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Dissecting the interplay of model-based control, impulsivity and compulsivity on self-control in daily life.

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  1. [1] § Methods › Regression model ↔ Data and code/SelfControl_2tasks_MLM_OSF.R, lines 218–266 · score 0.56 · random intercepts, random slopes, variables, fit, EEG, conflict strength

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  1. ################################################################################################################################################
  2. setwd('your directory')
  4. #### Load packages####
  6. # load required packages
  7. if(!require(readxl)){
  8. install.packages("readxl") # v 1.4.4
  9. library(readxl)
  10. }
  11. # load required packages
  12. if(!require(car)){
  13. install.packages("car") # v 3.1-3
  14. library(car)
  15. }
  16. # load required packages
  17. if(!require(tidyr)){
  18. install.packages("tidyr") # v 1.1.3
  19. library(tidyr)
  20. }
  21. # load required packages
  22. if(!require(dplyr)){
  23. install.packages("dplyr") # v 1.1.4
  24. library(dplyr)
  25. }
  26. # load required packages
  27. if(!require(psych)){
  28. install.packages("psych") # v 2.4.12
  29. library(psych)
  30. }
  31. # load required packages
  32. if(!require(stats)){
  33. install.packages("stats") # v 4.4.3
  34. library(stats)
  35. }
  36. # load required packages
  37. if(!require(DHARMa)){
  38. install.packages("DHARMa") # v 0.4.7
  39. library(DHARMa)
  40. }
  41. # load required packages
  42. if(!require(lme4)){
  43. install.packages("lme4") # v 1.1-36
  44. library(lme4)
  45. }
  46. # load required packages
  47. if(!require(emmeans)){
  48. install.packages("emmeans") # v 1.10.7
  49. library(emmeans)
  50. }
  51. # load required packages
  52. if(!require(sjPlot)){
  53. install.packages("sjPlot") # v 2.8.17
  54. library(sjPlot)
  55. }
  56. # load required packages
  57. if(!require(report)){
  58. install.packages("report") # v 0.6.1
  59. library(report)
  60. }
  61. # load required packages
  62. if(!require(interactions)){
  63. install.packages("interactions") # v 1.2.0
  64. library(interactions)
  65. }
  66. # load required packages
  67. if(!require(ggplot2)){
  68. install.packages("ggplot2") # v 3.5.1
  69. library(ggplot2)
  70. }
  73. #### Read Dataset ####
  75. SC_data_indiv <- openxlsx::read.xlsx("SC_2tasks_indiv.xlsx")
  77. SC_data_indiv <- dplyr::rename(SC_data_indiv, TotalMiss = Miss , TotalDes = Des , TotalCon = Con ,
  78. TotalConEnact = ConEnact , TotalResEnact = ResEnact ,
  79. MeanConStrength = ConStrength , MeanDesStrength = DesStrength,
  80. MeanDesStrengthEnact = DesStrengthEnact)
  82. names <- SC_data_indiv$name
  83. SC_data_indiv$namesNew <- factor(x = SC_data_indiv$name, levels = names)
  86. EMA_long <- read.csv("your directory\\EMA_longForm.csv")
  88. exclude <- setdiff(EMA_long$name, SC_data_indiv$name)
  89. EMA_long <- EMA_long[!EMA_long$name %in% exclude, ] #frame originally contains all participants
  91. AllData <- full_join(SC_data_indiv, EMA_long, by = c("name"))
  93. glmmData <- subset(AllData, select = c(1, 4, 7, 10, 13, 16, 21, 38, 39, 82, 86, 87, 89:93)) # only needed columns
  94. glmmData$resistance[glmmData$resistance == 0] <- -1
  95. # extract only conflicted situations
  96. glmmData_conflict <- (glmmData %>% filter(conflict==1))
  99. # scale all continuous predictors
  100. glmmData[,c(2:9 )]<-scale(glmmData[,c(2:9)],center=F,scale=T) #mean betas, BIS, OCI
  101. glmmData[,c(13, 15 )] <- scale(glmmData[,c(13, 15 )], center = F, scale = T) #conf and des strength
  102. glmmData_conflict[,c(2:9 )]<-scale(glmmData_conflict[,c(2:9)],center=F,scale=T) #mean betas, BIS, OCI
  103. glmmData_conflict[,c(13, 15 )] <- scale(glmmData_conflict[,c(13, 15 )], center = F, scale = T) #conf and des strength
  105. #### desriptives ####
  107. psych::describe(SC_data_indiv[, c(18:22, 30:32, 38:39, 52:81)])
  109. ##################################################################################
  110. # checking assumptions
  111. #
  112. # The assumptions of generalised linear mixed models are a combination of
  113. # the assumptions of GLMs and mixed models.
  114. #
  115. # The observed y are independent, conditional on some predictors x
  116. #
  117. # The response y come from a known distribution from the exponential family,
  118. # with a known mean variance relationship
  119. #
  120. # There is a straight line relationship between some known function (link) of the mean of y
  121. # and the predictors x and random effects z
  122. #
  123. # Random effects z are independent of y
  124. #
  125. # Random effects z are normally distributed
  127. ##################################################################################
  128. # Model Random Slope
  130. fit.bin <- glmer(enactment ~ 1 + desire_strength + conflict_strength + BIS + OCI +
  131. (1 + desire_strength + conflict_strength|name),
  132. family = binomial("logit"), data = glmmData,
  133. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  134. summary(fit.bin)
  136. # examine residual plots - plots difficult to interpret for binary data
  137. par(mfrow = c(1, 2))
  138. plot(residuals(fit.bin) ~ fitted(fit.bin), main = "residuals v.s. Fitted")
  139. qqnorm(residuals(fit.bin)) #seems ok
  141. #check dispersion
  143. # https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html#general-remarks-on-interperting-residual-patterns-and-tests
  144. # https://github.com/florianhartig/DHARMa/issues/181
  145. # https://github.com/florianhartig/DHARMa/issues/396
  146. ss <- DHARMa::simulateResiduals(fit.bin, plot = T) #straight diagnonal --> looks ok
  148. testDispersion(ss) #n.s.
  149. simulationOutput = recalculateResiduals(ss , group = glmmData$name)
  150. testDispersion(simulationOutput) #n.s.
  151. plot(simulationOutput) #KS test distribution, dispersion and residual vs. predicted values ok, but outlier test significant
  153. # check normality of RE
  155. r_int<- ranef(fit.bin)$name$`(Intercept)`
  156. shapiro.test(r_int) # p <.001
  157. qqPlot(r_int)
  159. r_conf<- ranef(fit.bin)$name$conflict_strength
  160. shapiro.test(r_conf) # p .63
  161. qqPlot(r_conf)
  163. r_des<- ranef(fit.bin)$name$desire_strength
  164. shapiro.test(r_des) # p .044
  165. qqPlot(r_des)
  167. ##################################################################################
  168. # Level 1 predictors will be desire and conflict strength (Krönke et al., 2020),
  169. # level 2 predictors will be impulsivity and compulsivity
  170. # enactment ~ desire strength * conflict strength * impulsivity * compulsivity + (desire strength * conflict strength | participant)
  171. ##################################################################################
  173. ##################################################################################
  174. # models for different situations
  175. ##################################################################################
  176. ## all situations
  177. # fit null model
  178. fit0a <- glm(enactment ~ 1, family = binomial("logit"), data = glmmData)
  179. summary(fit0a)
  180. # fit baseline model (2 levels)
  181. fit0b <- glmer(enactment ~ (1|name), family = binomial("logit"), data = glmmData)
  182. summary(fit0b)
  183. # see if random effects warranted - if significant then yes
  184. null.id = -2 * logLik(fit0a) + 2 * logLik(fit0b)
  185. pchisq(as.numeric(null.id), df=1, lower.tail=F) #yes: p 8.7e-238
  186. ## conflict situations
  187. # fit null model
  188. fit0a <- glm(enactment ~ 1, family = binomial("logit"), data = glmmData_conflict)
  189. summary(fit0a)
  190. # fit baseline model (2 levels)
  191. fit0b <- glmer(enactment ~ (1|name), family = binomial("logit"), data = glmmData_conflict)
  192. summary(fit0b)
  193. # see if random effects warranted - if significant then yes
  194. null.id = -2 * logLik(fit0a) + 2 * logLik(fit0b)
  195. pchisq(as.numeric(null.id), df=1, lower.tail=F) #yes: p 4.3e-31
  197. # examine estimates of the community effects or residuals
  198. u0 <- ranef(fit0b, condVar = TRUE)
  199. u0se <- sqrt(attr(u0[[1]], "postVar")[1, , ])
  200. commid <- as.numeric((str_sub(rownames(u0[[1]]), start = -4)))
  201. u0tab <- cbind("commid" = commid, "u0" = u0[[1]], "u0se" = u0se)
  202. colnames(u0tab)[2] <- "u0"
  203. u0tab <- u0tab[order(u0tab$u0), ]
  204. u0tab <- cbind(u0tab, c(1:dim(u0tab)[1]))
  205. u0tab <- u0tab[order(u0tab$commid), ]
  206. colnames(u0tab)[4] <- "u0rank"
  207. # The plot shows the estimated residuals for all 236 participants in the sample. For a
  208. # substantial number of participants, the 95% confidence interval does not overlap
  209. # the horizontal line at zero, indicating that enactment of desires in these
  210. # participants is significantly above average (above the zero line) or below average
  211. # (below the zero line)
  212. plot(u0tab$u0rank, u0tab$u0, type = "n", xlab = "u_rank",
  213. ylab = "conditional modes of r.e. for comm_id:_cons", ylim = c(-4, 4))
  214. segments(u0tab$u0rank, u0tab$u0 - 1.96*u0tab$u0se, u0tab$u0rank, u0tab$u0 + 1.96*u0tab$u0se)
  215. points(u0tab$u0rank, u0tab$u0, col = "blue")
  216. abline(h = 0, col = "red")
  218. # add explanatory variables
  219. ## all situations
  220. #random intercept
  221. fit0c <- glmer(enactment ~ desire_strength + conflict_strength + (1|name),
  222. family = binomial("logit"), data = glmmData)
  223. summary(fit0c)
  224. # random slopes
  225. fit0c1 <- glmer(enactment ~ desire_strength + conflict_strength + (1 + conflict_strength + desire_strength|name),
  226. family = binomial("logit"), data = glmmData)
  227. summary(fit0c1)
  228. # see if random slopes warranted - if significant then yes
  229. null.id = -2 * logLik(fit0c) + 2 * logLik(fit0c1)
  230. pchisq(as.numeric(null.id), df=1, lower.tail=F) #yes: p 2.04-99
  231. ## conflict situations
  232. #random intercept
  233. fit0d <- glmer(enactment ~ desire_strength + conflict_strength + (1|name),
  234. family = binomial("logit"), data = glmmData_conflict)
  235. summary(fit0d)
  236. # random slopes
  237. fit0d1 <- glmer(enactment ~ desire_strength + conflict_strength + (1 + conflict_strength + desire_strength|name),
  238. family = binomial("logit"), data = glmmData_conflict)
  239. summary(fit0d1)
  241. ###############################################################################################################
  242. ############ explore effect of EEG ###########################################################
  243. ###############################################################################################################
  245. ########## two step task
  247. # for all situations
  249. # add FRN and P3 (cor .14)
  250. fit4a0 <- glmer(enactment ~ desire_strength + conflict_strength +
  251. mean_beta_FRN + mean_beta_fbP3b +
  252. (1 + desire_strength + conflict_strength|name),
  253. family = binomial("logit"), data = glmmData,
  254. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  255. summary(fit4a0) #singular fit
  256. sjPlot::tab_model(fit4a0)
  257. report::report(fit4a0)
  258. # add BIS and OCI
  259. fit4a <- glmer(enactment ~ desire_strength + conflict_strength +
  260. mean_beta_FRN + mean_beta_fbP3b + BIS + OCI +
  261. (1 + desire_strength + conflict_strength|name),
  262. family = binomial("logit"), data = glmmData,
  263. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  264. summary(fit4a)
  265. sjPlot::tab_model(fit4a)
  266. report::report(fit4a)
  268. # add interactions with BIS and OCI
  269. fit4b <- glmer(enactment ~ desire_strength + conflict_strength +
  270. mean_beta_FRN + mean_beta_FRN:BIS + mean_beta_FRN:OCI + mean_beta_FRN:BIS:OCI +
  271. mean_beta_fbP3b + mean_beta_fbP3b:BIS + mean_beta_fbP3b:OCI + mean_beta_fbP3b:BIS:OCI +
  272. (1 + desire_strength + conflict_strength|name),
  273. family = binomial("logit"), data = glmmData,
  274. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  275. summary(fit4b)
  276. sjPlot::tab_model(fit4b)
  277. report::report(fit4b)
  280. #for conflicts
  282. # add FRN and P3
  283. fit4a02 <- glmer(enactment ~ desire_strength + conflict_strength +
  284. mean_beta_FRN + mean_beta_fbP3b +
  285. (1 + desire_strength + conflict_strength|name),
  286. family = binomial("logit"), data = glmmData_conflict,
  287. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  288. summary(fit4a02) #singular fit
  289. sjPlot::tab_model(fit4a02)
  290. report::report(fit4a02)
  291. # add BIS and OCI
  292. fit4a2 <- glmer(enactment ~ desire_strength + conflict_strength +
  293. mean_beta_FRN + mean_beta_fbP3b + BIS + OCI +
  294. (1 + desire_strength + conflict_strength|name),
  295. family = binomial("logit"), data = glmmData_conflict,
  296. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  297. summary(fit4a2)
  298. sjPlot::tab_model(fit4a2)
  299. report::report(fit4a2)
  300. # add interactions with BIS and OCI
  301. fit4b2 <- glmer(enactment ~ desire_strength + conflict_strength +
  302. mean_beta_FRN + mean_beta_FRN:BIS + mean_beta_FRN:OCI + mean_beta_FRN:BIS:OCI +
  303. mean_beta_fbP3b + mean_beta_fbP3b:BIS + mean_beta_fbP3b:OCI + mean_beta_fbP3b:BIS:OCI +
  304. (1 + desire_strength + conflict_strength|name),
  305. family = binomial("logit"), data = glmmData_conflict,
  306. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  307. summary(fit4b2)
  308. sjPlot::tab_model(fit4b2)
  309. report::report(fit4b2)
  312. ########## GONOGO task
  314. # for all situations
  316. # add N2 and P3 (cave: cor .65)
  317. fit5a0 <- glmer(enactment ~ desire_strength + conflict_strength +
  318. mean_beta_N2 + mean_beta_stimP3a +
  319. (1 + desire_strength + conflict_strength|name),
  320. family = binomial("logit"), data = glmmData,
  321. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  322. summary(fit5a0)
  323. sjPlot::tab_model(fit5a0)
  324. report::report(fit5a0)
  325. # add BIS and OCI
  326. fit5a <- glmer(enactment ~ desire_strength + conflict_strength +
  327. mean_beta_N2 + mean_beta_stimP3a + BIS + OCI +
  328. (1 + desire_strength + conflict_strength|name),
  329. family = binomial("logit"), data = glmmData,
  330. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  331. summary(fit5a)
  332. sjPlot::tab_model(fit5a)
  333. report::report(fit5a)
  334. # add interactions with BIS and OCI
  335. fit5b <- glmer(enactment ~ desire_strength + conflict_strength +
  336. mean_beta_N2 + mean_beta_N2:BIS + mean_beta_N2:OCI + mean_beta_N2:BIS:OCI +
  337. mean_beta_stimP3a + mean_beta_stimP3a:BIS + mean_beta_stimP3a:OCI + mean_beta_stimP3a:BIS:OCI +
  338. (1 + desire_strength + conflict_strength|name),
  339. family = binomial("logit"), data = glmmData,
  340. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  341. summary(fit5b)
  342. sjPlot::tab_model(fit5b)
  343. report::report(fit5b)
  346. ########### investigate interactions
  347. # Calculate mean and SD of the moderators (predictor2 and predictor3)
  348. mean_pred2 <- mean(glmmData$BIS)
  349. sd_pred2 <- sd(glmmData$BIS)
  350. mean_pred3 <- mean(glmmData$OCI)
  351. sd_pred3 <- sd(glmmData$OCI)
  353. ### Imp*Comp
  354. # Specify the values for predictor2 and predictor3 at which to estimate the simple slopes
  355. emm <- emmeans(fit4b,
  356. ~ mean_beta_FRN | BIS * OCI,
  357. at = list(BIS = c(mean_pred2 - sd_pred2, mean_pred2, mean_pred2 + sd_pred2),
  358. OCI = c(mean_pred3 - sd_pred3, mean_pred3, mean_pred3 + sd_pred3)))
  359. # View the estimated marginal means and simple slopes
  360. summary(emm)
  361. # Regrid the emmeans object to back-transform estimates
  362. regridded_emm <- regrid(emm)
  363. # Use emtrends to test the slope of FRN effect at different levels of Imp and Comp
  364. slopes <- emtrends(fit4b, ~ BIS * OCI, var = "mean_beta_FRN",
  365. at = list(BIS = c(mean_pred2 - sd_pred2, mean_pred2, mean_pred2 + sd_pred2),
  366. OCI = c(mean_pred3 - sd_pred3, mean_pred3, mean_pred3 + sd_pred3)))
  367. slopes2 <- emtrends(fit4b, ~ BIS * OCI, var = "mean_beta_FRN",
  368. at = list(BIS = c(mean_pred2 - sd_pred2, mean_pred2 + sd_pred2),
  369. OCI = c(mean_pred3 - sd_pred3, mean_pred3 + sd_pred3)))
  371. #compare slopes
  372. pairs(slopes, simple = "each")
  374. ## plot interactions
  375. glmmData$Impulsivity <- glmmData$BIS
  376. glmmData$Compulsivity <- glmmData$OCI
  377. glmmData$FRN <- glmmData$mean_beta_FRN
  378. glmmData$P3 <- glmmData$mean_beta_fbP3b
  379. glmmData$Enactment <- glmmData$enactment
  380. fit4plot <- glmer(Enactment ~ desire_strength + conflict_strength +
  381. FRN + FRN:Impulsivity + FRN:Compulsivity + Impulsivity:Compulsivity:FRN +
  382. P3 + P3:Impulsivity + P3:Compulsivity + Impulsivity:Compulsivity:P3 +
  383. (1 + desire_strength + conflict_strength|name),
  384. family = binomial("logit"), data = glmmData,
  385. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=100000)))
  386. summary(fit4plot)
  388. #FRN
  389. #big fonts get smaller when images are saved
  391. interact_plot(fit4plot, pred = FRN, modx = Impulsivity, mod2 = Compulsivity,
  392. outcome.scale = 'response', interval = T, x.label = "FRN effect", colors = "Dark2") +
  393. theme(axis.title.x = element_text( size = 30, face = "bold"),
  394. axis.title.y = element_text( size = 30, face = "bold"),
  395. plot.subtitle = element_text(size = 28),
  396. axis.text = element_text( size = 14),
  397. legend.title=element_text(size=22),
  398. legend.text=element_text(size=22))
  400. interact_plot(fit4plot, pred = FRN, modx = Compulsivity, mod2 = Impulsivity,
  401. outcome.scale = 'response', interval = T, x.label = "FRN effect", colors = "Dark2") +
  402. theme(axis.title.x = element_text( size = 30, face = "bold"),
  403. axis.title.y = element_text( size = 30, face = "bold"),
  404. plot.subtitle = element_text(size = 28),
  405. axis.text = element_text( size = 14),
  406. legend.title=element_text(size=22),
  407. legend.text=element_text(size=22))
  409. interact_plot(fit4plot, pred = FRN, modx = Compulsivity, interval = T, x.label = "FRN effect", colors = "Dark2") +
  410. geom_line(size = 1) +
  411. theme(axis.title.x = element_text( size = 30, face = "bold"),
  412. axis.title.y = element_text( size = 30, face = "bold"),
  413. axis.text = element_text( size = 14),
  414. legend.title=element_text(size=22),
  415. legend.text=element_text(size=22))
  416. interact_plot(fit4plot, pred = FRN, modx = Impulsivity, interval = T)
  417. geom_line(size = 10)
  419. #P3
  420. interact_plot(fit4plot, pred = P3, modx = Impulsivity, mod2 = Compulsivity,
  421. outcome.scale = 'response')
  422. interact_plot(fit4plot, pred = P3, modx = Compulsivity, mod2 = Impulsivity,
  423. outcome.scale = 'response')
  425. interact_plot(fit4plot, pred = P3, modx = Compulsivity)
  426. geom_line(size = 10)
  427. interact_plot(fit4plot, pred = P3, modx = Impulsivity)
  428. geom_line(size = 10)
  431. ###############################################################################################################
  432. ############ explore effect of desire and conflict occurrence ###########################################################
  433. ###############################################################################################################
  435. ### conflict occurence by EEG
  437. fit6a1 <- glmer(conflict ~ mean_beta_FRN + mean_beta_fbP3b + BIS + OCI + (1|name),
  438. family = binomial("logit"), data = glmmData,
  439. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e9)))
  440. summary(fit6a)
  442. fit6a2 <- glmer(conflict ~ mean_beta_FRN:BIS:OCI + mean_beta_fbP3b:BIS:OCI + BIS + OCI + (1|name),
  443. family = binomial("logit"), data = glmmData,
  444. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e9)))
  445. summary(fit6a2)
  446. sjPlot::tab_model(fit6a2)
  447. report::report(fit6a2)
  449. interact_plot(fit6a2, pred = mean_beta_FRN, modx = OCI,
  450. outcome.scale = 'response', interval = T)
  452. ggplot(glmmData, aes(x=OCI, y=conflict)) +
  453. labs(x="Compulsivity", y="Conflict") +
  454. geom_smooth(method=glm , color="#D95F02", ,size = 1.5, se=TRUE) +
  455. theme_minimal() +
  456. theme(panel.grid.minor = element_blank()) +
  457. theme(panel.grid.major = element_line(,size = 0.5,linetype = 5)) +
  458. theme(axis.title.x = element_text(color = "#34495e", size = 16, face = "bold"),
  459. axis.title.y = element_text(color = "#34495e", size = 16, face = "bold")) +
  460. ylim(0.0, 1.0)
  462. ### desire occurence
  464. fit7a <- glmer(desire ~ mean_beta_FRN:BIS:OCI + mean_beta_fbP3b:BIS:OCI + BIS + OCI + (1|name),
  465. family = binomial("logit"), data = glmmData,
  466. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e9)))
  467. summary(fit7a)
  468. sjPlot::tab_model(fit7a)
  469. report::report(fit7a)
  471. interact_plot(fit7a, pred = mean_beta_FRN, modx = OCI,
  472. outcome.scale = 'response', interval = T)
  474. ggplot(glmmData, aes(x=OCI, y=desire)) +
  475. labs(x="Compulsivity", y="Desire") +
  476. geom_smooth(method=glm, color = "#D95F02" ,size = 1.5, se=TRUE) +
  477. theme_minimal() +
  478. theme(panel.grid.minor = element_blank()) +
  479. theme(panel.grid.major = element_line(size = 0.5,linetype = 5)) +
  480. theme(axis.title.x = element_text( size = 16, face = "bold", color = "#34495e"),
  481. axis.title.y = element_text( size = 16, face = "bold", color = "#34495e")) +
  482. ylim(0.0, 1.0)
  485. ### explore FRN and EMA
  486. fit8a <- glmer(enactment ~ conflict_strength*desire_strength + conflict_strength:mean_beta_FRN:BIS + conflict_strength:mean_beta_FRN:OCI +
  487. desire_strength:mean_beta_FRN:BIS + desire_strength:mean_beta_FRN:OCI +(1 + desire_strength + conflict_strength|name),
  488. family = binomial("logit"), data = glmmData,
  489. control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e9)))
  490. summary(fit8a) #only EMA data predicts enactment

SelfControl_2tasks_MLM_OSF.R, no license · at the source

Overview

Authors: Kerstin Dück1, Rebecca Overmeyer1, Raoul Wüllhorst1, Tanja Endrass1
ORCID iDs: Kerstin Dück
  1. Faculty of Psychology Clinical Psychology and Addiction Research, Technische Universität Dresden, Chemnitzer Str. 46a 01187, 01062 Dresden, Germany
Institutions: Technische Universität Dresden (Germany)
Journal: Scientific reports, volume 16, issue 1, article 19167
Dates: received 10 February 2026; accepted 11 June 2026; published online 19 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-58046-4 · PMID 42321288 · PMCID PMC13282391 · OpenAlex W7165121732
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials
Keywords: Diseases, Neuroscience, Psychology
MeSH: Compulsive Behavior*, Impulsive Behavior*, Self-Control*, Adult, Decision Making, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Behavioral Health and Interventions (Applied Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft; Technische Universität Dresden
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Impaired self-control is linked to maladaptive behavior and psychopathology and may be shaped by transdiagnostic factors such as impulsivity and compulsivity. Both traits show associations with model-based control, which supports goal-directed behavior by representing long-term action consequences. To investigate how model-based control and personality traits relate to everyday self-control, we combined single-trial EEG data (modulation of the feedback-related negativity [FRN] and P3) from 236 participants during a two-step decision-making task with self-reported impulsivity (BIS-11) and compulsivity (OCI-R) and a seven-day ecological momentary assessment of daily-life self-control. Mixed-effects models revealed that desire enactment was more likely when desires were stronger and perceived conflicts weaker. The effect of model-based control varied with impulsivity and compulsivity: In individuals low in compulsivity, stronger modulations of the FRN, suggesting higher model-based control, were connected to fewer desire enactments, suggesting a protecting role of model-based control. This pattern reversed at high compulsivity levels, possibly due to greater conflict awareness but reduced behavioral regulation capacity. Impulsivity moderated compulsivity effects, such that model-based control consistently predicted reduced enactment at high impulsivity levels. These findings highlight how compulsivity and impulsivity shape the translation of cognitive control into everyday behavior and offer insights into mechanisms underlying self-control deficits.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-58046-4.

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

Repository

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OSF vjnhw

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 4 files, 1 script
Software Heritage: not checked
Found in: the text, “Regression model”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: car (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), lme4 (1 file), psych (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/vjnhw/

Tracing map

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  • 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);
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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

Data and analysis routines are available under [https://osf.io/vjnhw/].

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

Versions

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Version 2, 28 September 2026

  • Funding: added Deutsche Forschungsgemeinschaft; Technische Universität Dresden

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 10 MeSH terms, 75 references.

Cite

This paper

Dück, K., Overmeyer, R., Wüllhorst, R., & Endrass, T. (2026). Dissecting the interplay of model-based control, impulsivity and compulsivity on self-control in daily life. Scientific reports, 16(1), 19167. https://doi.org/10.1038/s41598-026-58046-4

BibTeX

@article{duck2026dissecting,
author = {Dück, Kerstin and Overmeyer, Rebecca and Wüllhorst, Raoul and Endrass, Tanja},
title = {{Dissecting the interplay of model-based control, impulsivity and compulsivity on self-control in daily life}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {19167},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-58046-4},
url = {https://doi.org/10.1038/s41598-026-58046-4},
pmid = {42321288},
pmcid = {PMC13282391}
}

RIS

TY - JOUR
AU - Dück, Kerstin
AU - Overmeyer, Rebecca
AU - Wüllhorst, Raoul
AU - Endrass, Tanja
TI - Dissecting the interplay of model-based control, impulsivity and compulsivity on self-control in daily life
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/19
VL - 16
IS - 1
SP - 19167
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-58046-4
UR - https://doi.org/10.1038/s41598-026-58046-4
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13282391",
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"issued": {
"date-parts": [
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2026,
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19
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