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Altered semantic prediction error processing with increasing schizotypal and autistic traits.

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  1. ---
  2. title: "R Notebook"
  3. output:
  4. html_document:
  5. df_print: paged
  6. ---
  7. List of figures:
  8. 1. Mean confidence for entropy conditions split by number of channels
  9. 2. Mean clarity for entropy conditions split by number of channels
  10. 3. Proportion of words, correct words, no words for entropy conditions --> zoom in for nowords --> split by channels
  11. 4. Scatterplot for SPQ and number of nowords --> split by channels
  13. # load packages
  14. ```{r}
  15. library(ggplot2)
  16. library(svglite)
  17. library(ggcorrplot)
  18. library(dplyr)
  19. library(ggpubr)
  20. library(RColorBrewer)
  21. library(ggExtra)
  22. library(tidyr)
  23. library(psych)
  24. library(afex)
  25. library(report)
  26. library(apa)
  27. library(ggfortify)
  28. library(emmeans)
  29. library(flextable)
  30. library(stringr)
  31. ```
  33. # read data
  34. ```{r}
  35. df_clean_55 <- read.csv2(".../df_clean_55_allvariables_final_updated.csv", header=TRUE)
  36. ```
  38. # define factors
  39. ```{r}
  40. df_clean_55$participant_private_id<-factor(df_clean_55$participant_private_id)
  41. df_clean_55$condition<-factor(df_clean_55$condition, levels = c("el", "em", "eh", "elmm"))
  42. df_clean_55$channel_nr<-factor(df_clean_55$channel_nr, levels = c("12", "6", "3", "1"))
  43. df_clean_55$word_noword<-factor(df_clean_55$word_noword, levels = c("correctword", "word", "noword"))
  44. ```
  46. # select colours for channels and condition
  47. ```{r}
  48. colours_channels = c('#bfe6c3','#6fcf97','#2f9e44','#1b5e20')
  49. colours_clinicaltraits <- c("#a5a5a5", "#284b63", "#549DA0")
  50. colours_condition = c('#ffadbc', '#ff4769', '#b80022','#660013')
  51. ```
  53. # 1. Mean confidence for entropy conditions split by number of channels
  54. --> x: condition, y: confidence, group by: channel number -> without noword
  56. # 1.1 ANOVA
  57. ```{r}
  58. # Exlude nowords
  59. df_clean_55_wonowords <- df_clean_55 %>%
  60. filter(word_noword == "correctword" | word_noword =="word") %>%
  61. select(participant_private_id, condition, confidence, word_noword, channel_nr) %>%
  62. group_by(participant_private_id, condition, channel_nr, .drop = FALSE) %>%
  63. summarize(mean_confidence = mean(confidence)*100)
  65. # ANOVA
  66. anova_confidence <- aov_ez('participant_private_id', 'mean_confidence', df_clean_55_wonowords, within = c('condition', 'channel_nr'))
  67. anova_confidence
  68. anova_apa(anova_confidence)
  69. summary(anova_confidence)
  70. knitr::kable(nice(anova_confidence, correction = "GG"))
  72. ```
  73. ```{r}
  74. interaction.plot(df_clean_55_wonowords$condition, df_clean_55_wonowords$channel_nr, df_clean_55_wonowords$mean_confidence)
  76. ```
  79. ```{r}
  80. interaction_effect <- emmeans(anova_confidence, ~condition|channel_nr)
  82. interaction_effect
  83. ```
  85. ```{r}
  86. pairs(interaction_effect, adjust = "tukey")
  87. ```
  90. ```{r}
  91. results = as.data.frame(pairs(interaction_effect, adjust = "tukey"))
  92. results <- as.data.frame(lapply(results, function(x) if(is.numeric(x)) round(x, 2) else x))
  94. results_table = flextable(results)
  95. print(results_table, preview = "docx")
  96. ```
  98. # 1.2 Plot
  99. ```{r}
  100. ggplot(data = df_clean_55_wonowords, aes (x = condition, y = mean_confidence)) +
  101. labs(x = "Entropy Condition", y = "Mean Confidence in %") +
  102. #stat_boxplot(geom='errorbar', aes(fill = channel_nr)) +
  103. geom_boxplot(aes(fill = channel_nr)) +
  104. scale_fill_manual(values = colours_condition, name="Entropy Condition", breaks=c("el", "em", "eh", "elmm"), labels=c("low", "medium", "high", "low (mismatch)" )) +
  105. scale_fill_manual(values = colours_channels, name = "Channel Number") +
  106. scale_x_discrete(labels=c("el" = "low", "em" = "medium",
  107. "eh" = "high", "elmm" = "low (mismatch)")) +
  108. scale_y_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  109. theme_minimal() +
  110. theme(axis.text = element_text(size = 21)) +
  111. theme(axis.title = element_text(size = 24)) +
  112. theme(legend.position="none")
  113. ggsave(".../55_confidence_entropy_bychannels_boxplots.svg", width=10, height=6, dpi = 600, device = "svg")
  114. ```
  115. ## Correlations
  116. # SPQ
  117. ```{r}
  118. df_clean_55_spq <- df_clean_55 %>%
  119. #filter(word_noword == "correctword") %>%
  120. select(participant_private_id, confidence, word_noword, condition, total_score_spq) %>%
  121. group_by(participant_private_id, condition, total_score_spq, .drop = FALSE) %>%
  122. summarize(mean_confidence = mean(confidence)*100)
  124. ggscatter(df_clean_55_spq, x = "mean_confidence", y = "total_score_spq",
  125. size = 1.5,
  126. add = "reg.line", # Add regression line
  127. conf.int = TRUE, # Add confidence interval
  128. color = "condition", palette = colours_condition
  129. )+
  130. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  131. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  132. stat_cor(method = "pearson", aes(color = condition), label.x = 0) +
  133. labs(x = "Mean Confidence Rating", y = "SPQ") +
  134. theme_minimal() + theme(legend.position = "none")
  136. ggsave(".../55_scatter_SPQ_confidence.svg", width=6, height=6, dpi = 600, device = "svg")
  137. ```
  138. # AQ
  139. ```{r}
  140. df_clean_55_spq <- df_clean_55 %>%
  141. #filter(word_noword == "correctword") %>%
  142. select(participant_private_id, confidence, word_noword, condition, total_score_aq) %>%
  143. group_by(participant_private_id, condition, total_score_aq, .drop = FALSE) %>%
  144. summarize(mean_confidence = mean(confidence)*100)
  146. ggscatter(df_clean_55_spq, x = "mean_confidence", y = "total_score_aq",
  147. size = 1.5,
  148. add = "reg.line", # Add regression line
  149. conf.int = TRUE, # Add confidence interval
  150. color = "condition", palette = colours_condition
  151. )+
  152. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  153. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  154. stat_cor(method = "pearson", aes(color = condition), label.x = 0) +
  155. labs(x = "Mean Confidence Rating", y = "AQ") +
  156. theme_minimal() + theme(legend.position = "none")
  158. ggsave(".../55_scatter_AQ_confidence.svg", width=6, height=6, dpi = 600, device = "svg")
  159. ```
  160. # VCI
  161. ```{r}
  162. df_clean_55_spq <- df_clean_55 %>%
  163. #filter(word_noword == "correctword") %>%
  164. select(participant_private_id, confidence, word_noword, condition, total_score_SV) %>%
  165. group_by(participant_private_id, condition, total_score_SV, .drop = FALSE) %>%
  166. summarize(mean_confidence = mean(confidence)*100)
  168. ggscatter(df_clean_55_spq, x = "mean_confidence", y = "total_score_SV",
  169. size = 1.5,
  170. add = "reg.line", # Add regression line
  171. conf.int = TRUE, # Add confidence interval
  172. color = "condition", palette = colours_condition
  173. )+
  174. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  175. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  176. stat_cor(method = "pearson", aes(color = condition), label.x = 0) +
  177. labs(x = "Mean Confidence Rating", y = "VCI") +
  178. theme_minimal() + theme(legend.position = "none")
  180. ggsave(".../55_scatter_VCI_confidence.svg", width=6, height=6, dpi = 600, device = "svg")
  181. ```
  185. # 2. Mean clarity for entropy conditions split by number of channels
  186. --> x: condition, y: confidence, group by: channel number -> including nowords
  188. ## 2.1 ANOVA
  189. ```{r}
  190. # data set
  191. df_clean_55_clarity <- df_clean_55 %>%
  192. select(participant_private_id, condition, clarity, word_noword, channel_nr) %>%
  193. group_by(participant_private_id, condition, channel_nr, .drop = FALSE) %>%
  194. summarize(mean_clarity = mean(clarity)*100)
  196. # ANOVA
  197. anova_clarity <- aov_ez('participant_private_id', 'mean_clarity', df_clean_55_clarity, within = c('condition', 'channel_nr'))
  198. anova_clarity
  199. anova_apa(anova_clarity)
  200. summary(anova_clarity)
  201. table_clarity <- nice(anova_clarity, correction = "GG")
  202. table_clarity
  203. knitr::kable(nice(anova_clarity, correction = "GG"))
  204. ```
  205. ```{r}
  206. interaction.plot(df_clean_55_clarity$condition, df_clean_55_clarity$channel_nr, df_clean_55_clarity$mean_clarity)
  208. ```
  211. ```{r}
  212. interaction_effect <- emmeans(anova_clarity, ~condition|channel_nr)
  214. interaction_effect
  215. ```
  216. ```{r}
  217. pairs(interaction_effect, adjust = "tukey")
  218. ```
  221. ```{r}
  222. results = as.data.frame(pairs(interaction_effect, adjust = "tukey"))
  223. results <- as.data.frame(lapply(results, function(x) if(is.numeric(x)) round(x, 2) else x))
  225. results_table = flextable(results)
  226. print(results_table, preview = "docx")
  227. ```
  229. ## 2.2 Plot
  230. ```{r}
  231. ggplot(df_clean_55_clarity, aes (x = condition, y = mean_clarity)) +
  232. labs(x = "Entropy Condition", y = "Mean Clarity in %") +
  233. geom_boxplot(aes(fill = channel_nr)) +
  234. scale_fill_manual(values = colours_condition, name="Entropy Condition", breaks=c("el", "em", "eh", "elmm"), labels=c("low", "medium", "high", "low (mismatch)" )) +
  235. scale_fill_manual(values = colours_channels, name = "Channel Number") +
  236. theme_minimal() +
  237. scale_x_discrete(labels=c("el" = "low", "em" = "medium",
  238. "eh" = "high", "elmm" = "low (mismatch)")) +
  239. scale_y_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  240. theme(axis.text = element_text(size = 21)) +
  241. theme(axis.title = element_text(size = 24)) +
  242. theme(legend.position="none")
  243. ggsave(".../55_clarity_entropy_bychannels_boxplots.svg", width=10, height=6, dpi = 600, device = "svg")
  244. ```
  245. ## Correlations
  246. # SPQ
  247. ```{r}
  248. df_clean_55_spq <- df_clean_55 %>%
  249. #filter(word_noword == "correctword") %>%
  250. select(participant_private_id, clarity, word_noword, condition, total_score_spq) %>%
  251. group_by(participant_private_id, condition, total_score_spq, .drop = FALSE) %>%
  252. summarize(mean_clarity = mean(clarity)*100)
  254. ggscatter(df_clean_55_spq, x = "mean_clarity", y = "total_score_spq",
  255. size = 1.5,
  256. add = "reg.line", # Add regression line
  257. conf.int = TRUE, # Add confidence interval
  258. color = "condition", palette = colours_condition
  259. )+
  260. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  261. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  262. stat_cor(method = "pearson", aes(color = condition), label.x = 75) +
  263. labs(x = "Mean Clarity Rating", y = "SPQ") +
  264. theme_minimal() + theme(legend.position = "none")
  266. ggsave(".../55_scatter_SPQ_clarity.svg", width=6, height=6, dpi = 600, device = "svg")
  267. ```
  268. # AQ
  269. ```{r}
  270. df_clean_55_spq <- df_clean_55 %>%
  271. #filter(word_noword == "correctword") %>%
  272. select(participant_private_id, clarity, word_noword, condition, total_score_aq) %>%
  273. group_by(participant_private_id, condition, total_score_aq, .drop = FALSE) %>%
  274. summarize(mean_clarity = mean(clarity)*100)
  276. ggscatter(df_clean_55_spq, x = "mean_clarity", y = "total_score_aq",
  277. size = 1.5,
  278. add = "reg.line", # Add regression line
  279. conf.int = TRUE, # Add confidence interval
  280. color = "condition", palette = colours_condition
  281. )+
  282. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  283. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  284. stat_cor(method = "pearson", aes(color = condition), label.x = 75) +
  285. labs(x = "Mean Clarity Rating", y = "AQ")+
  286. theme_minimal() + theme(legend.position = "none")
  288. ggsave(".../55_scatter_AQ_clarity.svg", width=6, height=6, dpi = 600, device = "svg")
  289. ```
  290. # VCI
  291. ```{r}
  292. df_clean_55_spq <- df_clean_55 %>%
  293. #filter(word_noword == "correctword") %>%
  294. select(participant_private_id, clarity, word_noword, condition, total_score_SV) %>%
  295. group_by(participant_private_id, condition, total_score_SV, .drop = FALSE) %>%
  296. summarize(mean_clarity = mean(clarity)*100)
  298. ggscatter(df_clean_55_spq, x = "mean_clarity", y = "total_score_SV",
  299. size = 1.5,
  300. add = "reg.line", # Add regression line
  301. conf.int = TRUE, # Add confidence interval
  302. color = "condition", palette = colours_condition
  303. )+
  304. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  305. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  306. stat_cor(method = "pearson", aes(color = condition), label.x = 75) +
  307. labs(x = "Mean Clarity Rating", y = "VCI")+
  308. theme_minimal() + theme(legend.position = "none")
  310. ggsave(".../55_scatter_VCI_clarity.svg", width=6, height=6, dpi = 600, device = "svg")
  311. ```
  312. # 3. Facet Plot: Proportion of responses (correctword, word, noword) by entropy
  314. ## 3.1 ANOVA
  315. ```{r}
  316. # reshape df_clean
  317. df_clean_plots <- df_clean_55 %>%
  318. select(participant_private_id, channel_nr, condition, word_noword, clarity, confidence, total_score_spq, total_score_aq, total_score_SV) %>%
  319. group_by(participant_private_id,condition, word_noword, total_score_spq, total_score_aq, total_score_SV, .drop = FALSE) %>%
  320. summarize(
  321. word_noword_count = length(word_noword)
  322. )
  323. df_clean_plots$percent_perform <- NA
  324. df_clean_plots$percent_perform <- ifelse(df_clean_plots$condition == "em", df_clean_plots$word_noword_count/80*100, df_clean_plots$word_noword_count/40*100 )
  325. #df_clean_plots$percent_perform <- df_clean_plots$word_noword_count/200*100
  326. df_clean_plots$word_noword <- factor(df_clean_plots$word_noword, levels = c("correctword", "word", "noword"))
  327. df_clean_plots$condition<-factor(df_clean_plots$condition, levels = c("el", "em", "eh", "elmm"))
  328. ```
  330. ## Correct responses
  331. ```{r}
  332. df_clean_plots_corr <- df_clean_plots %>% filter(word_noword == "correctword")
  334. # ANOVA
  336. anova_proportionofcorrectresponses <- aov_ez(id = 'participant_private_id', dv ='percent_perform', data = df_clean_plots_corr, within = c('condition'))
  337. #anova_proportionofresponses <- ezANOVA(data = df_clean_plots, dv = .(percent_perform), wid = .(participant_private_id), within = .(condition, word_noword), type = 3, detailed = TRUE)
  339. anova_proportionofcorrectresponses
  340. anova_apa(anova_proportionofcorrectresponses)
  341. summary(anova_proportionofcorrectresponses)
  342. table_proportionofresponses <- nice(anova_proportionofcorrectresponses, correction = "GG")
  343. table_proportionofresponses
  344. knitr::kable(nice(anova_proportionofcorrectresponses, correction = "GG"))
  345. ```
  347. ```{r}
  348. interaction_effect <- emmeans(anova_proportionofcorrectresponses, ~condition)
  350. interaction_effect
  351. ```
  353. ```{r}
  354. pairs(interaction_effect, adjust = "tukey")
  355. ```
  358. ```{r}
  359. results = as.data.frame(pairs(interaction_effect, adjust = "tukey"))
  360. results <- as.data.frame(lapply(results, function(x) if(is.numeric(x)) round(x, 2) else x))
  362. results_table = flextable(results)
  363. print(results_table, preview = "docx")
  364. ```
  366. ## No responses
  367. ```{r}
  368. df_clean_plots_noword <- df_clean_plots %>% filter(word_noword == "noword")
  370. # ANOVA
  372. anova_proportionofnoresponses <- aov_ez(id = 'participant_private_id', dv ='percent_perform', data = df_clean_plots_noword, within = c('condition'))
  373. #anova_proportionofresponses <- ezANOVA(data = df_clean_plots, dv = .(percent_perform), wid = .(participant_private_id), within = .(condition, word_noword), type = 3, detailed = TRUE)
  375. anova_proportionofnoresponses
  376. anova_apa(anova_proportionofnoresponses)
  377. summary(anova_proportionofnoresponses)
  378. table_proportionofresponses <- nice(anova_proportionofnoresponses, correction = "GG")
  379. table_proportionofresponses
  380. knitr::kable(nice(anova_proportionofnoresponses, correction = "GG"))
  381. ```
  383. ```{r}
  384. interaction_effect <- emmeans(anova_proportionofnoresponses, ~condition)
  386. interaction_effect
  387. ```
  389. ```{r}
  390. pairs(interaction_effect, adjust = "tukey")
  391. ```
  394. ```{r}
  395. results = as.data.frame(pairs(interaction_effect, adjust = "tukey"))
  396. results <- as.data.frame(lapply(results, function(x) if(is.numeric(x)) round(x, 2) else x))
  398. results_table = flextable(results)
  399. print(results_table, preview = "docx")
  400. ```
  402. ## Task-based hallucinations / words
  403. ```{r}
  404. df_clean_plots_word <- df_clean_plots %>% filter(word_noword == "word")
  406. # ANOVA
  408. anova_proportionoftbh <- aov_ez(id = 'participant_private_id', dv ='percent_perform', data = df_clean_plots_word, within = c('condition'))
  409. #anova_proportionofresponses <- ezANOVA(data = df_clean_plots, dv = .(percent_perform), wid = .(participant_private_id), within = .(condition, word_noword), type = 3, detailed = TRUE)
  411. anova_proportionoftbh
  412. anova_apa(anova_proportionoftbh)
  413. summary(anova_proportionoftbh)
  414. table_proportionofresponses <- nice(anova_proportionoftbh, correction = "GG")
  415. table_proportionofresponses
  416. knitr::kable(nice(anova_proportionoftbh, correction = "GG"))
  417. ```
  419. ```{r}
  420. interaction_effect <- emmeans(anova_proportionoftbh, ~condition)
  422. interaction_effect
  423. ```
  425. ```{r}
  426. pairs(interaction_effect, adjust = "tukey")
  427. ```
  430. ```{r}
  431. results = as.data.frame(pairs(interaction_effect, adjust = "tukey"))
  432. results <- as.data.frame(lapply(results, function(x) if(is.numeric(x)) round(x, 2) else x))
  434. results_table = flextable(results)
  435. print(results_table, preview = "docx")
  436. ```
  438. ## 3.2 Plot
  439. ```{r}
  440. df_clean_plots <- df_clean_plots %>%
  441. mutate(condition = recode(condition, "el" = "low entropy", "em" = "medium entropy", "eh" = "high entropy", "elmm" = "low entropy (mismatch)"))
  443. df_clean_plots$condition <- factor(df_clean_plots$condition, levels = c("low entropy", "medium entropy", "high entropy", "low entropy (mismatch)"))
  446. ggplot(df_clean_plots, aes (x = word_noword, y = percent_perform)) +
  447. geom_boxplot() +
  448. # scale_fill_manual(values = colours_channels, name = "Number of Channels",
  449. #guide = guide_legend(reverse = FALSE)) +
  450. facet_wrap (~condition, ncol = 2) +
  451. labs(x = "Response Type", y = "Proportion of Response Types in %") +
  452. #scale_y_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  453. scale_x_discrete(labels=c("correctword" = "correct\nword", "noword" = "no\nword", "word" = "incorrect\nword")) +
  454. theme_bw() + theme( panel.grid.major = element_blank(),
  455. panel.grid.minor = element_blank()) +
  456. theme(panel.spacing=unit(1,"lines"))
  457. theme(axis.text = element_text(size = 40)) +
  458. theme(axis.title = element_text(size = 40)) +
  459. theme(strip.text = element_text(size = 40)) +
  460. theme(legend.position="none")
  462. ggsave(".../55_percentage_facet_entropy_boxplots.svg", width=9, height=8, dpi = 600, device = "svg")
  463. ```
  465. # 4. Correlations
  467. ## Correct words
  468. ### SPQ
  469. ```{r}
  470. # reshape df_clean
  471. df_clean_plots_corrwords <- df_clean_plots %>%
  472. filter(word_noword =="correctword")
  474. ggscatter(df_clean_plots_corrwords, x = "percent_perform", y = "total_score_spq",
  475. size = 1.5,
  476. add = "reg.line", # Add regression line
  477. conf.int = TRUE, # Add confidence interval
  478. color = "condition", palette = colours_condition,
  479. conf.int.fill = "#ccccccff"
  480. )+
  481. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  482. #scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  483. stat_cor(aes(color = condition), label.x = 3) +
  484. labs(x = "Proportion of Correct Words in %", y = "SPQ") +
  485. theme_minimal() + theme(legend.position = "none")
  487. ggsave(".../55_scatter_SPQ_correctwords.svg", width=6, height=6, dpi = 600, device = "svg")
  488. ```
  490. ```{r}
  491. ggscatter(df_clean_plots_corrwords, x = "percent_perform", y = "total_score_aq",
  492. size = 1.5,
  493. add = "reg.line", # Add regression line
  494. conf.int = TRUE, # Add confidence interval
  495. color = "condition", palette = colours_condition,
  496. conf.int.fill = "#ccccccff"
  497. )+
  498. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  499. #scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  500. stat_cor(aes(color = condition), label.x = 3) +
  501. labs(x = "Proportion of Correct Words in %", y = "AQ")+
  502. theme_minimal() + theme(legend.position = "none")
  504. ggsave(".../55_scatter_AQ_correctwords.svg", width=6, height=6, dpi = 600, device = "svg")
  505. ```
  507. ```{r}
  508. # reshape df_clean
  510. ggscatter(df_clean_plots_corrwords, x = "percent_perform", y = "total_score_SV",
  511. size = 1.5,
  512. add = "reg.line", # Add regression line
  513. conf.int = TRUE, # Add confidence interval
  514. color = "condition", palette = colours_condition,
  515. conf.int.fill = "#ccccccff"
  516. )+
  517. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  518. #scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  519. stat_cor(aes(color = condition), label.x = 3) +
  520. labs(x = "Proportion of Correct Words in %", y = "VCI")+
  521. theme_minimal() + theme(legend.position = "none")
  523. ggsave(".../55_scatter_VCI_correctwords.svg", width=6, height=6, dpi = 600, device = "svg")
  524. ```
  525. ## No words
  526. ### SPQ
  527. ```{r}
  528. # reshape df_clean
  529. df_clean_plots_nowords <- df_clean_plots %>%
  530. filter(word_noword =="noword")
  532. ggscatter(df_clean_plots_nowords, x = "percent_perform", y = "total_score_spq",
  533. size = 1.5,
  534. add = "reg.line", # Add regression line
  535. conf.int = TRUE, # Add confidence interval
  536. color = "condition", palette = colours_condition,
  537. conf.int.fill = "#ccccccff"
  538. )+
  539. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  540. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  541. stat_cor(aes(color = condition), label.x = 60) +
  542. labs(x = "Proportion of No Words in %", y = "SPQ")+
  543. theme_minimal() + theme(legend.position = "none")
  545. ggsave(".../55_scatter_SPQ_nowords.svg", width=6, height=6, dpi = 600, device = "svg")
  546. ```
  547. ```{r}
  548. ggscatter(df_clean_plots_nowords, x = "percent_perform", y = "total_score_aq",
  549. size = 1.5,
  550. add = "reg.line", # Add regression line
  551. conf.int = TRUE, # Add confidence interval
  552. color = "condition", palette = colours_condition,
  553. conf.int.fill = "#ccccccff"
  554. )+
  555. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  556. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  557. stat_cor(aes(color = condition), label.x = 60) +
  558. labs(x = "Proportion of No Words in %", y = "AQ")+
  559. theme_minimal() + theme(legend.position = "none")
  561. ggsave(".../figures/55_scatter_AQ_nowords.svg", width=6, height=6, dpi = 600, device = "svg")
  562. ```
  564. ```{r}
  565. # reshape df_clean
  567. ggscatter(df_clean_plots_nowords, x = "percent_perform", y = "total_score_SV",
  568. size = 1.5,
  569. add = "reg.line", # Add regression line
  570. conf.int = TRUE, # Add confidence interval
  571. color = "condition", palette = colours_condition,
  572. conf.int.fill = "#ccccccff"
  573. )+
  574. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  575. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  576. stat_cor(aes(color = condition), label.x = 60) +
  577. labs(x = "Proportion of No Words in %", y = "VCI")+
  578. theme_minimal() + theme(legend.position = "none")
  580. ggsave(".../55_scatter_VCI_nowords.svg", width=6, height=6, dpi = 600, device = "svg")
  581. ```
  582. ## Task-based hallucinations
  583. ### SPQ
  584. ```{r}
  585. # reshape df_clean
  586. df_clean_plots_tbh <- df_clean_plots %>%
  587. filter(word_noword =="word")
  589. ggscatter(df_clean_plots_tbh, x = "percent_perform", y = "total_score_spq",
  590. size = 1.5,
  591. add = "reg.line", # Add regression line
  592. conf.int = TRUE, # Add confidence interval
  593. color = "condition", palette = colours_condition,
  594. conf.int.fill = "#ccccccff"
  595. )+
  596. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  597. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  598. stat_cor(aes(color = condition), label.x = 60) +
  599. labs(x = "Proportion of Task-Based Hallucinations in %", y = "SPQ")+
  600. theme_minimal() + theme(legend.position = "none")
  602. ggsave(".../55_scatter_SPQ_words.svg", width=6, height=6, dpi = 600, device = "svg")
  603. ```
  604. ```{r}
  605. ggscatter(df_clean_plots_tbh, x = "percent_perform", y = "total_score_aq",
  606. size = 1.5,
  607. add = "reg.line", # Add regression line
  608. conf.int = TRUE, # Add confidence interval
  609. color = "condition", palette = colours_condition,
  610. conf.int.fill = "#ccccccff"
  611. )+
  612. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  613. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  614. stat_cor(aes(color = condition), label.x = 60) +
  615. labs(x = "Proportion of Task-Based Hallucinations in %", y = "AQ")+
  616. theme_minimal() + theme(legend.position = "none")
  618. ggsave(".../55_scatter_AQ_words.svg", width=6, height=6, dpi = 600, device = "svg")
  619. ```
  621. ```{r}
  622. # reshape df_clean
  624. ggscatter(df_clean_plots_tbh, x = "percent_perform", y = "total_score_SV",
  625. size = 1.5,
  626. add = "reg.line", # Add regression line
  627. conf.int = TRUE, # Add confidence interval
  628. color = "condition", palette = colours_condition,
  629. conf.int.fill = "#ccccccff"
  630. )+
  631. #scale_y_continuous(limits = c(0, 60), breaks = c(0, 10, 20, 30, 40, 50, 60)) +
  632. scale_x_continuous(limits = c(0, 100), breaks = c(0, 25, 50, 75, 100)) +
  633. stat_cor(aes(color = condition), label.x = 60) +
  634. labs(x = "Proportion of Task-Based Hallucinations in %", y = "VCI")+
  635. theme_minimal() + theme(legend.position = "none")
  637. ggsave(".../55_scatter_VCI_words.svg", width=6, height=6, dpi = 600, device = "svg")
  638. ```
  640. # psycholinguistic properties
  641. ```{r}
  642. sentences <- df_clean_55 %>% select(condition, Sentence, target_word, cloze_probability, entropy) %>% distinct()
  643. sentences <- sentences %>% mutate(nwords = str_count(Sentence, "\\S+"))
  644. sentences_summary <- sentences %>%
  645. group_by(condition) %>%
  646. summarise(
  647. mean_cloze = mean(cloze_probability, na.rm = TRUE),
  648. sd_cloze = sd(cloze_probability, na.rm = TRUE),
  649. mean_entropy = mean(entropy, na.rm = TRUE),
  650. sd_entropy = sd(entropy, na.rm = TRUE),
  651. mean_nwords = mean(nwords, na.rm = TRUE),
  652. sd_nwords = sd(nwords, na.rm = TRUE)
  653. ) %>%
  654. mutate(across(where(is.numeric), round, 2))
  655. sentences_summary
  656. ```

behavioural_analyses_plots_osf.Rmd, no license · at the source

Overview

Authors: Elisabeth F Sterner1,2,3, Verena F Demler1,2, Rachel Lenz4, Lucy J MacGregor5, Christoph Mathys6, Franziska Knolle1,2
ORCID iDs: Elisabeth F Sterner, Christoph Mathys, Franziska Knolle
  1. School of Medicine and Health, Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
  2. School of Medicine and Health, TUM-NIC Neuroimaging Center, Technical University of Munich, Munich, Germany
  3. Department of Experimental Psychology, Ludwig-Maximilians University Munich, Munich, Germany
  4. School of Medicine and Health, Department of Anesthesiology and Intensive Care, Technical University of Munich, Munich, Germany
  5. MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
  6. Interacting Minds Centre, Aarhus University, Aarhus, Denmark
Institutions: Technical University of Munich (Germany); Ludwig-Maximilians-Universität München (Germany); MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); Aarhus University (Denmark)
Journal: Translational psychiatry, volume 16, issue 1, article 502
Dates: received 17 February 2026; accepted 11 September 2026; published online 30 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41398-026-04479-4 · PMID 42816505 · PMCID PMC13627722 · OpenAlex W7214992370
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), autism (population), schizophrenia / psychosis (population), cognitive (subfield)
Methods: Preprocessing, Evoked potentials, Connectivity, Statistics
Keywords: Psychiatric disorders, Human behaviour
MeSH: Autism Spectrum Disorder*, Autistic Disorder*, Comprehension*, Schizophrenia*, Schizotypal Personality Disorder*, Semantics*, Adult, Bayes Theorem, Electroencephalography, Evoked Potentials, Female, Humans, Male, Young Adult (* major topic)
Topic: Autism Spectrum Disorder Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (529207886)
Citations: not cited yet (Europe PMC); 99 references in the paper

Abstract

Predictive processing has been proposed as a framework for symptom development in autism (ASD) and schizophrenia (SSD) spectrum disorders, with ASD being associated with an overweighting of (low-level) sensory evidence whereas SSD is characterized by an overweighting of (high-level) prior beliefs, both resulting in altered prediction error processing. This study investigated these hypotheses and their electrophysiological correlates in subclinical expressions of ASD and SSD during language processing. Participants completed an auditory comprehension task that manipulated the precision of high-level semantic prior beliefs and low-level sensory evidence. Hierarchical Bayesian belief-updating modeling and electroencephalography were used to examine whether imbalances in the weighting of prior beliefs and sensory evidence were reflected in altered processing of semantic prediction errors, characterized by mean N400 amplitudes. Computational modeling revealed that increasing schizotypal traits were associated with significant overweighting of prior beliefs, while autistic traits did not show a significant shift. Linear mixed-effect models of mean N400 amplitudes indicated that schizotypy-related overweighting of semantic prior beliefs was reflected in a reduced semantic prediction error signal, indexed by smaller N400 differences between high predictability sentences and both low predictability and high predictability mismatch sentences. A similar but weaker and less distinct pattern emerged for increasing autistic traits. These findings provide converging computational and electrophysiological support for overweighting of semantic prior beliefs with increasing subclinical schizotypy. In contrast, increasing autistic traits were not associated with overweighting of sensory evidence, with electrophysiological results instead suggesting subtle alterations in the weighting of semantic prior beliefs.

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

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OSF 3rv4m

License: none: the authors keep all their rights
State: the link answers, verified on 3 October 2026
Evidence: files inventoried
Languages: R (3)
Size: 6 files, 3 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: easystats (3 files), emmeans (3 files), ggpubr (3 files), tidyverse (3 files), lme4 (2 files), lmerTest (2 files), afex (1 file), ggplot2 (1 file), psych (1 file)
Availability: 1 check, the latest on 3 October 2026: the link answers (HTTP 200)
  • 3 October 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/3rv4m/overview

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Data and code may be accessed through: https://osf.io/3rv4m/overview.

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

Tracing map

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Version 1, 3 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 14 MeSH terms, 1 funder, 91 references.

Cite

This paper

Sterner, E. F., Demler, V. F., Lenz, R., MacGregor, L. J., Mathys, C., & Knolle, F. (2026). Altered semantic prediction error processing with increasing schizotypal and autistic traits. Translational psychiatry, 16(1), 502. https://doi.org/10.1038/s41398-026-04479-4

BibTeX

@article{sterner2026altered,
author = {Sterner, Elisabeth F and Demler, Verena F and Lenz, Rachel and MacGregor, Lucy J and Mathys, Christoph and Knolle, Franziska},
title = {{Altered semantic prediction error processing with increasing schizotypal and autistic traits}},
journal = {Translational psychiatry},
year = {2026},
month = sep,
volume = {16},
number = {1},
pages = {502},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04479-4},
url = {https://doi.org/10.1038/s41398-026-04479-4},
pmid = {42816505},
pmcid = {PMC13627722}
}

RIS

TY - JOUR
AU - Sterner, Elisabeth F
AU - Demler, Verena F
AU - Lenz, Rachel
AU - MacGregor, Lucy J
AU - Mathys, Christoph
AU - Knolle, Franziska
TI - Altered semantic prediction error processing with increasing schizotypal and autistic traits
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/09/30
VL - 16
IS - 1
SP - 502
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04479-4
UR - https://doi.org/10.1038/s41398-026-04479-4
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41398-026-04479-4",
"type": "article-journal",
"title": "Altered semantic prediction error processing with increasing schizotypal and autistic traits",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Sterner",
"given": "Elisabeth F"
},
{
"family": "Demler",
"given": "Verena F"
},
{
"family": "Lenz",
"given": "Rachel"
},
{
"family": "MacGregor",
"given": "Lucy J"
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{
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],
"container-title-short": "Transl Psychiatry",
"volume": "16",
"issue": "1",
"page": "502",
"DOI": "10.1038/s41398-026-04479-4",
"PMID": "42816505",
"PMCID": "PMC13627722",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04479-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
30
]
]
}
}

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