2174.839	1.0003644	-
as.factororder3	-1.9289152	1.3015284	-4.5978144	0.5823471	1513.234	1.0013690	-
as.factororder4	-0.5591404	1.3287323	-3.1714489	2.0463075	1617.316	1.0016575	-
scaleage.mos:condition_labelPolysemy	1.5918185	1.1851727	-0.6837457	3.9970124	1615.424	1.0025434	-
scaleage.mos:condition_labelMaterialversusObject	1.0037313	1.2061505	-1.3177059	3.3801183	1955.325	1.0003120	-
scaleage.mos:as.factororder2	-1.0956829	1.2285334	-3.5679940	1.2375705	2195.687	0.9996146	-
scaleage.mos:as.factororder3	-0.9754109	1.3205074	-3.6189035	1.6363170	1896.412	1.0026604	-
scaleage.mos:as.factororder4	1.2181224	1.3110295	-1.3470644	3.7846310	1879.957	1.0011565	-
condition_labelPolysemy:as.factororder2	-0.2821910	1.6801840	-3.5132537	3.0518774	2537.922	1.0010979	-
condition_labelMaterialversusObject:as.factororder2	1.7631593	1.8320106	-1.7709417	5.5097367	3100.713	1.0001597	-
condition_labelPolysemy:as.factororder3	0.6607266	1.6580097	-2.6224383	3.8674786	2036.441	1.0027021	-
condition_labelMaterialversusObject:as.factororder3	1.7250091	1.6697223	-1.6085651	5.0082231	2275.106	1.0017593	-
condition_labelPolysemy:as.factororder4	-1.9104601	1.6881953	-5.2361741	1.3349228	2381.447	1.0016507	-
condition_labelMaterialversusObject:as.factororder4	3.2021852	1.9236143	-0.4935752	7.0814173	2978.430	1.0004707	-
scaleage.mos:condition_labelPolysemy:as.factororder2	0.3578286	1.6096271	-2.8514888	3.5924277	2229.123	0.9999696	-
scaleage.mos:condition_labelMaterialversusObject:as.factororder2	-1.7845794	1.8761161	-5.5621365	1.9079806	3085.593	1.0001996	-
scaleage.mos:condition_labelPolysemy:as.factororder3	-0.6506507	1.7609547	-4.0460049	2.7563642	2396.873	1.0009213	-
scaleage.mos:condition_labelMaterialversusObject:as.factororder3	0.5109298	1.7020821	-2.7880974	3.9142093	2486.210	1.0001955	-
scaleage.mos:condition_labelPolysemy:as.factororder4	0.4641506	1.7130776	-2.7925852	3.9302908	2309.636	1.0010673	-
scaleage.mos:condition_labelMaterialversusObject:as.factororder4	-0.8148095	1.7471254	-4.1354795	2.6464533	2570.208	0.9998571	-

kable(convert_stan_to_dataframe(simpler_polysemy_effect_bayes),
     caption = "Simpler Bayesian model")

Simpler Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.658406	0.6446640	-2.967773	-0.4634405	1536.899	1.0018575	*
condition_labelPolysemy	3.420210	0.8938251	1.755651	5.2792661	1662.583	0.9998583	*
condition_labelMaterialversusObject	4.975715	0.9513267	3.212356	6.8924837	1858.110	1.0010043	*

kable(convert_stan_to_dataframe(unambiguous_kids),
      caption = "Unambiguous Control Condition Kids Bayesian model")

Unambiguous Control Condition Kids Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-2.048691	0.6544722	-3.459115	-0.907049	2456.031	0.9994982	*

kable(convert_stan_to_dataframe(polysemy_kids),
      caption = "Polysemy Condition Kids Bayesian model")

Polysemy Condition Kids Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	1.755284	0.5997693	0.6580427	3.026568	2493.183	1.001573	*

kable(convert_stan_to_dataframe(material_kids),
      caption = "Material vs. Object Condition Kids Bayesian model")

Material vs. Object Condition Kids Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	3.540103	0.8498518	2.095777	5.414366	2666.097	0.9996351	*

kable(convert_stan_to_dataframe(simpler_polysemy_effect_bayes_three),
      caption = "Simpler Bayesian model for three-year-olds")

Simpler Bayesian model for three-year-olds

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-0.8519557	0.7276756	-2.2723227	0.571773	2490.286	1.002322	-
condition_labelPolysemy	1.4760780	0.9930959	-0.4638941	3.439269	2412.286	1.001575	-
condition_labelMaterialversusObject	3.2538791	1.0680840	1.1800836	5.314217	2264.712	1.003143	*

kable(convert_stan_to_dataframe(simpler_polysemy_effect_bayes_four),
      caption = "Simpler Bayesian model for four-year-olds")

Simpler Bayesian model for four-year-olds

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.663656	0.7919165	-3.266265	-0.0910843	2521.875	1.0006280	*
condition_labelPolysemy	4.389636	1.1490809	2.125471	6.7088454	3034.897	1.0003714	*
condition_labelMaterialversusObject	4.982792	1.1931063	2.792607	7.4707139	2803.170	0.9999399	*

kable(convert_stan_to_dataframe(adult_polysemy_effect_bayes),
      caption = "Adult polysemy comparison")

Adult polysemy comparison

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.124954	0.9395835	-6.1043413	-2.371703	3180.929	1.000729	*
condition_labelPolysemy	7.138142	1.2203236	4.7981355	9.495228	4000.000	1.001193	*
condition_labelMaterialversusObject	2.782707	1.1619974	0.4192193	5.106435	3093.548	1.000816	*

kable(convert_stan_to_dataframe(adult_polysemy_effect_bayes_seq),
      caption = "Adult polysemy comparison with trial order")

Adult polysemy comparison with trial order

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.3340558	0.9605002	-6.2951909	-2.5176783	3189.047	1.0002359	*
condition_labelPolysemy	7.5663384	1.2577853	5.2156401	10.1575156	4000.000	0.9999075	*
condition_labelMaterialversusObject	2.9097237	1.1780320	0.5550778	5.2307297	3076.711	1.0003670	*
scaleseq	-0.5065682	0.6708219	-1.8153391	0.7996535	2930.468	0.9996920	-
condition_labelPolysemy:scaleseq	1.3757040	0.8709221	-0.3019466	3.1445580	3204.575	0.9996786	-
condition_labelMaterialversusObject:scaleseq	0.4570539	0.7846589	-1.1287104	1.9776996	3345.511	0.9997037	-

kable(convert_stan_to_dataframe(adult_polysemy_effect_bayes_list),
      caption = "Adult polysemy comparison with list")

Adult polysemy comparison with list

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.0407644	1.262987	-6.6100821	-1.700829	2271.121	1.0006199	*
condition_labelPolysemy	7.0302315	1.526801	4.1052493	10.117047	2468.370	0.9997604	*
condition_labelMaterialversusObject	2.5341650	1.416452	-0.2539199	5.386819	1648.843	1.0048045	-
as.factororder2	-0.1834690	1.484503	-3.0907460	2.705086	2252.257	1.0035527	-
as.factororder3	-0.6239205	1.582301	-3.8105095	2.409767	2520.475	1.0004959	-
as.factororder4	-0.5806267	1.507333	-3.5846789	2.295734	2422.238	0.9994657	-
condition_labelPolysemy:as.factororder2	-0.6927652	1.804088	-4.1245327	2.970871	2493.971	1.0005165	-
condition_labelMaterialversusObject:as.factororder2	1.1160691	1.750448	-2.2320880	4.594584	2306.240	1.0019842	-
condition_labelPolysemy:as.factororder3	2.6844388	1.988905	-1.0936912	6.773514	3159.939	1.0004458	-
condition_labelMaterialversusObject:as.factororder3	-0.3329184	1.861131	-4.0350093	3.397440	2554.632	1.0001411	-
condition_labelPolysemy:as.factororder4	1.3367496	1.870778	-2.2910853	5.043008	2878.355	1.0000218	-
condition_labelMaterialversusObject:as.factororder4	0.9194244	1.813384	-2.6328385	4.410732	2513.266	0.9999518	-

kable(convert_stan_to_dataframe(unambiguous_adults),
      caption = "Unambiguous Control Condition Adults Bayesian model")

Unambiguous Control Condition Adults Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-5.116761	1.391304	-8.367355	-2.986699	2316.487	0.999977	*

kable(convert_stan_to_dataframe(polysemy_adults),
      caption = "Polysemy Condition Adults model")

Polysemy Condition Adults model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	3.625019	1.058251	1.853144	6.016717	2617.961	1.00092	*

kable(convert_stan_to_dataframe(material_adults),
      caption = "Material vs. Object Condition Adults Bayesian model")

Material vs. Object Condition Adults Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.105366	0.8803796	-2.885603	0.5896289	1459.728	1.004094	-

kable(convert_stan_to_dataframe(material_adults_rep),
      caption = "Material vs. Object Condition Replication Adults Bayesian model")

Material vs. Object Condition Replication Adults Bayesian model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3709052	0.6956155	-0.9786634	1.750289	1409.107	1.000419	-

1.4  Homophone condition

Homophone Condition kids model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.147042	0.5483706	-2.279511	-0.1364286	1939.067	1.000251	*

Homophone Condition Adults model

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.844643	0.5981932	-3.202179	-0.862164	1648.464	1.001383	*

Homophone Condition kids model with item order as factor

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.2538517	0.5686213	-2.442316	-0.1983250	1916.281	1.003917	*
scaleseq	-0.6650393	0.2730021	-1.223305	-0.1502611	4000.000	1.001515	*

Homophone Condition Adults model with item order as factor

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-2.1164177	0.6831474	-3.694561	-1.0004937	1992.817	1.0000748	*
scaleseq	-0.8174398	0.4167121	-1.669726	-0.0519515	4000.000	0.9998914	*

Homophone Condition kids model with list as factor

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.0701005	0.8676364	-2.836024	0.5620098	2744.866	1.0000967	-
as.factororder2	0.6603825	1.2002765	-1.684476	2.9865393	2967.367	0.9997442	-
as.factororder3	0.5015958	1.1867697	-1.870588	2.8505095	2843.609	1.0002915	-
as.factororder4	-1.8090588	1.2514056	-4.299726	0.5788340	3447.208	0.9999392	-

Homophone Condition Adults model with list as factor

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.8195256	1.032767	-4.072920	-0.0112893	2547.585	1.0005958	*
as.factororder2	-0.0675844	1.317717	-2.658726	2.5397704	2975.091	1.0014160	-
as.factororder3	-1.1131648	1.403120	-4.009844	1.6167089	3078.858	0.9997943	-
as.factororder4	-0.9106137	1.381628	-3.553219	1.8141380	3394.435	1.0002816	-
scaleseq	-0.8859788	0.440117	-1.798595	-0.0815603	4000.000	0.9991981	*

Homophone Condition comparison for kids

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.181556	0.6058472	-2.375047	-0.0128153	1575.856	0.9993584	*
homophone_comparison_labelUnambiguous	-0.983387	0.8755436	-2.707072	0.7214412	1444.187	1.0000546	-
homophone_comparison_labelPolysemy	2.943072	0.8756607	1.290539	4.6764080	1592.775	1.0001955	*

Homophone Condition comparison for adults

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-1.955781	0.5995563	-3.261604	-0.8735774	3290.294	0.9997760	*
homophone_comparison_labelUnambiguous	-3.067668	1.1522886	-5.539591	-1.0278839	4000.000	1.0001541	*
homophone_comparison_labelPolysemy	4.812457	0.9603414	3.061728	6.8513451	4000.000	0.9996258	*

1.5 Object first comparison

a=0
adult.SB.expt1.obj <- read_csv("./Expt1_ChoiceTask/Data/Adults_ObjectFirst.csv")
adult.SB.expt1.obj$age_group = "Adult"

child.SB.expt1.obj <- read_csv("./Expt1_ChoiceTask/Data/Child_ObjectFirst.csv")

## Warning: Missing column names filled in: 'X24' [24]

child.SB.expt1.obj$age_group = "Child"

combined.SB.expt1.obj = bind_rows(adult.SB.expt1.obj,child.SB.expt1.obj)

combined.SB.expt1.obj$AgeGroup = factor(ifelse(combined.SB.expt1.obj$age_group == "Child", "Children","Adults"), levels = c("Children","Adults"))


combined.SB.expt1.obj <- combined.SB.expt1.obj %>%
  left_join(labels, by = "condition") %>%
  filter(Exclude == "no",type == "test", condition != 4)

combined.SB.expt1.obj.graph =  combined.SB.expt1.obj %>%
  dplyr::select(AgeGroup,condition_label,subject,response) %>%
  #mutate(response = -1*(response-1)) %>%
  group_by(AgeGroup,condition_label,subject)%>%
  dplyr::summarise(response.m = mean(response,na.rm = T)) %>%
  group_by(AgeGroup,condition_label) %>%
  dplyr::summarise(response.mean = mean(response.m,na.rm = T), response.ci.low = ci.low(response.m),response.ci.high = ci.high(response.m))
  
dodge <- position_dodge(width=0.9)


ggplot(combined.SB.expt1.obj.graph, aes(condition_label, response.mean, fill = condition_label))+
  geom_bar(stat="identity")+
  facet_grid(AgeGroup~.)+
  geom_errorbar(data= combined.SB.expt1.obj.graph, aes(ymax = ifelse(response.mean > 0.5,response.ci.low,response.ci.high), 
                    ymin = response.mean
                      ), width=0, position = dodge, size = 1.1) +
  ylim(c(0,1))+
  ylab("Proportion choosing material-match object")+
  geom_hline(yintercept = 0.5, lty = 2)+
  theme_cowplot()+
  theme(axis.title.x=element_blank(),
        #axis.text.x=element_blank(),
        axis.ticks.x=element_blank())+
  guides(fill=FALSE)+
  scale_fill_brewer(palette="Set1")

kable(combined.SB.expt1.obj.graph)

AgeGroup	condition_label	response.mean	response.ci.low	response.ci.high
Children	Unambiguous	0.171875	0.03125	0.3441406
Children	Polysemy	0.750000	0.56250	0.9375000
Adults	Unambiguous	0.046875	0.00000	0.1406250
Adults	Polysemy	0.875000	0.68750	1.0000000

a=1

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_child_order),
     caption = "Child Object First with Item Order effect")

Child Object First with Item Order effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-2.8601241	0.9967589	-4.9398360	-1.011789	2934.701	1.0010748	*
condition_labelPolysemy	4.8557074	1.3098208	2.3556925	7.565550	3465.150	1.0002149	*
scaleseq	0.3412886	0.5071282	-0.6408636	1.357316	4000.000	0.9995080	-
condition_labelPolysemy:scaleseq	-0.3238561	0.7260867	-1.7607592	1.134458	4000.000	0.9998351	-

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_child_list),
     caption = "Child Object First with List effect")

Child Object First with List effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-3.3973868	1.301923	-6.063196	-0.8750734	2045.158	1.0015670	*
condition_labelPolysemy	4.5937932	1.410879	1.957073	7.5170686	2382.792	1.0010036	*
as.factororder2	2.0342907	1.583492	-1.026873	5.2223797	2570.997	1.0006913	-
as.factororder3	0.7608927	1.584926	-2.315293	3.8913391	2364.310	1.0000122	-
as.factororder4	-1.1728811	1.697950	-4.468234	2.1999807	3319.813	1.0015167	-
condition_labelPolysemy:as.factororder2	2.4934398	2.001443	-1.414125	6.4526324	4000.000	0.9999397	-
condition_labelPolysemy:as.factororder3	-2.1556325	1.976045	-6.078596	1.8233277	3023.730	1.0001143	-
condition_labelPolysemy:as.factororder4	1.6873334	1.892029	-2.075079	5.4916884	3263.920	1.0007477	-

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_child),
     caption = "Child Object First")

Child Object First

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-2.745599	0.9846825	-4.718500	-0.8553959	2829.687	1.001448	*
condition_labelPolysemy	4.735804	1.2706249	2.295339	7.2757132	3373.335	1.000036	*

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_adult),
     caption = "Adult Object First")

Adult Object First

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.075909	0.9863563	-6.120660	-2.300718	3530.714	1.0000642	*
condition_labelPolysemy	7.036680	1.2322164	4.807798	9.582091	4000.000	0.9996956	*

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_adult_seq2),
     caption = "Adult Object First with Item Order info")

Adult Object First with Item Order info

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.3147618	1.0085878	-6.482478	-2.5262756	3545.434	1.001078	*
condition_labelPolysemy	7.3244422	1.2771385	5.008797	9.9287877	4000.000	1.000049	*
scaleseq	-0.5585296	0.6556420	-1.906011	0.6642044	4000.000	1.000137	-
condition_labelPolysemy:scaleseq	0.5167193	0.8358234	-1.089667	2.1912742	4000.000	1.000039	-

kable(convert_stan_to_dataframe(obj_first_polysemy_effect_adult_list),
     caption = "Adult Object First with List info")

Adult Object First with List info

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-3.2216849	1.231039	-5.831134	-1.012619	3258.463	0.9999276	*
condition_labelPolysemy	6.9907329	1.441814	4.193309	9.921450	4000.000	0.9993883	*
as.factororder2	-1.9491761	1.678113	-5.351460	1.209100	4000.000	0.9995184	-
as.factororder3	-0.9415750	1.615318	-4.102888	2.154795	4000.000	0.9993705	-
as.factororder4	-1.9524012	1.640244	-5.247905	1.121749	4000.000	1.0003492	-
condition_labelPolysemy:as.factororder2	0.3577327	1.923900	-3.269800	4.197150	4000.000	0.9999023	-
condition_labelPolysemy:as.factororder3	2.4386341	1.931625	-1.274677	6.345777	4000.000	0.9995361	-
condition_labelPolysemy:as.factororder4	0.3125015	1.856240	-3.309687	3.948314	4000.000	1.0000006	-

kable(convert_stan_to_dataframe(obj_first_unambig_child),
     caption = "Child Unambiguous")

Child Unambiguous

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-3.042559	0.9849422	-5.248135	-1.406009	2300.325	1.002405	*

kable(convert_stan_to_dataframe(obj_first_polysemy_child),
     caption = "Child Polysemy")

Child Polysemy

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	2.693368	1.106706	0.7209793	5.086964	2051.23	1.001517	*

kable(convert_stan_to_dataframe(obj_first_unambig_adult),
     caption = "Adult Unambiguous")

Adult Unambiguous

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	-4.918188	1.513809	-8.604185	-2.697794	2368.978	1.003281	*

kable(convert_stan_to_dataframe(obj_first_polysemy_adult),
     caption = "Adult Polysemy")

Adult Polysemy

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	4.257288	1.299581	2.090701	7.239842	2602.776	0.9994165	*

2 Experiment 2 - sorting

## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

Object_label	condition_label	response.mean	response.ci.low	response.ci.high
Standard	Unambiguous	1.000000	1.000000	1.000000
Standard	Polysemy	0.984375	0.953125	1.000000
Shape & Material Match	Unambiguous	1.000000	1.000000	1.000000
Shape & Material Match	Polysemy	0.984375	0.953125	1.000000
Shape Match	Unambiguous	0.875000	0.718750	0.984375
Shape Match	Polysemy	0.375000	0.187500	0.593750
Material Match	Unambiguous	0.140625	0.015625	0.296875
Material Match	Polysemy	0.609375	0.390625	0.812500
No Match	Unambiguous	0.000000	0.000000	0.000000
No Match	Polysemy	0.015625	0.000000	0.046875

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Children shape match (lower in polysemy condition)

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.6284987	0.0658381	0.4984367	0.7602331	1412.140	1.000804	*
condition_label.L	-0.3564537	0.0955207	-0.5468839	-0.1699841	1397.614	1.000770	*

Children material match (higher in polysemy condition)

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3754633	0.0722458	0.2265267	0.5153060	640.6423	1.008115	*
condition_label.L	0.3377231	0.0990098	0.1395509	0.5300239	667.8182	1.003904	*

Children shape match (lower in polysemy condition) with item order effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.6228120	0.0643018	0.4954988	0.7529499	1303.056	1.0045674	*
condition_label.L	-0.3526086	0.0918181	-0.5289497	-0.1706050	1236.817	1.0037907	*
scaleseq_items	0.0279595	0.0246143	-0.0199844	0.0778845	4000.000	0.9994114	-

Children material match (higher in polysemy condition) with item order effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3653354	0.0723321	0.2233976	0.5037863	581.1796	1.0101662	*
condition_label.L	0.3363461	0.1004266	0.1332592	0.5288822	684.4867	1.0053844	*
scaleseq_items	-0.0350822	0.0192009	-0.0731600	0.0046804	4000.0000	0.9999587	-

Children shape match (lower in polysemy condition) with list effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.5290754	0.1236389	0.2812928	0.7689620	2100.175	1.001387	*
condition_label.L	-0.6585056	0.1702302	-0.9909300	-0.3304163	1760.838	1.000591	*
as.factororder2	0.1264647	0.1660331	-0.2077182	0.4522777	2193.907	1.000487	-
as.factororder3	0.2541701	0.1720801	-0.0933261	0.5945924	2094.176	1.002771	-
as.factororder4	0.0106971	0.1672448	-0.3112502	0.3666812	2160.998	1.001362	-
condition_label.L:as.factororder2	0.7059174	0.2331483	0.2406493	1.1623659	1806.764	1.000262	*
condition_label.L:as.factororder3	0.4335330	0.2344826	-0.0498325	0.8900840	1980.257	1.000376	-
condition_label.L:as.factororder4	0.0787045	0.2224547	-0.3541668	0.5303403	1801.495	1.001249	-

Children material match (higher in polysemy condition) with list effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.5013933	0.1325811	0.2359387	0.7630440	2365.843	1.0005386	*
condition_label.L	0.7003464	0.1809375	0.3436625	1.0592840	2244.016	1.0003006	*
as.factororder2	-0.1871060	0.1847360	-0.5427178	0.1853323	2462.802	1.0012957	-
as.factororder3	-0.3742410	0.1797635	-0.7256491	-0.0235435	2458.702	1.0009142	*
as.factororder4	0.0360624	0.1803100	-0.3292777	0.3732621	2184.320	1.0021188	-
condition_label.L:as.factororder2	-0.6961279	0.2481002	-1.1786086	-0.2126392	2361.326	1.0006690	*
condition_label.L:as.factororder3	-0.5219674	0.2487127	-1.0200244	-0.0244300	2207.182	0.9997524	*
condition_label.L:as.factororder4	-0.2221950	0.2413959	-0.7051453	0.2516740	2198.052	1.0002162	-

2.1  Adults

## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding factor and character vector,
## coercing into character vector

## Warning in bind_rows_(x, .id): binding character and factor vector,
## coercing into character vector

Object_label	condition_label	response.mean	response.ci.low	response.ci.high
Standard	Unambiguous	1.0000000	1.0000000	1.0000000
Standard	Polysemy	1.0000000	1.0000000	1.0000000
Shape & Material Match	Unambiguous	1.0000000	1.0000000	1.0000000
Shape & Material Match	Polysemy	0.9705882	0.9117647	1.0000000
Shape Match	Unambiguous	0.8125000	0.6093750	0.9687500
Shape Match	Polysemy	0.1911765	0.0441176	0.3676471
Material Match	Unambiguous	0.0468750	0.0000000	0.1406250
Material Match	Polysemy	0.7352941	0.5588235	0.8974265
No Match	Unambiguous	0.0000000	0.0000000	0.0000000
No Match	Polysemy	0.0000000	0.0000000	0.0000000

## Compiling the C++ model

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Adults Shape match (lower in polysemy condition)

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.5012592	0.0638496	0.3781891	0.6266477	1108.7260	1.002196	*
condition_label.L	-0.4409511	0.0914113	-0.6283881	-0.2651783	981.3098	1.002561	*

Adults Material match (higher in polysemy condition)

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3907080	0.0555038	0.2808900	0.5027294	1064.198	1.002529	*
condition_label.L	0.4890035	0.0756413	0.3480519	0.6403309	1146.333	1.008134	*

Adults Shape match (lower in polysemy condition) with item order effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.5044989	0.0651119	0.3818475	0.6343617	674.2651	1.0029339	*
condition_label.L	-0.4400917	0.0934596	-0.6205864	-0.2540861	695.6893	1.0027466	*
scaleseq_items	0.0306587	0.0192390	-0.0071262	0.0689817	4000.0000	0.9998967	-

Adults Material match (higher in polysemy condition) with item order effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3913567	0.0556007	0.2821919	0.5004927	1065.790	1.0022617	*
condition_label.L	0.4875236	0.0787351	0.3345661	0.6458055	1168.872	1.0002591	*
scaleseq_items	-0.0101747	0.0193685	-0.0474325	0.0269161	4000.000	0.9995808	-

Adults Shape match (lower in polysemy condition) with list effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.3412865	0.1187568	0.1042156	0.5812695	2203.155	1.001141	*
condition_label.L	-0.2166554	0.1652288	-0.5358250	0.1128446	1685.654	1.000152	-
as.factororder2	0.0350974	0.1665923	-0.2939479	0.3687458	2032.700	1.000236	-
as.factororder3	0.4402020	0.1649233	0.1203598	0.7768351	2169.979	1.000316	*
as.factororder4	0.1625287	0.1646769	-0.1647612	0.4861583	2194.618	1.000632	-
condition_label.L:as.factororder2	-0.3125455	0.2365969	-0.7953276	0.1574251	1599.094	1.003123	-
condition_label.L:as.factororder3	-0.0899130	0.2274175	-0.5478019	0.3627851	1695.239	1.000558	-
condition_label.L:as.factororder4	-0.4060044	0.2177526	-0.8342130	0.0126461	2012.668	1.001532	-

Adults Material match (higher in polysemy condition) with list effect

	Estimate	Est.Error	l.95..CI	u.95..CI	Eff.Sample	Rhat	Diff_from_zero
Intercept	0.4038196	0.1162098	0.1743811	0.6270174	2601.000	0.9994852	*
condition_label.L	0.5782294	0.1640005	0.2527588	0.9030761	2162.548	1.0022864	*
as.factororder2	0.1248648	0.1662692	-0.2019916	0.4407658	2908.724	1.0008046	-
as.factororder3	-0.1527257	0.1647413	-0.4698936	0.1737877	2794.892	1.0000032	-
as.factororder4	-0.0017436	0.1604867	-0.3168405	0.3146724	2631.280	0.9999636	-
condition_label.L:as.factororder2	-0.1887090	0.2358311	-0.6493804	0.2692028	2529.499	0.9999348	-
condition_label.L:as.factororder3	-0.2233393	0.2280599	-0.6637114	0.2341126	2275.786	1.0012328	-
condition_label.L:as.factororder4	-0.0018922	0.2278718	-0.4590293	0.4448887	2265.186	1.0021053	-

child.sort.summary$AgeGroup = "Children"
adult.sort.summary$AgeGroup = "Adults"
combine_graph = bind_rows(adult.sort.summary,child.sort.summary)

combine_graph$AgeGroup = ordered(combine_graph$AgeGroup, levels = c("Children","Adults"))
kable(combine_graph)

Object_label	condition_label	response.mean	response.ci.low	response.ci.high	AgeGroup
Standard	Unambiguous	1.0000000	1.0000000	1.0000000	Adults
Standard	Polysemy	1.0000000	1.0000000	1.0000000	Adults
Shape & Material Match	Unambiguous	1.0000000	1.0000000	1.0000000	Adults
Shape & Material Match	Polysemy	0.9705882	0.9117647	1.0000000	Adults
Shape Match	Unambiguous	0.8125000	0.6093750	0.9687500	Adults
Shape Match	Polysemy	0.1911765	0.0441176	0.3676471	Adults
Material Match	Unambiguous	0.0468750	0.0000000	0.1406250	Adults
Material Match	Polysemy	0.7352941	0.5588235	0.8974265	Adults
No Match	Unambiguous	0.0000000	0.0000000	0.0000000	Adults
No Match	Polysemy	0.0000000	0.0000000	0.0000000	Adults
Standard	Unambiguous	1.0000000	1.0000000	1.0000000	Children
Standard	Polysemy	0.9843750	0.9531250	1.0000000	Children
Shape & Material Match	Unambiguous	1.0000000	1.0000000	1.0000000	Children
Shape & Material Match	Polysemy	0.9843750	0.9531250	1.0000000	Children
Shape Match	Unambiguous	0.8750000	0.7187500	0.9843750	Children
Shape Match	Polysemy	0.3750000	0.1875000	0.5937500	Children
Material Match	Unambiguous	0.1406250	0.0156250	0.2968750	Children
Material Match	Polysemy	0.6093750	0.3906250	0.8125000	Children
No Match	Unambiguous	0.0000000	0.0000000	0.0000000	Children
No Match	Polysemy	0.0156250	0.0000000	0.0468750	Children

ggplot(combine_graph, aes(Object_label, response.mean, fill = Object_label))+
  facet_wrap(AgeGroup~condition_label)+
  geom_bar(stat="identity")+
  geom_errorbar(data = combine_graph, aes(ymax = ifelse(response.mean > 0.5,response.ci.low,response.ci.high), 
                    ymin = response.mean
                      ), width=0, position = dodge, size = 1.1) +
  ylim(c(0,1))+
  ylab("Proportion sorting object into target category")+
  geom_hline(yintercept = 0.5, lty = 2)+
  theme_cowplot()+
  theme(axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank())+
  labs(fill = "Object Type")+
  scale_fill_brewer(palette="Set1")

child.sort.strat$AgeGroup = "Children"
adult.sort.strat$AgeGroup = "Adults"
combine_graph.sort = bind_rows(adult.sort.strat,child.sort.strat)
combine_graph.sort$categorizations_label = gsub(" ", "\n", combine_graph.sort$categorizations_label, fixed = TRUE)

combine_graph.sort$AgeGroup = ordered(combine_graph.sort$AgeGroup, levels = c("Children","Adults"))
combine_graph.sort$condition_label = ordered(combine_graph.sort$condition_label, levels = c("Unambiguous","Polysemy"))

ggplot(combine_graph.sort, aes(y = response,categorizations_label, fill = categorizations_label))+
  geom_bar(stat = "identity")+
  facet_wrap(AgeGroup~condition_label)+
  xlab("Sorting Strategy")+
  ylab("Count of participants")+
  guides(fill=FALSE)+
  scale_fill_brewer(palette="Set1")