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Social Anxiety Predicts Enhanced Electrocortical Sensitivity to the Mental State Meanings Revealed by Gaze Patterns.

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  1. #############################################################################################################
  2. ##### SOCIAL ANXIETY PREDICTS THE TIMELINESS OF GAZE MEANING DETECTION: AN ELECTROCORTICAL INVESTIGATION ####
  3. ################## IRENE MELANI, MARTHA K. BERG, JAMES GLAZER, AND SHINOBU KITAYAMA #########################
  4. #############################################################################################################
  6. #load packages
  7. library(psych)
  8. library(lme4)
  9. library(lmerTest)
  10. library(ggplot2)
  11. library(emmeans)
  12. library(tidyverse)
  13. library(dplyr)
  14. library(Hmisc)
  15. library(patchwork)
  17. #set path
  18. setwd('C:\\Users\\irene\\Documents\\Michigan\\RME\\Data\\R Script')
  20. #########################
  21. # Load and prepare data
  22. #########################
  24. #load data
  25. RME <- read.csv('Data_RME.csv')
  26. CTRL <- read.csv('Data_CTRL.csv')
  28. #combine data from RME and Control tasks
  29. all <- rbind(RME, CTRL)
  32. #########################
  33. # Descriptive Statistics
  34. #########################
  36. #Descriptive statistics for RME condition
  37. #Main variables
  38. RME %>% group_by(Subject) %>% summarise(sex_cont = mean(sex_cont)) %>% count(sex_cont)
  39. RME %>%
  40. group_by(Subject) %>%
  41. summarise(
  42. age = mean(age),
  43. socanx = mean(socanx),
  44. accuracy = mean(accuracy),
  45. rt = mean(rt)) %>%
  46. summarise(
  47. age_mean = mean(age), age_sd = sd(age),
  48. socanx_mean = mean(socanx), socanx_sd = sd(socanx),
  49. acc_mean = mean(accuracy), acc_sd = sd(accuracy),
  50. rt_mean = mean(rt), rt_sd = sd(rt))
  51. #Supplementary variables
  52. RME %>%
  53. group_by(Subject) %>%
  54. summarise(
  55. indep = mean(indep),
  56. interdep = mean(interdep),
  57. open = mean(open),
  58. conscient = mean(conscient),
  59. extro = mean(extro),
  60. agree = mean(agree),
  61. neurot = mean(neurot)) %>%
  62. summarise(
  63. indep_mean = mean(indep, na.rm = TRUE), indep_sd = sd(indep, na.rm = TRUE),
  64. interdep_mean = mean(interdep, na.rm = TRUE), interdep_sd = sd(interdep, na.rm = TRUE),
  65. open_mean = mean(open), open_sd = sd(open),
  66. conscient_mean = mean(conscient), conscient_sd = sd(conscient),
  67. extro_mean = mean(extro), extro_sd = sd(extro),
  68. agree_mean = mean(agree), agree_sd = sd(agree),
  69. neurot_mean = mean(neurot), neurot_sd = sd(neurot))
  71. #Descriptive statistics for Control condition
  72. #Main variables
  73. CTRL %>% group_by(Subject) %>% summarise(sex_cont = mean(sex_cont)) %>% count(sex_cont)
  74. CTRL %>%
  75. group_by(Subject) %>%
  76. summarise(
  77. age = mean(age),
  78. socanx = mean(socanx),
  79. accuracy = mean(accuracy),
  80. rt = mean(rt)) %>%
  81. summarise(
  82. age_mean = mean(age), age_sd = sd(age),
  83. socanx_mean = mean(socanx), socanx_sd = sd(socanx),
  84. acc_mean = mean(accuracy), acc_sd = sd(accuracy),
  85. rt_mean = mean(rt), rt_sd = sd(rt))
  86. #Supplementary variables
  87. CTRL %>%
  88. group_by(Subject) %>%
  89. summarise(
  90. indep = mean(indep),
  91. interdep = mean(interdep),
  92. open = mean(open),
  93. conscient = mean(conscient),
  94. extro = mean(extro),
  95. agree = mean(agree),
  96. neurot = mean(neurot)) %>%
  97. summarise(
  98. indep_mean = mean(indep, na.rm = TRUE), indep_sd = sd(indep, na.rm = TRUE),
  99. interdep_mean = mean(interdep, na.rm = TRUE), interdep_sd = sd(interdep, na.rm = TRUE),
  100. open_mean = mean(open), open_sd = sd(open),
  101. conscient_mean = mean(conscient), conscient_sd = sd(conscient),
  102. extro_mean = mean(extro), extro_sd = sd(extro),
  103. agree_mean = mean(agree), agree_sd = sd(agree),
  104. neurot_mean = mean(neurot), neurot_sd = sd(neurot))
  106. #Compare descriptive statistics across tasks
  107. #Main variables
  108. prop.test(x = c(43, 25), n = c(57, 50))
  109. all %>% group_by(Subject, task) %>% summarise(age = mean(age)) %>% t.test(age ~ task, data = .)
  110. all %>% group_by(Subject, task) %>% summarise(socanx = mean(socanx)) %>% t.test(socanx ~ task, data = .)
  111. all %>% group_by(Subject, task) %>% summarise(accuracy = mean(accuracy)) %>% t.test(accuracy ~ task, data = .)
  112. all %>% group_by(Subject, task) %>% summarise(rt = mean(rt)) %>% t.test(rt ~ task, data = .)
  113. #Supplementary variables
  114. all %>% group_by(Subject, task) %>% summarise(indep = mean(indep)) %>% t.test(indep ~ task, data = .)
  115. all %>% group_by(Subject, task) %>% summarise(interdep = mean(interdep)) %>% t.test(interdep ~ task, data = .)
  116. all %>% group_by(Subject, task) %>% summarise(open = mean(open)) %>% t.test(open ~ task, data = .)
  117. all %>% group_by(Subject, task) %>% summarise(conscient = mean(conscient)) %>% t.test(conscient ~ task, data = .)
  118. all %>% group_by(Subject, task) %>% summarise(extro = mean(extro)) %>% t.test(extro ~ task, data = .)
  119. all %>% group_by(Subject, task) %>% summarise(agree = mean(agree)) %>% t.test(agree ~ task, data = .)
  120. all %>% group_by(Subject, task) %>% summarise(neurot = mean(neurot)) %>% t.test(neurot ~ task, data = .)
  123. ################
  124. # Main Analyses
  125. ################
  127. #Task Performance
  128. #Predict accuracy from congruence * social anxiety
  129. RME.accmod <- glmer(accuracy ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  130. data = RME, family = binomial)
  131. summary(RME.accmod)
  132. confint(RME.accmod)
  134. CTRL.accmod <- glmer(accuracy ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  135. data = CTRL, family = binomial)
  136. summary(CTRL.accmod)
  137. confint(CTRL.accmod)
  139. #Predict accuracy from congruence * social anxiety * task
  140. all.accmod <- glmer(accuracy ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  141. data = all, family = binomial)
  142. summary(all.accmod)
  143. confint(all.accmod)
  145. #Drop incorrectly answered trials for neural and reaction time analyses
  146. RME.correct <- subset(RME, RME$acc==1) #dropped 1573 trials = 21.71% of eligible trials
  147. CTRL.correct <- subset(CTRL, CTRL$acc==1) #dropped 999 trials = 15.41% of eligible trials
  148. all.correct <- rbind(RME.correct, CTRL.correct)
  150. #Perform analyses on correctly answered trials
  152. #N400 (Cz, CPz, Pz)
  153. #Predict amplitude from congruence * social anxiety
  154. RME.erpmod <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  155. data = RME.correct, REML = TRUE)
  156. summary(RME.erpmod)
  157. confint(RME.erpmod)
  159. CTRL.erpmod <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  160. data = CTRL.correct, REML = TRUE)
  161. summary(CTRL.erpmod)
  162. confint(CTRL.erpmod)
  164. #Predict amplitude from congruence * social anxiety * task
  165. all.erpmod <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  166. data = all.correct, REML = TRUE)
  167. summary(all.erpmod)
  168. confint(all.erpmod)
  171. #Response Time
  172. #Predict RT from congruence * social anxiety
  173. RME.rtmod <- lmer(rt ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  174. data = RME.correct, REML = TRUE)
  175. summary(RME.rtmod)
  176. confint(RME.rtmod)
  178. CTRL.rtmod <- lmer(rt ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  179. data = CTRL.correct, REML = TRUE)
  180. summary(CTRL.rtmod)
  181. confint(CTRL.rtmod)
  183. #Predict RT from congruence * social anxiety * task
  184. all.rtmod <- lmer(rt ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  185. data = all.correct, REML = TRUE)
  186. summary(all.rtmod)
  187. confint(all.rtmod)
  189. ##############
  190. # Plot Figure
  191. ##############
  193. #Increase computing power so that the subsequent command can be run
  194. emm_options(pbkrtest.limit = 11158, lmerTest.limit = 11158)
  195. #Compute estimated marginal means from the ERP model for plotting
  196. figure_pred <- emmeans(all.erpmod, ~c_socanx * congruence_cont * task,
  197. at = list(c_socanx = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5),
  198. congruence_cont = c(-0.5, 0.5),
  199. task = c(0.5, -0.5))) %>%
  200. as.data.frame() %>%
  201. mutate(Congruence = congruence_cont %>% recode(`-0.5` = "Incongruent", `0.5` = "Congruent")) %>%
  202. mutate(Condition = task %>% recode(`0.5` = "RME Task", `-0.5` = "Control Task"))
  204. #Create figure
  205. #RME Task plot
  206. figure_rme <- figure_pred %>%
  207. filter(Condition == "RME Task") %>%
  208. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  209. geom_line(linewidth = 1.1) +
  210. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  211. scale_fill_grey(start = 0.3, end = 0.7) +
  212. scale_color_grey(start = 0.3, end = 0.7) +
  213. scale_y_reverse(limits = c(4, -5)) +
  214. theme_classic() +
  215. labs(title = "RME Task",
  216. x = "Social anxiety (centered)",
  217. y = expression(atop("N400 amplitude (" * mu * "V)", "Midline centroparietal (Cz, CPz, Pz)"))) +
  218. theme(axis.title.x = element_text(size = 14),
  219. axis.title.y = element_text(size = 14),
  220. axis.text.x = element_text(size = 12),
  221. axis.text.y = element_text(size = 12),
  222. legend.position = "none")
  223. figure_rme
  224. #Control Task plot
  225. figure_control <- figure_pred %>%
  226. filter(Condition == "Control Task") %>%
  227. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  228. geom_line(linewidth = 1.1) +
  229. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  230. scale_fill_grey(start = 0.3, end = 0.7) +
  231. scale_color_grey(start = 0.3, end = 0.7) +
  232. scale_y_reverse(limits = c(4, -5)) +
  233. theme_classic() +
  234. labs(title = "Control Task",
  235. x = "Social anxiety (centered)",
  236. y = expression(atop("N400 amplitude (" * mu * "V)", "Midline centroparietal (Cz, CPz, Pz)"))) +
  237. theme(axis.title.x = element_text(size = 14),
  238. axis.title.y = element_text(size = 14),
  239. axis.text.x = element_text(size = 12),
  240. axis.text.y = element_text(size = 12),
  241. legend.position = "none")
  242. figure_control
  243. #Combine RME and Control plots
  244. figure_combined <- (figure_rme + figure_control) +
  245. plot_layout(ncol = 2, guides = "collect") &
  246. theme(legend.position = "bottom", legend.title = element_blank(),
  247. legend.text = element_text(size = 11.5))
  248. #Display
  249. figure_combined
  251. #Export figure
  252. ggsave('Figure_1C_251225.png', figure_rme, width = 5, height = 4)
  253. ggsave('Figure_1D_251225.png', figure_control, width = 5, height = 4)
  255. #########################
  256. # Supplementary analyses
  257. #########################
  259. #Test primary model with sex as a moderator
  260. sexmoderator <- lmer(c_erp_avg ~ sex_cont * c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  261. data = RME.correct, REML = TRUE)
  262. summary(sexmoderator)
  263. confint(sexmoderator)
  265. #Test valence interaction in primary model
  266. valence.rme.erpmod <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont * Valence_factor + (1 | Subject) + (1 | FaceNum),
  267. data = RME.correct, REML = TRUE)
  268. summary(valence.rme.erpmod)
  269. confint(valence.rme.erpmod)
  271. #Test primary model controlling for other measures
  273. #Independent self-construal
  274. indep <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  275. c_indep:congruence_cont + c_indep +
  276. (1 | Subject) + (1 | FaceNum),
  277. data = RME.correct, REML = TRUE)
  278. summary(indep)
  279. confint(indep)
  281. #Interdependent self-construal
  282. interdep <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  283. c_interdep:congruence_cont + c_interdep +
  284. (1 | Subject) + (1 | FaceNum),
  285. data = RME.correct, REML = TRUE)
  286. summary(interdep)
  287. confint(interdep)
  289. #Openness to experience
  290. open <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  291. c_open:congruence_cont + c_open +
  292. (1 | Subject) + (1 | FaceNum),
  293. data = RME.correct, REML = TRUE)
  294. summary(open)
  295. confint(open)
  297. #Conscientiousness
  298. conscient <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  299. c_conscient:congruence_cont + c_conscient +
  300. (1 | Subject) + (1 | FaceNum),
  301. data = RME.correct, REML = TRUE)
  302. summary(conscient)
  303. confint(conscient)
  305. #Extraversion
  306. extro <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  307. c_extro:congruence_cont + c_extro +
  308. (1 | Subject) + (1 | FaceNum),
  309. data = RME.correct, REML = TRUE)
  310. summary(extro)
  311. confint(extro)
  313. #Agreeableness
  314. agree <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  315. c_agree:congruence_cont + c_agree +
  316. (1 | Subject) + (1 | FaceNum),
  317. data = RME.correct, REML = TRUE)
  318. summary(agree)
  319. confint(agree)
  321. #Neuroticism
  322. neurot <- lmer(c_erp_avg ~ sex_cont + c_socanx * congruence_cont +
  323. c_neurot:congruence_cont + c_neurot +
  324. (1 | Subject) + (1 | FaceNum),
  325. data = RME.correct, REML = TRUE)
  326. summary(neurot)
  327. confint(neurot)
  330. #N400 (Centroparietal [including lateral and midline]: Cz, C3, C4, CPz, CP1, CP2, CP5, CP6, Pz, P3, P4, P7, P8)
  331. #Predict amplitude from congruence * social anxiety
  332. RME.erp.centroparietal.mod <- lmer(c_n400_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  333. data = RME.correct, REML = TRUE)
  334. summary(RME.erp.centroparietal.mod)
  335. confint(RME.erp.centroparietal.mod)
  337. CTRL.erp.centroparietal.mod <- lmer(c_n400_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  338. data = CTRL.correct, REML = TRUE)
  339. summary(CTRL.erp.centroparietal.mod)
  340. confint(CTRL.erp.centroparietal.mod)
  342. #Predict amplitude from congruence * social anxiety * task
  343. all.erp.centroparietal.mod <- lmer(c_n400_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  344. data = all.correct, REML = TRUE)
  345. summary(all.erp.centroparietal.mod)
  346. confint(all.erp.centroparietal.mod)
  349. #LPP (Cz, CPz, Pz)
  350. #Predict amplitude from congruence * social anxiety
  351. RME.lpp.czcpzpz.mod <- lmer(c_lpp_cz_cpz_pz_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  352. data = RME.correct, REML = TRUE)
  353. summary(RME.lpp.czcpzpz.mod)
  354. confint(RME.lpp.czcpzpz.mod)
  356. CTRL.lpp.czcpzpz.mod <- lmer(c_lpp_cz_cpz_pz_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  357. data = CTRL.correct, REML = TRUE)
  358. summary(CTRL.lpp.czcpzpz.mod)
  359. confint(CTRL.lpp.czcpzpz.mod)
  361. #Predict amplitude from congruence * social anxiety * task
  362. all.lpp.czcpzpz.mod <- lmer(c_lpp_cz_cpz_pz_avg ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  363. data = all.correct, REML = TRUE)
  364. summary(all.lpp.czcpzpz.mod)
  365. confint(all.lpp.czcpzpz.mod)
  368. #LPP (Centroparietal [including lateral and midline]: Cz, C3, C4, CPz, CP1, CP2, CP5, CP6, Pz, P3, P4, P7, P8)
  369. #Predict amplitude from congruence * social anxiety
  370. RME.lpp.centroparietal.mod <- lmer(c_lpp_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  371. data = RME.correct, REML = TRUE)
  372. summary(RME.lpp.centroparietal.mod)
  373. confint(RME.lpp.centroparietal.mod)
  375. CTRL.lpp.centroparietal.mod <- lmer(c_lpp_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont + (1 | Subject) + (1 | FaceNum),
  376. data = CTRL.correct, REML = TRUE)
  377. summary(CTRL.lpp.centroparietal.mod)
  378. confint(CTRL.lpp.centroparietal.mod)
  380. #Predict amplitude from congruence * social anxiety * task
  381. all.lpp.centroparietal.mod <- lmer(c_lpp_centroparietal_avg ~ sex_cont + c_socanx * congruence_cont * task + (1 | Subject) + (1 | FaceNum),
  382. data = all.correct, REML = TRUE)
  383. summary(all.lpp.centroparietal.mod)
  384. confint(all.lpp.centroparietal.mod)
  387. ###########################
  388. # Plot Supplemental Figure
  389. ###########################
  391. #N400 (Centroparietal [including lateral and midline])
  393. #Increase computing power so that the subsequent command can be run
  394. #Compute estimated marginal means from the ERP model for plotting
  395. figure_pred_n400_centroparietal <- emmeans(all.erp.centroparietal.mod, ~c_socanx * congruence_cont * task,
  396. at = list(c_socanx = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5),
  397. congruence_cont = c(-0.5, 0.5),
  398. task = c(0.5, -0.5))) %>%
  399. as.data.frame() %>%
  400. mutate(Congruence = congruence_cont %>% recode(`-0.5` = "Incongruent", `0.5` = "Congruent")) %>%
  401. mutate(Condition = task %>% recode(`0.5` = "RME Task", `-0.5` = "Control Task"))
  403. #Create figure
  404. #RME Task plot
  405. figure_rme_n400_centroparietal <- figure_pred_n400_centroparietal %>%
  406. filter(Condition == "RME Task") %>%
  407. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  408. geom_line(linewidth = 1.1) +
  409. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  410. scale_fill_grey(start = 0.3, end = 0.7) +
  411. scale_color_grey(start = 0.3, end = 0.7) +
  412. scale_y_reverse(limits = c(4, -4)) +
  413. theme_classic() +
  414. labs(title = "RME Task",
  415. x = "Social anxiety (centered)",
  416. y = expression(atop("N400 amplitude (" * mu * "V)", "Midline and lateral centroparietal"))) +
  417. theme(axis.title.x = element_text(size = 14),
  418. axis.title.y = element_text(size = 14),
  419. axis.text.x = element_text(size = 12),
  420. axis.text.y = element_text(size = 12),
  421. legend.position = "none")
  422. figure_rme_n400_centroparietal
  423. #Control Task plot
  424. figure_control_n400_centroparietal <- figure_pred_n400_centroparietal %>%
  425. filter(Condition == "Control Task") %>%
  426. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  427. geom_line(linewidth = 1.1) +
  428. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  429. scale_fill_grey(start = 0.3, end = 0.7) +
  430. scale_color_grey(start = 0.3, end = 0.7) +
  431. scale_y_reverse(limits = c(4, -4)) +
  432. theme_classic() +
  433. labs(title = "Control Task",
  434. x = "Social anxiety (centered)",
  435. y = expression(atop("N400 amplitude (" * mu * "V)", "Midline and lateral centroparietal"))) +
  436. theme(axis.title.x = element_text(size = 14),
  437. axis.title.y = element_text(size = 14),
  438. axis.text.x = element_text(size = 12),
  439. axis.text.y = element_text(size = 12),
  440. legend.position = "none")
  441. figure_control_n400_centroparietal
  442. #Combine RME and Control plots
  443. figure_combined_n400_centroparietal <- (figure_rme_n400_centroparietal + figure_control_n400_centroparietal) +
  444. plot_layout(ncol = 2, guides = "collect") &
  445. theme(legend.position = "bottom", legend.title = element_blank(),
  446. legend.text = element_text(size = 11.5))
  447. #Display
  448. figure_combined_n400_centroparietal
  450. #Export figure
  451. ggsave('Figure_S1C_251225.png', figure_rme_n400_centroparietal, width = 5, height = 4)
  452. ggsave('Figure_S1D_251225.png', figure_control_n400_centroparietal, width = 5, height = 4)
  455. #LPP (Centroparietal midline: Cz, CPz, Pz)
  457. #Compute estimated marginal means from the ERP model for plotting
  458. figure_pred_lpp_czcpzpz <- emmeans(all.lpp.czcpzpz.mod, ~c_socanx * congruence_cont * task,
  459. at = list(c_socanx = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5),
  460. congruence_cont = c(-0.5, 0.5),
  461. task = c(0.5, -0.5))) %>%
  462. as.data.frame() %>%
  463. mutate(Congruence = congruence_cont %>% recode(`-0.5` = "Incongruent", `0.5` = "Congruent")) %>%
  464. mutate(Condition = task %>% recode(`0.5` = "RME Task", `-0.5` = "Control Task"))
  466. #Create figure
  467. #RME Task plot
  468. figure_rme_lpp_czcpzpz <- figure_pred_lpp_czcpzpz %>%
  469. filter(Condition == "RME Task") %>%
  470. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  471. geom_line(linewidth = 1.1) +
  472. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  473. scale_fill_grey(start = 0.3, end = 0.7) +
  474. scale_color_grey(start = 0.3, end = 0.7) +
  475. scale_y_continuous(limits = c(-3.7, 5.0)) +
  476. theme_classic() +
  477. labs(title = "",
  478. x = "Social anxiety (centered)",
  479. y = expression(atop("LPP amplitude (" * mu * "V)", "Midline centroparietal (Cz, CPz, Pz)"))) +
  480. theme(axis.title.x = element_text(size = 14),
  481. axis.title.y = element_text(size = 14),
  482. axis.text.x = element_text(size = 12),
  483. axis.text.y = element_text(size = 12),
  484. legend.position = "none")
  485. figure_rme_lpp_czcpzpz
  486. #Control Task plot
  487. figure_control_lpp_czcpzpz <- figure_pred_lpp_czcpzpz %>%
  488. filter(Condition == "Control Task") %>%
  489. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  490. geom_line(linewidth = 1.1) +
  491. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  492. scale_fill_grey(start = 0.3, end = 0.7) +
  493. scale_color_grey(start = 0.3, end = 0.7) +
  494. scale_y_continuous(limits = c(-3.7, 5.0)) +
  495. theme_classic() +
  496. labs(title = "",
  497. x = "Social anxiety (centered)",
  498. y = expression(atop("LPP amplitude (" * mu * "V)", "Midline centroparietal (Cz, CPz, Pz)"))) +
  499. theme(axis.title.x = element_text(size = 14),
  500. axis.title.y = element_text(size = 14),
  501. axis.text.x = element_text(size = 12),
  502. axis.text.y = element_text(size = 12),
  503. legend.position = "none")
  504. figure_control_lpp_czcpzpz
  505. #Combine RME and Control plots
  506. figure_combined_lpp_czcpzpz <- (figure_rme_lpp_czcpzpz + figure_control_lpp_czcpzpz) +
  507. plot_layout(ncol = 2, guides = "collect") &
  508. theme(legend.position = "bottom", legend.title = element_blank(),
  509. legend.text = element_text(size = 11.5))
  510. #Display
  511. figure_combined_lpp_czcpzpz
  513. #Export figure
  514. ggsave('Figure_S3AB_251228.png', figure_combined_lpp_czcpzpz, width = 8, height = 4)
  517. #LPP (Centroparietal [including lateral and midline])
  519. #Compute estimated marginal means from the ERP model for plotting
  520. figure_pred_lpp_centroparietal <- emmeans(all.lpp.centroparietal.mod, ~c_socanx * congruence_cont * task,
  521. at = list(c_socanx = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5),
  522. congruence_cont = c(-0.5, 0.5),
  523. task = c(0.5, -0.5))) %>%
  524. as.data.frame() %>%
  525. mutate(Congruence = congruence_cont %>% recode(`-0.5` = "Incongruent", `0.5` = "Congruent")) %>%
  526. mutate(Condition = task %>% recode(`0.5` = "RME Task", `-0.5` = "Control Task"))
  528. #Create figure
  529. #RME Task plot
  530. figure_rme_lpp_centroparietal <- figure_pred_lpp_centroparietal %>%
  531. filter(Condition == "RME Task") %>%
  532. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  533. geom_line(linewidth = 1.1) +
  534. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  535. scale_fill_grey(start = 0.3, end = 0.7) +
  536. scale_color_grey(start = 0.3, end = 0.7) +
  537. scale_y_continuous(limits = c(-3, 4.7)) +
  538. theme_classic() +
  539. labs(title = "A) RME Task",
  540. x = "Social anxiety (centered)",
  541. y = expression(atop("LPP amplitude (" * mu * "V)", "Midline and lateral centroparietal"))) +
  542. theme(axis.title.x = element_text(size = 14),
  543. axis.title.y = element_text(size = 14),
  544. axis.text.x = element_text(size = 12),
  545. axis.text.y = element_text(size = 12),
  546. legend.position = "none")
  547. figure_rme_lpp_centroparietal
  548. #Control Task plot
  549. figure_control_lpp_centroparietal <- figure_pred_lpp_centroparietal %>%
  550. filter(Condition == "Control Task") %>%
  551. ggplot(aes(x = c_socanx, y = emmean, linetype = Congruence)) +
  552. geom_line(linewidth = 1.1) +
  553. geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE, fill = Congruence), alpha = 0.25) +
  554. scale_fill_grey(start = 0.3, end = 0.7) +
  555. scale_color_grey(start = 0.3, end = 0.7) +
  556. scale_y_continuous(limits = c(-3, 4.7)) +
  557. theme_classic() +
  558. labs(title = "B) Control Task",
  559. x = "Social anxiety (centered)",
  560. y = expression(atop("LPP amplitude (" * mu * "V)", "Midline and lateral centroparietal"))) +
  561. theme(axis.title.x = element_text(size = 14),
  562. axis.title.y = element_text(size = 14),
  563. axis.text.x = element_text(size = 12),
  564. axis.text.y = element_text(size = 12),
  565. legend.position = "none")
  566. figure_control_lpp_centroparietal
  567. #Combine RME and Control plots
  568. figure_combined_lpp_centroparietal <- (figure_rme_lpp_centroparietal + figure_control_lpp_centroparietal) +
  569. plot_layout(ncol = 2, guides = "collect") &
  570. theme(legend.position = "bottom", legend.title = element_blank(),
  571. legend.text = element_text(size = 11.5))
  572. #Display
  573. figure_combined_lpp_centroparietal
  575. #Export figure
  576. ggsave('Figure_S3CD_251225.png', figure_combined_lpp_centroparietal, width = 8, height = 4)

SocAnx_ERP_260119.R, no license · at the source

Overview

Authors: Irene Melani1, Martha K Berg1, James Glazer2, Shinobu Kitayama1
ORCID iDs: Irene Melani, Martha K Berg, James Glazer, Shinobu Kitayama
  1. Department of Psychology, University of Michigan, Ann Arbor, Michigan, USA
  2. Department of Psychology, Northwestern University, Evanston, Illinois, USA
Institutions: University of Michigan (United States); Northwestern University (United States)
Journal: Psychophysiology, volume 63, issue 10, article e70409
Dates: received 5 April 2025; accepted 17 September 2026; published online 28 September 2026; in print October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70409 · PMID 42806618 · PMCID PMC13620438 · OpenAlex W7214732656
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), other condition (population)
Methods: Connectivity, Preprocessing, Evoked potentials, Statistics
Keywords: event‐related potential, mental state inference, N400, semantic integration, social anxiety
MeSH: Anxiety*, Cerebral Cortex*, Evoked Potentials*, Fixation, Ocular*, Social Perception*, Theory of Mind*, Adolescent, Adult, Electroencephalography, Female, Humans, Male, Semantics, Young Adult (* major topic)
Topic: Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: National Science Foundation (BCS 0717982, SES 1325881)
Citations: not cited yet (Europe PMC); 32 references in the paper

Abstract

Social anxiety—a fear of social interaction and negative evaluation—may be accompanied by heightened vigilance toward others' mental states, particularly those conveyed through gaze, as they provide cues about how one is being evaluated. We thus investigated whether individual differences in social anxiety predict neural sensitivity to the mental‐state meanings conveyed by gaze patterns. Fifty‐seven European American young adults varying in social anxiety viewed different gaze patterns, immediately followed by a mental‐state word that was either congruent or incongruent with the culturally recognized meaning of the gaze, while their electroencephalogram (EEG) was monitored. Our focus was N400—an event‐related potential (ERP) component indexing semantic expectation violations. We found the N400 amplitude to be more negative for incongruent (vs. congruent) mental‐state words. Notably, this N400 incongruity effect increased significantly with social anxiety. Moreover, the social anxiety effect was specific to mental state inference and was not observed in a comparable N400 incongruity effect involving animal stereotypes (n = 50). These findings suggest that social anxiety may confer enhanced neural sensitivity during the inference of others' mental states from gaze, even in the absence of overt performance differences. We discuss the implications of these findings with respect to the potential functional benefits of social anxiety.

Reproduced under the paper's license (CC BY), from the paper cited above.

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OSF tw5rp

License: none: the authors keep all their rights
State: the link answers, verified on 1 October 2026
Evidence: files inventoried
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), patchwork (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 1 October 2026: the link answers (HTTP 200)
  • 1 October 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/tw5rp/

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

Tracing map

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Data

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Data Availability Statement

Deidentified data and R codes are available on Open Science Framework (https://osf.io/tw5rp/).

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 1 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 14 MeSH terms, 1 funder, 28 references.

Cite

This paper

Melani, I., Berg, M. K., Glazer, J., & Kitayama, S. (2026). Social Anxiety Predicts Enhanced Electrocortical Sensitivity to the Mental State Meanings Revealed by Gaze Patterns. Psychophysiology, 63(10), e70409. https://doi.org/10.1111/psyp.70409

BibTeX

@article{melani2026social,
author = {Melani, Irene and Berg, Martha K and Glazer, James and Kitayama, Shinobu},
title = {{Social Anxiety Predicts Enhanced Electrocortical Sensitivity to the Mental State Meanings Revealed by Gaze Patterns}},
journal = {Psychophysiology},
year = {2026},
month = oct,
volume = {63},
number = {10},
pages = {e70409},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70409},
url = {https://doi.org/10.1111/psyp.70409},
pmid = {42806618},
pmcid = {PMC13620438}
}

RIS

TY - JOUR
AU - Melani, Irene
AU - Berg, Martha K
AU - Glazer, James
AU - Kitayama, Shinobu
TI - Social Anxiety Predicts Enhanced Electrocortical Sensitivity to the Mental State Meanings Revealed by Gaze Patterns
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/10/01
VL - 63
IS - 10
SP - e70409
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70409
UR - https://doi.org/10.1111/psyp.70409
LA - en
ER -

CSL-JSON

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"id": "10.1111/psyp.70409",
"type": "article-journal",
"title": "Social Anxiety Predicts Enhanced Electrocortical Sensitivity to the Mental State Meanings Revealed by Gaze Patterns",
"container-title": "Psychophysiology",
"author": [
{
"family": "Melani",
"given": "Irene"
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"family": "Berg",
"given": "Martha K"
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{
"family": "Glazer",
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"given": "Shinobu"
}
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"container-title-short": "Psychophysiology",
"volume": "63",
"issue": "10",
"page": "e70409",
"DOI": "10.1111/psyp.70409",
"PMID": "42806618",
"PMCID": "PMC13620438",
"ISSN": "0048-5772",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/psyp.70409",
"language": "en",
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"date-parts": [
[
2026,
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1
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]
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}

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