specify paths
US_PATH <- "data/1_raw_data/US/"
CN_PATH <- "data/1_raw_data/CN/"
MERGED_DATA_PATH <- here("data/2_merged/merged_all.csv")
MERGED_DEMOGS_PATH <- here("data/2_merged/merged_demogs.csv")
US_TBA_FD_PATH <- here("data/to_be_annotated/US/us_tba_fd.csv")
US_TBA_CA_PATH <- here("data/to_be_annotated/US/us_tba_ca.csv")
US_SYMS_LABEL_PATH <- here("data/to_be_annotated/US/us_syms_label.csv")
#funnel debriefing and sanity check
US_HUMANREAD_PATH <- here("data/to_be_annotated/US/us_hr.csv")
CN_TBA_FD_PATH <- here("data/to_be_annotated/CN/cn_tba_fd.csv")
CN_TBA_CA_PATH <- here("data/to_be_annotated/CN/cn_tba_ca.csv")
CN_SYMS_LABEL_PATH <- here("data/to_be_annotated/CN/cn_syms_label.csv")
CN_HUMANREAD_PATH <- here("data/to_be_annotated/CN/cn_hr.csv")
# task-based exclusion and completion based exclusion
TRIMMED_DATA_PATH <- here("data/3_trimmed/trimmed_all.csv")
TRIMMED_DEMOGS_PATH <- here("data/3_trimmed/trimmed_demogs.csv")
# tidy data
PROCESSED_MAIN_PATH <- here("data/4_processed/tidy_main.csv")
CONSERV_MAIN_PATH <- here("data/4_processed/tidy_conserv.csv")HAND_EXCLUSION <- c(
# participant requested
"SS1609806859776"
)
tidy_d <- read_csv(PROCESSED_MAIN_PATH) %>%
filter(! subject %in% HAND_EXCLUSION)## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## X1 = col_double(),
## subject = col_character(),
## culture = col_character(),
## task_name = col_character(),
## task_info = col_character(),
## trial_info = col_double(),
## resp_type = col_character(),
## resp = col_double()
## )## Warning: 17830 parsing failures.
## row col expected actual file
## 9113 trial_info a double RMTS '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/4_processed/tidy_main.csv'
## 9114 trial_info a double RMTS '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/4_processed/tidy_main.csv'
## 9115 trial_info a double RMTS '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/4_processed/tidy_main.csv'
## 9116 trial_info a double RMTS '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/4_processed/tidy_main.csv'
## 9117 trial_info a double RMTS '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/4_processed/tidy_main.csv'
## .... .......... ........ ...... ...............................................................................
## See problems(...) for more details.tidy_d %>%
group_by(subject, culture) %>%
summarise(n = n()) %>%
group_by(culture) %>%
count()## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## # A tibble: 2 x 2
## # Groups: culture [2]
## culture n
## <chr> <int>
## 1 CN 160
## 2 US 108plot proportion relation match
#get means by ppt and group by country (0 is object match, 1 is relation)
RMTS_ms <- tidy_d %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) %>%
group_by(culture) %>%
tidyboot_mean(mean, na.rm=T)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## Warning: `data_frame()` is deprecated as of tibble 1.1.0.
## Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: Problem with `mutate()` input `strap`.
## ℹ `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
## ℹ Input `strap` is `purrr::map(strap, dplyr::as_data_frame)`.## Warning: `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot means and CIs
ggplot(RMTS_ms,
aes(x = culture, y = mean, col = culture)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
ylab("Proportion relation match in ambiguous RMTS") +
xlab("Country") +
ylim(c(0,1)) +
geom_hline(yintercept = .5, lty = 2) +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
response histogram
#plot histogram
tidy_d %>%
filter(task_name == "RMTS") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) %>%
ggplot(aes(x = mean)) +
geom_histogram() +
facet_wrap(~culture)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#get means by ppt and group by country
RV_ms <- tidy_d %>%
filter(task_name == "RV") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) %>%
group_by(culture) %>%
tidyboot_mean(mean, na.rm=T) ## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`RV_mss <- tidy_d %>%
filter(task_name == "RV") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp))## `summarise()` regrouping output by 'subject' (override with `.groups` argument)#plot means and CIs
ggplot(RV_ms,
aes(x = culture, y = mean,color = culture)) +
geom_violin(data = RV_mss, show.legend = FALSE, bw = 0.08)+
geom_jitter(data = RV_mss, width = .2, alpha = 0.2) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper), show.legend = FALSE) +
guides(colour = guide_legend(override.aes = list(alpha = 1)))+
ylab("Raven's SPM performance") +
xlab("Country") +
ylim(0,1) +
scale_y_continuous(breaks = seq(0,1,0.25),
labels = {function(x) paste0(as.character(x*100),"%")})+
labs(color='Country') +
theme_classic() +
ggthemes::scale_color_solarized() +
scale_color_manual(values = c("red", "blue"))+
theme(legend.position = "bottom")## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
score histogram
#plot histogram
tidy_d %>%
filter(task_name == "RV") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) %>%
ggplot(aes(x = mean)) +
geom_histogram(binwidth = .1) +
facet_wrap(~culture)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)
RT_raven <- read_csv(TRIMMED_DATA_PATH) %>%
filter(variable_type == "raven") %>%
select(subject, culture, rt) %>%
mutate(rt = as.numeric(rt))## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## .default = col_character(),
## X1 = col_double(),
## trial_index = col_double(),
## time_elapsed = col_double(),
## correct = col_logical(),
## answer_correct = col_double(),
## passed = col_logical(),
## labels_locations = col_logical(),
## unique_position = col_double(),
## activate = col_logical(),
## startTime = col_double(),
## endTime = col_double(),
## RT = col_double()
## )## See spec(...) for full column specifications.## Warning: 149 parsing failures.
## row col expected actual file
## 19952 labels_locations 1/0/T/F/TRUE/FALSE {"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":170.25833518600868,\"y\":66,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":170.25833518600868,"label_y":66},{"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":399.6837836859094,\"y\":339,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":399.6837836859094,"label_y":339} '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/3_trimmed/trimmed_all.csv'
## 20102 labels_locations 1/0/T/F/TRUE/FALSE {"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":144.89555371841578,\"y\":202,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":144.89555371841578,"label_y":202},{"label_text":"{\"attrs\":{\"x\":144.89555371841578,\"y\":202,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":144.89555371841578,"label_y":202},{"label_text":"{\"attrs\":{\"text\":\"爷爷奶奶\",\"x\":293.63488080021466,\"y\":159,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":293.63488080021466,"label_y":159},{"label_text":"{\"attrs\":{\"text\":\"妈妈\",\"x\":56.339822922176054,\"y\":294,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":56.339822922176054,"label_y":294},{"label_text":"{\"attrs\":{\"text\":\"爸爸\",\"x\":169.97685681117767,\"y\":314,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":169.97685681117767,"label_y":314},{"label_text":"{\"attrs\":{\"x\":169.97685681117767,\"y\":314,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":169.97685681117767,"label_y":314} '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/3_trimmed/trimmed_all.csv'
## 20318 labels_locations 1/0/T/F/TRUE/FALSE {"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":102.59422209179507,\"y\":269,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":102.59422209179507,"label_y":269},{"label_text":"{\"attrs\":{\"text\":\"爸爸\",\"x\":167.37401686262075,\"y\":116,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":167.37401686262075,"label_y":116},{"label_text":"{\"attrs\":{\"x\":167.37401686262075,\"y\":116,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":167.37401686262075,"label_y":116},{"label_text":"{\"attrs\":{\"x\":167.37401686262075,\"y\":116,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":167.37401686262075,"label_y":116},{"label_text":"{\"attrs\":{\"text\":\"妈妈\",\"x\":192.86419686396277,\"y\":261,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":192.86419686396277,"label_y":261},{"label_text":"{\"attrs\":{\"text\":\"姥姥\",\"x\":251.64161672015584,\"y\":404,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":251.64161672015584,"label_y":404},{"label_text":"{\"attrs\":{\"text\":\"姥爷\",\"x\":79.59956896751928,\"y\":390,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":79.59956896751928,"label_y":390} '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/3_trimmed/trimmed_all.csv'
## 20468 labels_locations 1/0/T/F/TRUE/FALSE {"label_text":"{\"attrs\":{\"text\":\"爸爸\",\"x\":113.10290798430026,\"y\":71,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":113.10290798430026,"label_y":71},{"label_text":"{\"attrs\":{\"x\":113.10290798430026,\"y\":71,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":113.10290798430026,"label_y":71},{"label_text":"{\"attrs\":{\"text\":\"妈妈\",\"x\":393.79695953753725,\"y\":106,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":393.79695953753725,"label_y":106},{"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":239.48518681059687,\"y\":386,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":239.48518681059687,"label_y":386} '/Users/caoanjie/Desktop/projects/CCRR_analysis/data/3_trimmed/trimmed_all.csv'
## 20672 labels_locations 1/0/T/F/TRUE/FALSE {"label_text":"{\"attrs\":{\"text\":\"我\",\"x\":96.79810430798162,\"y\":100,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":96.79810430798162,"label_y":100},{"label_text":"{\"attrs\":{\"text\":\"爸爸\",\"x\":444.4342560238989,\"y\":252,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":444.4342560238989,"label_y":252},{"label_text":"{\"attrs\":{\"text\":\"妈妈\",\"x\":185.2839556687078,\"y\":251,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":185.2839556687078,"label_y":251},{"label_text":"{\"attrs\":{\"text\":\"爷爷\",\"x\":401.5049024320361,\"y\":26,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":401.5049024320361,"label_y":26},{"label_text":"{\"attrs\":{\"text\":\"奶奶\",\"x\":270.39817079971493,\"y\":69,\"fontSize\":20,\"fill\":\"white\"},\"className\":\"Text\"}","label_x":270.39817079971493,"label_y":RT_raven_ms <- RT_raven %>%
group_by(subject, culture) %>%
summarise(mean = mean(rt)) %>%
group_by(culture) %>%
tidyboot_mean(mean, na.rm=T)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`RT_raven %>%
ggplot(aes(x=rt))+
geom_histogram()+
facet_wrap(~culture)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(RT_raven_ms,
aes(x = culture, y = mean, col = culture)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
ylab("Raven RT") +
xlab("Country") +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
Plot proportion conforming choice, with size of mean scaled by n
#pen choice means
pen_ms <- tidy_d %>%
filter(task_name == "CP") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot pen choice means
ggplot(pen_ms,
aes(x = culture, y = mean, col = culture)) +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_point(aes(size=n)) +
ylab("Proportion conforming choice") +
xlab("Country") +
theme_classic() +
ylim(0,1) +
scale_size_area(breaks = seq(0,50,5)) +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
Quick stats check (not necessarily preregistered).
cp_lm_data <- tidy_d %>%
filter(task_name == "CP")
cp_mod <- glm(resp ~ culture, data = cp_lm_data, family = "binomial")
summary(cp_mod)##
## Call:
## glm(formula = resp ~ culture, family = "binomial", data = cp_lm_data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.4490 -1.2899 0.9282 0.9282 1.0689
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.6190 0.1657 3.735 0.000188 ***
## cultureUS -0.3583 0.2552 -1.404 0.160354
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 357.05 on 267 degrees of freedom
## Residual deviance: 355.08 on 266 degrees of freedom
## AIC: 359.08
##
## Number of Fisher Scoring iterations: 4si_ratio_ms <- tidy_d %>%
filter(task_name == "SI") %>%
filter(resp_type == "inflation_score_ratio") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot pen choice means
#
ggplot(si_ratio_ms,
aes(x = culture, y = mean, col = culture)) +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_point(aes(size=n)) +
ylab("(me_radius *2) / (other_radius * 2)") +
xlab("Country") +
theme_classic() +
scale_size_area(breaks = seq(0,50,5)) +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
si_diff_ms <- tidy_d %>%
filter(task_name == "SI") %>%
filter(resp_type == "inflation_score_diff") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`ggplot(si_diff_ms,
aes(x = culture, y = mean, col = culture)) +
geom_linerange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_point(aes(size=n)) +
ylab("me_radius - other_radius") +
xlab("Country") +
theme_classic() +
ylim(0, -30)+
scale_size_area(breaks = seq(0,50,5)) +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
tidy_d %>%
filter(task_name == "SI") %>%
filter(resp_type == "inflation_score_ratio") %>%
ggplot(aes(x = resp)) +
geom_histogram(binwidth = .1) +
facet_wrap(~culture)
tidy_d %>%
filter(task_name == "SI") %>%
filter(resp_type == "inflation_score_diff") %>%
ggplot(aes(x = resp)) +
geom_histogram(binwidth = .1) +
facet_wrap(~culture)
Plot means of person and situation attributions
#get means by ppt and group by country
CA_ms <- tidy_d %>%
filter(task_name == "CA") %>%
group_by(culture, resp_type, subject) %>%
summarise(subject_mean = mean(resp)) %>%
group_by(culture, resp_type) %>%
tidyboot_mean(subject_mean, na.rm=T)## `summarise()` regrouping output by 'culture', 'resp_type' (override with `.groups` argument)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot means and CIs
ggplot(CA_ms,
aes(x = culture, y = mean, col = resp_type)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper), position = position_dodge2(.1)) +
ylab("Average number of attributions per trial") +
xlab("Country") +
ylim(c(0,1)) +
geom_hline(yintercept = .5, lty = 2) +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
tidy_d %>%
filter(task_name == "CA") %>%
ggplot(aes(x = resp)) +
geom_histogram(binwidth = 1) +
facet_wrap(resp_type~culture)
tidy_d %>%
filter(task_name == "CA") %>%
group_by(culture, resp_type, subject) %>%
summarise(subject_mean = mean(resp)) %>%
ggplot(aes(x = subject_mean)) +
geom_histogram(binwidth = 1) +
facet_wrap(resp_type~culture)## `summarise()` regrouping output by 'culture', 'resp_type' (override with `.groups` argument)
Plot mean height of the horizon line
# horizon height means
height_ms <- tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type=="hz_height") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`# plot horizon height means
ggplot(height_ms,
aes(x = culture, y = mean, col = culture)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_jitter(data = tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type=="hz_height"),
aes(x = culture, y = resp, col = culture), width = .1) +
ylab("Average horizon height") +
xlab("Country") +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
Plot horizon height histogram
#horizon height histogram
tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type == "hz_height") %>%
ggplot(aes(x = resp)) +
geom_histogram() +
facet_wrap(~culture)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot mean number of stickers used in the horizon task
#sticker count means
stickerCount_ms <- tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type=="stkr_count") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot sticker count means
ggplot(stickerCount_ms,
aes(x = culture, y = mean, col = culture)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_jitter(data = stickerCount_ms <- tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type=="stkr_count") ,
aes(x = culture, y = resp, col = culture), width = .1) +
ylab("Average number of stickers") +
xlab("Country") +
ylim(0, 20) +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")## Warning: Removed 26 rows containing missing values (geom_point).
#sticker count histogram
tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type == "stkr_count") %>%
ggplot(aes(x = resp)) +
geom_histogram() +
facet_wrap(~culture)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot average area covered by stickers in the horizon task
#sticker area means
stickerArea_ms <- tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type=="stkr_area") %>%
group_by(culture) %>%
tidyboot_mean(resp, na.rm=T)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`#plot sticker count means
ggplot(stickerArea_ms,
aes(x = culture, y = mean, col = culture)) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
ylab("Average area covered by stickers") +
xlab("Country") +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
Plot area covered by stickers for each participant in the horizon task
#sticker area histogram
tidy_d %>%
filter(task_name == "HZ") %>%
filter(resp_type == "stkr_area") %>%
ggplot(aes(x = resp)) +
geom_histogram() +
facet_wrap(~culture)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
IC_ms <- tidy_d %>%
filter(task_name == "EBB") %>%
filter(task_info %in% c("IL","NC")) %>%
group_by(subject, task_info, trial_info, culture) %>%
summarise(mean = mean(resp)) %>%
group_by(task_info, trial_info, culture) %>%
tidyboot_mean(mean, na.rm=T)## `summarise()` regrouping output by 'subject', 'task_info', 'trial_info' (override with `.groups` argument)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`ggplot(IC_ms, aes(x = trial_info, y = mean, col = culture)) +
facet_wrap(~task_info) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper)) +
geom_smooth(method = "lm", formula = y ~ x + I(x^2)) +
ylab("Accuracy") +
xlab("Size Difference") +
theme_classic() +
ggthemes::scale_color_solarized() +
theme(legend.position = "bottom")
Proportion of first-mentioned objects that are focal objects (first_mention_focal), per subject across stimuli. Plot means/CIs and histogram by culture.
# Free Description, first mention
#Proportion focal object first mention
mention_ms <- tidy_d %>%
filter(task_name == "FD") %>%
filter(resp_type=="first_mention_focal") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) %>%
group_by(culture) %>%
tidyboot_mean(mean, na.rm=T)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)## Warning: `cols` is now required when using unnest().
## Please use `cols = c(strap)`mention_mss <- tidy_d %>%
filter(task_name == "FD") %>%
filter(resp_type=="first_mention_focal") %>%
group_by(subject, culture) %>%
summarise(mean = mean(resp)) ## `summarise()` regrouping output by 'subject' (override with `.groups` argument)#plot first mention proportions, averaged by subject and then culture
ggplot(mention_ms,
aes(x = culture, y = mean, col = culture)) +
geom_violin(data = mention_mss, show.legend = FALSE, bw = 0.08)+
geom_jitter(data = mention_mss, width = .2, alpha = 0.2) +
geom_pointrange(aes(ymin = ci_lower, ymax = ci_upper), show.legend = FALSE) +
guides(colour = guide_legend(override.aes = list(alpha = 1)))+
scale_y_continuous(breaks = seq(0,1,0.25),
labels = {function(x) paste0(as.character(x*100),"%")})+
ylab("Proportion of focal object first mentions") +
xlab("Country") +
labs(color='Country') +
theme_classic() +
scale_color_manual(values = c("red", "blue"))+
theme(legend.position = "bottom")
Description score (bckgrd_description - focal_description), per subject across stimuli. Plot means/CIs and histogram by culture.
# missing b/c of no coding criteriaThere are no significant differences between cultures on the proportion of first mention being focal objects
FD_item_proportion
Also coded proportion of participants mentioning the picture looks “photoshopped” / faked
FD_item_psmention
Also roughly estimated the average description length for each item
FD_item_descriptionlength