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Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia.

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  1. ```{r setup, include=FALSE, warning=FALSE}
  2. knitr::opts_chunk$set(echo = FALSE)
  3. library(plyr)
  4. library(dplyr)
  5. library(reshape2)
  6. library(ggplot2)
  7. library(effectsize)
  8. library(knitr)
  9. library(RColorBrewer)
  10. library(effsize)
  12. ```
  15. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  17. # load files
  18. cur_wd = " " ### Insert your directory
  19. # load files
  20. df_visits = read.csv(paste0(cur_wd,'CIT_visits.csv'))
  22. df_visits$sub = as.factor(df_visits$sub)
  23. fixation_clean = read.csv(paste0(cur_wd,'CIT_fixations.csv'))
  25. df_visits = df_visits[df_visits$sub!="507",]
  26. df_visits = df_visits[df_visits$sub!="607",]
  27. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="507",]
  28. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="607",]
  29. df_visits = df_visits[df_visits$sub!="510",]
  30. df_visits = df_visits[df_visits$sub!="610",]
  31. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="510",]
  32. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="610",]
  33. df_visits = df_visits[df_visits$sub!="511",]
  34. df_visits = df_visits[df_visits$sub!="611",]
  35. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="511",]
  36. fixation_clean = fixation_clean[fixation_clean$RECORDING_SESSION_LABEL!="611",]
  38. nsub = length(unique(fixation_clean$RECORDING_SESSION_LABEL))
  40. ```
  43. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  45. # prepare data - first visit
  46. df_for_analysis = fixation_clean
  47. df_for_analysis$image_type = ifelse(df_for_analysis$familiar==0,"Unfamiliar",
  48. ifelse(df_for_analysis$answer_in_quest==2,
  49. "Image & name",
  50. "Name only"))
  51. # remove cases of typical participants that did not recognize the image
  52. df_for_analysis = df_for_analysis[!(df_for_analysis$Group=="control" & df_for_analysis$image_type=="Name only"),]
  54. # summary trial
  55. trial_summary_IA = df_for_analysis %>%
  56. group_by(RECORDING_SESSION_LABEL,Group, TRIAL_INDEX,image_type,CURRENT_FIX_INTEREST_AREA_ID) %>%
  57. dplyr::summarise(Visit_time = min(CURRENT_FIX_START))
  59. trial_summary = trial_summary_IA %>%
  60. group_by(RECORDING_SESSION_LABEL,Group,TRIAL_INDEX,image_type) %>%
  61. dplyr::summarise(Visit_time_by_trial = mean(Visit_time))
  63. summary_first_visit_time =
  64. trial_summary %>% group_by(RECORDING_SESSION_LABEL,Group,image_type) %>%
  65. dplyr::summarise(mean_visit_time = mean(Visit_time_by_trial))
  67. summary_first_visit_time$RECORDING_SESSION_LABEL =
  68. as.factor(summary_first_visit_time$RECORDING_SESSION_LABEL)
  70. # prepare data - number of visits & duration
  72. df_for_analysis = df_visits
  73. df_for_analysis$image_type = ifelse(df_for_analysis$familiar==0,"Unfamiliar",
  74. ifelse(df_for_analysis$answer_in_quest==2,
  75. "Image & name",
  76. "Name only"))
  77. df_for_analysis = df_for_analysis[!(df_for_analysis$Group=="control" & df_for_analysis$image_type=="Name only"),]
  79. # summary trial
  80. visit_summary_IA = df_for_analysis %>%
  81. group_by(sub, trial,visitsOrder, Group,image_type) %>%
  82. dplyr::summarise(mean_vistsDur_IA = mean(visitDur, na.rm = T),
  83. num_visitCount_IA = n())
  85. visit_summary_trial = visit_summary_IA %>%
  86. group_by(sub, trial, Group,image_type) %>%
  87. dplyr::summarise(mean_vistsDur_trial = mean(mean_vistsDur_IA, na.rm = T),
  88. num_visitCount_trial = mean(num_visitCount_IA))
  90. summary_n_visits_and_duration = visit_summary_trial %>% group_by(sub, Group,image_type) %>%
  91. dplyr::summarise(mean_vistsDur = mean(mean_vistsDur_trial, na.rm = T),
  92. num_visitCount = mean(num_visitCount_trial))
  94. summary_n_visits_and_duration$sub = as.factor(summary_n_visits_and_duration$sub)
  96. # combine datasets
  97. summary = merge(summary_first_visit_time,summary_n_visits_and_duration,
  98. by.x = c("RECORDING_SESSION_LABEL","Group","image_type"),
  99. by.y = c("sub","Group","image_type"))
  100. summary$RECORDING_SESSION_LABEL = as.factor(summary$RECORDING_SESSION_LABEL)
  102. df_plot = summary %>%
  103. group_by(Group,image_type) %>%
  104. dplyr::summarise(first_visit_time_mean=mean(mean_visit_time),
  105. first_visit_time_sd=sd(mean_visit_time),
  106. visit_duration_mean = mean(mean_vistsDur),
  107. visit_duration_sd = sd(mean_vistsDur),
  108. number_of_visits_mean = mean(num_visitCount),
  109. number_of_visits_sd = sd(num_visitCount))
  110. ```
  112. # First visit time
  114. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  115. ggplot(df_plot, aes(x = Group, y = first_visit_time_mean, fill = image_type)) +
  116. geom_bar(stat = "identity",
  117. position = position_dodge(0.8),
  118. alpha = 0.6,
  119. width = c(0.7,0.7,0.7,0.5,0.5)) +
  120. # 🔑 Add raw participant points
  121. geom_jitter(
  122. data = summary,
  123. aes(x = Group, y = mean_visit_time, color = image_type),
  124. position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.8),
  125. #alpha = 0.8,
  126. size = 2,
  127. inherit.aes = FALSE
  128. ) +
  129. geom_errorbar(aes(
  130. ymin = first_visit_time_mean - first_visit_time_sd/sqrt(nsub),
  131. ymax = first_visit_time_mean + first_visit_time_sd/sqrt(nsub)
  132. ),
  133. width = 0.2,
  134. position = position_dodge(0.8)) +
  135. coord_cartesian(ylim = c(500, 1500)) +
  136. theme_minimal() +
  137. scale_fill_manual(values = c(
  138. "Unfamiliar" = "#add8e6", # Light blue
  139. "Name only" = "#4682b4", # Medium blue
  140. "Image & name" = "#00008b" # Dark blue
  141. )) +
  142. scale_color_manual(values = c(
  143. "Unfamiliar" = "#add8e6", # Light blue
  144. "Name only" = "#4682b4", # Medium blue
  145. "Image & name" = "#00008b" # Dark blue
  146. ), guide = "none") + # hide point legend
  147. theme(text = element_text(size = 12),
  148. legend.position = "top") +
  149. labs(x = "Group", y = "Mean First Visit Time (ms)", fill = "Image Type")
  151. ggsave("CIT_first_visit_time.pdf", width = 8, height = 6)
  153. ```
  155. # Analysis: control vs. CP
  156. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  158. # aov for CP: familiairty + Group
  160. df_analysis_CP_control = summary[summary$image_type!="Name only",]
  161. aov_visit_time =
  162. aov(mean_visit_time~image_type*Group+Error(RECORDING_SESSION_LABEL/image_type),
  163. data = df_analysis_CP_control)
  164. summary(aov_visit_time)
  166. # t-test for control: familiar vs. unfamiliar
  167. t.test(df_analysis_CP_control$mean_visit_time[
  168. df_analysis_CP_control$image_type=="Unfamiliar" &
  169. df_analysis_CP_control$Group=="control"], df_analysis_CP_control$mean_visit_time[
  170. df_analysis_CP_control$image_type=="Image & name"&
  171. df_analysis_CP_control$Group=="control"], paired = T)
  173. cohen.d(df_analysis_CP_control$mean_visit_time[
  174. df_analysis_CP_control$image_type=="Unfamiliar" &
  175. df_analysis_CP_control$Group=="control"], df_analysis_CP_control$mean_visit_time[
  176. df_analysis_CP_control$image_type=="Image & name"&
  177. df_analysis_CP_control$Group=="control"], paired = T)
  179. t.test(df_analysis_CP_control$mean_visit_time[
  180. df_analysis_CP_control$image_type=="Unfamiliar" &
  181. df_analysis_CP_control$Group=="CP"], df_analysis_CP_control$mean_visit_time[
  182. df_analysis_CP_control$image_type=="Image & name"&
  183. df_analysis_CP_control$Group=="CP"], paired = T)
  185. cohen.d(df_analysis_CP_control$mean_visit_time[
  186. df_analysis_CP_control$image_type=="Unfamiliar" &
  187. df_analysis_CP_control$Group=="CP"], df_analysis_CP_control$mean_visit_time[
  188. df_analysis_CP_control$image_type=="Image & name"&
  189. df_analysis_CP_control$Group=="CP"], paired = T)
  191. ```
  193. # Analysis: Familiar by name (CP only)
  194. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  196. df_analysis_CP = summary[summary$Group=="CP",]
  197. aov_visit_time =
  198. aov(mean_visit_time~image_type+Error(RECORDING_SESSION_LABEL/image_type),
  199. data = df_analysis_CP)
  200. summary(aov_visit_time)
  202. # Image & name vs. Name only
  203. t.test(df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Name only"], paired = T)
  205. cohen.d(df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Name only"], paired = T)
  207. # Unfamiliar vs. Name only
  208. t.test(df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Name only"], paired = T)
  210. cohen.d(df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_visit_time[df_analysis_CP$image_type=="Name only"], paired = T)
  212. ```
  214. ## Visits duration
  215. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  216. ggplot(df_plot, aes(x = Group, y = visit_duration_mean, fill = image_type)) +
  217. geom_bar(stat = "identity",
  218. position = position_dodge(0.8),
  219. alpha = 0.6,
  220. width = c(0.7,0.7,0.7,0.5,0.5)) +
  221. # 🔑 Add raw participant points
  222. geom_jitter(
  223. data = summary,
  224. aes(x = Group, y = mean_vistsDur, color = image_type),
  225. position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.8),
  226. #alpha = 0.8,
  227. size = 2,
  228. inherit.aes = FALSE
  229. ) +
  230. geom_errorbar(aes(
  231. ymin = visit_duration_mean - visit_duration_sd/sqrt(nsub),
  232. ymax = visit_duration_mean + visit_duration_sd/sqrt(nsub)
  233. ),
  234. width = 0.2,
  235. position = position_dodge(0.8)) +
  236. coord_cartesian(ylim = c(200, 650)) +
  237. theme_minimal() +
  238. scale_fill_manual(values = c(
  239. "Unfamiliar" = "#add8e6", # Light blue
  240. "Name only" = "#4682b4", # Medium blue
  241. "Image & name" = "#00008b" # Dark blue
  242. )) +
  243. scale_color_manual(values = c(
  244. "Unfamiliar" = "#add8e6", # Light blue
  245. "Name only" = "#4682b4", # Medium blue
  246. "Image & name" = "#00008b" # Dark blue
  247. ), guide = "none") + # hide point legend
  248. theme(text = element_text(size = 12),
  249. legend.position = "top") +
  250. labs(x = "Group", y = "Mean Visit Duration (ms)", fill = "Image Type")
  252. ggsave("CIT_visit_duration.pdf", width = 8, height = 6)
  254. ```
  257. # Analysis: control vs. CP
  258. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  260. # aov for CP: familiairty + Group
  262. df_analysis_CP_control = summary[summary$image_type!="Name only",]
  263. aov_visit_duration =
  264. aov(mean_vistsDur~image_type*Group+Error(RECORDING_SESSION_LABEL/image_type),
  265. data = df_analysis_CP_control)
  266. summary(aov_visit_duration)
  268. # t-test for control: familiar vs. unfamiliar
  269. t.test(df_analysis_CP_control$mean_vistsDur[
  270. df_analysis_CP_control$image_type=="Unfamiliar" &
  271. df_analysis_CP_control$Group=="control"],
  272. df_analysis_CP_control$mean_vistsDur[
  273. df_analysis_CP_control$image_type=="Image & name"&
  274. df_analysis_CP_control$Group=="control"], paired = T)
  276. cohen.d(df_analysis_CP_control$mean_vistsDur[
  277. df_analysis_CP_control$image_type=="Unfamiliar" &
  278. df_analysis_CP_control$Group=="control"],
  279. df_analysis_CP_control$mean_vistsDur[
  280. df_analysis_CP_control$image_type=="Image & name"&
  281. df_analysis_CP_control$Group=="control"], paired = T)
  283. t.test(df_analysis_CP_control$mean_vistsDur[
  284. df_analysis_CP_control$image_type=="Unfamiliar" &
  285. df_analysis_CP_control$Group=="CP"],
  286. df_analysis_CP_control$mean_vistsDur[
  287. df_analysis_CP_control$image_type=="Image & name"&
  288. df_analysis_CP_control$Group=="CP"], paired = T)
  290. cohen.d(df_analysis_CP_control$mean_vistsDur[
  291. df_analysis_CP_control$image_type=="Unfamiliar" &
  292. df_analysis_CP_control$Group=="CP"],
  293. df_analysis_CP_control$mean_vistsDur[
  294. df_analysis_CP_control$image_type=="Image & name"&
  295. df_analysis_CP_control$Group=="CP"], paired = T)
  299. ```
  301. # Analysis: Familiar by name (CP only)
  302. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  304. df_analysis_CP = summary[summary$Group=="CP",]
  305. aov_visit_duration =
  306. aov(mean_vistsDur~image_type+Error(RECORDING_SESSION_LABEL/image_type),
  307. data = df_analysis_CP)
  308. summary(aov_visit_duration)
  310. # Image & name vs. Name only
  311. t.test(df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Name only"], paired = T)
  313. cohen.d(df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Name only"],paired = T)
  315. # Unfamiliar vs. Name only
  316. t.test(df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Name only"], paired = T)
  318. cohen.d(df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_vistsDur[df_analysis_CP$image_type=="Name only"], paired = T)
  322. ```
  324. ## Number of visits
  325. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  326. ggplot(df_plot, aes(x = Group, y = number_of_visits_mean, fill = image_type)) +
  327. geom_bar(stat = "identity",
  328. position = position_dodge(0.8),
  329. alpha = 0.6,
  330. width = c(0.7,0.7,0.7,0.5,0.5)) +
  331. # 🔑 Add raw participant points
  332. geom_jitter(
  333. data = summary,
  334. aes(x = Group, y = num_visitCount, color = image_type),
  335. position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.8),
  336. #alpha = 0.8,
  337. size = 2,
  338. inherit.aes = FALSE
  339. ) +
  340. geom_errorbar(aes(
  341. ymin = number_of_visits_mean - number_of_visits_sd/sqrt(nsub),
  342. ymax = number_of_visits_mean + number_of_visits_sd/sqrt(nsub)
  343. ),
  344. width = 0.2,
  345. position = position_dodge(0.8)) +
  346. coord_cartesian(ylim = c(1, 3.5)) +
  347. theme_minimal() +
  348. scale_fill_manual(values = c(
  349. "Unfamiliar" = "#add8e6", # Light blue
  350. "Name only" = "#4682b4", # Medium blue
  351. "Image & name" = "#00008b" # Dark blue
  352. )) +
  353. scale_color_manual(values = c(
  354. "Unfamiliar" = "#add8e6", # Light blue
  355. "Name only" = "#4682b4", # Medium blue
  356. "Image & name" = "#00008b" # Dark blue
  357. ), guide = "none") + # hide point legend
  358. theme(text = element_text(size = 12),
  359. legend.position = "top") +
  360. labs(x = "Group", y = "Mean Number of Visits", fill = "Image Type")
  362. ggsave("CIT_visit_count.pdf", width = 8, height = 6)
  363. ```
  364. # Analysis: control vs. CP
  365. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  367. # aov for CP: familiairty + Group
  369. df_analysis_CP_control = summary[summary$image_type!="Name only",]
  370. aov_n_visits =
  371. aov(num_visitCount~image_type*Group+Error(RECORDING_SESSION_LABEL/image_type),
  372. data = df_analysis_CP_control)
  373. summary(aov_n_visits)
  375. # t-test for control: familiar vs. unfamiliar
  376. t.test(df_analysis_CP_control$num_visitCount[
  377. df_analysis_CP_control$image_type=="Unfamiliar" &
  378. df_analysis_CP_control$Group=="control"],
  379. df_analysis_CP_control$num_visitCount[
  380. df_analysis_CP_control$image_type=="Image & name"&
  381. df_analysis_CP_control$Group=="control"], paired = T)
  383. cohen.d(df_analysis_CP_control$num_visitCount[
  384. df_analysis_CP_control$image_type=="Unfamiliar" &
  385. df_analysis_CP_control$Group=="control"],
  386. df_analysis_CP_control$num_visitCount[
  387. df_analysis_CP_control$image_type=="Image & name"&
  388. df_analysis_CP_control$Group=="control"], paired = T)
  390. t.test(df_analysis_CP_control$num_visitCount[
  391. df_analysis_CP_control$image_type=="Unfamiliar" &
  392. df_analysis_CP_control$Group=="CP"],
  393. df_analysis_CP_control$num_visitCount[
  394. df_analysis_CP_control$image_type=="Image & name"&
  395. df_analysis_CP_control$Group=="CP"], paired = T)
  397. cohen.d(df_analysis_CP_control$num_visitCount[
  398. df_analysis_CP_control$image_type=="Unfamiliar" &
  399. df_analysis_CP_control$Group=="CP"],
  400. df_analysis_CP_control$num_visitCount[
  401. df_analysis_CP_control$image_type=="Image & name"&
  402. df_analysis_CP_control$Group=="CP"], paired = T)
  406. ```
  408. # Analysis: Familiar by name (CP only)
  409. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  411. df_analysis_CP = summary[summary$Group=="CP",]
  412. aov_n_visits =
  413. aov(num_visitCount~image_type+Error(RECORDING_SESSION_LABEL/image_type),
  414. data = df_analysis_CP)
  415. summary(aov_n_visits)
  417. # Image & name vs. Name only
  418. t.test(df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Name only"], paired = T)
  420. cohen.d(df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Name only"], paired = T)
  422. # Unfamiliar vs. Name only
  423. t.test(df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Name only"], paired = T)
  425. cohen.d(df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$num_visitCount[df_analysis_CP$image_type=="Name only"], paired = T)
  429. ```
  431. #Behavioral Analysis
  432. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  433. df_behavioral_clean = read.csv(paste0(cur_wd,"CIT_behavioral.csv"))
  435. df_behavioral_clean$image_type = ifelse(is.na(df_behavioral_clean$answer_in_quest),"Unfamiliar",
  436. ifelse(df_behavioral_clean$answer_in_quest==2,
  437. "Image & name",
  438. "Name only"))
  440. df_behavioral_by_sub = df_behavioral_clean %>%
  441. group_by(sub, Group, image_type) %>%
  442. dplyr::summarise(mean_accuracy_by_sub = mean(correct),
  443. mean_RT_by_sub = mean(RT_press))
  445. df_behavioral_by_sub =
  446. df_behavioral_by_sub[!(df_behavioral_by_sub$Group=="control" &
  447. df_behavioral_by_sub$image_type=="Name only"),]
  449. df_behavioral_plot <- df_behavioral_by_sub %>%
  450. group_by(Group, image_type) %>%
  451. dplyr::summarise(
  452. mean_accuracy = mean(mean_accuracy_by_sub),
  453. sd_accuracy = sd(mean_accuracy_by_sub),
  454. mean_RT = mean(mean_RT_by_sub),
  455. sd_RT = sd(mean_RT_by_sub)
  456. )
  457. df_behavioral_by_sub$sub = as.factor(df_behavioral_by_sub$sub)
  458. ```
  460. ## Accuracy
  462. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  464. ggplot(df_behavioral_plot,
  465. aes(x = Group, y = mean_accuracy, fill = image_type)) +
  466. geom_bar(stat = "identity", position = position_dodge(0.8), alpha = 0.6,
  467. width = c(0.7,0.7,0.7,0.5,0.5)) +
  468. #coord_cartesian(ylim = c(500, 1600)) +
  469. geom_errorbar(aes(ymin = mean_accuracy - sd_accuracy,
  470. ymax = mean_accuracy + sd_accuracy),
  471. width = 0.2, position = position_dodge(0.8)) +
  472. geom_hline(yintercept = 0.5, linetype = "dashed", color = "black") + # Add dashed line
  473. theme_minimal() +
  474. scale_fill_manual(values = c("Unfamiliar" = "#add8e6", # Light blue
  475. "Name only" = "#4682b4", # Medium blue
  476. "Image & name" = "#00008b" # Dark blue
  477. ))+
  478. theme(text = element_text(size = 12),
  479. legend.position = "top") + # Optional: Move legend to top for better readability
  480. labs(x = "Group", y = "Mean Accuracy", fill = "Image Type")
  483. ```
  485. # Analysis: control vs. CP
  486. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  488. df_analysis_CP_control = df_behavioral_by_sub[df_behavioral_by_sub$image_type!="Name only",]
  489. aov_accuracy =
  490. aov(mean_accuracy_by_sub~image_type*Group+Error(sub/image_type),
  491. data = df_analysis_CP_control)
  492. summary(aov_accuracy)
  494. # t-test for control: familiar vs. unfamiliar
  495. t.test(df_analysis_CP_control$mean_accuracy_by_sub[
  496. df_analysis_CP_control$image_type=="Unfamiliar" &
  497. df_analysis_CP_control$Group=="control"],
  498. df_analysis_CP_control$mean_accuracy_by_sub[
  499. df_analysis_CP_control$image_type=="Image & name"&
  500. df_analysis_CP_control$Group=="control"], paired = T)
  502. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  503. df_analysis_CP_control$image_type=="Unfamiliar" &
  504. df_analysis_CP_control$Group=="control"],
  505. df_analysis_CP_control$mean_accuracy_by_sub[
  506. df_analysis_CP_control$image_type=="Image & name"&
  507. df_analysis_CP_control$Group=="control"], paired = T)
  509. t.test(df_analysis_CP_control$mean_accuracy_by_sub[
  510. df_analysis_CP_control$image_type=="Unfamiliar" &
  511. df_analysis_CP_control$Group=="CP"],
  512. df_analysis_CP_control$mean_accuracy_by_sub[
  513. df_analysis_CP_control$image_type=="Image & name"&
  514. df_analysis_CP_control$Group=="CP"], paired = T)
  516. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  517. df_analysis_CP_control$image_type=="Unfamiliar" &
  518. df_analysis_CP_control$Group=="CP"],
  519. df_analysis_CP_control$mean_accuracy_by_sub[
  520. df_analysis_CP_control$image_type=="Image & name"&
  521. df_analysis_CP_control$Group=="CP"], paired = T)
  523. ```
  525. # Analysis: Familiar by name (CP only)
  526. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  528. df_analysis_CP = df_behavioral_by_sub[df_behavioral_by_sub$Group=="CP",]
  529. aov_accuracy =
  530. aov(mean_accuracy_by_sub~image_type+Error(sub/image_type),
  531. data = df_analysis_CP)
  532. summary(aov_accuracy)
  534. # Image & name vs. Name only
  535. t.test(df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  537. cohen.d(df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  539. # Unfamiliar vs. Name only
  540. t.test(df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  542. cohen.d(df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_accuracy_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  546. ```
  548. # Analysis: compare to chance
  549. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  550. t.test(df_analysis_CP_control$mean_accuracy_by_sub[
  551. df_analysis_CP_control$Group=="control" &
  552. df_analysis_CP_control$image_type=="Image & name"], mu = 0.5)
  553. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  554. df_analysis_CP_control$Group=="control" &
  555. df_analysis_CP_control$image_type=="Image & name"], mu = 0.5, f = NA)
  557. t.test(df_analysis_CP_control$mean_accuracy_by_sub[
  558. df_analysis_CP_control$Group=="control" &
  559. df_analysis_CP_control$image_type=="Unfamiliar"], mu = 0.5)
  560. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  561. df_analysis_CP_control$Group=="control" &
  562. df_analysis_CP_control$image_type=="Unfamiliar"], mu = 0.5, f = NA)
  564. t.test(df_analysis_CP_control$mean_accuracy_by_sub[
  565. df_analysis_CP_control$Group=="CP" &
  566. df_analysis_CP_control$image_type=="Image & name"], mu = 0.5)
  567. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  568. df_analysis_CP_control$Group=="CP" &
  569. df_analysis_CP_control$image_type=="Image & name"], mu = 0.5, f = NA)
  571. t.test(df_analysis_CP$mean_accuracy_by_sub[
  572. df_analysis_CP_control$Group=="CP" &
  573. df_analysis_CP_control$image_type=="Unfamiliar"], mu = 0.5)
  574. cohen.d(df_analysis_CP_control$mean_accuracy_by_sub[
  575. df_analysis_CP_control$Group=="CP" &
  576. df_analysis_CP_control$image_type=="Unfamiliar"], mu = 0.5, f = NA)
  578. t.test(df_analysis_CP$mean_accuracy_by_sub[
  579. df_analysis_CP$image_type=="Name only"], mu = 0.5)
  580. cohen.d(df_analysis_CP$mean_accuracy_by_sub[
  581. df_analysis_CP$image_type=="Name only"], mu = 0.5, f = NA)
  584. ```
  587. ## RT
  588. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  590. ggplot(df_behavioral_plot,
  591. aes(x = Group, y = mean_RT, fill = image_type)) +
  592. geom_bar(stat = "identity", position = position_dodge(0.8), alpha = 0.6,
  593. width = c(0.7,0.7,0.7,0.5,0.5)) +
  594. coord_cartesian(ylim = c(500, 1300)) +
  595. geom_errorbar(aes(ymin = mean_RT - sd_RT,
  596. ymax = mean_RT + sd_RT),
  597. width = 0.2, position = position_dodge(0.8)) +
  598. theme_minimal() +
  599. scale_fill_manual(values = c("Unfamiliar" = "#add8e6", # Light blue
  600. "Name only" = "#4682b4", # Medium blue
  601. "Image & name" = "#00008b" # Dark blue
  602. ))+
  603. theme(text = element_text(size = 12),
  604. legend.position = "top") + # Optional: Move legend to top for better readability
  605. labs(x = "Group", y = "Mean RT (ms)", fill = "Image Type")
  608. ```
  610. # Analysis: control vs. CP
  611. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  613. df_analysis_CP_control = df_behavioral_by_sub[df_behavioral_by_sub$image_type!="Name only",]
  614. aov_RT =
  615. aov(mean_RT_by_sub~image_type*Group+Error(sub/image_type),
  616. data = df_analysis_CP_control)
  617. summary(aov_RT)
  619. # t-test for control: familiar vs. unfamiliar
  620. t.test(df_analysis_CP_control$mean_RT_by_sub[
  621. df_analysis_CP_control$image_type=="Unfamiliar" &
  622. df_analysis_CP_control$Group=="control"],
  623. df_analysis_CP_control$mean_RT_by_sub[
  624. df_analysis_CP_control$image_type=="Image & name"&
  625. df_analysis_CP_control$Group=="control"], paired = T)
  627. cohen.d(df_analysis_CP_control$mean_RT_by_sub[
  628. df_analysis_CP_control$image_type=="Unfamiliar" &
  629. df_analysis_CP_control$Group=="control"],
  630. df_analysis_CP_control$mean_accuracy_by_sub[
  631. df_analysis_CP_control$image_type=="Image & name"&
  632. df_analysis_CP_control$Group=="control"], paired = T)
  634. t.test(df_analysis_CP_control$mean_RT_by_sub[
  635. df_analysis_CP_control$image_type=="Unfamiliar" &
  636. df_analysis_CP_control$Group=="CP"],
  637. df_analysis_CP_control$mean_RT_by_sub[
  638. df_analysis_CP_control$image_type=="Image & name"&
  639. df_analysis_CP_control$Group=="CP"], paired = T)
  641. cohen.d(df_analysis_CP_control$mean_RT_by_sub[
  642. df_analysis_CP_control$image_type=="Unfamiliar" &
  643. df_analysis_CP_control$Group=="CP"],
  644. df_analysis_CP_control$mean_RT_by_sub[
  645. df_analysis_CP_control$image_type=="Image & name"&
  646. df_analysis_CP_control$Group=="CP"], paired = T)
  648. ```
  650. # Analysis: Familiar by name (CP only)
  651. ```{r echo=FALSE,warning=FALSE, message=FALSE}
  653. df_analysis_CP = df_behavioral_by_sub[df_behavioral_by_sub$Group=="CP",]
  654. aov_RT =
  655. aov(mean_RT_by_sub~image_type+Error(sub/image_type),
  656. data = df_analysis_CP)
  657. summary(aov_RT)
  660. # Image & name vs. Name only
  661. t.test(df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  663. cohen.d(df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Image & name"], df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  665. # Unfamiliar vs. Name only
  666. t.test(df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  668. cohen.d(df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Unfamiliar"], df_analysis_CP$mean_RT_by_sub[df_analysis_CP$image_type=="Name only"], paired = T)
  672. ```

Analysis_CIT_for_paper_OSF.Rmd, no license · at the source

Overview

Authors: Adi Mizrachi1, Oryah Lancry-Dayan2, Yoni Pertzov2, Galia Avidan1
  1. Department of Psychology, Ben-Gurion University of the Negev, Beer Sheba, Israel
  2. Department of Psychology, The Hebrew University of Jerusalem, Jerusalem, Israel
Institutions: Ben-Gurion University of the Negev (Israel); Hebrew University of Jerusalem (Israel)
Journal: Scientific reports, volume 16, issue 1, article 12540
Dates: received 24 April 2025; accepted 28 January 2026; published online 7 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-37933-w · PMID 41794815 · PMCID PMC13087275 · OpenAlex W7134176217
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: behavior only (modality), human (organism), cognitive (subfield)
Methods: Statistics, Preprocessing, Physiology & signal measures
Keywords: Human behaviour, Attention, Learning and memory
MeSH: Facial Recognition*, Fixation, Ocular*, Prosopagnosia*, Recognition, Psychology*, Adult, Attention, Face, Female, Humans, Male, Middle Aged, Pattern Recognition, Visual, Photic Stimulation, Reaction Time, Young Adult (* major topic)
Topic: Face Recognition and Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Israel Science Foundation (2414/20)
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

OSF hpfng

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 6 files, 2 scripts
Software Heritage: not checked
Found in: the text, “Data analysis”
Holds: 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (2 files), reshape2 (2 files), tidyverse (2 files), easystats (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/hpfng/?view_only=386d3c0631f842e6aef7493e1202a354

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Code and data availability statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41598-026-37933-w.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 15 MeSH terms, 1 funder, 38 references.

Cite

This paper

Mizrachi, A., Lancry-Dayan, O., Pertzov, Y., & Avidan, G. (2026). Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia. Scientific reports, 16(1), 12540. https://doi.org/10.1038/s41598-026-37933-w

BibTeX

@article{mizrachi2026gaze,
author = {Mizrachi, Adi and Lancry-Dayan, Oryah and Pertzov, Yoni and Avidan, Galia},
title = {{Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia}},
journal = {Scientific reports},
year = {2026},
month = mar,
volume = {16},
number = {1},
pages = {12540},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-37933-w},
url = {https://doi.org/10.1038/s41598-026-37933-w},
pmid = {41794815},
pmcid = {PMC13087275}
}

RIS

TY - JOUR
AU - Mizrachi, Adi
AU - Lancry-Dayan, Oryah
AU - Pertzov, Yoni
AU - Avidan, Galia
TI - Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/03/07
VL - 16
IS - 1
SP - 12540
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-37933-w
UR - https://doi.org/10.1038/s41598-026-37933-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-37933-w",
"type": "article-journal",
"title": "Gaze dynamics toward familiar and unfamiliar faces in prosopagnosia",
"container-title": "Scientific reports",
"author": [
{
"family": "Mizrachi",
"given": "Adi"
},
{
"family": "Lancry-Dayan",
"given": "Oryah"
},
{
"family": "Pertzov",
"given": "Yoni"
},
{
"family": "Avidan",
"given": "Galia"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "12540",
"DOI": "10.1038/s41598-026-37933-w",
"PMID": "41794815",
"PMCID": "PMC13087275",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-37933-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
7
]
]
}
}

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