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A neurocognitive interactive activation model of semantic priming in lexical decisions.

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  1. ---
  2. title: "Analyses"
  3. author: "LeoSokolovic"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. editor_options:
  7. chunk_output_type: console
  8. ---
  10. # Load Libraries
  12. ```{r}
  13. rm(list = ls())
  14. library(correlation)
  15. library(reshape2)
  16. library(readr)
  17. library(ggplot2)
  18. library(patchwork)
  19. library(lme4)
  20. library(lmerTest)
  21. library(emmeans)
  22. library(performance)
  23. library(dplyr)
  24. library(xtable)
  25. library(gvlma)
  27. my_colors <- c("#D95319", "#EDB120", "#0072BD", "#4DBEEE")
  28. ```
  30. # Read in the Data
  32. ```{r}
  33. # dataW <- read_csv2("dataW.csv")
  34. #
  35. #
  36. # data <- data.frame()
  37. #
  38. # for(p in 6:68){
  39. # sd <-dataW[c(1:5,p)]
  40. # sd$ID <- rep(p-5,200)
  41. # colnames(sd)[6] <- "RT"
  42. # data <- rbind(data,sd)
  43. # }
  44. # data$DirectAssociates <- NA
  45. # data$DirectAssociates[grep("HA",data$Condition)] <- "Strong"
  46. # data$DirectAssociates[grep("NA",data$Condition)] <- "No"
  47. # data$IndirectAssociates <- NA
  48. # data$IndirectAssociates[grep("H2",data$Condition)] <- "Many"
  49. # data$IndirectAssociates[grep("N2",data$Condition)] <- "No"
  50. # data$Experiment <- ifelse(data$ID<33,1,2)
  51. # data$ID <- factor(data$ID)
  52. # data$DirectAssociates <- factor(data$DirectAssociates, levels=c("Strong","No"))
  53. # data$IndirectAssociates <- factor(data$IndirectAssociates, levels = c("Many","No"))
  54. # data$SOA <- stringr::str_to_title(data$SOA)
  55. # data$SOA <- factor(data$SOA, levels = c("Long", "Short"))
  56. # data$Experiment <- factor(data$Experiment)
  57. # save(data,file = 'observed_data.RData')
  58. ```
  60. ```{r}
  61. # Load observed data
  62. load('observed_data.RData')
  63. # Load SAROM simulated data
  64. load('model_simulated_data.RData')
  65. # Load combined data
  66. load('combined_data.RData')
  67. # Load SAROM parameter estimates
  68. load('parameters_long_format.RData')
  70. levels(comb_data$IndirectAssociates) <- c("Many","Few")
  71. levels(data$IndirectAssociates) <- c("Many","Few")
  72. levels(dataM$IndirectAssociates) <- c("Many","Few")
  74. ```
  77. ## Plots
  78. ### Response Times
  80. ```{r echo=FALSE}
  81. meanRT <- data %>% group_by(Experiment,SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanRT = mean(RT,na.rm = T), se = sd(RT,na.rm = T))
  82. meanRT
  83. ```
  86. ### Number of Errors
  88. ```{r echo=FALSE}
  89. errors <- data %>% group_by(Experiment,ID,SOA, DirectAssociates, IndirectAssociates) %>% summarise(nErrors = sum(is.na(RT))/length(RT))
  91. mean_errors <- errors %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanErrors = mean(nErrors), se = sd(nErrors))
  92. mean_errors
  93. ```
  96. ```{r echo=FALSE}
  97. errors <- data %>% group_by(Experiment,ID,SOA, DirectAssociates, IndirectAssociates) %>% summarise(nErrors = sum(is.na(RT))/length(RT))
  99. mean_errors <- errors %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanErrors = mean(nErrors), se = sd(nErrors)/sqrt(length(nErrors)))
  100. mean_errors
  101. ```
  104. # Model Simulated Data
  106. ```{r}
  107. # dataM <- read_delim("simdata.csv",
  108. # delim = ";", escape_double = FALSE, locale = locale(decimal_mark = ",",
  109. # grouping_mark = "."),na = "NaN", trim_ws = TRUE)
  110. # dataM$PairNumber <- 1:200
  111. # dataM$PairNumber <- factor(dataM$PairNumber)
  112. # dataM$Prime <- NULL
  113. # dataM$Target <- NULL
  114. # dataM$DirectAssociates <- NA
  115. # dataM$DirectAssociates[grep("HA",dataM$Condition)] <- "Strong"
  116. # dataM$DirectAssociates[grep("NA",dataM$Condition)] <- "No"
  117. # dataM$IndirectAssociates <- NA
  118. # dataM$IndirectAssociates[grep("H2",dataM$Condition)] <- "Many"
  119. # dataM$IndirectAssociates[grep("N2",dataM$Condition)] <- "No"
  120. #
  121. #
  122. # dataM$DirectAssociates <- factor(dataM$DirectAssociates, levels=c("Strong","No"))
  123. # dataM$IndirectAssociates <- factor(dataM$IndirectAssociates, levels = c("Many","No"))
  124. # dataM$SOA <- stringr::str_to_title(dataM$SOA)
  125. # dataM$SOA <- factor(dataM$SOA, levels = c("Long", "Short"))
  126. # # dataM$Simulation <- NA
  127. # # dataM$Simulation <- rep(c(1:100),each = 200)
  128. # # dataM$Simulation <- factor(dataM$Simulation)
  129. #
  130. # dataM <- melt(dataM,id.vars = c("PairNumber","Condition","SOA","DirectAssociates","IndirectAssociates"),variable.name = "ID",value.name = "RT")
  131. # dataM$Experiment <- ifelse(as.numeric(dataM$ID)<33,1,2)
  132. # dataM$ID <- factor(dataM$ID)
  133. # dataM$Experiment <- factor(dataM$Experiment)
  134. # save(dataM, file = 'model_simulated_data_new.RData')
  135. ```
  139. ### Simulated response Times
  141. ```{r echo=FALSE}
  142. meanRTSim <- dataM %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanRT = mean(RT,na.rm = T), sd = sd(RT,na.rm = T))
  143. meanRTSim
  144. ```
  146. ```{r echo=FALSE}
  147. meanRTSim <- dataM %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanRT = mean(RT,na.rm = T), se = sd(RT,na.rm = T)/sqrt(length(RT)))
  148. meanRTSim
  149. ```
  151. ### Simulated number of Errors
  153. ```{r echo=FALSE}
  154. errorsSim <- dataM %>% group_by(Experiment, ID,SOA, DirectAssociates, IndirectAssociates) %>% summarise(nErrors = sum(is.na(RT)/length(RT)))
  156. mean_errorsSim <- errorsSim %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanErrors = mean(nErrors), sd = sd(nErrors))
  157. mean_errorsSim
  158. ```
  161. ```{r echo=FALSE}
  162. errorsSim <- dataM %>% group_by(Experiment, ID,SOA, DirectAssociates, IndirectAssociates) %>% summarise(nErrors = sum(is.na(RT)/length(RT)))
  164. mean_errorsSim <- errorsSim %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(meanErrors = mean(nErrors), se = sd(nErrors)/sqrt(length(nErrors)))
  165. mean_errorsSim
  166. ```
  168. ```{r}
  169. # data$Source <- "Observed"
  170. # data$Prime <- NULL
  171. # data$Target <- NULL
  172. # dataM$Source <- "Simulated"
  173. #
  174. # comb_data <- rbind(data,dataM)
  175. # comb_data$Source <- factor(comb_data$Source, levels = c("Observed","Simulated"))
  176. # comb_data$Error <- as.numeric(is.na(comb_data$RT))
  177. #
  178. # save(comb_data, file = 'combined_data_new.RData')
  179. ```
  183. ## Compare directly
  185. ### Response Times
  187. Empirical response times
  189. ```{r echo=FALSE}
  190. meanRT <- data %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(
  191. meanRT = mean(RT,na.rm = T),
  192. se = sd(RT,na.rm = T)/sqrt(length(RT)))
  193. meanRT
  194. ```
  196. Simulated response times
  198. ```{r echo=FALSE}
  199. meanRTSim <- dataM %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarise(
  200. meanRT = mean(RT,na.rm = T),
  201. se = sd(RT,na.rm = T)/sqrt(length(RT)))
  202. meanRTSim
  203. ```
  205. ```{r}
  206. meanRT$Source <- "Observed"
  207. meanRTSim$Source <- "Simulated"
  208. mean_errors$Source <- "Observed"
  209. mean_errorsSim$Source <- "Simulated"
  212. dataPlotRT <- rbind(meanRT, meanRTSim)
  213. dataPlotRT$Source <- factor(dataPlotRT$Source)
  215. dataPlotErrors <- rbind(mean_errors,mean_errorsSim)
  216. dataPlotErrors$Source <- factor(dataPlotErrors$Source)
  217. ```
  219. ```{r}
  220. RTplot <- ggplot(data = dataPlotRT,mapping = aes(x = Source,y = meanRT, ymin = meanRT-1.96*se, ymax = meanRT+1.96*se, fill = DirectAssociates:IndirectAssociates)) +
  221. scale_fill_manual(values = my_colors) + facet_grid(SOA~Experiment, labeller = label_both) +
  222. geom_bar(stat = "identity", position = position_dodge(.5), width = .5) +
  223. geom_errorbar(stat = "identity", position = position_dodge(.5),width = .2) +
  224. xlab(NULL)+
  225. ylab("Mean response time in seconds")+
  226. coord_cartesian(ylim = c(0.5,.75))+
  227. labs(tag = "A") +
  228. theme_classic() +
  229. theme(legend.position = "none",text = element_text(size = 14),
  230. line = element_line(linewidth = 1),
  231. axis.text = element_text(size = 14),
  232. axis.title.x = element_blank())
  234. ```
  236. ### Number of Errors
  238. ```{r}
  239. errorPlot <- ggplot(data = dataPlotErrors,mapping = aes(x = Source,y = meanErrors, ymin = meanErrors-1.96*se, ymax = meanErrors+1.96*se, fill = DirectAssociates:IndirectAssociates)) +
  240. scale_fill_manual(values = my_colors) +
  241. facet_grid(SOA~Experiment, labeller = label_both) +
  242. geom_bar(stat = "identity", position = position_dodge(.5), width = .5) +
  243. geom_errorbar(stat = "identity", position = position_dodge(.5),width = .2) +
  244. ylim(c(0,.125))+
  245. xlab("Data source")+
  246. ylab("Proportion of errors")+
  247. labs(tag = "B",fill = "Direct association \nx common associates") +
  248. theme_classic() +
  249. theme(legend.position = "bottom",
  250. legend.direction = "horizontal",
  251. line = element_line(linewidth = 1),
  252. text = element_text(size = 14),
  253. axis.text = element_text(size = 14))
  256. ```
  259. ## Correlations between observed and simulated data
  261. #### Response times
  262. ```{r}
  263. mean_data <- comb_data %>% group_by(Experiment,ID,SOA, DirectAssociates, IndirectAssociates,Source) %>% summarize(mean = mean(RT,na.rm = T))
  265. mean_data <- reshape2::dcast(mean_data, Experiment+ID+SOA+DirectAssociates+IndirectAssociates~Source)
  267. mean_data %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarize(cor = cor.test(Observed, Simulated, method = "spearman",exact = F)$estimate, p = round(p.adjust(cor.test(Observed, Simulated, method = "spearman",exact = F)$p.value,method = "bonferroni"),4))
  268. ```
  272. ```{r}
  273. RT_cors_plot <- ggplot(data = mean_data, mapping = aes(x = Observed, y = Simulated, color = DirectAssociates:IndirectAssociates)) + scale_color_manual(values = my_colors)+
  274. facet_grid(SOA~Experiment, labeller = label_both) + geom_point() + geom_smooth(method = lm) + theme_classic()+
  275. labs(tag = "B",
  276. x = "Observed response times in seconds",
  277. y = "Simulated response times in seconds",
  278. color = "Direct association \nx common associates") + ylim(c(.4,1)) + xlim(c(.4,1))+
  279. theme_classic() +
  280. theme(legend.position = "none", line = element_line(linewidth = 1),text = element_text(size = 12), axis.text = element_text(size = 12))
  282. ```
  284. #### Errors
  285. ```{r}
  286. mean_data <- comb_data %>% group_by(Experiment, ID,SOA, DirectAssociates, IndirectAssociates,Source) %>% summarize(mean = mean(Error,na.rm = T))
  288. mean_data <- reshape2::dcast(mean_data, Experiment + ID+SOA+DirectAssociates+IndirectAssociates~Source)
  290. mean_data %>% group_by(Experiment, SOA, DirectAssociates, IndirectAssociates) %>% summarize(cor = cor.test(Observed, Simulated, method = "spearman",exact = F)$estimate, p = round(p.adjust(cor.test(Observed, Simulated, method = "spearman",exact = F)$p.value,method = "bonferroni"),4))
  291. ```
  294. ```{r}
  295. Error_cor_plot <- ggplot(data = mean_data, mapping = aes(x = Observed, y = Simulated, color = DirectAssociates:IndirectAssociates)) + facet_grid(SOA~Experiment, labeller = label_both) +
  296. scale_color_manual(values = my_colors)+
  297. geom_point() + geom_smooth(method = lm) + theme_classic()+
  298. labs(tag = "D",color = NULL,
  299. x = "Observed error proportion",
  300. y = "Simulated error proportion") + ylim(c(0,.4)) + xlim(c(0,.4))+
  301. theme_classic() +
  302. theme(legend.position = "bottom", line = element_line(linewidth = 1),text = element_text(size = 12), axis.text = element_text(size = 12))
  303. ```
  305. #Overall correlation
  307. ```{r}
  308. mean_RT_ID <- aggregate(.~ID, data[,c("ID","RT")], function(x) c(RT = mean(x, na.rm = T), errors = 200 - length(x)))
  309. mean_RT_sim_ID <- aggregate(.~ID, dataM[,c("ID","RT")], function(x) c(RT = mean(x, na.rm = T), errors = 200 - length(x)))
  310. cors <- cor.test(mean_RT_ID[,2][,1],mean_RT_sim_ID[,2][,1])
  311. cors$estimate^2
  312. ```
  315. ```{r}
  316. cors_err <- cor.test(mean_RT_ID[,2][,2],mean_RT_sim_ID[,2][,2])
  317. cors_err$estimate
  318. ```
  321. ## Plot eCDF of simulated and observed response times
  323. ```{r}
  324. comb_data$Experiment <- factor(comb_data$Experiment, labels = c('Exp. 1', 'Exp. 2'))
  325. comb_data$SOA <- factor(comb_data$SOA, labels = c('Long SOA', 'Short SOA'))
  326. ```
  328. ```{r}
  329. eCDF_plot <- ggplot(data = comb_data, mapping = aes(x = RT, color = Source, linetype = Source, linewidth = Source))+
  330. facet_grid(SOA~Experiment) +
  331. geom_density(stat = "ecdf",na.rm = T) +
  332. xlim(0,1.5) +
  333. scale_color_manual(values = c(my_colors[1],my_colors[4]))+
  334. scale_linetype_manual(values = c(1,1)) +
  335. scale_linewidth_manual(values = c(1.5,.8))+
  336. xlab("Response time in seconds") +
  337. ylab("Empirical CDF")+
  338. labs(color = "Response times: ", linetype = "Response times: ",linewidth = "Response times: ")+
  339. theme_classic()+
  340. theme(legend.position = "bottom",
  341. line = element_line(linewidth = 1),
  342. text = element_text(size = 12),
  343. axis.text = element_text(size = 12))
  344. eCDF_plot
  345. ggsave(filename = "RT_eCDF.jpeg",device = "jpeg",dpi = 600,height = 8, width = 13, units = "cm")
  347. ```
  349. ```{r}
  350. (RTplot + RT_cors_plot) / (errorPlot + Error_cor_plot)
  351. ggsave(filename = "RT_errors_plot.jpeg",device = "jpeg",dpi = 600,height = 18, width = 25, units = "cm")
  352. ```
  357. # Model parameters
  359. ```{r}
  360. # parameters <- read_csv2('parameters.csv')
  361. # parameters$Experiment <- ifelse(parameters$ID<33,1,2)
  362. # short_pars <- parameters[c(1,2,4,6,7,8,10,11)]
  363. # long_pars <- parameters[c(1,3,5,6,7,9,10,11)]
  364. # colnames(short_pars) <- c("ID", "Kappa","Beta","Xi","NDT","Theta","Strength", "Experiment")
  365. # colnames(long_pars) <- c("ID", "Kappa","Beta","Xi","NDT","Theta","Strength","Experiment")
  366. # short_pars$Lambda <- short_pars$Kappa - short_pars$Beta
  367. # short_pars$SOA <- "Short"
  368. # long_pars$Lambda <- long_pars$Kappa - long_pars$Beta
  369. # long_pars$SOA <- "Long"
  370. # short_pars$AO_excitation <- ifelse(short_pars$Strength >.02,'strong','weak')
  371. # short_pars$AO_excitation <- factor(short_pars$AO_excitation, levels = c('weak','strong'))
  372. # long_pars$AO_excitation <- ifelse(long_pars$Strength >.02,'strong','weak')
  373. # long_pars$AO_excitation <- factor(long_pars$AO_excitation, levels = c('weak','strong'))
  374. #
  375. # parametersL <- rbind(short_pars,long_pars)
  376. # parametersL$ID <- factor(parametersL$ID)
  377. # parametersL$SOA <- factor(parametersL$SOA)
  378. # parametersL$Experiment <- factor(parametersL$Experiment)
  379. # parametersL$AO_excitation <- factor(parametersL$AO_excitation)
  380. #
  381. # save(parametersL, file = 'parameters_long_format.RData')
  382. ```
  385. ## Average parameter values
  387. ```{r}
  388. aggregate(.~Experiment:SOA, parametersL[,-1], function(x) c(mean = round(mean(x),2), sd = round(sd(x),2)))
  389. ```
  392. ## Correlations between the parameters in the short and long SOA
  393. ### Experiment 1
  394. ```{r}
  395. correlation(parametersL[parametersL$SOA == "Short" & parametersL$Experiment == "1",c(2:7,9)],method = "spearman",p_adjust = "bonferroni")
  396. ```
  398. ```{r}
  399. correlation(parametersL[parametersL$SOA == "Long" & parametersL$Experiment == "1",c(2:7,9)],method = "spearman",p_adjust = "bonferroni")
  400. ```
  402. ### Experiment 2
  403. ```{r}
  404. correlation(parametersL[parametersL$SOA == "Short" & parametersL$Experiment=="2",c(2:7,9)],method = "spearman",p_adjust = "bonferroni")
  405. ```
  407. ```{r}
  408. correlation(parametersL[parametersL$SOA == "Long" & parametersL$Experiment =="2",c(2:7, 9)],method = "spearman",p_adjust = "bonferroni")
  409. ```
  414. ## Differences between parameter values in the short and long SOA
  416. ### Kappa
  417. #### Experiment 1
  418. ```{r}
  419. wilcox.test(short_pars$Kappa[short_pars$Experiment=="1"], long_pars$Kappa[long_pars$Experiment == "1"], paired = T, conf.int = T)
  420. ```
  422. #### Experiment 2
  423. ```{r}
  424. wilcox.test(short_pars$Kappa[short_pars$Experiment=="2"], long_pars$Kappa[long_pars$Experiment == "2"], paired = T, conf.int = T)
  425. ```
  429. ### Beta
  431. #### Experiment 1
  432. ```{r}
  433. wilcox.test(short_pars$Beta[short_pars$Experiment=="1"], long_pars$Beta[long_pars$Experiment == "1"], paired = T, conf.int = T)
  434. ```
  436. #### Experiment 2
  437. ```{r}
  438. wilcox.test(short_pars$Beta[short_pars$Experiment=="2"], long_pars$Beta[long_pars$Experiment == "2"], paired = T, conf.int = T)
  439. ```
  441. ### Theta
  443. #### Experiment 1
  444. ```{r}
  445. wilcox.test(short_pars$Theta[short_pars$Experiment=="1"], long_pars$Theta[long_pars$Experiment == "1"], paired = T, conf.int = T)
  446. ```
  448. #### Experiment 2
  449. ```{r}
  450. wilcox.test(short_pars$Theta[short_pars$Experiment=="2"], long_pars$Theta[long_pars$Experiment == "2"], paired = T, conf.int = T)
  451. ```
  454. ### Lambda
  455. #### Experiment 1
  456. ```{r}
  457. wilcox.test(short_pars$Lambda[short_pars$Experiment=="1"], long_pars$Lambda[long_pars$Experiment == "1"], paired = T, conf.int = T)
  458. ```
  460. #### Experiment 2
  461. ```{r}
  462. wilcox.test(short_pars$Lambda[short_pars$Experiment=="2"], long_pars$Lambda[long_pars$Experiment == "2"], paired = T, conf.int = T)
  463. ```
  467. ## Differences between strong and weak AO excitation groups
  469. ```{r}
  470. ex1 <- subset(parametersL, Experiment == '1')
  471. ex2 <- subset(parametersL, Experiment == '2')
  472. ```
  475. ### Kappa
  477. #### Experiment 1
  479. ```{r}
  480. kappa_fit1 <- lmer(Kappa ~ SOA + AO_excitation + (1|ID), data = ex1)
  481. summary(kappa_fit1)
  482. ```
  484. #### Experiment 2
  486. ```{r}
  487. kappa_fit2 <- lmer(Kappa ~ SOA + AO_excitation + (1|ID), data = ex2)
  488. summary(kappa_fit2)
  489. ```
  491. ### Beta
  493. #### Experiment 1
  495. ```{r}
  496. beta_fit1 <- lmer(Beta ~ SOA + AO_excitation + (1|ID), data = ex1)
  497. summary(beta_fit1)
  498. ```
  500. #### Experiment 2
  502. ```{r}
  503. beta_fit2 <- lmer(Beta ~ SOA + AO_excitation + (1|ID), data = ex2)
  504. summary(beta_fit2)
  505. ```
  507. ### Theta
  509. #### Experiment 1
  511. ```{r}
  512. theta_fit1 <- lmer(Theta ~ SOA + AO_excitation + (1|ID), data = ex1)
  513. summary(theta_fit1)
  514. ```
  516. #### Experiment 2
  518. ```{r}
  519. theta_fit2 <- lmer(Theta ~ SOA + AO_excitation + (1|ID), data = ex2)
  520. summary(theta_fit2)
  521. ```
  524. ### Xi
  526. #### Experiment 1
  528. ```{r}
  529. xi_fit1 <- lmer(Xi ~ SOA + AO_excitation + (1|ID), data = ex1)
  530. summary(xi_fit1)
  531. ```
  533. #### Experiment 2
  535. ```{r}
  536. xi_fit2 <- lmer(Xi ~ SOA + AO_excitation + (1|ID), data = ex2)
  537. summary(xi_fit2)
  538. ```
  541. ### NDT
  543. #### Experiment 1
  545. ```{r}
  546. NDT_fit1 <- lmer(NDT ~ SOA + AO_excitation + (1|ID), data = ex1)
  547. summary(NDT_fit1)
  548. ```
  550. #### Experiment 2
  552. ```{r}
  553. NDT_fit2 <- lmer(NDT ~ SOA + AO_excitation + (1|ID), data = ex2)
  554. summary(NDT_fit2)
  555. ```
  558. # Model parameters, response times and errors
  560. ```{r}
  561. parametersL$AO_excitation <- NULL
  562. datap <- merge(data, parametersL, by =c("ID","SOA","Experiment"))
  563. datap$Error <- as.numeric(is.na(datap$RT))
  564. datap$Lambda <- datap$Kappa-datap$Beta
  565. datap$too_fast <- datap$RT<.2
  567. datap$too_fast[is.na(datap$too_fast)] <- F
  568. datap <- subset(datap, too_fast ==F)
  569. ```
  572. ## Criterion, Kappa, Beta
  574. ```{r}
  575. sum_data <- datap %>% group_by(Experiment, ID, SOA) %>% summarize(
  576. RT = exp(mean(log(RT), na.rm = T)),
  577. PC = 1-mean(Error),
  578. Theta = mean(Theta),
  579. Kappa = mean(Kappa),
  580. Beta = mean(Beta),
  581. Xi = mean(Xi),
  582. Strength = mean(Strength),
  583. NDT = mean(NDT))
  584. sum_data$Lambda <- sum_data$Kappa-sum_data$Beta
  585. ```
  587. ## Experiment 1
  589. ```{r}
  590. correlation(sum_data[sum_data$SOA == "Short" & sum_data$Experiment=="1",3:10],method = "spearman",p_adjust = "bonferroni")
  591. ```
  593. ```{r}
  594. correlation(sum_data[sum_data$SOA == "Long"&sum_data$Experiment=="1",3:10],method = "spearman",p_adjust = "bonferroni")
  595. ```
  598. ## Experiment 2
  600. ```{r}
  601. correlation(sum_data[sum_data$SOA == "Short" & sum_data$Experiment=="2",3:10],method = "spearman",p_adjust = "bonferroni")
  602. ```
  604. ```{r}
  605. correlation(sum_data[sum_data$SOA == "Long"&sum_data$Experiment=="2",3:10],method = "spearman",p_adjust = "bonferroni")
  606. ```
  609. ## Xi, AO and NDT
  611. ```{r}
  612. sum_data2 <- datap %>% group_by(Experiment, ID) %>% summarize(
  613. RT = exp(mean(log(RT), na.rm = T)),
  614. PC = 1-mean(Error),
  615. Xi = mean(Xi),
  616. Strength = mean(Strength),
  617. NDT = mean(NDT))
  618. ```
  620. ### Experiment 1
  621. ```{r}
  622. correlation(sum_data2[sum_data2$Experiment == "1",3:7],method = "spearman",p_adjust = "bonferroni")
  623. ```
  625. ### Experiment 2
  626. ```{r}
  627. correlation(sum_data2[sum_data2$Experiment == "2",3:7],method = "spearman",p_adjust = "bonferroni")
  628. ```
  630. ## Figure 6
  632. ```{r}
  633. load('parameters_long_format.RData')
  634. pars <- subset(parametersL, Experiment==2)
  635. ```
  637. ```{r}
  638. pdiffs <- pars %>% group_by(ID) %>% summarize(
  639. Theta = Theta[SOA == "Long"]-Theta[SOA =="Short"],
  640. Lambda = Lambda[SOA == "Long"]-Lambda[SOA =="Short"])
  641. pdiffs$ID <- NULL
  642. pdiffs$ID <- 1:31
  643. pdiffs$Theta <- scale(pdiffs$Theta)
  644. ```
  647. ### Decision threshold
  649. ```{r}
  650. data_threshold <- read.csv2('decision_threshold_bold.csv')
  651. ```
  653. ```{r}
  654. data_threshold <- data_threshold %>% group_by(ID, region, SOA) %>% summarise(adjustedBOLD = mean(adjustedBOLD), threshold = mean(decision.threshold))
  656. data_threshold$region <- factor(data_threshold$region)
  657. data_threshold$SOA <- factor(data_threshold$SOA)
  658. data_threshold$region <- factor(data_threshold$region)
  659. ```
  661. ```{r}
  662. ggplot(data = data_threshold, mapping = aes(x = threshold,y = adjustedBOLD, color = SOA)) + geom_vline(xintercept = 0,linetype = 2) +
  663. facet_wrap(~region,nrow = 2)+
  664. geom_point() +
  665. geom_smooth(method = MASS::rlm, alpha= .3) +
  666. scale_color_manual(values = c("black","#42a4f5"))+
  667. ylim(-20,20) + xlim(-2.5,2.5) +
  668. theme_classic() +
  669. ylab("SPM's adjusted \nBOLD amplitude") +
  670. xlab("standardized decision threshold")+
  671. theme(legend.position = "none",text = element_text(size = 14, family = "serif"), line = element_line(linewidth = 1), strip.text.x = element_text(size = 14,face = "bold"))
  672. ```
  674. ### Effective differential leakage
  676. ```{r}
  677. lambda <- read.csv2('lambda_bold.csv')
  678. ```
  680. ```{r}
  681. lambda <- lambda %>% group_by(ID, region, SOA) %>% summarise(adjustedBOLD = mean(adjustedBOLD), lambda = mean(lambda))
  683. data_threshold$region <- factor(data_threshold$region)
  684. data_threshold$SOA <- factor(data_threshold$SOA)
  685. data_threshold$region <- factor(data_threshold$region)
  686. ```
  688. ```{r}
  689. ggplot(data = lambda, mapping = aes(x = lambda,y = adjustedBOLD, color = SOA)) + geom_vline(xintercept = 0,linetype = 2) +
  690. facet_grid(~region)+
  691. geom_point() +
  692. geom_smooth(method = MASS::rlm, alpha= .3) +
  693. scale_color_manual(values = c("black","#42a4f5"))+
  694. ylim(-20,20) + xlim(-2.5,2.5) +
  695. theme_classic() +
  696. ylab("SPM's adjusted \nBOLD amplitude") +
  697. xlab("standardized effective differential leakage")+
  698. guides(color = guide_legend(nrow=1))+
  699. theme(legend.position = "bottom",text = element_text(size = 14, family = "serif"), line = element_line(linewidth = 1), strip.text.x = element_text(size = 14, face = "bold"), legend.text = element_text(size = 14))
  700. ```
  703. ## Does SROM recover the priming effects?
  705. ```{r}
  706. priming_effects <- comb_data %>%
  707. group_by(Source, Experiment, ID, SOA,DirectAssociates,IndirectAssociates) %>%
  708. summarise(RT = mean(RT, na.rm = T),
  709. Error = mean(Error, na.rm = T))
  711. priming_effects <- priming_effects %>% group_by(Source, Experiment, ID,SOA) %>%
  712. summarise(
  713. DirPE_RT = RT[DirectAssociates == "Strong" & IndirectAssociates == "Few"] - RT[DirectAssociates == "No" & IndirectAssociates == "Few"],
  714. DirPE_ER = Error[DirectAssociates == "Strong" & IndirectAssociates == "Few"] - Error[DirectAssociates == "No" & IndirectAssociates == "Few"],
  715. ComPE_RT = RT[DirectAssociates == "No" & IndirectAssociates == "Many"] - RT[DirectAssociates == "No" & IndirectAssociates == "Few"],
  716. ComPE_ER = Error[DirectAssociates == "No" & IndirectAssociates == "Many"] - Error[DirectAssociates == "No" & IndirectAssociates == "Few"],
  717. DCPE_RT = RT[DirectAssociates == "Strong" & IndirectAssociates == "Many"] - RT[DirectAssociates == "No" & IndirectAssociates == "Few"],
  718. DCPE_ER = Error[DirectAssociates == "Strong" & IndirectAssociates == "Many"] - Error[DirectAssociates == "No" & IndirectAssociates == "Few"])
  720. priming_effects1 <- melt(priming_effects,id.vars = c("ID","Source","Experiment","SOA"),
  721. measure.vars = c("DirPE_RT","DirPE_ER","ComPE_RT","ComPE_ER","DCPE_RT","DCPE_ER"),
  722. variable.name = "Condition",value.name = "Priming_effect")
  724. priming_effects1$Outcome <- NA
  725. priming_effects1$Outcome[grep('ER', priming_effects1$Condition)] <- "Error"
  726. priming_effects1$Outcome[grep('RT', priming_effects1$Condition)] <- "RT"
  728. priming_effects1$Priming <- NA
  729. priming_effects1$Priming[grep('Dir', priming_effects1$Condition)] <- "Associative"
  730. priming_effects1$Priming[grep('Com', priming_effects1$Condition)] <- "Semantic"
  731. priming_effects1$Priming[grep('DC', priming_effects1$Condition)] <- "Combined"
  734. priming_RT <- subset(priming_effects1, Outcome == "RT")
  735. priming_ER <- subset(priming_effects1, Outcome == "Error")
  737. ```
  740. ### Plots
  743. ```{r}
  744. ggplot(data = priming_RT, mapping = aes(x = Priming, y = Priming_effect, fill = Source)) + facet_grid(Experiment~SOA) +
  745. geom_bar(position = position_dodge(1),stat = "summary") +
  746. geom_errorbar(position = position_dodge(1), width = .1,stat = "summary")+
  747. theme_classic()
  748. ```
  752. ```{r}
  753. ggplot(data = priming_ER, mapping = aes(x = Priming, y = Priming_effect, fill = Source)) + facet_grid(Experiment~SOA) +
  754. geom_bar(position = position_dodge(1),stat = "summary") +
  755. geom_errorbar(position = position_dodge(1), width = .1,stat = "summary")+
  756. theme_classic()
  757. ```
  762. ### Perform t-Tests
  764. #### Descriptives
  766. ```{r}
  767. priming_effects1 %>% summarise(.by = c('Source','Experiment','SOA','Priming','Outcome'),
  768. mean = round(mean(Priming_effect),2),
  769. se = round(sd(Priming_effect)/sqrt(n()),3))
  770. ```
  772. #### t-Tests
  774. ```{r}
  775. priming_effects1 %>% summarise(.by = c('Source','Experiment','SOA','Priming','Outcome'),
  776. t = round(t.test(Priming_effect)$statistic,2),
  777. p = round(t.test(Priming_effect)$p.value,3), df = t.test(Priming_effect)$parameter)
  778. ```

Analyses.Rmd, no license · at the source

Overview

Authors: Leo Sokolovič1,2,3, Juraj Kukolja2,3, Markus Hofmann1
ORCID iDs: Leo Sokolovič
  1. Department of General and Biological Psychology, University of Wuppertal,42119 Wuppertal, Germany
  2. Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal,42883 Wuppertal, Germany
  3. Faculty of Health, Witten/Herdecke University,58488 Witten, Germany
Institutions: University of Wuppertal (Germany); Helios Universitätsklinikum Wuppertal (Germany); Witten/Herdecke University (Germany)
Journal: Scientific reports, volume 16, issue 1, article 19183
Dates: received 20 February 2026; accepted 17 June 2026; published online 20 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-58866-4 · PMID 42323482 · PMCID PMC13283216 · OpenAlex W7165403616
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, fMRI & imaging, Spectral & time-frequency
Keywords: associative and semantic priming, leaky competing accumulators, interactive activation model, fMRI, Neuroscience, Psychology
MeSH: Cognition*, Decision Making*, Semantics*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Memory, Short-Term, Reaction Time, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Bergische Universität Wuppertal (3089)
Citations: cited by 1 paper (Europe PMC); 114 references in the paper

Abstract

This study introduces the sequential read-out model (SROM) to investigate the strategic and decision-making contributions to the semantic priming effect in a lexical-decision task (LDT). We use behavioral and fMRI data from two experiments (n = 32 and 31), which factorially manipulated the association strength, semantic similarity, and stimulus-onset-asynchrony. Using a leaky competing accumulator layer for lexical decisions, the SROM successfully accounted for behavioral data and showed that switching from short to long stimulus-onset-asynchrony changed the decision-making mode from competitive to independent race regime. We then used the individually estimated SROM parameters to predict the interindividual differences in BOLD responses to targets. We found that lexical-orthographic evidence modulated BOLD amplitudes in the left lingual gyrus, suggesting that it maintains lexico-orthographic evidence in working memory, which is then used for lexical decisions. The decision threshold, inhibition parameters and top-down semantic excitation predicted activation differences in the left inferior frontal gyrus, suggesting it regulates the decision process. Decision noise and top-down semantic excitation were associated with the left angular gyrus, supporting its involvement in evidence accumulation. Overall, the SROM provided mechanistic interpretations of brain activations in the regions involved in the semantically primed LDT, while accounting for associative semantic and strategic factors.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-58866-4.

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Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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This paper

Sokolovič, L., Kukolja, J., & Hofmann, M. (2026). A neurocognitive interactive activation model of semantic priming in lexical decisions. Scientific reports, 16(1), 19183. https://doi.org/10.1038/s41598-026-58866-4

BibTeX

@article{sokolovic2026neurocognitive,
author = {Sokolovič, Leo and Kukolja, Juraj and Hofmann, Markus},
title = {{A neurocognitive interactive activation model of semantic priming in lexical decisions}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {19183},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58866-4},
url = {https://doi.org/10.1038/s41598-026-58866-4},
pmid = {42323482},
pmcid = {PMC13283216}
}

RIS

TY - JOUR
AU - Sokolovič, Leo
AU - Kukolja, Juraj
AU - Hofmann, Markus
TI - A neurocognitive interactive activation model of semantic priming in lexical decisions
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/20
VL - 16
IS - 1
SP - 19183
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58866-4
UR - https://doi.org/10.1038/s41598-026-58866-4
LA - en
ER -

CSL-JSON

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