Spatial Word Learning Adults
17 September, 2026
df.response_blind_adults <- read.csv("../../../data/blind_adults/PROCESSED_DATA/response_wl.csv")
df.response_blind_children <- read.csv("../../../data/blind_children/PROCESSED_DATA/response_wl.csv")
df.response_combined <- bind_rows(df.response_blind_adults, df.response_blind_children)Combined Data
We didn’t meet the partial/no vision quota for blind adults, but did for blind kids.
df.demog_age <- df.response_combined %>%
group_by(PID, group) %>%
slice(1) %>%
ungroup()
df.demog_age_summary_by_group <- df.demog_age %>%
group_by(group) %>%
summarise(mean_age = mean(age_years, na.rm = T),
sd_age = sd(age_years, na.rm = T),
min_age = min(age_years, na.rm = T),
max_age = max(age_years, na.rm = T))
df.demog_gender <- df.response_combined %>%
group_by(PID, group) %>%
slice(1) %>%
ungroup()
df.demog_gender_summary_by_group <- df.demog_gender %>%
group_by(group, sex) %>%
count()
df.demog_vision <- df.response_combined %>%
group_by(PID, group) %>%
slice(1) %>%
ungroup()
df.demog_vision_summary_by_group <- df.demog_vision %>%
group_by(group, vision_group) %>%
count()Descriptives
Bias test
Most participants showed a consistent geocentric bias. Some participants cannot be categorized.
df.response_combined_bias <- df.response_combined %>%
group_by(PID, group) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
group_by(group, bias_category) %>%
summarise(n = n())## `summarise()` has regrouped the output.
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID and group.
## ℹ Output is grouped by PID.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, group))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.df.response_combined_bias## # A tibble: 6 × 3
## # Groups: group [2]
## group bias_category n
## <chr> <chr> <int>
## 1 blind_adults Egocentric 7
## 2 blind_adults Geocentric 23
## 3 blind_adults <NA> 8
## 4 blind_children Egocentric 13
## 5 blind_children Geocentric 24
## 6 blind_children <NA> 3df.response_combined_bias_by_vision <- df.response_combined %>%
group_by(PID, vision_group, group) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
group_by(group, vision_group, bias_category) %>%
summarise(n = n())## `summarise()` has regrouped the output.
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, vision_group, and group.
## ℹ Output is grouped by PID and vision_group.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, vision_group, group))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.df.response_combined_bias_by_vision## # A tibble: 11 × 4
## # Groups: group, vision_group [4]
## group vision_group bias_category n
## <chr> <chr> <chr> <int>
## 1 blind_adults none Egocentric 7
## 2 blind_adults none Geocentric 21
## 3 blind_adults none <NA> 6
## 4 blind_adults partial Geocentric 2
## 5 blind_adults partial <NA> 2
## 6 blind_children none Egocentric 10
## 7 blind_children none Geocentric 19
## 8 blind_children none <NA> 1
## 9 blind_children partial Egocentric 3
## 10 blind_children partial Geocentric 5
## 11 blind_children partial <NA> 2Plotting bias by age, kids and adults side by side (each on its own age scale). Seems like the older kids are more egocentric? Though later regressions show no age effects (in both kids-confirmatory and adults-exploratory).
plot_bias_age_by_group <- function(df, group_name) {
ggplot(df %>% filter(!is.na(bias_geocentric) & group == group_name),
aes(x = age_years, y = bias_geocentric)) +
geom_jitter(width = 0, height = 0.05, alpha = 0.6) +
#geom_smooth(method = "loess", se = TRUE) +
geom_smooth(method = "glm", method.args = list(family = "quasibinomial"), se = TRUE) +
labs(title = group_name, x = "Age (years)", y = "Proportion geocentric bias") +
coord_cartesian(ylim = c(-0.1,1.1))
}df.response_combined_bias_age_continuous <- df.response_combined %>%
filter(task == "Bias Test") %>%
group_by(PID, group, age_months) %>%
summarise(bias_geocentric = mean(is_geocentric_bias), .groups = "drop") %>%
mutate(age_years = age_months / 12)
df.response_combined_bias_age_category <- df.response_combined %>%
group_by(PID, group, age_months) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
ungroup() %>%
mutate(age_years = age_months / 12) %>%
mutate(bias_geocentric = case_when(
bias_category == "Geocentric" ~ 1,
bias_category == "Egocentric" ~ 0
))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, group, and age_months.
## ℹ Output is grouped by PID and group.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, group, age_months))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.plot_grid(
plot_bias_age_by_group(df.response_combined_bias_age_continuous, "blind_children"),
plot_bias_age_by_group(df.response_combined_bias_age_continuous, "blind_adults"),
plot_bias_age_by_group(df.response_combined_bias_age_category, "blind_children"),
plot_bias_age_by_group(df.response_combined_bias_age_category, "blind_adults"),
nrow = 2, ncol = 2
)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
df.response_blind_adults_bias_summary <- df.response_combined_bias_age_continuous %>%
filter(group == "blind_adults") %>%
filter(!is.na(bias_geocentric)) %>%
summarise(
mean_bias = mean(bias_geocentric),
n = n(),
ci_low = binom.test(sum(bias_geocentric), n())$conf.int[1],
ci_high = binom.test(sum(bias_geocentric), n())$conf.int[2]
)
df.response_blind_children_bias_age <- df.response_combined_bias_age_continuous %>%
filter(group == "blind_children")
# place the adults' reference point past the oldest kid on the same x-axis
adults_x_pos <- max(df.response_blind_children_bias_age$age_months, na.rm = TRUE) + 20
ggplot(df.response_blind_children_bias_age %>% filter(!is.na(bias_geocentric)),
aes(x = age_months, y = bias_geocentric)) +
geom_jitter(height = 0.05, alpha = 0.6) +
geom_smooth(method = "glm", method.args = list(family = "quasibinomial"), se = TRUE) +
geom_vline(xintercept = adults_x_pos - 10, linetype = "dotted", alpha = 0.4) +
geom_pointrange(data = df.response_blind_adults_bias_summary,
aes(x = adults_x_pos, y = mean_bias, ymin = ci_low, ymax = ci_high),
inherit.aes = FALSE, color = "red") +
annotate("text", x = adults_x_pos, y = 1.05, label = "Adults\nmean", color = "red", size = 3) +
labs(x = "Age (months)", y = "Proportion geocentric bias") +
coord_cartesian(ylim = c(-0.1,1.1))## `geom_smooth()` using formula = 'y ~ x'
Number of feedback trials
In both adults and kids, they need more feedback trials for the egocentric compared to geocentric condition.
df.response_combined %>%
filter(task == "Feedback Task") %>%
group_by(PID, group, word_meaning) %>%
summarise(n_feedback_trials = mean(n_feedback_trials, na.rm = T)) %>%
group_by(group, word_meaning) %>%
summarise(mean_num_feedback_trials = mean(n_feedback_trials, na.rm = T))## `summarise()` has regrouped the output.
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, group, and word_meaning.
## ℹ Output is grouped by PID and group.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, group, word_meaning))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 4 × 3
## # Groups: group [2]
## group word_meaning mean_num_feedback_trials
## <chr> <chr> <dbl>
## 1 blind_adults Egocentric 16.8
## 2 blind_adults Geocentric 10.7
## 3 blind_children Egocentric 14.6
## 4 blind_children Geocentric 11.2df.response_combined_n_feedback <- df.response_combined %>%
group_by(PID, word_meaning, group, age_months) %>%
slice(1) %>%
summarise(n_feedback_trials = n_feedback_trials)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, word_meaning, group, and age_months.
## ℹ Output is grouped by PID, word_meaning, and group.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, group, age_months))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.ggplot(df.response_combined_n_feedback,
aes(x = word_meaning, y = n_feedback_trials, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
facet_grid(~group)## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.## Warning: Removed 2 rows containing missing values or values outside the scale range
## (`stat_bindot()`).## Warning: Removed 2 rows containing non-finite outside the scale range
## (`stat_summary()`).
Older kids seem to be better.
ggplot(df.response_combined_n_feedback %>%
filter(group == "blind_children"),
aes(x = age_months / 12, y = n_feedback_trials)) +
geom_smooth(method = "lm", se = TRUE) +
geom_jitter(width = 0,
alpha = 0.3) +
labs(x = "Age (years)")## `geom_smooth()` using formula = 'y ~ x'
Post-test performance
1 kid was missing post-test responses.
df.response_combined %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning, group) %>%
summarise(sum_correct_resp = mean(response_coded, na.rm = T)) %>%
group_by(word_meaning, group) %>%
summarise(mean_correct_resp = mean(sum_correct_resp, na.rm = T))## `summarise()` has regrouped the output.
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, word_meaning, and group.
## ℹ Output is grouped by PID and word_meaning.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, group))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 4 × 3
## # Groups: word_meaning [2]
## word_meaning group mean_correct_resp
## <chr> <chr> <dbl>
## 1 Egocentric blind_adults 0.882
## 2 Egocentric blind_children 0.770
## 3 Geocentric blind_adults 0.914
## 4 Geocentric blind_children 0.912ggplot(df.response_combined %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning, group) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = word_meaning, y = mean_correct_resp, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
facet_grid(~group) +
ylim(0, 1)## `summarise()` has regrouped the output.
## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
## ℹ Summaries were computed grouped by PID, word_meaning, and group.
## ℹ Output is grouped by PID and word_meaning.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, group))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## Warning: Removed 1 row containing missing values or values outside the scale range
## (`stat_bindot()`).## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_summary()`).
Older kids are better.
ggplot(df.response_combined %>%
filter(task == "Post-test" & group == "blind_children") %>%
group_by(PID, word_meaning, age_months) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months / 12, y = mean_correct_resp)) +
geom_smooth(method = "lm", se = TRUE) +
geom_jitter(width = 0,
alpha = 0.3) +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
labs(x = "Age (years)") +
coord_cartesian(ylim = c(-0.1,1.1))## `summarise()` has regrouped the output.
## `geom_smooth()` using formula = 'y ~ x'
## ℹ Summaries were computed grouped by PID, word_meaning, and age_months.
## ℹ Output is grouped by PID and word_meaning.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, age_months))` for per-operation
## grouping (`?dplyr::dplyr_by`) instead.## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
Word extension task
Both adults and kids showed better performance in the geocentric condition. Adults were better in the egocentric-doll facing same direction condition, suggesting that they are not flipping the axis when the doll rotated. Kids were at chance in the egocentric condition.
df.response_combined %>%
filter(task == "Word Extension Task") %>%
group_by(PID, word_meaning, anchor_facing, group) %>%
summarise(sum_correct_resp = mean(response_coded)) %>%
group_by(word_meaning, anchor_facing, group) %>%
summarise(mean_correct_resp = mean(sum_correct_resp))## `summarise()` has regrouped the output.
## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by PID, word_meaning, anchor_facing, and
## group.
## ℹ Output is grouped by PID, word_meaning, and anchor_facing.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, anchor_facing, group))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.## # A tibble: 8 × 4
## # Groups: word_meaning, anchor_facing [4]
## word_meaning anchor_facing group mean_correct_resp
## <chr> <chr> <chr> <dbl>
## 1 Egocentric back blind_adults 0.671
## 2 Egocentric back blind_children 0.5
## 3 Egocentric front blind_adults 0.829
## 4 Egocentric front blind_children 0.612
## 5 Geocentric back blind_adults 0.829
## 6 Geocentric back blind_children 0.675
## 7 Geocentric front blind_adults 0.763
## 8 Geocentric front blind_children 0.662ggplot(df.response_combined %>%
filter(task == "Word Extension Task") %>%
group_by(PID, word_meaning, anchor_facing, group) %>%
mutate(anchor_facing = recode_factor(anchor_facing,
"back" = "Doll facing opposite dir.",
"front" = "Doll facing same dir.")) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = word_meaning, y = mean_correct_resp, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 1.5) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
facet_grid(group~anchor_facing) +
ylim(0, 1)## `summarise()` has regrouped the output.
## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
## ℹ Summaries were computed grouped by PID, word_meaning, anchor_facing, and
## group.
## ℹ Output is grouped by PID, word_meaning, and anchor_facing.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, anchor_facing, group))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.
Because later regression shows that there is some difference between partial vision vs. no vision kids in this task, their performance is split up here.
Based on this plot, kids with no vision are not succeeding at this task in either egocentric or geocentric FoR.
ggplot(df.response_combined %>%
filter(task == "Word Extension Task") %>%
mutate(group_detailed = case_when(
group == "blind_children" & vision_group == "none" ~ "blind_children-no_visual",
group == "blind_children" & vision_group == "partial" ~ "blind_children-partial_visual",
group == "blind_adults" ~ "blind_adults"
)) %>%
group_by(PID, word_meaning, anchor_facing, group_detailed) %>%
mutate(anchor_facing = recode_factor(anchor_facing,
"back" = "Doll facing opposite dir.",
"front" = "Doll facing same dir.")) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = word_meaning, y = mean_correct_resp, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 1.5) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
facet_grid(group_detailed~anchor_facing) ## `summarise()` has regrouped the output.
## Bin width defaults to 1/30 of the range of the data. Pick better value with
## `binwidth`.
## ℹ Summaries were computed grouped by PID, word_meaning, anchor_facing, and
## group_detailed.
## ℹ Output is grouped by PID, word_meaning, and anchor_facing.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, anchor_facing, group_detailed))`
## for per-operation grouping (`?dplyr::dplyr_by`) instead.
Older kids are better, except for(?) the egocentric-doll facing opposite
dir. condition.
ggplot(df.response_combined %>%
filter(task == "Word Extension Task" & group == "blind_children") %>%
group_by(PID, word_meaning, anchor_facing, age_months) %>%
mutate(anchor_facing = recode_factor(anchor_facing,
"back" = "Doll facing opposite dir.",
"front" = "Doll facing same dir.")) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months / 12, y = mean_correct_resp,
color = word_meaning, fill = word_meaning)) +
geom_smooth(method = "lm", se = TRUE) +
geom_point(alpha = 0.3) +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
facet_grid(~anchor_facing) +
labs(x = "Age (years)") +
coord_cartesian(ylim = c(-0.1,1.1))## `summarise()` has regrouped the output.
## `geom_smooth()` using formula = 'y ~ x'
## ℹ Summaries were computed grouped by PID, word_meaning, anchor_facing, and
## age_months.
## ℹ Output is grouped by PID, word_meaning, and anchor_facing.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, anchor_facing, age_months))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.
Older kids are better, except for(?) the egocentric-doll facing opposite dir. condition.
ggplot(df.response_combined %>%
filter(task == "Word Extension Task" & group == "blind_children") %>%
group_by(PID, word_meaning, anchor_facing, age_months) %>%
mutate(anchor_facing = recode_factor(anchor_facing,
"back" = "Doll facing opposite dir.",
"front" = "Doll facing same dir.")) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months / 12, y = mean_correct_resp,
color = word_meaning, fill = word_meaning)) +
geom_smooth(method = "lm", se = TRUE) +
geom_point(alpha = 0.3) +
geom_hline(yintercept = 0.5,
linetype = "dashed") +
facet_grid(~anchor_facing) +
labs(x = "Age (years)") +
coord_cartesian(ylim = c(-0.1,1.1))## `summarise()` has regrouped the output.
## `geom_smooth()` using formula = 'y ~ x'
## ℹ Summaries were computed grouped by PID, word_meaning, anchor_facing, and
## age_months.
## ℹ Output is grouped by PID, word_meaning, and anchor_facing.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(PID, word_meaning, anchor_facing, age_months))` for
## per-operation grouping (`?dplyr::dplyr_by`) instead.
Regression
Blind adults
Pre-test bias
Model: pretraining FoR bias (0/1) ~ 1 + (1|site/participant)
Significant geocentric bias, but not significant once site
is not considered as a random effect. (Model is singular, might need to
take out site)
fit.bias_adults <- glmer(is_geocentric_bias ~ 1 + (1|PID) + (1|site),
data = df.response_combined %>%
filter(task == "Bias Test" & group == "blind_adults"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))## boundary (singular) fit: see help('isSingular')summary(fit.bias_adults)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: is_geocentric_bias ~ 1 + (1 | PID) + (1 | site)
## Data: df.response_combined %>% filter(task == "Bias Test" & group ==
## "blind_adults")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 90.3 97.2 -42.1 84.3 73
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2484 -0.5438 0.2488 0.2488 0.8010
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 6.982 2.642
## site (Intercept) 0.000 0.000
## Number of obs: 76, groups: PID, 38; site, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.9681 0.5751 3.422 0.000622 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Exploratory: is there an effect of age for adults? No (even if we
remove site to prevent singularity issues).
fit.bias_adults_age <- glmer(is_geocentric_bias ~ age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Bias Test" & group == "blind_adults") %>%
mutate(age_zscored = (age_months - mean(age_months)) / sd(age_months)),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.bias_adults_age)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: is_geocentric_bias ~ age_zscored + (1 | PID)
## Data: df.response_combined %>% filter(task == "Bias Test" & group ==
## "blind_adults") %>% mutate(age_zscored = (age_months - mean(age_months))/sd(age_months))
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 88.1 95.1 -41.1 82.1 73
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5613 -0.4922 0.2631 0.3025 0.8980
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 5.654 2.378
## Number of obs: 76, groups: PID, 38
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.8322 0.8491 2.158 0.0309 *
## age_zscored 0.8788 0.6686 1.314 0.1887
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## age_zscored 0.297Post-test response
Model: word learning response (0/1) ~ Word Meaning (Egocentric /
Geocentric) + (1|site/participant) No effect of word meaning. Blind
adults don’t perform differently between learning egocentric
vs. geocentric frames. Need to consider dropping site. This
model is singular again, and also showed a null intercept, which does
not track with overwhelming adult success.
fit.post_test_adults <- glmer(response_coded ~ word_meaning + (1|PID) + (1|site),
data = df.response_combined %>%
filter(task == "Post-test" & group == "blind_adults"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))## boundary (singular) fit: see help('isSingular')summary(fit.post_test_adults)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: response_coded ~ word_meaning + (1 | PID) + (1 | site)
## Data: df.response_combined %>% filter(task == "Post-test" & group ==
## "blind_adults")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 99.7 114.6 -45.8 91.7 300
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7719 0.0058 0.0058 0.0068 0.9819
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 135.1 11.62
## site (Intercept) 0.0 0.00
## Number of obs: 304, groups: PID, 38; site, 2
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.9315 5.3990 1.84 0.0658 .
## word_meaningGeocentric 0.3328 8.2401 0.04 0.9678
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## wrd_mnngGcn -0.655
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Kids
These analyses are pending because we are still waiting for sighted kids’ data for comparison.
Pre-test bias
Model: pretraining FoR bias (0/1) ~ 1 + (1|site/participant) No
site random effects included for now, because all blind
kids came from 1 site in this task. Significant geocentric bias, no
effect of age.
fit.bias_kids <- glmer(is_geocentric_bias ~ age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Bias Test" & group == "blind_children"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.bias_kids)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: is_geocentric_bias ~ age_zscored + (1 | PID)
## Data: df.response_combined %>% filter(task == "Bias Test" & group ==
## "blind_children")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 73.0 80.1 -33.5 67.0 77
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.02767 -0.12215 0.00506 0.01197 0.97818
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 411.6 20.29
## Number of obs: 80, groups: PID, 40
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.183 2.117 4.338 1.44e-05 ***
## age_zscored -1.179 1.885 -0.625 0.532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## age_zscored -0.283There is no effect of partial vs. no vision group in kids.
fit.bias_kids_vision <- glmer(is_geocentric_bias ~ vision_group + age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Bias Test" & group == "blind_children"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.bias_kids_vision)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: is_geocentric_bias ~ vision_group + age_zscored + (1 | PID)
## Data: df.response_combined %>% filter(task == "Bias Test" & group ==
## "blind_children")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 74.9 84.4 -33.5 66.9 76
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.02843 -0.12252 0.00426 0.01321 0.97947
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 405.2 20.13
## Number of obs: 80, groups: PID, 40
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.4136 2.4042 3.915 9.02e-05 ***
## vision_grouppartial -0.9006 3.1160 -0.289 0.773
## age_zscored -1.3395 2.1276 -0.630 0.529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vsn_gr
## vsn_grpprtl -0.423
## age_zscored -0.429 0.330Post-test response
Model: word learning response (0/1) ~ Word Meaning (Egocentric /
Geocentric) + age + (1|site/participant) No site random
effects included for now, because all blind kids came from 1 site in
this task.
Kids performed better in geocentric FoR condition compared to egocentric, but succeeded overall. No significant age effect.
fit.post_test_kids <- glmer(response_coded ~ word_meaning + age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Post-test" & group == "blind_children"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.post_test_kids)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: response_coded ~ word_meaning + age_zscored + (1 | PID)
## Data: df.response_combined %>% filter(task == "Post-test" & group ==
## "blind_children")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 229.9 244.9 -111.0 221.9 307
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8182 0.1071 0.1860 0.2706 1.2562
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 2.873 1.695
## Number of obs: 311, groups: PID, 39
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.7774 0.5214 3.409 0.000653 ***
## word_meaningGeocentric 1.8760 0.7749 2.421 0.015483 *
## age_zscored 0.6295 0.3570 1.763 0.077895 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) wrd_mG
## wrd_mnngGcn -0.471
## age_zscored 0.004 0.173No effect of partial vs. no visual experience (neither main effects or interaction with word meaning). Adding visual experience as an effect did not improve the model.
fit.post_test_kids_vision <- glmer(response_coded ~ vision_group * word_meaning + age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Post-test" & group == "blind_children"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.post_test_kids_vision)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: response_coded ~ vision_group * word_meaning + age_zscored +
## (1 | PID)
## Data: df.response_combined %>% filter(task == "Post-test" & group ==
## "blind_children")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 231.4 253.8 -109.7 219.4 305
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.2421 0.1104 0.1657 0.2700 1.2630
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 2.526 1.589
## Number of obs: 311, groups: PID, 39
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.3140 0.5532 2.375 0.0175
## vision_grouppartial 1.7023 1.2131 1.403 0.1605
## word_meaningGeocentric 2.1258 0.8777 2.422 0.0154
## age_zscored 0.7850 0.3811 2.060 0.0394
## vision_grouppartial:word_meaningGeocentric -0.8280 2.0173 -0.410 0.6815
##
## (Intercept) *
## vision_grouppartial
## word_meaningGeocentric *
## age_zscored *
## vision_grouppartial:word_meaningGeocentric
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vsn_gr wrd_mG ag_zsc
## vsn_grpprtl -0.445
## wrd_mnngGcn -0.560 0.427
## age_zscored -0.188 0.420 0.372
## vsn_grpp:_G 0.297 -0.656 -0.511 -0.385anova(fit.post_test_kids_vision, fit.post_test_kids, type = 3)## Data: df.response_combined %>% filter(task == "Post-test" & group == ...
## Models:
## fit.post_test_kids: response_coded ~ word_meaning + age_zscored + (1 | PID)
## fit.post_test_kids_vision: response_coded ~ vision_group * word_meaning + age_zscored + (1 | PID)
## npar AIC BIC logLik -2*log(L) Chisq Df
## fit.post_test_kids 4 229.95 244.91 -110.97 221.95
## fit.post_test_kids_vision 6 231.40 253.84 -109.70 219.40 2.5483 2
## Pr(>Chisq)
## fit.post_test_kids
## fit.post_test_kids_vision 0.2797Exploratory analyses
Number of feedback trials
Model: number of feedback trials ~ Visual Experience (Blind - No Visual / Blind - Some Visual) * Word Meaning (Geocentric / Egocentric) + Age + (1|site)
Fewer trials in the geocentric condition, fewer trials with older kids. No effect of visual experience.
fit.feedback_trials_kids <- lm(n_feedback_trials ~ vision_group * word_meaning + age_zscored,
data = df.response_combined %>%
filter(task == "Post-test" & group == "blind_children") %>%
#n_feedback_trials is on every row, so just need 1 row per PID
group_by(PID) %>%
slice(1))
summary(fit.feedback_trials_kids)##
## Call:
## lm(formula = n_feedback_trials ~ vision_group * word_meaning +
## age_zscored, data = df.response_combined %>% filter(task ==
## "Post-test" & group == "blind_children") %>% group_by(PID) %>%
## slice(1))
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.174 -4.061 -1.438 3.878 10.621
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.7355 1.4413 10.917 8.12e-13
## vision_grouppartial -3.4191 2.9206 -1.171 0.2496
## word_meaningGeocentric -4.9941 2.0621 -2.422 0.0208
## age_zscored -2.1616 0.9476 -2.281 0.0287
## vision_grouppartial:word_meaningGeocentric 4.7553 4.2472 1.120 0.2705
##
## (Intercept) ***
## vision_grouppartial
## word_meaningGeocentric *
## age_zscored *
## vision_grouppartial:word_meaningGeocentric
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.441 on 35 degrees of freedom
## Multiple R-squared: 0.2125, Adjusted R-squared: 0.1225
## F-statistic: 2.361 on 4 and 35 DF, p-value: 0.0722df.response_combined %>%
filter(task == "Post-test" & group == "blind_children")## PID word_meaning order task trial response BT.response_position
## 1 WX038 Geocentric 1 Post-test T1 Y
## 2 WX038 Geocentric 1 Post-test T2 Y
## 3 WX038 Geocentric 1 Post-test T3 Y
## 4 WX038 Geocentric 1 Post-test T4 Y
## 5 WX038 Geocentric 1 Post-test T5 Y
## 6 WX038 Geocentric 1 Post-test T6 Y
## 7 WX038 Geocentric 1 Post-test T7 Y
## 8 WX038 Geocentric 1 Post-test T8 Y
## 9 YZ065 Geocentric 1 Post-test T1 Y
## 10 YZ065 Geocentric 1 Post-test T2 Y
## 11 YZ065 Geocentric 1 Post-test T3 Y
## 12 YZ065 Geocentric 1 Post-test T4 Y
## 13 YZ065 Geocentric 1 Post-test T5 Y
## 14 YZ065 Geocentric 1 Post-test T6 Y
## 15 YZ065 Geocentric 1 Post-test T7 Y
## 16 YZ065 Geocentric 1 Post-test T8 Y
## 17 OP021 Geocentric 1 Post-test T1 Y
## 18 OP021 Geocentric 1 Post-test T2 N
## 19 OP021 Geocentric 1 Post-test T3 N
## 20 OP021 Geocentric 1 Post-test T4 Y
## 21 OP021 Geocentric 1 Post-test T5 N
## 22 OP021 Geocentric 1 Post-test T6 N
## 23 OP021 Geocentric 1 Post-test T7 N
## 24 OP021 Geocentric 1 Post-test T8 N
## 25 QR022 Geocentric 2 Post-test T1 Y
## 26 QR022 Geocentric 2 Post-test T2 Y
## 27 QR022 Geocentric 2 Post-test T3 Y
## 28 QR022 Geocentric 2 Post-test T4 Y
## 29 QR022 Geocentric 2 Post-test T5 Y
## 30 QR022 Geocentric 2 Post-test T6 Y
## 31 QR022 Geocentric 2 Post-test T7 Y
## 32 QR022 Geocentric 2 Post-test T8 Y
## 33 ST023 Egocentric 1 Post-test T1 Y
## 34 ST023 Egocentric 1 Post-test T2 Y
## 35 ST023 Egocentric 1 Post-test T3 Y
## 36 ST023 Egocentric 1 Post-test T4 Y
## 37 ST023 Egocentric 1 Post-test T5 N
## 38 ST023 Egocentric 1 Post-test T6 N
## 39 ST023 Egocentric 1 Post-test T7 N
## 40 ST023 Egocentric 1 Post-test T8 N
## 41 UV024 Egocentric 1 Post-test T1 Y
## 42 UV024 Egocentric 1 Post-test T2 Y
## 43 UV024 Egocentric 1 Post-test T3 Y
## 44 UV024 Egocentric 1 Post-test T4 N
## 45 UV024 Egocentric 1 Post-test T5 Y
## 46 UV024 Egocentric 1 Post-test T6 N
## 47 UV024 Egocentric 1 Post-test T7 Y
## 48 UV024 Egocentric 1 Post-test T8 N
## 49 WX025 Geocentric 1 Post-test T1 Y
## 50 WX025 Geocentric 1 Post-test T2 Y
## 51 WX025 Geocentric 1 Post-test T3 Y
## 52 WX025 Geocentric 1 Post-test T4 Y
## 53 WX025 Geocentric 1 Post-test T5 N
## 54 WX025 Geocentric 1 Post-test T6 N
## 55 WX025 Geocentric 1 Post-test T7 N
## 56 WX025 Geocentric 1 Post-test T8 N
## 57 YZ026 Egocentric 2 Post-test T1 Y
## 58 YZ026 Egocentric 2 Post-test T2 Y
## 59 YZ026 Egocentric 2 Post-test T3 Y
## 60 YZ026 Egocentric 2 Post-test T4 Y
## 61 YZ026 Egocentric 2 Post-test T5 Y
## 62 YZ026 Egocentric 2 Post-test T6 Y
## 63 YZ026 Egocentric 2 Post-test T7 Y
## 64 YZ026 Egocentric 2 Post-test T8 Y
## 65 AB027 Geocentric 1 Post-test T1 Y
## 66 AB027 Geocentric 1 Post-test T2 Y
## 67 AB027 Geocentric 1 Post-test T3 Y
## 68 AB027 Geocentric 1 Post-test T4 Y
## 69 AB027 Geocentric 1 Post-test T5 Y
## 70 AB027 Geocentric 1 Post-test T6 Y
## 71 AB027 Geocentric 1 Post-test T7 Y
## 72 AB027 Geocentric 1 Post-test T8 Y
## 73 CD028 Egocentric 2 Post-test T1 Y
## 74 CD028 Egocentric 2 Post-test T2 Y
## 75 CD028 Egocentric 2 Post-test T3 Y
## 76 CD028 Egocentric 2 Post-test T4 Y
## 77 CD028 Egocentric 2 Post-test T5 N
## 78 CD028 Egocentric 2 Post-test T6 N
## 79 CD028 Egocentric 2 Post-test T7 N
## 80 CD028 Egocentric 2 Post-test T8 Y
## 81 EF029 Egocentric 1 Post-test T1 N
## 82 EF029 Egocentric 1 Post-test T2 N
## 83 EF029 Egocentric 1 Post-test T3 Y
## 84 EF029 Egocentric 1 Post-test T4 Y
## 85 EF029 Egocentric 1 Post-test T5 N
## 86 EF029 Egocentric 1 Post-test T6 N
## 87 EF029 Egocentric 1 Post-test T7 N
## 88 EF029 Egocentric 1 Post-test T8 Y
## 89 GH030 Geocentric 2 Post-test T1 Y
## 90 GH030 Geocentric 2 Post-test T2 Y
## 91 GH030 Geocentric 2 Post-test T3 Y
## 92 GH030 Geocentric 2 Post-test T4 Y
## 93 GH030 Geocentric 2 Post-test T5 Y
## 94 GH030 Geocentric 2 Post-test T6 Y
## 95 GH030 Geocentric 2 Post-test T7 Y
## 96 GH030 Geocentric 2 Post-test T8 Y
## 97 IJ031 Egocentric 2 Post-test T1
## 98 IJ031 Egocentric 2 Post-test T2
## 99 IJ031 Egocentric 2 Post-test T3
## 100 IJ031 Egocentric 2 Post-test T4
## 101 IJ031 Egocentric 2 Post-test T5
## 102 IJ031 Egocentric 2 Post-test T6
## 103 IJ031 Egocentric 2 Post-test T7
## 104 IJ031 Egocentric 2 Post-test T8
## 105 KL032 Geocentric 2 Post-test T1 Y
## 106 KL032 Geocentric 2 Post-test T2 Y
## 107 KL032 Geocentric 2 Post-test T3 Y
## 108 KL032 Geocentric 2 Post-test T4 Y
## 109 KL032 Geocentric 2 Post-test T5 Y
## 110 KL032 Geocentric 2 Post-test T6 Y
## 111 KL032 Geocentric 2 Post-test T7 Y
## 112 KL032 Geocentric 2 Post-test T8 Y
## 113 OP034 Geocentric 2 Post-test T1 Y
## 114 OP034 Geocentric 2 Post-test T2 Y
## 115 OP034 Geocentric 2 Post-test T3 Y
## 116 OP034 Geocentric 2 Post-test T4 Y
## 117 OP034 Geocentric 2 Post-test T5 Y
## 118 OP034 Geocentric 2 Post-test T6 Y
## 119 OP034 Geocentric 2 Post-test T7 Y
## 120 OP034 Geocentric 2 Post-test T8 Y
## 121 QR035 Geocentric 1 Post-test T1 Y
## 122 QR035 Geocentric 1 Post-test T2 Y
## 123 QR035 Geocentric 1 Post-test T3 N
## 124 QR035 Geocentric 1 Post-test T4 Y
## 125 QR035 Geocentric 1 Post-test T5 Y
## 126 QR035 Geocentric 1 Post-test T6 Y
## 127 QR035 Geocentric 1 Post-test T7 Y
## 128 QR035 Geocentric 1 Post-test T8 Y
## 129 ST036 Egocentric 2 Post-test T1 Y
## 130 ST036 Egocentric 2 Post-test T2 Y
## 131 ST036 Egocentric 2 Post-test T3 Y
## 132 ST036 Egocentric 2 Post-test T4 Y
## 133 ST036 Egocentric 2 Post-test T5 N
## 134 ST036 Egocentric 2 Post-test T6 N
## 135 ST036 Egocentric 2 Post-test T7 N
## 136 ST036 Egocentric 2 Post-test T8 N
## 137 UV037 Egocentric 2 Post-test T1 Y
## 138 UV037 Egocentric 2 Post-test T2 Y
## 139 UV037 Egocentric 2 Post-test T3 Y
## 140 UV037 Egocentric 2 Post-test T4 Y
## 141 UV037 Egocentric 2 Post-test T5 Y
## 142 UV037 Egocentric 2 Post-test T6 N
## 143 UV037 Egocentric 2 Post-test T7 Y
## 144 UV037 Egocentric 2 Post-test T8 Y
## 145 YZ039 Geocentric 2 Post-test T1 Y
## 146 YZ039 Geocentric 2 Post-test T2 Y
## 147 YZ039 Geocentric 2 Post-test T3 Y
## 148 YZ039 Geocentric 2 Post-test T4 Y
## 149 YZ039 Geocentric 2 Post-test T5 Y
## 150 YZ039 Geocentric 2 Post-test T6 Y
## 151 YZ039 Geocentric 2 Post-test T7 Y
## 152 YZ039 Geocentric 2 Post-test T8 Y
## 153 AB040 Geocentric 2 Post-test T1 Y
## 154 AB040 Geocentric 2 Post-test T2 Y
## 155 AB040 Geocentric 2 Post-test T3 Y
## 156 AB040 Geocentric 2 Post-test T4 Y
## 157 AB040 Geocentric 2 Post-test T5 Y
## 158 AB040 Geocentric 2 Post-test T6 Y
## 159 AB040 Geocentric 2 Post-test T7 Y
## 160 AB040 Geocentric 2 Post-test T8 Y
## 161 CD041 Geocentric 1 Post-test T1 Y
## 162 CD041 Geocentric 1 Post-test T2 Y
## 163 CD041 Geocentric 1 Post-test T3 Y
## 164 CD041 Geocentric 1 Post-test T4 Y
## 165 CD041 Geocentric 1 Post-test T5 Y
## 166 CD041 Geocentric 1 Post-test T6 Y
## 167 CD041 Geocentric 1 Post-test T7 Y
## 168 CD041 Geocentric 1 Post-test T8 Y
## 169 EF042 Geocentric 1 Post-test T1 Y
## 170 EF042 Geocentric 1 Post-test T2 Y
## 171 EF042 Geocentric 1 Post-test T3 Y
## 172 EF042 Geocentric 1 Post-test T4 Y
## 173 EF042 Geocentric 1 Post-test T5 Y
## 174 EF042 Geocentric 1 Post-test T6 Y
## 175 EF042 Geocentric 1 Post-test T7 Y
## 176 EF042 Geocentric 1 Post-test T8 N
## 177 GH043 Geocentric 2 Post-test T1 Y
## 178 GH043 Geocentric 2 Post-test T2 Y
## 179 GH043 Geocentric 2 Post-test T3 Y
## 180 GH043 Geocentric 2 Post-test T4 Y
## 181 GH043 Geocentric 2 Post-test T5 Y
## 182 GH043 Geocentric 2 Post-test T6 Y
## 183 GH043 Geocentric 2 Post-test T7 Y
## 184 GH043 Geocentric 2 Post-test T8 N
## 185 KL045 Egocentric 2 Post-test T1 Y
## 186 KL045 Egocentric 2 Post-test T2 Y
## 187 KL045 Egocentric 2 Post-test T3 Y
## 188 KL045 Egocentric 2 Post-test T4 Y
## 189 KL045 Egocentric 2 Post-test T5 Y
## 190 KL045 Egocentric 2 Post-test T6 Y
## 191 KL045 Egocentric 2 Post-test T7 Y
## 192 KL045 Egocentric 2 Post-test T8 Y
## 193 QR048 Egocentric 1 Post-test T1 Y
## 194 QR048 Egocentric 1 Post-test T2 Y
## 195 QR048 Egocentric 1 Post-test T3 Y
## 196 QR048 Egocentric 1 Post-test T4 Y
## 197 QR048 Egocentric 1 Post-test T5 Y
## 198 QR048 Egocentric 1 Post-test T6 Y
## 199 QR048 Egocentric 1 Post-test T7 Y
## 200 QR048 Egocentric 1 Post-test T8 Y
## 201 MN046 Egocentric 1 Post-test T1 Y
## 202 MN046 Egocentric 1 Post-test T2 Y
## 203 MN046 Egocentric 1 Post-test T3 Y
## 204 MN046 Egocentric 1 Post-test T4 Y
## 205 MN046 Egocentric 1 Post-test T5 N
## 206 MN046 Egocentric 1 Post-test T6 N
## 207 MN046 Egocentric 1 Post-test T7 N
## 208 MN046 Egocentric 1 Post-test T8 N
## 209 OP047 Egocentric 1 Post-test T1 Y
## 210 OP047 Egocentric 1 Post-test T2 Y
## 211 OP047 Egocentric 1 Post-test T3 Y
## 212 OP047 Egocentric 1 Post-test T4 Y
## 213 OP047 Egocentric 1 Post-test T5 Y
## 214 OP047 Egocentric 1 Post-test T6 Y
## 215 OP047 Egocentric 1 Post-test T7 Y
## 216 OP047 Egocentric 1 Post-test T8 Y
## 217 ST049 Geocentric 2 Post-test T1 Y
## 218 ST049 Geocentric 2 Post-test T2 Y
## 219 ST049 Geocentric 2 Post-test T3 Y
## 220 ST049 Geocentric 2 Post-test T4 Y
## 221 ST049 Geocentric 2 Post-test T5 Y
## 222 ST049 Geocentric 2 Post-test T6 Y
## 223 ST049 Geocentric 2 Post-test T7 N
## 224 ST049 Geocentric 2 Post-test T8 Y
## 225 UV050 Egocentric 1 Post-test T1 Y
## 226 UV050 Egocentric 1 Post-test T2 Y
## 227 UV050 Egocentric 1 Post-test T3 Y
## 228 UV050 Egocentric 1 Post-test T4 Y
## 229 UV050 Egocentric 1 Post-test T5 N
## 230 UV050 Egocentric 1 Post-test T6 N
## 231 UV050 Egocentric 1 Post-test T7 N
## 232 UV050 Egocentric 1 Post-test T8 N
## 233 WX051 Egocentric 1 Post-test T1 Y
## 234 WX051 Egocentric 1 Post-test T2 Y
## 235 WX051 Egocentric 1 Post-test T3 Y
## 236 WX051 Egocentric 1 Post-test T4 Y
## 237 WX051 Egocentric 1 Post-test T5 Y
## 238 WX051 Egocentric 1 Post-test T6 Y
## 239 WX051 Egocentric 1 Post-test T7 Y
## 240 WX051 Egocentric 1 Post-test T8 Y
## 241 AB053 Geocentric 1 Post-test T1 Y
## 242 AB053 Geocentric 1 Post-test T2 Y
## 243 AB053 Geocentric 1 Post-test T3 Y
## 244 AB053 Geocentric 1 Post-test T4 Y
## 245 AB053 Geocentric 1 Post-test T5 Y
## 246 AB053 Geocentric 1 Post-test T6 Y
## 247 AB053 Geocentric 1 Post-test T7 Y
## 248 AB053 Geocentric 1 Post-test T8 Y
## 249 CD054 Egocentric 1 Post-test T1 Y
## 250 CD054 Egocentric 1 Post-test T2 Y
## 251 CD054 Egocentric 1 Post-test T3 Y
## 252 CD054 Egocentric 1 Post-test T4 Y
## 253 CD054 Egocentric 1 Post-test T5 Y
## 254 CD054 Egocentric 1 Post-test T6 Y
## 255 CD054 Egocentric 1 Post-test T7 Y
## 256 CD054 Egocentric 1 Post-test T8 Y
## 257 EF055 Egocentric 2 Post-test T1 Y
## 258 EF055 Egocentric 2 Post-test T2 Y
## 259 EF055 Egocentric 2 Post-test T3 Y
## 260 EF055 Egocentric 2 Post-test T4 Y
## 261 EF055 Egocentric 2 Post-test T5 Y
## 262 EF055 Egocentric 2 Post-test T6 Y
## 263 EF055 Egocentric 2 Post-test T7 Y
## 264 EF055 Egocentric 2 Post-test T8 Y
## 265 GH056 Geocentric 2 Post-test T1 Y
## 266 GH056 Geocentric 2 Post-test T2 Y
## 267 GH056 Geocentric 2 Post-test T3 Y
## 268 GH056 Geocentric 2 Post-test T4 Y
## 269 GH056 Geocentric 2 Post-test T5 Y
## 270 GH056 Geocentric 2 Post-test T6 Y
## 271 GH056 Geocentric 2 Post-test T7 Y
## 272 GH056 Geocentric 2 Post-test T8 Y
## 273 IJ057 Geocentric 1 Post-test T1 Y
## 274 IJ057 Geocentric 1 Post-test T2 Y
## 275 IJ057 Geocentric 1 Post-test T3 Y
## 276 IJ057 Geocentric 1 Post-test T4 Y
## 277 IJ057 Geocentric 1 Post-test T5 Y
## 278 IJ057 Geocentric 1 Post-test T6 Y
## 279 IJ057 Geocentric 1 Post-test T7 Y
## 280 IJ057 Geocentric 1 Post-test T8 Y
## 281 MN059 Egocentric 2 Post-test T1 Y
## 282 MN059 Egocentric 2 Post-test T2 Y
## 283 MN059 Egocentric 2 Post-test T3 Y
## 284 MN059 Egocentric 2 Post-test T4 Y
## 285 MN059 Egocentric 2 Post-test T5 Y
## 286 MN059 Egocentric 2 Post-test T6 Y
## 287 MN059 Egocentric 2 Post-test T7 Y
## 288 MN059 Egocentric 2 Post-test T8 Y
## 289 OP060 Egocentric 2 Post-test T1 Y
## 290 OP060 Egocentric 2 Post-test T2 Y
## 291 OP060 Egocentric 2 Post-test T3 Y
## 292 OP060 Egocentric 2 Post-test T4 Y
## 293 OP060 Egocentric 2 Post-test T5 Y
## 294 OP060 Egocentric 2 Post-test T6 Y
## 295 OP060 Egocentric 2 Post-test T7 N
## 296 OP060 Egocentric 2 Post-test T8 Y
## 297 ST062 Egocentric 2 Post-test T1 Y
## 298 ST062 Egocentric 2 Post-test T2 Y
## 299 ST062 Egocentric 2 Post-test T3 Y
## 300 ST062 Egocentric 2 Post-test T4 Y
## 301 ST062 Egocentric 2 Post-test T5 N
## 302 ST062 Egocentric 2 Post-test T6 N
## 303 ST062 Egocentric 2 Post-test T7 Y
## 304 ST062 Egocentric 2 Post-test T8 Y
## 305 UV063 Egocentric 2 Post-test T1 Y
## 306 UV063 Egocentric 2 Post-test T2 Y
## 307 UV063 Egocentric 2 Post-test T3 Y
## 308 UV063 Egocentric 2 Post-test T4 Y
## 309 UV063 Egocentric 2 Post-test T5 N
## 310 UV063 Egocentric 2 Post-test T6 N
## 311 UV063 Egocentric 2 Post-test T7 N
## 312 UV063 Egocentric 2 Post-test T8 N
## 313 WX064 Geocentric 1 Post-test T1 Y
## 314 WX064 Geocentric 1 Post-test T2 Y
## 315 WX064 Geocentric 1 Post-test T3 Y
## 316 WX064 Geocentric 1 Post-test T4 Y
## 317 WX064 Geocentric 1 Post-test T5 Y
## 318 WX064 Geocentric 1 Post-test T6 Y
## 319 WX064 Geocentric 1 Post-test T7 Y
## 320 WX064 Geocentric 1 Post-test T8
## group vision_group age_years age_months sex experimenter_LC
## 1 blind_children partial 9 111 M <NA>
## 2 blind_children partial 9 111 M <NA>
## 3 blind_children partial 9 111 M <NA>
## 4 blind_children partial 9 111 M <NA>
## 5 blind_children partial 9 111 M <NA>
## 6 blind_children partial 9 111 M <NA>
## 7 blind_children partial 9 111 M <NA>
## 8 blind_children partial 9 111 M <NA>
## 9 blind_children none 10 129 M <NA>
## 10 blind_children none 10 129 M <NA>
## 11 blind_children none 10 129 M <NA>
## 12 blind_children none 10 129 M <NA>
## 13 blind_children none 10 129 M <NA>
## 14 blind_children none 10 129 M <NA>
## 15 blind_children none 10 129 M <NA>
## 16 blind_children none 10 129 M <NA>
## 17 blind_children none 6 83 M <NA>
## 18 blind_children none 6 83 M <NA>
## 19 blind_children none 6 83 M <NA>
## 20 blind_children none 6 83 M <NA>
## 21 blind_children none 6 83 M <NA>
## 22 blind_children none 6 83 M <NA>
## 23 blind_children none 6 83 M <NA>
## 24 blind_children none 6 83 M <NA>
## 25 blind_children none 6 77 M <NA>
## 26 blind_children none 6 77 M <NA>
## 27 blind_children none 6 77 M <NA>
## 28 blind_children none 6 77 M <NA>
## 29 blind_children none 6 77 M <NA>
## 30 blind_children none 6 77 M <NA>
## 31 blind_children none 6 77 M <NA>
## 32 blind_children none 6 77 M <NA>
## 33 blind_children partial 6 76 M <NA>
## 34 blind_children partial 6 76 M <NA>
## 35 blind_children partial 6 76 M <NA>
## 36 blind_children partial 6 76 M <NA>
## 37 blind_children partial 6 76 M <NA>
## 38 blind_children partial 6 76 M <NA>
## 39 blind_children partial 6 76 M <NA>
## 40 blind_children partial 6 76 M <NA>
## 41 blind_children partial 6 76 M <NA>
## 42 blind_children partial 6 76 M <NA>
## 43 blind_children partial 6 76 M <NA>
## 44 blind_children partial 6 76 M <NA>
## 45 blind_children partial 6 76 M <NA>
## 46 blind_children partial 6 76 M <NA>
## 47 blind_children partial 6 76 M <NA>
## 48 blind_children partial 6 76 M <NA>
## 49 blind_children none 7 89 M <NA>
## 50 blind_children none 7 89 M <NA>
## 51 blind_children none 7 89 M <NA>
## 52 blind_children none 7 89 M <NA>
## 53 blind_children none 7 89 M <NA>
## 54 blind_children none 7 89 M <NA>
## 55 blind_children none 7 89 M <NA>
## 56 blind_children none 7 89 M <NA>
## 57 blind_children none 8 101 M <NA>
## 58 blind_children none 8 101 M <NA>
## 59 blind_children none 8 101 M <NA>
## 60 blind_children none 8 101 M <NA>
## 61 blind_children none 8 101 M <NA>
## 62 blind_children none 8 101 M <NA>
## 63 blind_children none 8 101 M <NA>
## 64 blind_children none 8 101 M <NA>
## 65 blind_children none 9 115 M <NA>
## 66 blind_children none 9 115 M <NA>
## 67 blind_children none 9 115 M <NA>
## 68 blind_children none 9 115 M <NA>
## 69 blind_children none 9 115 M <NA>
## 70 blind_children none 9 115 M <NA>
## 71 blind_children none 9 115 M <NA>
## 72 blind_children none 9 115 M <NA>
## 73 blind_children none 12 145 M <NA>
## 74 blind_children none 12 145 M <NA>
## 75 blind_children none 12 145 M <NA>
## 76 blind_children none 12 145 M <NA>
## 77 blind_children none 12 145 M <NA>
## 78 blind_children none 12 145 M <NA>
## 79 blind_children none 12 145 M <NA>
## 80 blind_children none 12 145 M <NA>
## 81 blind_children none 7 87 M <NA>
## 82 blind_children none 7 87 M <NA>
## 83 blind_children none 7 87 M <NA>
## 84 blind_children none 7 87 M <NA>
## 85 blind_children none 7 87 M <NA>
## 86 blind_children none 7 87 M <NA>
## 87 blind_children none 7 87 M <NA>
## 88 blind_children none 7 87 M <NA>
## 89 blind_children none 7 91 M <NA>
## 90 blind_children none 7 91 M <NA>
## 91 blind_children none 7 91 M <NA>
## 92 blind_children none 7 91 M <NA>
## 93 blind_children none 7 91 M <NA>
## 94 blind_children none 7 91 M <NA>
## 95 blind_children none 7 91 M <NA>
## 96 blind_children none 7 91 M <NA>
## 97 blind_children none 9 111 M <NA>
## 98 blind_children none 9 111 M <NA>
## 99 blind_children none 9 111 M <NA>
## 100 blind_children none 9 111 M <NA>
## 101 blind_children none 9 111 M <NA>
## 102 blind_children none 9 111 M <NA>
## 103 blind_children none 9 111 M <NA>
## 104 blind_children none 9 111 M <NA>
## 105 blind_children none 6 79 M <NA>
## 106 blind_children none 6 79 M <NA>
## 107 blind_children none 6 79 M <NA>
## 108 blind_children none 6 79 M <NA>
## 109 blind_children none 6 79 M <NA>
## 110 blind_children none 6 79 M <NA>
## 111 blind_children none 6 79 M <NA>
## 112 blind_children none 6 79 M <NA>
## 113 blind_children none 11 141 M <NA>
## 114 blind_children none 11 141 M <NA>
## 115 blind_children none 11 141 M <NA>
## 116 blind_children none 11 141 M <NA>
## 117 blind_children none 11 141 M <NA>
## 118 blind_children none 11 141 M <NA>
## 119 blind_children none 11 141 M <NA>
## 120 blind_children none 11 141 M <NA>
## 121 blind_children partial 10 120 M <NA>
## 122 blind_children partial 10 120 M <NA>
## 123 blind_children partial 10 120 M <NA>
## 124 blind_children partial 10 120 M <NA>
## 125 blind_children partial 10 120 M <NA>
## 126 blind_children partial 10 120 M <NA>
## 127 blind_children partial 10 120 M <NA>
## 128 blind_children partial 10 120 M <NA>
## 129 blind_children none 12 145 M <NA>
## 130 blind_children none 12 145 M <NA>
## 131 blind_children none 12 145 M <NA>
## 132 blind_children none 12 145 M <NA>
## 133 blind_children none 12 145 M <NA>
## 134 blind_children none 12 145 M <NA>
## 135 blind_children none 12 145 M <NA>
## 136 blind_children none 12 145 M <NA>
## 137 blind_children none 6 82 M <NA>
## 138 blind_children none 6 82 M <NA>
## 139 blind_children none 6 82 M <NA>
## 140 blind_children none 6 82 M <NA>
## 141 blind_children none 6 82 M <NA>
## 142 blind_children none 6 82 M <NA>
## 143 blind_children none 6 82 M <NA>
## 144 blind_children none 6 82 M <NA>
## 145 blind_children none 9 117 M <NA>
## 146 blind_children none 9 117 M <NA>
## 147 blind_children none 9 117 M <NA>
## 148 blind_children none 9 117 M <NA>
## 149 blind_children none 9 117 M <NA>
## 150 blind_children none 9 117 M <NA>
## 151 blind_children none 9 117 M <NA>
## 152 blind_children none 9 117 M <NA>
## 153 blind_children none 11 140 M <NA>
## 154 blind_children none 11 140 M <NA>
## 155 blind_children none 11 140 M <NA>
## 156 blind_children none 11 140 M <NA>
## 157 blind_children none 11 140 M <NA>
## 158 blind_children none 11 140 M <NA>
## 159 blind_children none 11 140 M <NA>
## 160 blind_children none 11 140 M <NA>
## 161 blind_children none 9 115 M <NA>
## 162 blind_children none 9 115 M <NA>
## 163 blind_children none 9 115 M <NA>
## 164 blind_children none 9 115 M <NA>
## 165 blind_children none 9 115 M <NA>
## 166 blind_children none 9 115 M <NA>
## 167 blind_children none 9 115 M <NA>
## 168 blind_children none 9 115 M <NA>
## 169 blind_children none 11 135 M <NA>
## 170 blind_children none 11 135 M <NA>
## 171 blind_children none 11 135 M <NA>
## 172 blind_children none 11 135 M <NA>
## 173 blind_children none 11 135 M <NA>
## 174 blind_children none 11 135 M <NA>
## 175 blind_children none 11 135 M <NA>
## 176 blind_children none 11 135 M <NA>
## 177 blind_children none 10 128 M <NA>
## 178 blind_children none 10 128 M <NA>
## 179 blind_children none 10 128 M <NA>
## 180 blind_children none 10 128 M <NA>
## 181 blind_children none 10 128 M <NA>
## 182 blind_children none 10 128 M <NA>
## 183 blind_children none 10 128 M <NA>
## 184 blind_children none 10 128 M <NA>
## 185 blind_children none 11 135 M <NA>
## 186 blind_children none 11 135 M <NA>
## 187 blind_children none 11 135 M <NA>
## 188 blind_children none 11 135 M <NA>
## 189 blind_children none 11 135 M <NA>
## 190 blind_children none 11 135 M <NA>
## 191 blind_children none 11 135 M <NA>
## 192 blind_children none 11 135 M <NA>
## 193 blind_children none 12 148 M <NA>
## 194 blind_children none 12 148 M <NA>
## 195 blind_children none 12 148 M <NA>
## 196 blind_children none 12 148 M <NA>
## 197 blind_children none 12 148 M <NA>
## 198 blind_children none 12 148 M <NA>
## 199 blind_children none 12 148 M <NA>
## 200 blind_children none 12 148 M <NA>
## 201 blind_children none 9 117 M <NA>
## 202 blind_children none 9 117 M <NA>
## 203 blind_children none 9 117 M <NA>
## 204 blind_children none 9 117 M <NA>
## 205 blind_children none 9 117 M <NA>
## 206 blind_children none 9 117 M <NA>
## 207 blind_children none 9 117 M <NA>
## 208 blind_children none 9 117 M <NA>
## 209 blind_children partial 10 120 M <NA>
## 210 blind_children partial 10 120 M <NA>
## 211 blind_children partial 10 120 M <NA>
## 212 blind_children partial 10 120 M <NA>
## 213 blind_children partial 10 120 M <NA>
## 214 blind_children partial 10 120 M <NA>
## 215 blind_children partial 10 120 M <NA>
## 216 blind_children partial 10 120 M <NA>
## 217 blind_children none 10 129 M <NA>
## 218 blind_children none 10 129 M <NA>
## 219 blind_children none 10 129 M <NA>
## 220 blind_children none 10 129 M <NA>
## 221 blind_children none 10 129 M <NA>
## 222 blind_children none 10 129 M <NA>
## 223 blind_children none 10 129 M <NA>
## 224 blind_children none 10 129 M <NA>
## 225 blind_children none 12 145 M <NA>
## 226 blind_children none 12 145 M <NA>
## 227 blind_children none 12 145 M <NA>
## 228 blind_children none 12 145 M <NA>
## 229 blind_children none 12 145 M <NA>
## 230 blind_children none 12 145 M <NA>
## 231 blind_children none 12 145 M <NA>
## 232 blind_children none 12 145 M <NA>
## 233 blind_children partial 11 138 M <NA>
## 234 blind_children partial 11 138 M <NA>
## 235 blind_children partial 11 138 M <NA>
## 236 blind_children partial 11 138 M <NA>
## 237 blind_children partial 11 138 M <NA>
## 238 blind_children partial 11 138 M <NA>
## 239 blind_children partial 11 138 M <NA>
## 240 blind_children partial 11 138 M <NA>
## 241 blind_children partial 10 127 M <NA>
## 242 blind_children partial 10 127 M <NA>
## 243 blind_children partial 10 127 M <NA>
## 244 blind_children partial 10 127 M <NA>
## 245 blind_children partial 10 127 M <NA>
## 246 blind_children partial 10 127 M <NA>
## 247 blind_children partial 10 127 M <NA>
## 248 blind_children partial 10 127 M <NA>
## 249 blind_children none 10 121 M <NA>
## 250 blind_children none 10 121 M <NA>
## 251 blind_children none 10 121 M <NA>
## 252 blind_children none 10 121 M <NA>
## 253 blind_children none 10 121 M <NA>
## 254 blind_children none 10 121 M <NA>
## 255 blind_children none 10 121 M <NA>
## 256 blind_children none 10 121 M <NA>
## 257 blind_children none 12 150 M <NA>
## 258 blind_children none 12 150 M <NA>
## 259 blind_children none 12 150 M <NA>
## 260 blind_children none 12 150 M <NA>
## 261 blind_children none 12 150 M <NA>
## 262 blind_children none 12 150 M <NA>
## 263 blind_children none 12 150 M <NA>
## 264 blind_children none 12 150 M <NA>
## 265 blind_children partial 12 151 M <NA>
## 266 blind_children partial 12 151 M <NA>
## 267 blind_children partial 12 151 M <NA>
## 268 blind_children partial 12 151 M <NA>
## 269 blind_children partial 12 151 M <NA>
## 270 blind_children partial 12 151 M <NA>
## 271 blind_children partial 12 151 M <NA>
## 272 blind_children partial 12 151 M <NA>
## 273 blind_children partial 12 145 M <NA>
## 274 blind_children partial 12 145 M <NA>
## 275 blind_children partial 12 145 M <NA>
## 276 blind_children partial 12 145 M <NA>
## 277 blind_children partial 12 145 M <NA>
## 278 blind_children partial 12 145 M <NA>
## 279 blind_children partial 12 145 M <NA>
## 280 blind_children partial 12 145 M <NA>
## 281 blind_children partial 10 124 M <NA>
## 282 blind_children partial 10 124 M <NA>
## 283 blind_children partial 10 124 M <NA>
## 284 blind_children partial 10 124 M <NA>
## 285 blind_children partial 10 124 M <NA>
## 286 blind_children partial 10 124 M <NA>
## 287 blind_children partial 10 124 M <NA>
## 288 blind_children partial 10 124 M <NA>
## 289 blind_children none 11 140 M <NA>
## 290 blind_children none 11 140 M <NA>
## 291 blind_children none 11 140 M <NA>
## 292 blind_children none 11 140 M <NA>
## 293 blind_children none 11 140 M <NA>
## 294 blind_children none 11 140 M <NA>
## 295 blind_children none 11 140 M <NA>
## 296 blind_children none 11 140 M <NA>
## 297 blind_children none 11 139 M <NA>
## 298 blind_children none 11 139 M <NA>
## 299 blind_children none 11 139 M <NA>
## 300 blind_children none 11 139 M <NA>
## 301 blind_children none 11 139 M <NA>
## 302 blind_children none 11 139 M <NA>
## 303 blind_children none 11 139 M <NA>
## 304 blind_children none 11 139 M <NA>
## 305 blind_children none 11 139 M <NA>
## 306 blind_children none 11 139 M <NA>
## 307 blind_children none 11 139 M <NA>
## 308 blind_children none 11 139 M <NA>
## 309 blind_children none 11 139 M <NA>
## 310 blind_children none 11 139 M <NA>
## 311 blind_children none 11 139 M <NA>
## 312 blind_children none 11 139 M <NA>
## 313 blind_children none 10 127 M <NA>
## 314 blind_children none 10 127 M <NA>
## 315 blind_children none 10 127 M <NA>
## 316 blind_children none 10 127 M <NA>
## 317 blind_children none 10 127 M <NA>
## 318 blind_children none 10 127 M <NA>
## 319 blind_children none 10 127 M <NA>
## 320 blind_children none 10 127 M <NA>
## experimenter_retest site word_prompted word_prompted_hindi anchor_facing
## 1 NA siteC ziv sib front
## 2 NA siteC kern jan front
## 3 NA siteC kern jan front
## 4 NA siteC ziv sib front
## 5 NA siteC kern jan back
## 6 NA siteC ziv sib back
## 7 NA siteC kern jan back
## 8 NA siteC ziv sib back
## 9 NA siteC ziv sib front
## 10 NA siteC kern jan front
## 11 NA siteC kern jan front
## 12 NA siteC ziv sib front
## 13 NA siteC kern jan back
## 14 NA siteC ziv sib back
## 15 NA siteC kern jan back
## 16 NA siteC ziv sib back
## 17 NA siteC ziv sib front
## 18 NA siteC kern jan front
## 19 NA siteC kern jan front
## 20 NA siteC ziv sib front
## 21 NA siteC kern jan back
## 22 NA siteC ziv sib back
## 23 NA siteC kern jan back
## 24 NA siteC ziv sib back
## 25 NA siteC ziv sib front
## 26 NA siteC kern jan front
## 27 NA siteC ziv sib front
## 28 NA siteC kern jan front
## 29 NA siteC kern jan back
## 30 NA siteC ziv sib back
## 31 NA siteC ziv sib back
## 32 NA siteC kern jan back
## 33 NA siteC ziv sib front
## 34 NA siteC kern jan front
## 35 NA siteC kern jan front
## 36 NA siteC ziv sib front
## 37 NA siteC kern jan back
## 38 NA siteC ziv sib back
## 39 NA siteC kern jan back
## 40 NA siteC ziv sib back
## 41 NA siteC ziv sib front
## 42 NA siteC kern jan front
## 43 NA siteC kern jan front
## 44 NA siteC ziv sib front
## 45 NA siteC kern jan back
## 46 NA siteC ziv sib back
## 47 NA siteC kern jan back
## 48 NA siteC ziv sib back
## 49 NA siteC ziv sib front
## 50 NA siteC kern jan front
## 51 NA siteC kern jan front
## 52 NA siteC ziv sib front
## 53 NA siteC kern jan back
## 54 NA siteC ziv sib back
## 55 NA siteC kern jan back
## 56 NA siteC ziv sib back
## 57 NA siteC ziv sib front
## 58 NA siteC kern jan front
## 59 NA siteC ziv sib front
## 60 NA siteC kern jan front
## 61 NA siteC kern jan back
## 62 NA siteC ziv sib back
## 63 NA siteC ziv sib back
## 64 NA siteC kern jan back
## 65 NA siteC ziv sib front
## 66 NA siteC kern jan front
## 67 NA siteC kern jan front
## 68 NA siteC ziv sib front
## 69 NA siteC kern jan back
## 70 NA siteC ziv sib back
## 71 NA siteC kern jan back
## 72 NA siteC ziv sib back
## 73 NA siteC ziv sib front
## 74 NA siteC kern jan front
## 75 NA siteC ziv sib front
## 76 NA siteC kern jan front
## 77 NA siteC kern jan back
## 78 NA siteC ziv sib back
## 79 NA siteC ziv sib back
## 80 NA siteC kern jan back
## 81 NA siteC ziv sib front
## 82 NA siteC kern jan front
## 83 NA siteC kern jan front
## 84 NA siteC ziv sib front
## 85 NA siteC kern jan back
## 86 NA siteC ziv sib back
## 87 NA siteC kern jan back
## 88 NA siteC ziv sib back
## 89 NA siteC ziv sib front
## 90 NA siteC kern jan front
## 91 NA siteC ziv sib front
## 92 NA siteC kern jan front
## 93 NA siteC kern jan back
## 94 NA siteC ziv sib back
## 95 NA siteC ziv sib back
## 96 NA siteC kern jan back
## 97 NA siteC ziv sib front
## 98 NA siteC kern jan front
## 99 NA siteC ziv sib front
## 100 NA siteC kern jan front
## 101 NA siteC kern jan back
## 102 NA siteC ziv sib back
## 103 NA siteC ziv sib back
## 104 NA siteC kern jan back
## 105 NA siteC ziv sib front
## 106 NA siteC kern jan front
## 107 NA siteC ziv sib front
## 108 NA siteC kern jan front
## 109 NA siteC kern jan back
## 110 NA siteC ziv sib back
## 111 NA siteC ziv sib back
## 112 NA siteC kern jan back
## 113 NA siteC ziv sib front
## 114 NA siteC kern jan front
## 115 NA siteC ziv sib front
## 116 NA siteC kern jan front
## 117 NA siteC kern jan back
## 118 NA siteC ziv sib back
## 119 NA siteC ziv sib back
## 120 NA siteC kern jan back
## 121 NA siteC ziv sib front
## 122 NA siteC kern jan front
## 123 NA siteC kern jan front
## 124 NA siteC ziv sib front
## 125 NA siteC kern jan back
## 126 NA siteC ziv sib back
## 127 NA siteC kern jan back
## 128 NA siteC ziv sib back
## 129 NA siteC ziv sib front
## 130 NA siteC kern jan front
## 131 NA siteC ziv sib front
## 132 NA siteC kern jan front
## 133 NA siteC kern jan back
## 134 NA siteC ziv sib back
## 135 NA siteC ziv sib back
## 136 NA siteC kern jan back
## 137 NA siteC ziv sib front
## 138 NA siteC kern jan front
## 139 NA siteC ziv sib front
## 140 NA siteC kern jan front
## 141 NA siteC kern jan back
## 142 NA siteC ziv sib back
## 143 NA siteC ziv sib back
## 144 NA siteC kern jan back
## 145 NA siteC ziv sib front
## 146 NA siteC kern jan front
## 147 NA siteC ziv sib front
## 148 NA siteC kern jan front
## 149 NA siteC kern jan back
## 150 NA siteC ziv sib back
## 151 NA siteC ziv sib back
## 152 NA siteC kern jan back
## 153 NA siteC ziv sib front
## 154 NA siteC kern jan front
## 155 NA siteC ziv sib front
## 156 NA siteC kern jan front
## 157 NA siteC kern jan back
## 158 NA siteC ziv sib back
## 159 NA siteC ziv sib back
## 160 NA siteC kern jan back
## 161 NA siteC ziv sib front
## 162 NA siteC kern jan front
## 163 NA siteC kern jan front
## 164 NA siteC ziv sib front
## 165 NA siteC kern jan back
## 166 NA siteC ziv sib back
## 167 NA siteC kern jan back
## 168 NA siteC ziv sib back
## 169 NA siteC ziv sib front
## 170 NA siteC kern jan front
## 171 NA siteC kern jan front
## 172 NA siteC ziv sib front
## 173 NA siteC kern jan back
## 174 NA siteC ziv sib back
## 175 NA siteC kern jan back
## 176 NA siteC ziv sib back
## 177 NA siteC ziv sib front
## 178 NA siteC kern jan front
## 179 NA siteC ziv sib front
## 180 NA siteC kern jan front
## 181 NA siteC kern jan back
## 182 NA siteC ziv sib back
## 183 NA siteC ziv sib back
## 184 NA siteC kern jan back
## 185 NA siteC ziv sib front
## 186 NA siteC kern jan front
## 187 NA siteC ziv sib front
## 188 NA siteC kern jan front
## 189 NA siteC kern jan back
## 190 NA siteC ziv sib back
## 191 NA siteC ziv sib back
## 192 NA siteC kern jan back
## 193 NA siteC ziv sib front
## 194 NA siteC kern jan front
## 195 NA siteC kern jan front
## 196 NA siteC ziv sib front
## 197 NA siteC kern jan back
## 198 NA siteC ziv sib back
## 199 NA siteC kern jan back
## 200 NA siteC ziv sib back
## 201 NA siteC ziv sib front
## 202 NA siteC kern jan front
## 203 NA siteC kern jan front
## 204 NA siteC ziv sib front
## 205 NA siteC kern jan back
## 206 NA siteC ziv sib back
## 207 NA siteC kern jan back
## 208 NA siteC ziv sib back
## 209 NA siteC ziv sib front
## 210 NA siteC kern jan front
## 211 NA siteC kern jan front
## 212 NA siteC ziv sib front
## 213 NA siteC kern jan back
## 214 NA siteC ziv sib back
## 215 NA siteC kern jan back
## 216 NA siteC ziv sib back
## 217 NA siteC ziv sib front
## 218 NA siteC kern jan front
## 219 NA siteC ziv sib front
## 220 NA siteC kern jan front
## 221 NA siteC kern jan back
## 222 NA siteC ziv sib back
## 223 NA siteC ziv sib back
## 224 NA siteC kern jan back
## 225 NA siteC ziv sib front
## 226 NA siteC kern jan front
## 227 NA siteC kern jan front
## 228 NA siteC ziv sib front
## 229 NA siteC kern jan back
## 230 NA siteC ziv sib back
## 231 NA siteC kern jan back
## 232 NA siteC ziv sib back
## 233 NA siteC ziv sib front
## 234 NA siteC kern jan front
## 235 NA siteC kern jan front
## 236 NA siteC ziv sib front
## 237 NA siteC kern jan back
## 238 NA siteC ziv sib back
## 239 NA siteC kern jan back
## 240 NA siteC ziv sib back
## 241 NA siteC ziv sib front
## 242 NA siteC kern jan front
## 243 NA siteC kern jan front
## 244 NA siteC ziv sib front
## 245 NA siteC kern jan back
## 246 NA siteC ziv sib back
## 247 NA siteC kern jan back
## 248 NA siteC ziv sib back
## 249 NA siteC ziv sib front
## 250 NA siteC kern jan front
## 251 NA siteC kern jan front
## 252 NA siteC ziv sib front
## 253 NA siteC kern jan back
## 254 NA siteC ziv sib back
## 255 NA siteC kern jan back
## 256 NA siteC ziv sib back
## 257 NA siteC ziv sib front
## 258 NA siteC kern jan front
## 259 NA siteC ziv sib front
## 260 NA siteC kern jan front
## 261 NA siteC kern jan back
## 262 NA siteC ziv sib back
## 263 NA siteC ziv sib back
## 264 NA siteC kern jan back
## 265 NA siteC ziv sib front
## 266 NA siteC kern jan front
## 267 NA siteC ziv sib front
## 268 NA siteC kern jan front
## 269 NA siteC kern jan back
## 270 NA siteC ziv sib back
## 271 NA siteC ziv sib back
## 272 NA siteC kern jan back
## 273 NA siteC ziv sib front
## 274 NA siteC kern jan front
## 275 NA siteC kern jan front
## 276 NA siteC ziv sib front
## 277 NA siteC kern jan back
## 278 NA siteC ziv sib back
## 279 NA siteC kern jan back
## 280 NA siteC ziv sib back
## 281 NA siteC ziv sib front
## 282 NA siteC kern jan front
## 283 NA siteC ziv sib front
## 284 NA siteC kern jan front
## 285 NA siteC kern jan back
## 286 NA siteC ziv sib back
## 287 NA siteC ziv sib back
## 288 NA siteC kern jan back
## 289 NA siteC ziv sib front
## 290 NA siteC kern jan front
## 291 NA siteC ziv sib front
## 292 NA siteC kern jan front
## 293 NA siteC kern jan back
## 294 NA siteC ziv sib back
## 295 NA siteC ziv sib back
## 296 NA siteC kern jan back
## 297 NA siteC ziv sib front
## 298 NA siteC kern jan front
## 299 NA siteC ziv sib front
## 300 NA siteC kern jan front
## 301 NA siteC kern jan back
## 302 NA siteC ziv sib back
## 303 NA siteC ziv sib back
## 304 NA siteC kern jan back
## 305 NA siteC ziv sib front
## 306 NA siteC kern jan front
## 307 NA siteC ziv sib front
## 308 NA siteC kern jan front
## 309 NA siteC kern jan back
## 310 NA siteC ziv sib back
## 311 NA siteC ziv sib back
## 312 NA siteC kern jan back
## 313 NA siteC ziv sib front
## 314 NA siteC kern jan front
## 315 NA siteC kern jan front
## 316 NA siteC ziv sib front
## 317 NA siteC kern jan back
## 318 NA siteC ziv sib back
## 319 NA siteC kern jan back
## 320 NA siteC ziv sib back
## correct_side_from_experimenter WE.table_used_from_experimenter
## 1 left
## 2 right
## 3 right
## 4 left
## 5 left
## 6 right
## 7 left
## 8 right
## 9 left
## 10 right
## 11 right
## 12 left
## 13 left
## 14 right
## 15 left
## 16 right
## 17 left
## 18 right
## 19 right
## 20 left
## 21 left
## 22 right
## 23 left
## 24 right
## 25 right
## 26 left
## 27 right
## 28 left
## 29 right
## 30 left
## 31 left
## 32 right
## 33 left
## 34 right
## 35 right
## 36 left
## 37 right
## 38 left
## 39 right
## 40 left
## 41 left
## 42 right
## 43 right
## 44 left
## 45 right
## 46 left
## 47 right
## 48 left
## 49 left
## 50 right
## 51 right
## 52 left
## 53 left
## 54 right
## 55 left
## 56 right
## 57 right
## 58 left
## 59 right
## 60 left
## 61 left
## 62 right
## 63 right
## 64 left
## 65 left
## 66 right
## 67 right
## 68 left
## 69 left
## 70 right
## 71 left
## 72 right
## 73 right
## 74 left
## 75 right
## 76 left
## 77 left
## 78 right
## 79 right
## 80 left
## 81 left
## 82 right
## 83 right
## 84 left
## 85 right
## 86 left
## 87 right
## 88 left
## 89 right
## 90 left
## 91 right
## 92 left
## 93 right
## 94 left
## 95 left
## 96 right
## 97 right
## 98 left
## 99 right
## 100 left
## 101 left
## 102 right
## 103 right
## 104 left
## 105 right
## 106 left
## 107 right
## 108 left
## 109 right
## 110 left
## 111 left
## 112 right
## 113 right
## 114 left
## 115 right
## 116 left
## 117 right
## 118 left
## 119 left
## 120 right
## 121 left
## 122 right
## 123 right
## 124 left
## 125 left
## 126 right
## 127 left
## 128 right
## 129 right
## 130 left
## 131 right
## 132 left
## 133 left
## 134 right
## 135 right
## 136 left
## 137 right
## 138 left
## 139 right
## 140 left
## 141 left
## 142 right
## 143 right
## 144 left
## 145 right
## 146 left
## 147 right
## 148 left
## 149 right
## 150 left
## 151 left
## 152 right
## 153 right
## 154 left
## 155 right
## 156 left
## 157 right
## 158 left
## 159 left
## 160 right
## 161 left
## 162 right
## 163 right
## 164 left
## 165 left
## 166 right
## 167 left
## 168 right
## 169 left
## 170 right
## 171 right
## 172 left
## 173 left
## 174 right
## 175 left
## 176 right
## 177 right
## 178 left
## 179 right
## 180 left
## 181 right
## 182 left
## 183 left
## 184 right
## 185 right
## 186 left
## 187 right
## 188 left
## 189 left
## 190 right
## 191 right
## 192 left
## 193 left
## 194 right
## 195 right
## 196 left
## 197 right
## 198 left
## 199 right
## 200 left
## 201 left
## 202 right
## 203 right
## 204 left
## 205 right
## 206 left
## 207 right
## 208 left
## 209 left
## 210 right
## 211 right
## 212 left
## 213 right
## 214 left
## 215 right
## 216 left
## 217 right
## 218 left
## 219 right
## 220 left
## 221 right
## 222 left
## 223 left
## 224 right
## 225 left
## 226 right
## 227 right
## 228 left
## 229 right
## 230 left
## 231 right
## 232 left
## 233 left
## 234 right
## 235 right
## 236 left
## 237 right
## 238 left
## 239 right
## 240 left
## 241 left
## 242 right
## 243 right
## 244 left
## 245 left
## 246 right
## 247 left
## 248 right
## 249 left
## 250 right
## 251 right
## 252 left
## 253 right
## 254 left
## 255 right
## 256 left
## 257 right
## 258 left
## 259 right
## 260 left
## 261 left
## 262 right
## 263 right
## 264 left
## 265 right
## 266 left
## 267 right
## 268 left
## 269 right
## 270 left
## 271 left
## 272 right
## 273 left
## 274 right
## 275 right
## 276 left
## 277 left
## 278 right
## 279 left
## 280 right
## 281 right
## 282 left
## 283 right
## 284 left
## 285 left
## 286 right
## 287 right
## 288 left
## 289 right
## 290 left
## 291 right
## 292 left
## 293 left
## 294 right
## 295 right
## 296 left
## 297 right
## 298 left
## 299 right
## 300 left
## 301 left
## 302 right
## 303 right
## 304 left
## 305 right
## 306 left
## 307 right
## 308 left
## 309 left
## 310 right
## 311 right
## 312 left
## 313 left
## 314 right
## 315 right
## 316 left
## 317 left
## 318 right
## 319 left
## 320 right
## BT.correct_response response_coded is_geocentric_bias n_feedback_trials
## 1 1 NA 8
## 2 1 NA 8
## 3 1 NA 8
## 4 1 NA 8
## 5 1 NA 8
## 6 1 NA 8
## 7 1 NA 8
## 8 1 NA 8
## 9 1 NA 8
## 10 1 NA 8
## 11 1 NA 8
## 12 1 NA 8
## 13 1 NA 8
## 14 1 NA 8
## 15 1 NA 8
## 16 1 NA 8
## 17 1 NA 24
## 18 0 NA 24
## 19 0 NA 24
## 20 1 NA 24
## 21 0 NA 24
## 22 0 NA 24
## 23 0 NA 24
## 24 0 NA 24
## 25 1 NA 8
## 26 1 NA 8
## 27 1 NA 8
## 28 1 NA 8
## 29 1 NA 8
## 30 1 NA 8
## 31 1 NA 8
## 32 1 NA 8
## 33 1 NA 24
## 34 1 NA 24
## 35 1 NA 24
## 36 1 NA 24
## 37 0 NA 24
## 38 0 NA 24
## 39 0 NA 24
## 40 0 NA 24
## 41 1 NA 8
## 42 1 NA 8
## 43 1 NA 8
## 44 0 NA 8
## 45 1 NA 8
## 46 0 NA 8
## 47 1 NA 8
## 48 0 NA 8
## 49 1 NA 17
## 50 1 NA 17
## 51 1 NA 17
## 52 1 NA 17
## 53 0 NA 17
## 54 0 NA 17
## 55 0 NA 17
## 56 0 NA 17
## 57 1 NA 17
## 58 1 NA 17
## 59 1 NA 17
## 60 1 NA 17
## 61 1 NA 17
## 62 1 NA 17
## 63 1 NA 17
## 64 1 NA 17
## 65 1 NA 8
## 66 1 NA 8
## 67 1 NA 8
## 68 1 NA 8
## 69 1 NA 8
## 70 1 NA 8
## 71 1 NA 8
## 72 1 NA 8
## 73 1 NA 8
## 74 1 NA 8
## 75 1 NA 8
## 76 1 NA 8
## 77 0 NA 8
## 78 0 NA 8
## 79 0 NA 8
## 80 1 NA 8
## 81 0 NA 24
## 82 0 NA 24
## 83 1 NA 24
## 84 1 NA 24
## 85 0 NA 24
## 86 0 NA 24
## 87 0 NA 24
## 88 1 NA 24
## 89 1 NA 17
## 90 1 NA 17
## 91 1 NA 17
## 92 1 NA 17
## 93 1 NA 17
## 94 1 NA 17
## 95 1 NA 17
## 96 1 NA 17
## 97 NA NA 18
## 98 NA NA 18
## 99 NA NA 18
## 100 NA NA 18
## 101 NA NA 18
## 102 NA NA 18
## 103 NA NA 18
## 104 NA NA 18
## 105 1 NA 10
## 106 1 NA 10
## 107 1 NA 10
## 108 1 NA 10
## 109 1 NA 10
## 110 1 NA 10
## 111 1 NA 10
## 112 1 NA 10
## 113 1 NA 13
## 114 1 NA 13
## 115 1 NA 13
## 116 1 NA 13
## 117 1 NA 13
## 118 1 NA 13
## 119 1 NA 13
## 120 1 NA 13
## 121 1 NA 8
## 122 1 NA 8
## 123 0 NA 8
## 124 1 NA 8
## 125 1 NA 8
## 126 1 NA 8
## 127 1 NA 8
## 128 1 NA 8
## 129 1 NA 8
## 130 1 NA 8
## 131 1 NA 8
## 132 1 NA 8
## 133 0 NA 8
## 134 0 NA 8
## 135 0 NA 8
## 136 0 NA 8
## 137 1 NA 21
## 138 1 NA 21
## 139 1 NA 21
## 140 1 NA 21
## 141 1 NA 21
## 142 0 NA 21
## 143 1 NA 21
## 144 1 NA 21
## 145 1 NA 16
## 146 1 NA 16
## 147 1 NA 16
## 148 1 NA 16
## 149 1 NA 16
## 150 1 NA 16
## 151 1 NA 16
## 152 1 NA 16
## 153 1 NA 8
## 154 1 NA 8
## 155 1 NA 8
## 156 1 NA 8
## 157 1 NA 8
## 158 1 NA 8
## 159 1 NA 8
## 160 1 NA 8
## 161 1 NA 8
## 162 1 NA 8
## 163 1 NA 8
## 164 1 NA 8
## 165 1 NA 8
## 166 1 NA 8
## 167 1 NA 8
## 168 1 NA 8
## 169 1 NA 8
## 170 1 NA 8
## 171 1 NA 8
## 172 1 NA 8
## 173 1 NA 8
## 174 1 NA 8
## 175 1 NA 8
## 176 0 NA 8
## 177 1 NA 8
## 178 1 NA 8
## 179 1 NA 8
## 180 1 NA 8
## 181 1 NA 8
## 182 1 NA 8
## 183 1 NA 8
## 184 0 NA 8
## 185 1 NA 8
## 186 1 NA 8
## 187 1 NA 8
## 188 1 NA 8
## 189 1 NA 8
## 190 1 NA 8
## 191 1 NA 8
## 192 1 NA 8
## 193 1 NA 16
## 194 1 NA 16
## 195 1 NA 16
## 196 1 NA 16
## 197 1 NA 16
## 198 1 NA 16
## 199 1 NA 16
## 200 1 NA 16
## 201 1 NA 17
## 202 1 NA 17
## 203 1 NA 17
## 204 1 NA 17
## 205 0 NA 17
## 206 0 NA 17
## 207 0 NA 17
## 208 0 NA 17
## 209 1 NA 9
## 210 1 NA 9
## 211 1 NA 9
## 212 1 NA 9
## 213 1 NA 9
## 214 1 NA 9
## 215 1 NA 9
## 216 1 NA 9
## 217 1 NA 8
## 218 1 NA 8
## 219 1 NA 8
## 220 1 NA 8
## 221 1 NA 8
## 222 1 NA 8
## 223 0 NA 8
## 224 1 NA 8
## 225 1 NA 24
## 226 1 NA 24
## 227 1 NA 24
## 228 1 NA 24
## 229 0 NA 24
## 230 0 NA 24
## 231 0 NA 24
## 232 0 NA 24
## 233 1 NA 9
## 234 1 NA 9
## 235 1 NA 9
## 236 1 NA 9
## 237 1 NA 9
## 238 1 NA 9
## 239 1 NA 9
## 240 1 NA 9
## 241 1 NA 8
## 242 1 NA 8
## 243 1 NA 8
## 244 1 NA 8
## 245 1 NA 8
## 246 1 NA 8
## 247 1 NA 8
## 248 1 NA 8
## 249 1 NA 8
## 250 1 NA 8
## 251 1 NA 8
## 252 1 NA 8
## 253 1 NA 8
## 254 1 NA 8
## 255 1 NA 8
## 256 1 NA 8
## 257 1 NA 15
## 258 1 NA 15
## 259 1 NA 15
## 260 1 NA 15
## 261 1 NA 15
## 262 1 NA 15
## 263 1 NA 15
## 264 1 NA 15
## 265 1 NA 16
## 266 1 NA 16
## 267 1 NA 16
## 268 1 NA 16
## 269 1 NA 16
## 270 1 NA 16
## 271 1 NA 16
## 272 1 NA 16
## 273 1 NA 15
## 274 1 NA 15
## 275 1 NA 15
## 276 1 NA 15
## 277 1 NA 15
## 278 1 NA 15
## 279 1 NA 15
## 280 1 NA 15
## 281 1 NA 17
## 282 1 NA 17
## 283 1 NA 17
## 284 1 NA 17
## 285 1 NA 17
## 286 1 NA 17
## 287 1 NA 17
## 288 1 NA 17
## 289 1 NA 8
## 290 1 NA 8
## 291 1 NA 8
## 292 1 NA 8
## 293 1 NA 8
## 294 1 NA 8
## 295 0 NA 8
## 296 1 NA 8
## 297 1 NA 9
## 298 1 NA 9
## 299 1 NA 9
## 300 1 NA 9
## 301 0 NA 9
## 302 0 NA 9
## 303 1 NA 9
## 304 1 NA 9
## 305 1 NA 24
## 306 1 NA 24
## 307 1 NA 24
## 308 1 NA 24
## 309 0 NA 24
## 310 0 NA 24
## 311 0 NA 24
## 312 0 NA 24
## 313 1 NA 8
## 314 1 NA 8
## 315 1 NA 8
## 316 1 NA 8
## 317 1 NA 8
## 318 1 NA 8
## 319 1 NA 8
## 320 NA NA 8
## bias_category condition age_zscored incorrect_n_feedback_trials
## 1 Geocentric NA -0.32630216 0
## 2 Geocentric NA -0.32630216 0
## 3 Geocentric NA -0.32630216 0
## 4 Geocentric NA -0.32630216 0
## 5 Geocentric NA -0.32630216 0
## 6 Geocentric NA -0.32630216 0
## 7 Geocentric NA -0.32630216 0
## 8 Geocentric NA -0.32630216 0
## 9 Geocentric NA 0.42359374 0
## 10 Geocentric NA 0.42359374 0
## 11 Geocentric NA 0.42359374 0
## 12 Geocentric NA 0.42359374 0
## 13 Geocentric NA 0.42359374 0
## 14 Geocentric NA 0.42359374 0
## 15 Geocentric NA 0.42359374 0
## 16 Geocentric NA 0.42359374 0
## 17 <NA> NA -1.49280689 0
## 18 <NA> NA -1.49280689 0
## 19 <NA> NA -1.49280689 0
## 20 <NA> NA -1.49280689 0
## 21 <NA> NA -1.49280689 0
## 22 <NA> NA -1.49280689 0
## 23 <NA> NA -1.49280689 0
## 24 <NA> NA -1.49280689 0
## 25 Geocentric NA -1.74277219 0
## 26 Geocentric NA -1.74277219 0
## 27 Geocentric NA -1.74277219 0
## 28 Geocentric NA -1.74277219 0
## 29 Geocentric NA -1.74277219 0
## 30 Geocentric NA -1.74277219 0
## 31 Geocentric NA -1.74277219 0
## 32 Geocentric NA -1.74277219 0
## 33 Geocentric NA -1.78443307 0
## 34 Geocentric NA -1.78443307 0
## 35 Geocentric NA -1.78443307 0
## 36 Geocentric NA -1.78443307 0
## 37 Geocentric NA -1.78443307 0
## 38 Geocentric NA -1.78443307 0
## 39 Geocentric NA -1.78443307 0
## 40 Geocentric NA -1.78443307 0
## 41 <NA> NA -1.78443307 0
## 42 <NA> NA -1.78443307 0
## 43 <NA> NA -1.78443307 0
## 44 <NA> NA -1.78443307 0
## 45 <NA> NA -1.78443307 0
## 46 <NA> NA -1.78443307 0
## 47 <NA> NA -1.78443307 0
## 48 <NA> NA -1.78443307 0
## 49 Egocentric NA -1.24284159 0
## 50 Egocentric NA -1.24284159 0
## 51 Egocentric NA -1.24284159 0
## 52 Egocentric NA -1.24284159 0
## 53 Egocentric NA -1.24284159 0
## 54 Egocentric NA -1.24284159 0
## 55 Egocentric NA -1.24284159 0
## 56 Egocentric NA -1.24284159 0
## 57 Geocentric NA -0.74291099 0
## 58 Geocentric NA -0.74291099 0
## 59 Geocentric NA -0.74291099 0
## 60 Geocentric NA -0.74291099 0
## 61 Geocentric NA -0.74291099 0
## 62 Geocentric NA -0.74291099 0
## 63 Geocentric NA -0.74291099 0
## 64 Geocentric NA -0.74291099 0
## 65 Geocentric NA -0.15965862 0
## 66 Geocentric NA -0.15965862 0
## 67 Geocentric NA -0.15965862 0
## 68 Geocentric NA -0.15965862 0
## 69 Geocentric NA -0.15965862 0
## 70 Geocentric NA -0.15965862 0
## 71 Geocentric NA -0.15965862 0
## 72 Geocentric NA -0.15965862 0
## 73 Egocentric NA 1.09016787 0
## 74 Egocentric NA 1.09016787 0
## 75 Egocentric NA 1.09016787 0
## 76 Egocentric NA 1.09016787 0
## 77 Egocentric NA 1.09016787 0
## 78 Egocentric NA 1.09016787 0
## 79 Egocentric NA 1.09016787 0
## 80 Egocentric NA 1.09016787 0
## 81 Geocentric NA -1.32616336 0
## 82 Geocentric NA -1.32616336 0
## 83 Geocentric NA -1.32616336 0
## 84 Geocentric NA -1.32616336 0
## 85 Geocentric NA -1.32616336 0
## 86 Geocentric NA -1.32616336 0
## 87 Geocentric NA -1.32616336 0
## 88 Geocentric NA -1.32616336 0
## 89 Geocentric NA -1.15951982 0
## 90 Geocentric NA -1.15951982 0
## 91 Geocentric NA -1.15951982 0
## 92 Geocentric NA -1.15951982 0
## 93 Geocentric NA -1.15951982 0
## 94 Geocentric NA -1.15951982 0
## 95 Geocentric NA -1.15951982 0
## 96 Geocentric NA -1.15951982 0
## 97 Egocentric NA -0.32630216 0
## 98 Egocentric NA -0.32630216 0
## 99 Egocentric NA -0.32630216 0
## 100 Egocentric NA -0.32630216 0
## 101 Egocentric NA -0.32630216 0
## 102 Egocentric NA -0.32630216 0
## 103 Egocentric NA -0.32630216 0
## 104 Egocentric NA -0.32630216 0
## 105 Geocentric NA -1.65945042 0
## 106 Geocentric NA -1.65945042 0
## 107 Geocentric NA -1.65945042 0
## 108 Geocentric NA -1.65945042 0
## 109 Geocentric NA -1.65945042 0
## 110 Geocentric NA -1.65945042 0
## 111 Geocentric NA -1.65945042 0
## 112 Geocentric NA -1.65945042 0
## 113 Geocentric NA 0.92352434 0
## 114 Geocentric NA 0.92352434 0
## 115 Geocentric NA 0.92352434 0
## 116 Geocentric NA 0.92352434 0
## 117 Geocentric NA 0.92352434 0
## 118 Geocentric NA 0.92352434 0
## 119 Geocentric NA 0.92352434 0
## 120 Geocentric NA 0.92352434 0
## 121 Geocentric NA 0.04864579 0
## 122 Geocentric NA 0.04864579 0
## 123 Geocentric NA 0.04864579 0
## 124 Geocentric NA 0.04864579 0
## 125 Geocentric NA 0.04864579 0
## 126 Geocentric NA 0.04864579 0
## 127 Geocentric NA 0.04864579 0
## 128 Geocentric NA 0.04864579 0
## 129 Geocentric NA 1.09016787 0
## 130 Geocentric NA 1.09016787 0
## 131 Geocentric NA 1.09016787 0
## 132 Geocentric NA 1.09016787 0
## 133 Geocentric NA 1.09016787 0
## 134 Geocentric NA 1.09016787 0
## 135 Geocentric NA 1.09016787 0
## 136 Geocentric NA 1.09016787 0
## 137 Geocentric NA -1.53446777 0
## 138 Geocentric NA -1.53446777 0
## 139 Geocentric NA -1.53446777 0
## 140 Geocentric NA -1.53446777 0
## 141 Geocentric NA -1.53446777 0
## 142 Geocentric NA -1.53446777 0
## 143 Geocentric NA -1.53446777 0
## 144 Geocentric NA -1.53446777 0
## 145 Egocentric NA -0.07633686 0
## 146 Egocentric NA -0.07633686 0
## 147 Egocentric NA -0.07633686 0
## 148 Egocentric NA -0.07633686 0
## 149 Egocentric NA -0.07633686 0
## 150 Egocentric NA -0.07633686 0
## 151 Egocentric NA -0.07633686 0
## 152 Egocentric NA -0.07633686 0
## 153 Geocentric NA 0.88186346 0
## 154 Geocentric NA 0.88186346 0
## 155 Geocentric NA 0.88186346 0
## 156 Geocentric NA 0.88186346 0
## 157 Geocentric NA 0.88186346 0
## 158 Geocentric NA 0.88186346 0
## 159 Geocentric NA 0.88186346 0
## 160 Geocentric NA 0.88186346 0
## 161 Geocentric NA -0.15965862 0
## 162 Geocentric NA -0.15965862 0
## 163 Geocentric NA -0.15965862 0
## 164 Geocentric NA -0.15965862 0
## 165 Geocentric NA -0.15965862 0
## 166 Geocentric NA -0.15965862 0
## 167 Geocentric NA -0.15965862 0
## 168 Geocentric NA -0.15965862 0
## 169 Geocentric NA 0.67355904 0
## 170 Geocentric NA 0.67355904 0
## 171 Geocentric NA 0.67355904 0
## 172 Geocentric NA 0.67355904 0
## 173 Geocentric NA 0.67355904 0
## 174 Geocentric NA 0.67355904 0
## 175 Geocentric NA 0.67355904 0
## 176 Geocentric NA 0.67355904 0
## 177 Geocentric NA 0.38193286 0
## 178 Geocentric NA 0.38193286 0
## 179 Geocentric NA 0.38193286 0
## 180 Geocentric NA 0.38193286 0
## 181 Geocentric NA 0.38193286 0
## 182 Geocentric NA 0.38193286 0
## 183 Geocentric NA 0.38193286 0
## 184 Geocentric NA 0.38193286 0
## 185 Egocentric NA 0.67355904 0
## 186 Egocentric NA 0.67355904 0
## 187 Egocentric NA 0.67355904 0
## 188 Egocentric NA 0.67355904 0
## 189 Egocentric NA 0.67355904 0
## 190 Egocentric NA 0.67355904 0
## 191 Egocentric NA 0.67355904 0
## 192 Egocentric NA 0.67355904 0
## 193 Egocentric NA 1.21515052 0
## 194 Egocentric NA 1.21515052 0
## 195 Egocentric NA 1.21515052 0
## 196 Egocentric NA 1.21515052 0
## 197 Egocentric NA 1.21515052 0
## 198 Egocentric NA 1.21515052 0
## 199 Egocentric NA 1.21515052 0
## 200 Egocentric NA 1.21515052 0
## 201 Geocentric NA -0.07633686 0
## 202 Geocentric NA -0.07633686 0
## 203 Geocentric NA -0.07633686 0
## 204 Geocentric NA -0.07633686 0
## 205 Geocentric NA -0.07633686 0
## 206 Geocentric NA -0.07633686 0
## 207 Geocentric NA -0.07633686 0
## 208 Geocentric NA -0.07633686 0
## 209 <NA> NA 0.04864579 0
## 210 <NA> NA 0.04864579 0
## 211 <NA> NA 0.04864579 0
## 212 <NA> NA 0.04864579 0
## 213 <NA> NA 0.04864579 0
## 214 <NA> NA 0.04864579 0
## 215 <NA> NA 0.04864579 0
## 216 <NA> NA 0.04864579 0
## 217 Geocentric NA 0.42359374 0
## 218 Geocentric NA 0.42359374 0
## 219 Geocentric NA 0.42359374 0
## 220 Geocentric NA 0.42359374 0
## 221 Geocentric NA 0.42359374 0
## 222 Geocentric NA 0.42359374 0
## 223 Geocentric NA 0.42359374 0
## 224 Geocentric NA 0.42359374 0
## 225 Geocentric NA 1.09016787 0
## 226 Geocentric NA 1.09016787 0
## 227 Geocentric NA 1.09016787 0
## 228 Geocentric NA 1.09016787 0
## 229 Geocentric NA 1.09016787 0
## 230 Geocentric NA 1.09016787 0
## 231 Geocentric NA 1.09016787 0
## 232 Geocentric NA 1.09016787 0
## 233 Egocentric NA 0.79854169 0
## 234 Egocentric NA 0.79854169 0
## 235 Egocentric NA 0.79854169 0
## 236 Egocentric NA 0.79854169 0
## 237 Egocentric NA 0.79854169 0
## 238 Egocentric NA 0.79854169 0
## 239 Egocentric NA 0.79854169 0
## 240 Egocentric NA 0.79854169 0
## 241 Geocentric NA 0.34027198 0
## 242 Geocentric NA 0.34027198 0
## 243 Geocentric NA 0.34027198 0
## 244 Geocentric NA 0.34027198 0
## 245 Geocentric NA 0.34027198 0
## 246 Geocentric NA 0.34027198 0
## 247 Geocentric NA 0.34027198 0
## 248 Geocentric NA 0.34027198 0
## 249 Egocentric NA 0.09030668 0
## 250 Egocentric NA 0.09030668 0
## 251 Egocentric NA 0.09030668 0
## 252 Egocentric NA 0.09030668 0
## 253 Egocentric NA 0.09030668 0
## 254 Egocentric NA 0.09030668 0
## 255 Egocentric NA 0.09030668 0
## 256 Egocentric NA 0.09030668 0
## 257 Egocentric NA 1.29847229 0
## 258 Egocentric NA 1.29847229 0
## 259 Egocentric NA 1.29847229 0
## 260 Egocentric NA 1.29847229 0
## 261 Egocentric NA 1.29847229 0
## 262 Egocentric NA 1.29847229 0
## 263 Egocentric NA 1.29847229 0
## 264 Egocentric NA 1.29847229 0
## 265 Egocentric NA 1.34013317 0
## 266 Egocentric NA 1.34013317 0
## 267 Egocentric NA 1.34013317 0
## 268 Egocentric NA 1.34013317 0
## 269 Egocentric NA 1.34013317 0
## 270 Egocentric NA 1.34013317 0
## 271 Egocentric NA 1.34013317 0
## 272 Egocentric NA 1.34013317 0
## 273 Egocentric NA 1.09016787 0
## 274 Egocentric NA 1.09016787 0
## 275 Egocentric NA 1.09016787 0
## 276 Egocentric NA 1.09016787 0
## 277 Egocentric NA 1.09016787 0
## 278 Egocentric NA 1.09016787 0
## 279 Egocentric NA 1.09016787 0
## 280 Egocentric NA 1.09016787 0
## 281 Geocentric NA 0.21528933 0
## 282 Geocentric NA 0.21528933 0
## 283 Geocentric NA 0.21528933 0
## 284 Geocentric NA 0.21528933 0
## 285 Geocentric NA 0.21528933 0
## 286 Geocentric NA 0.21528933 0
## 287 Geocentric NA 0.21528933 0
## 288 Geocentric NA 0.21528933 0
## 289 Egocentric NA 0.88186346 0
## 290 Egocentric NA 0.88186346 0
## 291 Egocentric NA 0.88186346 0
## 292 Egocentric NA 0.88186346 0
## 293 Egocentric NA 0.88186346 0
## 294 Egocentric NA 0.88186346 0
## 295 Egocentric NA 0.88186346 0
## 296 Egocentric NA 0.88186346 0
## 297 Egocentric NA 0.84020257 0
## 298 Egocentric NA 0.84020257 0
## 299 Egocentric NA 0.84020257 0
## 300 Egocentric NA 0.84020257 0
## 301 Egocentric NA 0.84020257 0
## 302 Egocentric NA 0.84020257 0
## 303 Egocentric NA 0.84020257 0
## 304 Egocentric NA 0.84020257 0
## 305 Geocentric NA 0.84020257 0
## 306 Geocentric NA 0.84020257 0
## 307 Geocentric NA 0.84020257 0
## 308 Geocentric NA 0.84020257 0
## 309 Geocentric NA 0.84020257 0
## 310 Geocentric NA 0.84020257 0
## 311 Geocentric NA 0.84020257 0
## 312 Geocentric NA 0.84020257 0
## 313 Geocentric NA 0.34027198 0
## 314 Geocentric NA 0.34027198 0
## 315 Geocentric NA 0.34027198 0
## 316 Geocentric NA 0.34027198 0
## 317 Geocentric NA 0.34027198 0
## 318 Geocentric NA 0.34027198 0
## 319 Geocentric NA 0.34027198 0
## 320 Geocentric NA 0.34027198 0Word extension response
Model: word extension response (0/1) ~ Visual Experience (Blind - No Visual Experience / Blind - Some Visual Experience) * Word Meaning (Geocentric / Egocentric) * Anchor Direction (Front / Back) + Age + (1|site/participant)
Main effects are all significant. - Partial vision kids are better than no vision. (based on the plots above, it seems like no vision kids are not really succeeding at this task). - geocentric FoR is better than egocentric - doll facing the same direction is better than facing opposite direction - kids succeed more with increasing age
There is a significant interaction effect of vision group x anchor_facing. Most meaningfully, when the doll is facing the opposite direction, then partial vision kids perform better than no vision kids.
fit.word_extension_kids_vision <- glmer(response_coded ~ vision_group * word_meaning * anchor_facing + age_zscored + (1|PID),
data = df.response_combined %>%
filter(task == "Word Extension Task" & group == "blind_children"),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.word_extension_kids_vision)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: response_coded ~ vision_group * word_meaning * anchor_facing +
## age_zscored + (1 | PID)
## Data: df.response_combined %>% filter(task == "Word Extension Task" &
## group == "blind_children")
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
##
## AIC BIC logLik -2*log(L) df.resid
## 402.7 440.4 -191.4 382.7 310
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8687 -0.9396 0.3468 0.7666 1.7458
##
## Random effects:
## Groups Name Variance Std.Dev.
## PID (Intercept) 0.4123 0.6421
## Number of obs: 320, groups: PID, 40
##
## Fixed effects:
## Estimate
## (Intercept) -0.5847
## vision_grouppartial 2.4741
## word_meaningGeocentric 1.0617
## anchor_facingfront 0.8911
## age_zscored 0.4082
## vision_grouppartial:word_meaningGeocentric 0.1019
## vision_grouppartial:anchor_facingfront -1.8175
## word_meaningGeocentric:anchor_facingfront -0.8147
## vision_grouppartial:word_meaningGeocentric:anchor_facingfront 0.4502
## Std. Error
## (Intercept) 0.3295
## vision_grouppartial 0.8019
## word_meaningGeocentric 0.4719
## anchor_facingfront 0.3922
## age_zscored 0.1808
## vision_grouppartial:word_meaningGeocentric 1.3990
## vision_grouppartial:anchor_facingfront 0.8969
## word_meaningGeocentric:anchor_facingfront 0.5530
## vision_grouppartial:word_meaningGeocentric:anchor_facingfront 1.5647
## z value Pr(>|z|)
## (Intercept) -1.774 0.07602
## vision_grouppartial 3.085 0.00203
## word_meaningGeocentric 2.250 0.02444
## anchor_facingfront 2.272 0.02308
## age_zscored 2.258 0.02394
## vision_grouppartial:word_meaningGeocentric 0.073 0.94192
## vision_grouppartial:anchor_facingfront -2.026 0.04272
## word_meaningGeocentric:anchor_facingfront -1.473 0.14065
## vision_grouppartial:word_meaningGeocentric:anchor_facingfront 0.288 0.77354
##
## (Intercept) .
## vision_grouppartial **
## word_meaningGeocentric *
## anchor_facingfront *
## age_zscored *
## vision_grouppartial:word_meaningGeocentric
## vision_grouppartial:anchor_facingfront *
## word_meaningGeocentric:anchor_facingfront
## vision_grouppartial:word_meaningGeocentric:anchor_facingfront
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vsn_gr wrd_mG anchr_ ag_zsc vs_:_G vsn_:_ wr_G:_
## vsn_grpprtl -0.463
## wrd_mnngGcn -0.723 0.364
## anchr_fcngf -0.601 0.267 0.427
## age_zscored -0.208 0.300 0.253 0.033
## vsn_grpp:_G 0.271 -0.566 -0.370 -0.144 -0.229
## vsn_grppr:_ 0.277 -0.662 -0.205 -0.445 -0.064 0.368
## wrd_mnngG:_ 0.426 -0.188 -0.592 -0.709 -0.021 0.200 0.315
## vsn_gr:_G:_ -0.154 0.368 0.214 0.252 0.025 -0.739 -0.568 -0.354Anova(fit.word_extension_kids_vision, type = 3)## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_coded
## Chisq Df Pr(>Chisq)
## (Intercept) 3.1480 1 0.076018 .
## vision_group 9.5188 1 0.002034 **
## word_meaning 5.0629 1 0.024443 *
## anchor_facing 5.1621 1 0.023084 *
## age_zscored 5.0991 1 0.023939 *
## vision_group:word_meaning 0.0053 1 0.941923
## vision_group:anchor_facing 4.1063 1 0.042724 *
## word_meaning:anchor_facing 2.1709 1 0.140646
## vision_group:word_meaning:anchor_facing 0.0828 1 0.773538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1fit.word_extension_kids_vision %>%
emmeans(specs = pairwise ~ vision_group + anchor_facing,
adjust = "none")## NOTE: Results may be misleading due to involvement in interactions## $emmeans
## vision_group anchor_facing emmean SE df asymp.LCL asymp.UCL
## none back -0.0391 0.228 Inf -0.48508 0.407
## partial back 2.4860 0.668 Inf 1.17759 3.794
## none front 0.4446 0.229 Inf -0.00408 0.893
## partial front 1.3773 0.481 Inf 0.43393 2.321
##
## Results are averaged over the levels of: word_meaning
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## none back - partial back -2.525 0.705 Inf -3.582 0.0003
## none back - none front -0.484 0.277 Inf -1.748 0.0804
## none back - partial front -1.416 0.532 Inf -2.662 0.0078
## partial back - none front 2.041 0.700 Inf 2.914 0.0036
## partial back - partial front 1.109 0.735 Inf 1.508 0.1315
## none front - partial front -0.933 0.529 Inf -1.764 0.0778
##
## Results are averaged over the levels of: word_meaning
## Results are given on the log odds ratio (not the response) scale.pairs(emmeans(
fit.word_extension_kids_vision,
~ vision_group | anchor_facing
), adjust = "none")## NOTE: Results may be misleading due to involvement in interactions## anchor_facing = back:
## contrast estimate SE df z.ratio p.value
## none - partial -2.525 0.705 Inf -3.582 0.0003
##
## anchor_facing = front:
## contrast estimate SE df z.ratio p.value
## none - partial -0.933 0.529 Inf -1.764 0.0778
##
## Results are averaged over the levels of: word_meaning
## Results are given on the log odds ratio (not the response) scale.anova(fit.post_test_kids_vision, fit.post_test_kids, type = 3)## Data: df.response_combined %>% filter(task == "Post-test" & group == ...
## Models:
## fit.post_test_kids: response_coded ~ word_meaning + age_zscored + (1 | PID)
## fit.post_test_kids_vision: response_coded ~ vision_group * word_meaning + age_zscored + (1 | PID)
## npar AIC BIC logLik -2*log(L) Chisq Df
## fit.post_test_kids 4 229.95 244.91 -110.97 221.95
## fit.post_test_kids_vision 6 231.40 253.84 -109.70 219.40 2.5483 2
## Pr(>Chisq)
## fit.post_test_kids
## fit.post_test_kids_vision 0.2797————-EARLIER ANALYSES—————
Blind Adults Data
df.demog_age <- df.response_blind_adults %>%
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_blind_adults %>%
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()Descriptives
Bias test
# Most blind adults are geocentric, 8 are not categorizeable.
df.response_blind_adults_bias <- df.response_blind_adults %>%
group_by(PID, word_meaning) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
group_by(word_meaning, bias_category) %>%
summarise(n = n())Bias by age (each adult’s overall Egocentric/Geocentric classification from the bias test):
df.response_blind_adults_bias_age <- df.response_blind_adults %>%
group_by(PID, age_years) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
ungroup() %>%
mutate(bias_geocentric = case_when(
bias_category == "Geocentric" ~ 1,
bias_category == "Egocentric" ~ 0
))
ggplot(df.response_blind_adults_bias_age %>% filter(!is.na(bias_geocentric)),
aes(x = age_years, y = bias_geocentric)) +
geom_jitter(height = 0.05, alpha = 0.6) +
geom_smooth(method = "glm", method.args = list(family = "binomial"), se = TRUE) +
labs(x = "Age (years)", y = "Proportion geocentric bias") +
ylim(-0.1, 1.1)Number of feedback trials
Participants need more trials in the egocentric condition, but they are not perfect
df.response_blind_adults %>%
filter(task == "Feedback Task") %>%
group_by(PID, word_meaning) %>%
summarise(n_feedback_trials = mean(n_feedback_trials)) %>%
group_by(word_meaning) %>%
summarise(mean_num_feedback_trials = mean(n_feedback_trials))df.response_blind_adults_n_feedback <- df.response_blind_adults %>%
group_by(PID, word_meaning) %>%
slice(1) %>%
summarise(n_feedback_trials = n_feedback_trials)
ggplot(df.response_blind_adults_n_feedback,
aes(x = word_meaning, y = n_feedback_trials, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange")Post-test performance
Blindfolded participants were perfect 4/5 non-blindfold participants were perfect. One non-blindfold ppt rotated the axis (i.e., treated the terms egocentrically after being rotated). Note that this ppt did not show an egocentric bias in the bias test, and also did not rotate the axis during the feedback trials.
df.response_blind_adults %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning) %>%
summarise(sum_correct_resp = mean(response_coded)) %>%
group_by(word_meaning) %>%
summarise(mean_correct_resp = mean(sum_correct_resp))
ggplot(df.response_blind_adults %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning) %>%
summarise(mean_correct_resp = mean(response_coded)),
aes(x = word_meaning, y = mean_correct_resp, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
ylim(0, 1)Word extension task
df.response_blind_adults %>%
filter(task == "Word Extension Task") %>%
group_by(PID, word_meaning, anchor_facing) %>%
summarise(sum_correct_resp = mean(response_coded)) %>%
group_by(word_meaning, anchor_facing) %>%
summarise(mean_correct_resp = mean(sum_correct_resp))Regression
post-feedback word learning response (0/1) ~ Visual Cues (Blindfold / No Blindfold) * Word Meaning (Egocentric / Geocentric) + (1|participant)
fit.post_test <- glmer(response_coded ~ word_meaning * Condition + (1|PID),
data = df.response_blind_adults %>%
filter(task == "Post-test") %>%
mutate(Condition = factor(Condition, levels = c("No Blindfold", "Blindfold"))),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.post_test)pretraining geocentric bias ~ Visual Cues (Blindfold / No Blindfold) + (1|participant)
fit.bias <- glmer(geocentric_bias ~ Condition + (1|PID),
data = df.response_blind_adults %>%
filter(task == "Bias Test") %>%
mutate(geocentric_bias = case_when(
word_meaning == "Egocentric" & Response == "correct" ~ 0,
word_meaning == "Egocentric" & Response == "incorrect" ~ 1,
word_meaning == "Geocentric" & Response == "correct" ~ 1,
word_meaning == "Geocentric" & Response == "incorrect" ~ 0)) %>%
mutate(Condition = factor(Condition, levels = c("No Blindfold", "Blindfold"))),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.bias)geocentric bias (from the bias test) ~ Age (one row per participant, so no random effect needed)
fit.bias_age <- glm(bias_geocentric ~ age_years,
data = df.response_blind_adults_bias_age,
family = binomial(link = 'logit'))
summary(fit.bias_age)Blind Kids Data
df.demog_age <- df.response_blind_children %>%
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_blind_children %>%
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()Descriptives
Bias test
# More blind kids are geocentric, but less so than adults (62.5%), 2 are not categorizeable.
df.response_blind_children_bias <- df.response_blind_children %>%
group_by(PID, word_meaning) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
group_by(word_meaning, bias_category) %>%
summarise(n = n())Performance by age:
ggplot(df.response_blind_children %>%
filter(task == "Bias Test") %>%
group_by(PID, word_meaning, age_months) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months, y = mean_correct_resp, color = word_meaning)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = TRUE) +
labs(x = "Age (months)", y = "Mean bias test accuracy") +
ylim(0, 1)Bias by age (each kid’s overall Egocentric/Geocentric classification from the bias test), with adults’ mean geocentric bias shown as a reference point on the right:
df.response_blind_children_bias_age <- df.response_blind_children %>%
group_by(PID, age_months) %>%
slice(1) %>%
summarise(bias_category = bias_category) %>%
ungroup() %>%
mutate(bias_geocentric = case_when(
bias_category == "Geocentric" ~ 1,
bias_category == "Egocentric" ~ 0
))Number of feedback trials
Participants need more trials in the egocentric condition, but they are not perfect. Kids did not need that many trials more than adults (probably not different statistically.)
df.response_blind_children %>%
filter(task == "Feedback Task") %>%
group_by(PID, word_meaning) %>%
summarise(n_feedback_trials = mean(n_feedback_trials)) %>%
group_by(word_meaning) %>%
summarise(mean_num_feedback_trials = mean(n_feedback_trials))df.response_blind_children_n_feedback <- df.response_blind_children %>%
group_by(PID, word_meaning, age_months) %>%
slice(1) %>%
summarise(n_feedback_trials = n_feedback_trials)
ggplot(df.response_blind_children_n_feedback,
aes(x = word_meaning, y = n_feedback_trials, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange")Performance by age:
ggplot(df.response_blind_children_n_feedback,
aes(x = age_months, y = n_feedback_trials, color = word_meaning)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = TRUE) +
labs(x = "Age (months)", y = "Number of feedback trials")Post-test performance
Blindfolded participants were perfect 4/5 non-blindfold participants were perfect. One non-blindfold ppt rotated the axis (i.e., treated the terms egocentrically after being rotated). Note that this ppt did not show an egocentric bias in the bias test, and also did not rotate the axis during the feedback trials.
df.response_blind_children %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning) %>%
summarise(sum_correct_resp = mean(response_coded, na.rm = T)) %>%
group_by(word_meaning) %>%
summarise(mean_correct_resp = mean(sum_correct_resp, na.rm = T))
ggplot(df.response_blind_children %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = word_meaning, y = mean_correct_resp, fill = word_meaning)) +
geom_dotplot(binaxis = "y", stackdir = "center",
alpha = 0.5,
dotsize = 0.75) +
stat_summary(fun.data = "mean_cl_boot",
geom = "pointrange") +
ylim(0, 1)Performance by age:
ggplot(df.response_blind_children %>%
filter(task == "Post-test") %>%
group_by(PID, word_meaning, age_months) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months, y = mean_correct_resp, color = word_meaning)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = TRUE) +
labs(x = "Age (months)", y = "Mean post-test accuracy") +
ylim(0, 1)Word extension task
Kids are around chance for the word extension task.
df.response_blind_children %>%
filter(task == "Word Extension Task") %>%
group_by(PID, word_meaning, anchor_facing) %>%
summarise(sum_correct_resp = mean(response_coded)) %>%
group_by(word_meaning, anchor_facing) %>%
summarise(mean_correct_resp = mean(sum_correct_resp))Performance by age:
ggplot(df.response_blind_children %>%
filter(task == "Word Extension Task") %>%
group_by(PID, word_meaning, anchor_facing, age_months) %>%
summarise(mean_correct_resp = mean(response_coded, na.rm = T)),
aes(x = age_months, y = mean_correct_resp, color = word_meaning)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = TRUE) +
facet_grid(~anchor_facing) +
labs(x = "Age (months)", y = "Mean word extension accuracy") +
ylim(0, 1)Regression
post-feedback word learning response (0/1) ~ Visual Cues (Blindfold / No Blindfold) * Word Meaning (Egocentric / Geocentric) + (1|participant)
fit.post_test <- glmer(response_coded ~ word_meaning * Condition + (1|PID),
data = df.response_blind_children %>%
filter(task == "Post-test") %>%
mutate(Condition = factor(Condition, levels = c("No Blindfold", "Blindfold"))),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.post_test)pretraining geocentric bias ~ Visual Cues (Blindfold / No Blindfold) + (1|participant)
fit.bias <- glmer(geocentric_bias ~ Condition + (1|PID),
data = df.response_blind_children %>%
filter(task == "Bias Test") %>%
mutate(geocentric_bias = case_when(
word_meaning == "Egocentric" & Response == "correct" ~ 0,
word_meaning == "Egocentric" & Response == "incorrect" ~ 1,
word_meaning == "Geocentric" & Response == "correct" ~ 1,
word_meaning == "Geocentric" & Response == "incorrect" ~ 0)) %>%
mutate(Condition = factor(Condition, levels = c("No Blindfold", "Blindfold"))),
family = binomial(link = 'logit'),
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=100000)))
summary(fit.bias)geocentric bias (from the bias test) ~ Age (one row per participant, so no random effect needed)
fit.bias_age <- glm(bias_geocentric ~ age_months,
data = df.response_blind_children_bias_age,
family = binomial(link = 'logit'))
summary(fit.bias_age)Session Info
session_info()