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Attentional processing in individual anxiety: Electrocortical correlates of threat- and safety-related declarative memory formation.

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  1. [1] § Method › Statistical analysis ↔ EEG-SME OSF.R, lines 160–199 · score 0.68 · median split, Kenward Roger, inferential, variable, memory ratings, lme4

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The authors' code

R · 697 lines · 30 KB · no license · 1 match

  1. library(tidyverse)
  2. library(stringr)
  3. library(afex)
  4. library(ez)
  5. library(apa)
  6. require(r2glmm)
  7. require(psych)
  9. options(warn = 1)
  11. # Describe demographics
  12. # Please note for ethical reasons we did not upload the diagnosis information to the OSF,
  13. #please reach out to "[email hidden]" for access to the full demographic data on request.
  15. sampledata <- read.csv("Sampledata_osf.csv")
  17. # Describe demographics
  19. sampledata %>% filter(include_final == 1) %>%
  20. select(ASI_sum_real,mA,Alter,Geschlecht) %>%
  21. describe()
  23. # Correlate things
  25. sampledata %>% filter(include_final == 1) %>%
  26. select(ASI_sum_labor,mA,Alter,Geschlecht,total_remembered,csp_remembered,csm_remembered) %>%
  27. cor() %>%
  28. corrplot::corrplot(method = "number",diag = F, type = "upper",col = 'black', cl.pos = 'n')
  30. # Histogram of ASI values
  31. sampledata %>% filter(include_final == 1) %>%
  32. ggplot(aes(ASI_sum_real)) +
  33. geom_histogram(bins = 10,color="black", fill = "darkgoldenrod3") +
  34. geom_vline(xintercept = mean(sampledata$ASI_sum_real), color= "black") +
  35. geom_vline(xintercept = mean(sampledata$ASI_sum_real)+sd(sampledata$ASI_sum_real), color= "black", linetype=2) +
  36. geom_vline(xintercept = mean(sampledata$ASI_sum_real)-sd(sampledata$ASI_sum_real), color= "black", linetype=2) +
  37. annotate("text", label ="µ = 23.3", x = 30, y = 12, color = "black")+
  38. annotate("text", label ="σ = 16.7", x = 46.5, y = 12, color = "black")+
  39. scale_y_continuous("Count", expand=c(0,0), breaks=c(0,2,4,6,8,10,12)) +
  40. scale_x_continuous("ASI levels", breaks=c(0,10,20,30,40,50,60)) +
  41. theme_classic()
  43. mean(sampledata$ASI_sum_real)
  44. sd(sampledata$ASI_sum_real)
  47. # Behavioral Analysis
  49. # Read Raw Rating Data
  50. ratings <- read.csv2("C:/Users/yas70ym/Documents/Experimente/Experiment JW - EEG SME/Logfiles/Rating/Rating.txt", skip = 4, header=F) %>%
  51. select(-V5) %>%
  52. rename(ID = V1, stim = V2, rating = V3, score = V4) %>% #rename columns
  53. filter(stim != "") %>% #remove empty cells
  54. mutate(ID = str_remove(ID,"_Rating")) %>% #clean up ID
  55. filter(ID != "Test") %>% #Just take the memory ratings
  56. filter(stim != "alle") %>%
  57. mutate(cue = factor(ifelse(str_sub(stim,1,3) == "CS+",2,1)), #recode CS+ -> 2, CS- -> 1
  58. score = as.numeric(score))
  60. # Now take the memory ratings and categorize (if they are correctly remembered)
  62. memory_ratings <- ratings %>% filter(rating == "Sch") %>%
  63. mutate(memorized = ifelse(cue == 2 & score > 2, 1,0), # CS+ was correctly remembered when score was > +2
  64. memorized = ifelse(cue == 1 & score < -2, 1, memorized)) # CS- was correctly remembered when score was < -2
  66. # Calculate number of correctly remembered items, total and per CS
  67. #total_rem <- memory_ratings %>% group_by(ID) %>%
  68. # summarize(total_remembered = sum(memorized))
  70. #cs_rem <- memory_ratings %>% group_by(ID,cue) %>%
  71. # mutate(cue = ifelse(cue == 1,"csm_remembered","csp_remembered")) %>%
  72. # summarize(remembered = sum(memorized)) %>%
  73. # pivot_wider(names_from = cue, values_from = remembered)
  75. # Tests
  77. memory_ratings %>%
  78. left_join(sampledata, by = "ID")%>%
  79. filter(include_final == 1) %>%
  80. group_by(ID,cue) %>%
  81. summarize(remembered = sum(memorized),
  82. mean_score = mean(score)) %>%
  83. group_by(cue) %>%
  84. summarize(remembered_m = mean(remembered)/40,
  85. remembered_sd = sd(remembered)/40,
  86. score_m = mean(mean_score),
  87. score_sd = sd(mean_score))
  89. lm <- memory_ratings %>%
  90. left_join(sampledata, by = "ID")%>%
  91. filter(include_final == 1) %>%
  92. group_by(ID,cue,ASI_sum_labor) %>%
  93. summarize(mean_score = mean(score)) %>%
  94. lmer(mean_score ~ cue * ASI_sum_labor + (1|ID), data = .) #cue, memory, as factors, asi as continuous, id as random
  95. anova(lm,ddf="Kenward-Roger")
  96. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  98. lm <- memory_ratings %>%
  99. left_join(sampledata, by = "ID")%>%
  100. filter(include_final == 1) %>%
  101. group_by(ID,cue,ASI_sum_labor) %>%
  102. summarize(remembered = sum(memorized),
  103. mean_score = mean(score)) %>%
  104. lmer(remembered ~ cue * ASI_sum_labor + (1|ID), data = .)
  105. anova(lm,ddf="Kenward-Roger")
  106. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  108. # Arousal & Valence
  110. # Plots
  111. aro_plot <- ratings %>%
  112. left_join(sampledata, by = "ID") %>%
  113. left_join(memory_ratings %>% select(ID,stim,memorized), by = c("ID","stim")) %>%
  114. filter(include_final == 1) %>%
  115. filter(rating == "Aro") %>%
  116. mutate(memorized = factor(memorized),
  117. ASI_median = factor(ASI_median)) %>%
  118. group_by(ID,cue, memorized, ASI_median) %>%
  119. summarize(mean_score = mean(score)) %>%
  120. group_by(cue,memorized,ASI_median) %>%
  121. summarize(score_m = mean(mean_score),
  122. score_se = sd(mean_score)/sqrt(n())) %>%
  123. ggplot(aes(x = cue, y = score_m, linetype = memorized, fill = ASI_median, group = interaction(ASI_median,memorized))) +
  124. geom_errorbar(aes(ymin = score_m-score_se, ymax=score_m+score_se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  125. geom_line(position = position_dodge(width=0.2)) +
  126. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  127. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  128. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +
  129. scale_x_discrete("",labels=c("CS-","CS+")) +
  130. scale_y_continuous("Arousal [0 - 100]") +
  131. theme_classic() +
  132. theme(legend.position = "none")
  134. val_plot <-ratings %>%
  135. left_join(sampledata, by = "ID") %>%
  136. left_join(memory_ratings %>% select(ID,stim,memorized), by = c("ID","stim")) %>%
  137. filter(include_final == 1) %>%
  138. filter(rating == "Val") %>%
  139. mutate(score = 100-score) %>%
  140. mutate(memorized = factor(memorized),
  141. ASI_median = factor(ASI_median)) %>%
  142. group_by(ID,cue, memorized, ASI_median) %>%
  143. summarize(mean_score = mean(score)) %>%
  144. group_by(cue,memorized,ASI_median) %>%
  145. summarize(score_m = mean(mean_score),
  146. score_se = sd(mean_score)/sqrt(n())) %>%
  147. ggplot(aes(x = cue, y = score_m, linetype = memorized, fill = ASI_median, group = interaction(ASI_median,memorized))) +
  148. geom_errorbar(aes(ymin = score_m-score_se, ymax=score_m+score_se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  149. geom_line(position = position_dodge(width=0.2)) +
  150. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  151. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  152. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +
  153. scale_x_discrete("",labels=c("CS-","CS+")) +
  154. scale_y_continuous("Unpleasantness [0 - 100]") +
  155. theme_classic() +
  156. theme(legend.title = element_blank())
  158. cowplot::plot_grid(aro_plot, val_plot, ncol=2, nrow=1, rel_widths=c(1, 1.3))
  160. # Inferential analysis
  161. # Valence
  162. lm <- ratings %>%
  163. left_join(sampledata, by = "ID")%>%
  164. left_join(memory_ratings %>% select(ID,stim,memorized), by = c("ID","stim")) %>%
  165. filter(include_final == 1) %>%
  166. filter(rating == "Val") %>%
  167. group_by(ID,cue, memorized,ASI_sum_labor) %>%
  168. summarize(mean_score = mean(score)) %>%
  169. lmer(mean_score ~ cue * memorized * ASI_sum_labor + (1|ID), data = .) #cue, memory, as factors, asi as continuous, id as random
  170. anova(lm,ddf="Kenward-Roger")
  171. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  173. # Arousal
  174. lm <- ratings %>%
  175. left_join(sampledata, by = "ID")%>%
  176. left_join(memory_ratings %>% select(ID,stim,memorized), by = c("ID","stim")) %>%
  177. filter(include_final == 1) %>%
  178. filter(rating == "Aro") %>%
  179. group_by(ID,cue, memorized,ASI_sum_labor) %>%
  180. summarize(mean_score = mean(score)) %>%
  181. lmer(mean_score ~ cue * memorized * ASI_sum_labor + (1|ID), data = .)#cue, memory, as factors, asi as continuous, id as random
  182. anova(lm,ddf="Kenward-Roger")
  183. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  186. # Pupil Analysis
  188. # Read preprocessed pupil data, there is memory and some other info already included
  189. ga_unified <- readRDS("C:/Users/yas70ym/Documents/Experimente/Experiment JW - EEG SME/ET_ga_unified.RData")
  190. pupil_df <- readRDS("C:/Users/yas70ym/Documents/Experimente/Experiment JW - EEG SME/ET_pupil_df.Rdata")
  192. # Combine with sampledata info
  193. ga_unified <- ga_unified %>% left_join(sampledata %>% select(-ASI_sum_labor, -Asi_sum_screening), by ="ID")
  194. pupil_df <- pupil_df %>% select(-ASI_sum_labor,-Gruppe) %>% left_join(sampledata %>% select(-Asi_sum_screening), by ="ID")
  196. # Insert ASI median split group in the trial variable for plotting ... this will load some minutes
  197. for (t in 1:nrow(ga_unified)) {
  198. ga_unified$diameter[[t]] = ga_unified$diameter[[t]] %>%
  199. mutate(ASI_median = ga_unified$ASI_median[[t]])}
  201. # Plot grand average
  202. pupil_wave_plot <- ga_unified %>%
  203. filter(include_final == 1) %>%
  204. .$diameter %>% bind_rows() %>%
  205. mutate(condition = as.factor(condition)) %>%
  206. mutate(condition = factor(condition,labels = c("CS-","CS+")),
  207. memorized = factor(memorized),
  208. ASI_median = factor(ASI_median)) %>%
  209. group_by(condition,memorized,samplepoint,ASI_median) %>%
  210. summarise(diameter = mean(diameter), time = mean(time)) %>%
  211. ggplot(., aes(x=time, y=diameter, color=ASI_median, group=interaction(ASI_median,memorized), linetype = memorized)) +
  212. facet_wrap(~condition)+
  213. geom_rect(xmin = 0.5, xmax = 3, ymin = -Inf, ymax = Inf, fill = "#EEEEEE", color = "transparent")+
  214. geom_path() +
  215. geom_hline(yintercept = 0) +
  216. scale_x_continuous("Time [s]")+ #,limits=c(-0.5, 6)) +
  217. scale_color_manual(labels=c("low ASI","high ASI"),
  218. values=c("darkgoldenrod3", "darkblue")) +
  219. scale_linetype_manual(values=c(2,1), labels = c("forgotten","remembered"))+
  220. scale_y_continuous("Pupil Diameter [mm]") +
  221. theme_classic() +
  222. theme(legend.position="bottom",
  223. legend.background = element_rect(fill = "transparent"),
  224. legend.title = element_blank(),
  225. strip.background.x = element_blank())
  227. pupil_plot <- pupil_df %>%
  228. filter(include_final == 1) %>% #take only included subjects
  229. select(ID,ASI_median,condition,memorized,dilation) %>% #this line is technically not necessary
  230. group_by(ID,condition,memorized,ASI_median) %>% # average over single trials
  231. summarise(dilation = mean(dilation)) %>% ungroup() %>%
  232. mutate(cue = factor(condition,labels = c("csm","csp")), #factorize factors
  233. memorized = factor(memorized),
  234. ASI_median = factor(ASI_median)) %>%
  235. group_by(cue,memorized,ASI_median) %>%
  236. summarize(dilation_m = mean(dilation),
  237. dilation_se = sd(dilation)/sqrt(n())) %>%
  238. ggplot(aes(x = cue, y = dilation_m, linetype = memorized, fill = ASI_median, group = interaction(ASI_median,memorized))) +
  239. geom_errorbar(aes(ymin = dilation_m-dilation_se, ymax=dilation_m+dilation_se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  240. geom_line(position = position_dodge(width=0.2)) +
  241. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  242. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  243. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1),guide = 'none') +
  244. scale_x_discrete("",labels=c("CS-","CS+")) +
  245. scale_y_continuous("Change in pupil dilation [mm]") +
  246. theme_classic() +
  247. theme(legend.title = element_blank(),
  248. legend.position = "bottom")
  250. cowplot::plot_grid(pupil_wave_plot,pupil_plot,ncol=2,rel_widths=c(2,1),labels=c("A","B"))
  253. # Calculate linear mixed model
  254. pupil_model <- pupil_df %>%
  255. filter(include_final == 1) %>% #take only included subjects
  256. select(ID,ASI_sum_labor,condition,memorized,dilation) %>% #this line is technically not necessary
  257. group_by(ID,condition,memorized,ASI_sum_labor) %>% # average over single trials
  258. summarise(dilation = mean(dilation)) %>% ungroup() %>%
  259. mutate(cue = factor(condition,labels = c("csm","csp")), #factorize factors
  260. memorized = factor(memorized)) %>%
  261. lmer(dilation ~ cue * memorized * ASI_sum_labor + (1|ID), data = .) #cue, memory, as factors, asi as continuous, id as random
  263. anova(pupil_model,ddf ="Kenward-Roger") # show results
  264. r2beta(pupil_model,method="kr") #Effectsize and 95-CI estimation
  266. # Follow up Tests:
  267. # Calculate linear mixed model
  268. lm <- pupil_df %>%
  269. filter(include_final == 1) %>% #take only included subjects
  270. select(ID,ASI_sum_labor,condition,memorized,dilation) %>% #this line is technically not necessary
  271. group_by(ID,condition,memorized,ASI_sum_labor) %>% # average over single trials
  272. summarise(dilation = mean(dilation)) %>% ungroup() %>%
  273. mutate(cue = factor(condition,labels = c("csm","csp")), #factorize factors
  274. memorized = factor(memorized)) %>%
  275. filter(cue == "csp") %>%
  276. lmer(dilation ~ memorized * ASI_sum_labor + (1|ID), data = .) #cue, memory, as factors, asi as continuous, id as random
  277. anova(lm,ddf ="Kenward-Roger") # show results
  278. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  280. # Calculate linear mixed model
  281. lm <- pupil_df %>%
  282. filter(include_final == 1) %>% #take only included subjects
  283. select(ID,ASI_sum_labor,condition,memorized,dilation) %>% #this line is technically not necessary
  284. group_by(ID,condition,memorized,ASI_sum_labor) %>% # average over single trials
  285. summarise(dilation = mean(dilation)) %>% ungroup() %>%
  286. mutate(cue = factor(condition,labels = c("csm","csp")), #factorize factors
  287. memorized = factor(memorized)) %>%
  288. filter(cue == "csm") %>%
  289. lmer(dilation ~ memorized * ASI_sum_labor + (1|ID), data = .)
  290. anova(lm,ddf ="Kenward-Roger") # show results
  291. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  296. # ERP Analysis
  298. p300Data = read.table("C:/Users/yas70ym/Documents/Experimente/Experiment JW - EEG SME/P300.txt",header=T) %>%
  299. mutate(ID = substr(ERPset,1,4),
  300. bini = factor(bini),
  301. condition = factor(bini,labels = c("CSp_re",
  302. "CSp_fo",
  303. "CSm_re",
  304. "CSm_fo",
  305. "US_re",
  306. "US_fo",
  307. "OM_re",
  308. "OM_fo",
  309. "CSp",
  310. "CSm",
  311. "US",
  312. "OM"))) %>%
  313. select(-ERPset,-bini) %>%
  314. separate(condition,into=c("condition","memory")) %>%
  315. mutate(memory = ifelse(is.na(memory),"all",memory)) %>%
  316. left_join(sampledata, by = "ID") %>%
  317. mutate(ID = factor(ID),
  318. condition = factor(condition),
  319. memory = factor(memory),
  320. ASI_median = factor(ASI_median, labels=c("low ASI", "high ASI")),
  321. chlabel = factor(chlabel))
  325. LPPData = read.table("C:/Users/yas70ym/Documents/Experimente/Experiment JW - EEG SME/LPP.txt",header=T) %>%
  326. mutate(ID = substr(ERPset,1,4),
  327. bini = factor(bini),
  328. condition = factor(bini,labels = c("CSp_re",
  329. "CSp_fo",
  330. "CSm_re",
  331. "CSm_fo",
  332. "US_re",
  333. "US_fo",
  334. "OM_re",
  335. "OM_fo",
  336. "CSp_all",
  337. "CSm_all",
  338. "US_all",
  339. "OM_all"))) %>%
  340. select(-ERPset,-bini) %>%
  341. separate(condition,into=c("condition","memory")) %>%
  342. mutate(memory = ifelse(is.na(memory),"all",memory)) %>%
  343. left_join(sampledata, by = "ID")%>%
  344. mutate(ID = factor(ID),
  345. condition = factor(condition),
  346. memory = factor(memory),
  347. ASI_median = factor(ASI_median, labels=c("low ASI", "high ASI")),
  348. chlabel = factor(chlabel))
  353. # Full analysis with Memory, ASI, and CS:
  355. # LPP
  356. # Pz biggest effects:
  357. # CS x Memory x ASI
  358. lm <- LPPData %>%
  359. filter(condition %in% c("CSp","CSm")) %>%
  360. filter(memory != "all") %>%
  361. filter(include_final == 1) %>%
  362. filter(chlabel == "Pz") %>%
  363. lmer(value ~ condition * memory * ASI_sum_labor + (1|ID), data = .)
  364. #cue, memory, as factors, asi as continuous, id as random
  365. anova(lm,ddf ="Kenward-Roger") # show results
  366. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  368. LPPData %>%
  369. filter(condition %in% c("CSp")) %>%
  370. filter(memory != "all") %>%
  371. filter(include_final == 1) %>%
  372. filter(chlabel == "Pz") %>%
  373. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .) %>%
  374. #cue, memory, as factors, asi as continuous, id as random
  375. anova() # show results
  377. LPPData %>%
  378. filter(condition %in% c("CSm")) %>%
  379. filter(memory != "all") %>%
  380. filter(include_final == 1) %>%
  381. filter(chlabel == "Pz") %>%
  382. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .) %>%
  383. #cue, memory, as factors, asi as continuous, id as random
  384. anova() # show results
  387. lpp_cue_plot <- LPPData %>%
  388. filter(condition %in% c("CSp","CSm")) %>%
  389. filter(memory != "all") %>%
  390. filter(chlabel == "Pz") %>%
  391. filter(include_final == 1) %>%
  392. group_by(condition,memory,ASI_median,chlabel) %>%
  393. summarise(mean = mean(value),
  394. se = sd(value)/sqrt(n())) %>%
  395. ggplot(aes(x = condition, y = mean, linetype = memory, fill = ASI_median, group = interaction(ASI_median,memory))) +
  396. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  397. geom_line(position = position_dodge(width=0.2)) +
  398. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  399. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  400. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  401. scale_x_discrete("",labels=c("CS-","CS+")) +
  402. scale_y_continuous("LPP [µV]") +
  403. theme_classic() +
  404. theme(legend.title = element_blank(),
  405. legend.position = "none")
  407. # P300
  408. # Pz biggest effects:
  409. # CS x Memory x ASI
  410. lm <- p300Data %>%
  411. filter(condition %in% c("CSp","CSm")) %>%
  412. filter(memory != "all") %>%
  413. filter(include_final == 1) %>%
  414. filter(chlabel == "Pz") %>%
  415. lmer(value ~ condition * memory * ASI_sum_labor + (1|ID), data = .)
  416. anova(lm,ddf ="Kenward-Roger") # show results
  417. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  420. p300Data %>%
  421. filter(condition %in% c("CSp")) %>%
  422. filter(memory != "all") %>%
  423. filter(include_final == 1) %>%
  424. filter(chlabel == "Pz") %>%
  425. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .) %>%
  426. #cue, memory, as factors, asi as continuous, id as random
  427. anova() # show results
  430. p300Data %>%
  431. filter(condition %in% c("CSm")) %>%
  432. filter(memory != "all") %>%
  433. filter(include_final == 1) %>%
  434. filter(chlabel == "Pz") %>%
  435. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .) %>%
  436. #cue, memory, as factors, asi as continuous, id as random
  437. anova() # show results
  440. p300_cue_plot <- p300Data %>%
  441. filter(condition %in% c("CSp","CSm")) %>%
  442. filter(memory != "all") %>%
  443. filter(chlabel == "Pz") %>%
  444. filter(include_final == 1) %>%
  445. group_by(condition,memory,ASI_median,chlabel) %>%
  446. summarise(mean = mean(value),
  447. se = sd(value)/sqrt(n())) %>%
  448. ggplot(aes(x = condition, y = mean, linetype = memory, fill = ASI_median, group = interaction(ASI_median,memory))) +
  449. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  450. geom_line(position = position_dodge(width=0.2)) +
  451. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  452. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  453. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  454. scale_x_discrete("",labels=c("CS-","CS+")) +
  455. scale_y_continuous("P300 [µV]") +
  456. theme_classic() +
  457. theme(legend.position = "none",
  458. legend.title = element_blank())
  460. plot_labels <- p300Data %>%
  461. filter(condition %in% c("CSp","CSm")) %>%
  462. filter(memory != "all") %>%
  463. filter(chlabel == "Pz") %>%
  464. filter(include_final == 1) %>%
  465. group_by(condition,memory,ASI_median,chlabel) %>%
  466. summarise(mean = mean(value),
  467. se = sd(value)/sqrt(n())) %>%
  468. ggplot(aes(x = condition, y = mean, linetype = memory, fill = ASI_median, group = interaction(ASI_median,memory))) +
  469. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  470. geom_line(position = position_dodge(width=0.2)) +
  471. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  472. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  473. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  474. scale_x_discrete("",labels=c("CS-","CS+")) +
  475. scale_y_continuous("P300 [µV]") +
  476. theme_void() +
  477. theme(legend.position = "bottom",
  478. legend.title = element_blank())
  480. legend <- ggpubr::as_ggplot(ggpubr::get_legend(plot_labels))
  481. cowplot::plot_grid(cowplot::plot_grid(p300_cue_plot,lpp_cue_plot,ncol=2,labels=c("A","B"),rel_widths = c(1,1)),legend, nrow=2, rel_heights = c(1,0.1))
  486. # OM: Memory x Channel x Gruppe
  488. lm <- LPPData %>%
  489. filter(condition %in% c("OM")) %>%
  490. filter(memory != "all") %>%
  491. filter(include_final == 1) %>%
  492. filter(chlabel == "Cz") %>%
  493. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .)
  494. anova(lm,ddf ="Kenward-Roger") # show results
  495. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  497. lpp_om_plot <- LPPData %>%
  498. filter(condition %in% c("OM")) %>%
  499. filter(memory != "all") %>%
  500. filter(chlabel == "Cz") %>%
  501. filter(include_final == 1) %>%
  502. group_by(memory,ASI_median) %>%
  503. summarise(mean = mean(value),
  504. se = sd(value)/sqrt(n())) %>%
  505. ggplot(aes(x = memory, y = mean, fill = ASI_median, group = ASI_median)) +
  506. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  507. geom_line(position = position_dodge(width=0.2)) +
  508. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  509. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  510. scale_x_discrete("",labels=c("forgotten","remembered")) +
  511. scale_y_continuous("LPP [µV]") +
  512. theme_classic() +
  513. theme(legend.position = "none",
  514. legend.title = element_blank())
  516. lm <- p300Data %>%
  517. filter(condition %in% c("OM")) %>%
  518. filter(memory != "all") %>%
  519. filter(include_final == 1) %>%
  520. filter(chlabel == "Cz") %>%
  521. #group_by(memory,ID,ASI_sum_labor) %>%
  522. #summarise(value = mean(value)) %>%
  523. lmer(value ~ memory * ASI_sum_labor + (1|ID), data = .)
  524. anova(lm,ddf ="Kenward-Roger") # show results
  525. r2beta(lm,method="kr") #Effectsize and 95-CI estimation
  527. p300_om_plot <- p300Data %>%
  528. filter(condition %in% c("OM")) %>%
  529. filter(memory != "all") %>%
  530. filter(chlabel == "Cz") %>%
  532. filter(include_final == 1) %>%
  533. group_by(memory,ASI_median) %>%
  534. summarise(mean = mean(value),
  535. se = sd(value)/sqrt(n())) %>%
  536. ggplot(aes(x = memory, y = mean, fill = ASI_median, group = ASI_median)) +
  537. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  538. geom_line(position = position_dodge(width=0.2)) +
  539. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  540. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  541. scale_x_discrete("",labels=c("forgotten","remembered")) +
  542. scale_y_continuous("P300 [µV]") +
  543. theme_classic() +
  544. theme(legend.position = "none",
  545. legend.title = element_blank())
  547. legend_om_plot <- p300Data %>%
  548. filter(condition %in% c("OM")) %>%
  549. filter(memory != "all") %>%
  550. filter(chlabel == "Cz") %>%
  551. filter(include_final == 1) %>%
  552. group_by(memory,ASI_median) %>%
  553. summarise(mean = mean(value),
  554. se = sd(value)/sqrt(n())) %>%
  555. ggplot(aes(x = memory, y = mean, fill = ASI_median, group = ASI_median)) +
  556. geom_errorbar(aes(ymin = mean-se, ymax=mean+se), linetype = 1, width = 0.1,,position = position_dodge(width=0.2)) +
  557. geom_line(position = position_dodge(width=0.2)) +
  558. geom_point(size = 3, shape = 21,position = position_dodge(width=0.2)) +
  559. scale_fill_manual(labels = c("low ASI", "high ASI"), values=c("darkgoldenrod3","darkblue"))+
  560. scale_x_discrete("",labels=c("forgotten","remembered")) +
  561. scale_y_continuous("P300 [µV]") +
  562. theme_classic() +
  563. theme(legend.position = "bottom",
  564. legend.title = element_blank())
  567. legend_om <- ggpubr::as_ggplot(ggpubr::get_legend(legend_om_plot))
  568. cowplot::plot_grid(cowplot::plot_grid(p300_om_plot,lpp_om_plot,ncol=2,labels=c("A","B")),
  569. legend_om,
  570. nrow=2, rel_heights = c(1,0.1))
  573. # Plot Waveforms
  575. high_csmfo <- read.table("Plots/Topographies/High__csmfo .txt", header = T) %>%
  576. select(time,Pz) %>% mutate(ASI_group = "high",
  577. condition = "csm",
  578. memory = "fo")
  580. high_cspfo <- read.table("Plots/Topographies/High__cspfo .txt", header = T) %>%
  581. select(time,Pz) %>% mutate(ASI_group = "high",
  582. condition = "csp",
  583. memory = "fo")
  585. high_csmre <- read.table("Plots/Topographies/High__csmre .txt", header = T) %>%
  586. select(time,Pz) %>% mutate(ASI_group = "high",
  587. condition = "csm",
  588. memory = "re")
  590. high_cspre <- read.table("Plots/Topographies/High__cspre .txt", header = T) %>%
  591. select(time,Pz) %>% mutate(ASI_group = "high",
  592. condition = "csp",
  593. memory = "re")
  595. high_omfo<- read.table("Plots/Topographies/High__omfo.txt", header = T) %>%
  596. select(time,Cz) %>% mutate(ASI_group = "high",
  597. condition = "om",
  598. memory = "fo")
  600. high_omre <- read.table("Plots/Topographies/High__omre.txt", header = T) %>%
  601. select(time,Cz) %>% mutate(ASI_group = "high",
  602. condition = "om",
  603. memory = "re")
  605. low_csmfo <- read.table("Plots/Topographies/Low__csmfo .txt", header = T) %>%
  606. select(time,Pz) %>% mutate(ASI_group = "low",
  607. condition = "csm",
  608. memory = "fo")
  610. low_cspfo <- read.table("Plots/Topographies/Low__cspfo .txt", header = T) %>%
  611. select(time,Pz) %>% mutate(ASI_group = "low",
  612. condition = "csp",
  613. memory = "fo")
  615. low_csmre <- read.table("Plots/Topographies/Low__csmre .txt", header = T) %>%
  616. select(time,Pz) %>% mutate(ASI_group = "low",
  617. condition = "csm",
  618. memory = "re")
  620. low_cspre <- read.table("Plots/Topographies/Low__cspre .txt", header = T) %>%
  621. select(time,Pz) %>% mutate(ASI_group = "low",
  622. condition = "csp",
  623. memory = "re")
  625. low_omfo<- read.table("Plots/Topographies/Low__omfo.txt", header = T) %>%
  626. select(time,Cz) %>% mutate(ASI_group = "low",
  627. condition = "om",
  628. memory = "fo")
  630. low_omre <- read.table("Plots/Topographies/Low__omre.txt", header = T) %>%
  631. select(time,Cz) %>% mutate(ASI_group = "low",
  632. condition = "om",
  633. memory = "re")
  635. cs_erps <- high_csmfo %>%
  636. rbind(high_cspfo) %>%
  637. rbind(high_csmre) %>%
  638. rbind(high_cspre) %>%
  639. rbind(low_csmfo) %>%
  640. rbind(low_cspfo) %>%
  641. rbind(low_csmre) %>%
  642. rbind(low_cspre) %>%
  643. mutate(ASI_group = factor(ASI_group),
  644. condition = factor(condition),
  645. memory = factor(memory))
  648. om_erps <- high_omfo %>%
  649. rbind(high_omre) %>%
  650. rbind(low_omfo) %>%
  651. rbind(low_omre)
  654. # Plot Cue-related Waveforms
  656. csp_erp_plot <- cs_erps %>%
  657. filter(condition %in% c("csp")) %>%
  658. ggplot(aes(x = time, y = Pz, color = ASI_group, linetype = memory)) +
  659. geom_vline(xintercept = 0,linetype = 2) +
  660. geom_line() +
  661. scale_color_manual(labels = c("high ASI", "low ASI"), values=c("darkblue","darkgoldenrod3"))+
  662. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  663. scale_x_continuous("Time [ms]", expand = c(0.05,0.05), breaks=c(-200,0,200,400,600,800,1000)) +
  664. scale_y_continuous("CS+ ERP [µV]") +
  665. theme_classic() +
  666. theme(legend.title = element_blank(),
  667. legend.position = "none")
  669. csm_erp_plot <- cs_erps %>%
  670. filter(condition %in% c("csm")) %>%
  671. ggplot(aes(x = time, y = Pz, color = ASI_group, linetype = memory)) +
  672. geom_vline(xintercept = 0,linetype = 2) +
  673. geom_line() +
  674. scale_color_manual(labels = c("high ASI", "low ASI"), values=c("darkblue","darkgoldenrod3"))+
  675. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  676. scale_x_continuous("Time [ms]", expand = c(0.05,0.05), breaks=c(-200,0,200,400,600,800,1000)) +
  677. scale_y_continuous("CS- ERP [µV]") +
  678. theme_classic() +
  679. theme(legend.title = element_blank(),
  680. legend.position = "none")
  682. cowplot::plot_grid(csm_erp_plot,csp_erp_plot,ncol=2,labels=c("D","E"),rel_widths = c(1,1))
  684. # Plot Omission-related Waveforms
  686. om_erps %>%
  687. filter(condition %in% c("om")) %>%
  688. ggplot(aes(x = time, y = Cz, color = ASI_group, linetype = memory)) +
  689. geom_vline(xintercept = 0,linetype = 2) +
  690. geom_line() +
  691. scale_color_manual(labels = c("high ASI", "low ASI"), values=c("darkblue","darkgoldenrod3"))+
  692. scale_linetype_manual(labels = c("forgotten", "remembered"), values=c(2,1)) +##,guide = 'none') +
  693. scale_x_continuous("Time [ms]", expand = c(0.05,0.05), breaks=c(-200,0,200,400,600,800,1000)) +
  694. scale_y_continuous("CS- offset ERP [µV]") +
  695. theme_classic() +
  696. theme(legend.title = element_blank(),
  697. legend.position = "none")

EEG-SME OSF.R, no license · at the source

Overview

Authors: Yannik Stegmann1, Valentina Glück1, Marta Andreatta2,3, Julian Wiemer1
ORCID iDs: Yannik Stegmann, Valentina Glück, Marta Andreatta, Julian Wiemer
  1. Department of Psychology, University of Würzburg,Marcusstraße 9-11, 97070 Würzburg, Germany
  2. Department of General Psychiatry and Psychotherapy, University Hospital Tübingen,Tübingen, Germany
  3. Tübingen Center for Mental Health, University Hospital Tübingen,Tübingen, Germany
Institutions: University of Würzburg (Germany); Universitätsklinikum Tübingen (Germany)
Journal: Cognitive, affective & behavioral neuroscience, volume 26, issue 5, pages 2297-2310
Dates: received 1 December 2025; accepted 6 April 2026; published online 12 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13415-026-01450-0 · PMID 42120709 · PMCID PMC13615150 · OpenAlex W7160910323
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), cognitive (subfield)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Statistics, Physiology & signal measures
Keywords: Memory, Conditioning, Event-related potentials, Anxiety, Anxiety sensitivity index
MeSH: Anxiety*, Attention*, Evoked Potentials*, Fear*, Memory*, Adolescent, Adult, Conditioning, Classical, Electroencephalography, Event-Related Potentials, P300, Female, Humans, Individuality, Male, Young Adult (* major topic)
Topic: Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (DFG) (378414384); Julius-Maximilians-Universität Würzburg (3088)
Citations: not cited yet (Europe PMC); 74 references in the paper

Abstract

The ability to accurately detect and remember threatening stimuli is essential for adaptive behavior in potentially dangerous environments. While threat-related defensive processes are increasingly well understood, recent research has begun to emphasize complementary safety-related attention and learning mechanisms, processes especially relevant to the development and maintenance of pathological anxiety. Prior fear conditioning studies have identified a key role for late positive event-related potentials, particularly the P300 and late positive potential (LPP) in the formation of declarative memories related to threat and safety. However, the influence of individual differences in anxiety on these mechanisms remains insufficiently explored. In the present study, we examined how anxiety sensitivity, a transdiagnostic risk factor for anxiety-related pathologies, modulates attention and memory processes during declarative threat and safety learning. Using a subsequent memory paradigm, we recorded event-related potentials as participants learned associations between neutral faces and either an aversive outcome or safety. Stronger P300 and LPP amplitudes were found for remembered compared with forgotten items. Crucially, elevated anxiety sensitivity was associated with increased P300 and LPP amplitudes at the onset of both threat and safety cues, indicating heightened attentional allocation to both types of stimuli. Despite these amplified neural responses during memory encoding, higher anxiety sensitivity did not correspond to enhanced memory performance. These results suggest that, while individuals at heightened risk for anxiety-related psychopathology exhibit generalized hypervigilance, this increased attentional engagement does not necessarily translate into enhanced memory formation during intentional learning.

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.3758/s13415-026-01450-0.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 83zbx

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 19 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: afex (1 file), cowplot (1 file), ggpubr (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/83zbx/

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.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data and code behind the analyses have been made publicly available at the Open Science Framework and can be accessed at https://osf.io/83zbx/.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → Springer Science+Business Media

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 15 MeSH terms, 2 funders, 71 references.

Cite

This paper

Stegmann, Y., Glück, V., Andreatta, M., & Wiemer, J. (2026). Attentional processing in individual anxiety: Electrocortical correlates of threat- and safety-related declarative memory formation. Cognitive, affective & behavioral neuroscience, 26(5), 2297-2310. https://doi.org/10.3758/s13415-026-01450-0

BibTeX

@article{stegmann2026attentional,
author = {Stegmann, Yannik and Glück, Valentina and Andreatta, Marta and Wiemer, Julian},
title = {{Attentional processing in individual anxiety: Electrocortical correlates of threat- and safety-related declarative memory formation}},
journal = {Cognitive, affective \& behavioral neuroscience},
year = {2026},
month = may,
volume = {26},
number = {5},
pages = {2297--2310},
publisher = {Springer Science+Business Media},
issn = {1530-7026},
doi = {10.3758/s13415-026-01450-0},
url = {https://doi.org/10.3758/s13415-026-01450-0},
pmid = {42120709},
pmcid = {PMC13615150}
}

RIS

TY - JOUR
AU - Stegmann, Yannik
AU - Glück, Valentina
AU - Andreatta, Marta
AU - Wiemer, Julian
TI - Attentional processing in individual anxiety: Electrocortical correlates of threat- and safety-related declarative memory formation
T2 - Cognitive, affective & behavioral neuroscience
J2 - Cogn Affect Behav Neurosci
PY - 2026
DA - 2026/05/12
VL - 26
IS - 5
SP - 2297
EP - 2310
SN - 1530-7026
PB - Springer Science+Business Media
DO - 10.3758/s13415-026-01450-0
UR - https://doi.org/10.3758/s13415-026-01450-0
LA - en
ER -

CSL-JSON

{
"id": "10.3758/s13415-026-01450-0",
"type": "article-journal",
"title": "Attentional processing in individual anxiety: Electrocortical correlates of threat- and safety-related declarative memory formation",
"container-title": "Cognitive, affective & behavioral neuroscience",
"author": [
{
"family": "Stegmann",
"given": "Yannik"
},
{
"family": "Glück",
"given": "Valentina"
},
{
"family": "Andreatta",
"given": "Marta"
},
{
"family": "Wiemer",
"given": "Julian"
}
],
"container-title-short": "Cogn Affect Behav Neurosci",
"volume": "26",
"issue": "5",
"page": "2297-2310",
"DOI": "10.3758/s13415-026-01450-0",
"PMID": "42120709",
"PMCID": "PMC13615150",
"ISSN": "1530-7026",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.3758/s13415-026-01450-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
12
]
]
}
}

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