OSCR

The association between the error-related negativity and self-control is moderated by impulsivity and compulsivity.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Results › The association between the ERN and self-control is diminished in high compulsivity and impulsivity ↔ Data and Code/SCF_analysis_GLM.R, lines 54 to 141 · score 0.73 · ERN loss, ERN gain, way interaction, motivational contexts, Simple slopes, predicted · Report a mismatch
  2. [2] § Methods › Data analysis › Profile regression to explore potential subgroups differing in self-control ↔ Data and Code/SCF_analysis_profileRegression.R, lines 43 to 82 · score 0.73 · fviz_nbclust, profile regression, PReMiuM, pam, medoids, covariates · Report a mismatch
  3. [3] § Methods › Data analysis ↔ Data and Code/Enactment_analysis_MLM.R, lines 167 to 232 · score 0.61 · Confidence Intervals, lme4, residual, BOBYQA, glmer, binomial · Report a mismatch
  4. [4] § Results › Cluster analysis reveals a compulsive/worry profile associated with increased self-control failures ↔ Data and Code/SCF_analysis_profileRegression.R, lines 43 to 82 · score 0.58 · profile regression, medoids, somatic, optimal, routine, premeditation · Report a mismatch
  5. [5] § Methods › Data analysis › Profile regression to explore potential subgroups differing in self-control ↔ Data and Code/SCF_analysis_profileRegression.R, lines 270 to 309 · score 0.56 · credible intervals, posterior distribution, regression, Profile, cluster · Report a mismatch
  6. [6] § Methods ↔ Data and Code/SCF_analysis_profileRegression.R, lines 1 to 41 · score 0.54 · COHS, STICSA, UPPS, somatic, routine, premeditation · Report a mismatch
  7. [7] § Results › Cluster analysis reveals a compulsive/worry profile associated with increased self-control failures ↔ Data and Code/SCF_analysis_profileRegression.R, lines 270 to 309 · score 0.50 · credible intervals, Posterior distribution, Empirical, Risk, plausible, cluster · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

R · 312 lines · 12 KB · no license · 5 matches

  1. # set directory
  2. setwd(" ")
  4. # load data
  5. library(readxl)
  6. library(tidyverse)
  7. EMAdata <- read.csv("EMAdata.csv")
  8. QUESTdata <- readxl::read_excel("QUESTdata.xlsx")
  9. # check data
  10. table((EMAdata$filename) == (QUESTdata$name))
  11. # make SCF variable
  12. EMAdata$SCF <- EMAdata$ConEnact/(EMAdata$All - EMAdata$Miss)
  14. # make new data
  15. RegressionData <- data.frame(SCF=rep(0, 221))
  16. RegressionData$SCF <- EMAdata$SCF
  17. RegressionData$Imp <- QUESTdata$BIS_SumScore
  18. RegressionData$Comp <- QUESTdata$OCI_SumScore
  19. RegressionData$UPPS_urgency <- QUESTdata$UPPS_Urge
  20. RegressionData$UPPS_premeditation <- QUESTdata$UPPS_Prem
  21. RegressionData$UPPS_perseverance <- QUESTdata$UPPS_Pers
  22. RegressionData$UPPS_sensation <- QUESTdata$UPPS_Sens
  23. RegressionData$DASS_Depression <- QUESTdata$DASS_Depression_Score
  24. RegressionData$DASS_Anxiety <- QUESTdata$DASS_Anxiety_Score
  25. RegressionData$STICSA_cognitive <- QUESTdata$STICSA_Cog
  26. RegressionData$STICSA_somatic <- QUESTdata$STICSA_Som
  27. RegressionData$PSWQ <- QUESTdata$PSWQ
  28. RegressionData$COHS_automaticity <- QUESTdata$COHS_Auto
  29. RegressionData$COHS_routine <- QUESTdata$COHS_Rout
  30. RegressionData$BISBAS_BIS <- QUESTdata$BIS_BISBAS
  31. RegressionData$BISBAS_BAS <- QUESTdata$BAS_BISBAS
  34. # Load the remotes package and install PReMiuM from GitHub
  35. #install.packages("remotes") # Install remotes if not already installed
  36. #remotes::install_github("silvialiverani/PReMiuM-R-package")
  38. # Load the PReMiuM package
  39. # https://premium-r-package.readthedocs.io/en/latest/manual/
  40. library(PReMiuM)
  41. set.seed(2024)
  43. # Define profiles and covariates
  44. # PReMiuM requires specific data structures for profile regression, including defining profiles and covariates.
  45. response <- "SCF" # response variable
  46. covariates <- c("Imp", "Comp",
  47. "UPPS_urgency", "UPPS_premeditation", "UPPS_perseverance", "UPPS_sensation",
  48. "DASS_Depression", "DASS_Anxiety", #"DASS_Stress",
  49. "STICSA_cognitive", "STICSA_somatic",
  50. "PSWQ", "COHS_automaticity", "COHS_routine",
  51. "BISBAS_BIS", "BISBAS_BAS") # covariates
  53. # Specify the variable types (e.g., "Bernoulli" for binary response, "Normal" for continuous)
  54. var_types <- c("Normal") # Adjust based on your response variable type
  56. # Create covariate matrix
  57. covariate_matrix <- as.matrix(RegressionData[covariates])
  59. ###############################################################################################
  60. ###############################################################################################
  62. # step 0: run pam() from cluster package to determine optimal number of cluster
  63. library(cluster)
  64. library(factoextra)
  65. cluster_dataframe <- scale(RegressionData)#[,c(1,3:17)])
  66. fviz_nbclust(cluster_dataframe, pam, method ="silhouette")+theme_minimal()
  67. fviz_nbclust(cluster_dataframe, pam, method ="gap_stat")+theme_minimal()
  68. opt_pam <- pam(cluster_dataframe, k=2, metric = "euclidean")
  69. ClusteredRegData <- cbind(RegressionData[,c(1,3:17)], opt_pam$clustering)
  70. head(ClusteredRegData)
  71. opt_pam$medoids
  72. fviz_cluster(opt_pam,
  73. palette =c("#007892","#D9455F"),
  74. ellipse.type ="euclid",
  75. repel =TRUE,
  76. ggtheme =theme_minimal())
  78. # optimal clustering: k = 2
  81. ###############################################################################################
  82. ###############################################################################################
  85. # step 1.1 - max 7 clusters
  86. # Perform profile regression
  87. # Use the profileReg function from the PReMiuM package to perform profile regression.
  89. # Run the MCMC algorithm
  90. runInfoObj <- profRegr(
  91. yModel = "Normal",
  92. xModel = "Normal",
  93. nSweeps = 100000,
  94. nBurn = 50000,
  95. data = RegressionData,
  96. output = "output",
  97. covNames = covariates,
  98. outcome = response,
  99. nFilter = 10,
  100. nClusInit = 20,
  101. varSelectType = "Continuous", #"BinaryCluster", #
  102. seed = 2024
  103. )
  105. # Calculate dissimilarity matrix
  106. dissimObj <- calcDissimilarityMatrix(runInfoObj)
  107. # calculate optimal clustering
  108. clusObj <- calcOptimalClustering(dissimObj, maxNClusters = 7, useLS=F)
  110. # Extract cluster assignments
  111. cluster_assignments <- clusObj$clustering
  112. # Calculate cluster sizes
  113. cluster_sizes <- table(cluster_assignments)
  114. print(cluster_sizes)
  116. # Calculate average risk and profiles
  117. riskProfileObj <- calcAvgRiskAndProfile(clusObj) #, includeFixedEffects = TRUE)
  118. # visualize
  119. clusterOrderObj <- plotRiskProfile(riskProfileObj,"1 - summary-1.png")
  121. # variable slecetion plots
  122. rho <- summariseVarSelectRho(runInfoObj)
  123. # Figure 3 (simple version)
  124. pdf("1 - varSelect-240710.pdf")
  125. par(mfrow = c(5, 2))
  126. for (k in 1:runInfoObj$nCovariates) {
  127. hist(rho$rho[, k], xlim = c(0, 1), main = "", xlab="")
  128. }
  129. dev.off()
  130. par(mfrow = c(1, 1))
  132. # step 1.2 - k = 2 clusters
  133. # Perform profile regression
  134. # Use the profileReg function from the PReMiuM package to perform profile regression.
  136. # calculate optimal clustering
  137. clusObj1.2 <- calcOptimalClustering(dissimObj, maxNClusters = 2, useLS=F)
  139. # Extract cluster assignments
  140. cluster_assignments1.2 <- clusObj1.2$clustering
  141. # Calculate cluster sizes
  142. cluster_sizes1.2 <- table(cluster_assignments1.2)
  143. print(cluster_sizes1.2)
  145. # Calculate average risk and profiles
  146. riskProfileObj1.2 <- calcAvgRiskAndProfile(clusObj1.2)
  147. # visualize
  148. clusterOrderObj1.2 <- plotRiskProfile(riskProfileObj1.2,"1 - summary-2clusters.png")
  150. ###############################################################################################
  151. ###############################################################################################
  153. # step 2 - remove covariates without explanatory properties: Imp, UPPs, DASS_D, STICSA, COHS, BISBAS
  154. covariates <- c("Comp",
  155. "DASS_Anxiety", #"DASS_Stress",
  156. "PSWQ") # covariates
  158. # step 2.1 - max 7 clusters
  159. # Perform profile regression
  160. # Use the profileReg function from the PReMiuM package to perform profile regression.
  162. # Run the MCMC algorithm
  163. runInfoObj2 <- profRegr(
  164. yModel = "Normal",
  165. xModel = "Normal",
  166. nSweeps = 100000,
  167. nBurn = 50000,
  168. data = RegressionData,
  169. output = "output",
  170. covNames = covariates,
  171. outcome = response,
  172. nFilter = 10,
  173. nClusInit = 20,
  174. varSelectType = "Continuous", #"BinaryCluster", #
  175. seed = 2024
  176. )
  178. # Calculate dissimilarity matrix
  179. dissimObj2 <- calcDissimilarityMatrix(runInfoObj2)
  180. # calculate optimal clustering
  181. clusObj2 <- calcOptimalClustering(dissimObj2, maxNClusters = 7, useLS=F)
  183. # Extract cluster assignments
  184. cluster_assignments2 <- clusObj2$clustering
  185. # Calculate cluster sizes
  186. cluster_sizes2 <- table(cluster_assignments2)
  187. print(cluster_sizes2)
  189. # Calculate average risk and profiles
  190. riskProfileObj2 <- calcAvgRiskAndProfile(clusObj2) #, includeFixedEffects = TRUE)
  191. # visualize
  192. clusterOrderObj2 <- plotRiskProfile(riskProfileObj2,"2 - summary-1.png")
  194. # variable slecetion plots
  195. rho <- summariseVarSelectRho(runInfoObj2)
  196. # Figure 3 (simple version)
  197. pdf("2 - varSelect-240710.pdf")
  198. par(mfrow = c(5, 2))
  199. for (k in 1:runInfoObj2$nCovariates) {
  200. hist(rho$rho[, k], xlim = c(0, 1), main = "", xlab="")
  201. }
  202. dev.off()
  203. par(mfrow = c(1, 1))
  205. # step 2.2 - k = 2 clusters
  206. # Perform profile regression
  207. # Use the profileReg function from the PReMiuM package to perform profile regression.
  209. # calculate optimal clustering
  210. clusObj2.2 <- calcOptimalClustering(dissimObj2, maxNClusters = 2, useLS=F)
  212. # Extract cluster assignments
  213. cluster_assignments2.2 <- clusObj2.2$clustering
  214. # Calculate cluster sizes
  215. cluster_sizes2.2 <- table(cluster_assignments2.2)
  216. print(cluster_sizes2.2)
  218. # Calculate average risk and profiles
  219. riskProfileObj2.2 <- calcAvgRiskAndProfile(clusObj2.2) #, includeFixedEffects = TRUE)
  220. # visualize
  221. clusterOrderObj2.2 <- plotRiskProfile(riskProfileObj2.2,"2 - summary-2clusters.png")
  223. # FOR VISUALIZATION
  224. # Get the posterior means for each cluster
  225. posteriorMeans <- calcAvgRiskAndProfile(clusObj2.2)
  226. # Extract mean outcome change for each cluster
  227. clusterMeans <- posteriorMeans[["risk"]]
  229. # Organize the posterior samples for each cluster into a data frame
  230. nClusters <- (posteriorMeans$riskProfClus$nClusters)
  231. nSamples <- length(posteriorMeans$risk) # Number of posterior samples
  232. # Create a data frame for plotting
  233. posteriorSamplesDF <- data.frame(unlist(posteriorMeans$risk))
  234. # Check the structure of the data frame
  235. head(posteriorSamplesDF)
  236. posteriorSamplesDF <- posteriorSamplesDF %>%
  237. rename(
  238. Cluster1 = X1,
  239. Cluster2 = X2
  240. )
  241. library(tidyr)
  242. posteriorSamplesLongDF <- pivot_longer(posteriorSamplesDF,
  243. cols = c(Cluster1, Cluster2),
  244. names_to = "Cluster",
  245. values_to = "MeanOutcome")
  246. # Check the reshaped data
  247. head(posteriorSamplesLongDF)
  249. # Calculate mean outcomes for each cluster
  250. meanCluster1 <- mean(posteriorSamplesDF$Cluster1)
  251. meanCluster2 <- mean(posteriorSamplesDF$Cluster2)
  252. # Colors for the clusters
  253. cluster_colors <- c("Cluster 1" = "#CC7722", "Cluster 2" = "#0047AB") # Ochre and cobalt blue
  254. # Plot the posterior distribution for both clusters
  255. ggplot(posteriorSamplesLongDF, aes(x = MeanOutcome, fill = Cluster, color = Cluster)) +
  256. # Density plot with transparency
  257. geom_density(alpha = 0.5) +
  258. # Custom colors for the clusters
  259. scale_fill_manual(values = cluster_colors) +
  260. scale_color_manual(values = cluster_colors) +
  261. # Dashed vertical lines for the means of each cluster
  262. geom_vline(xintercept = meanCluster1, color = "#CC7722", linetype = "dashed", size = 1) +
  263. geom_vline(xintercept = meanCluster2, color = "#0047AB", linetype = "dashed", size = 1) +
  264. # Labels and theme
  265. labs(title = "Posterior Distribution of Plausible Outcome Values for Cluster 1 and Cluster 2",
  266. x = "Mean Outcome",
  267. y = "Density") +
  268. theme_minimal()
  270. # Add the means for vertical lines into the original data frame
  271. posteriorSamplesLongDF$MeanLine <- NA
  272. posteriorSamplesLongDF$MeanLine[posteriorSamplesLongDF$Cluster == "Cluster1"] <- meanCluster1
  273. posteriorSamplesLongDF$MeanLine[posteriorSamplesLongDF$Cluster == "Cluster2"] <- meanCluster2
  274. # Plot the posterior distribution with separate plots for each cluster
  275. ggplot(posteriorSamplesLongDF, aes(x = MeanOutcome, fill = Cluster)) +
  276. # Density plot with different colors for each cluster
  277. geom_density(alpha = 0.5) +
  278. # Add dashed vertical lines for the means of each cluster
  279. geom_vline(aes(xintercept = MeanLine, color = Cluster), linetype = "dashed", size = 1) +
  280. # Custom colors for the fill and lines
  281. scale_fill_manual(values = cluster_colors) +
  282. scale_color_manual(values = cluster_colors) +
  283. # Facet by Cluster to create separate plots
  284. #facet_wrap(~ Cluster) +
  285. #scale_x_continuous(limits = c(0.09, 0.26)) +
  286. scale_y_continuous(breaks = c(10, 20)) +
  287. # Labels and theme
  288. labs(title = "Posterior Distribution of Plausible Outcome Values for Cluster 1 and Cluster 2",
  289. x = "Mean Outcome",
  290. y = "Density")
  292. # Calculate 95% credible intervals for Cluster 1
  293. ciCluster1 <- quantile(posteriorSamplesDF$Cluster1, probs = c(0.05, 0.95))
  294. # Calculate 95% credible intervals for Cluster 2
  295. ciCluster2 <- quantile(posteriorSamplesDF$Cluster2, probs = c(0.05, 0.95))
  296. # Display the credible intervals
  297. ciCluster1
  298. ciCluster2
  300. # empirical mean
  301. riskProfileObj2.2[["empiricals"]]
  302. # empirical CI
  303. # make new data
  304. EmpiricalData <- data.frame(SCF=rep(0, 221), Cluster=rep(0, 221))
  305. EmpiricalData$SCF <- clusObj2.2[["clusObjRunInfoObj"]][["yMat"]][,1]
  306. EmpiricalData$Cluster <- clusObj2.2[["clustering"]]
  308. lm1 <- lm(SCF ~ Cluster, data = EmpiricalData)
  309. summary(lm1)

SCF_analysis_profileRegression.R, no license · at the source

Overview

Authors: Rebecca Overmeyer1, Anja Kräplin2, Thomas Goschke1,3, Tanja Endrass1,3
ORCID iDs: Rebecca Overmeyer, Anja Kräplin, Tanja Endrass
  1. Faculty of Psychology, Dresden University of Technology,Dresden, Germany
  2. Department of Psychiatry and Psychotherapy, Dresden University of Technology,Dresden, Germany
  3. Neuroimaging Centre, Dresden University of Technology,Dresden, Germany
Institutions: Technische Universität Dresden (Germany)
Journal: Communications psychology, volume 4, issue 1, article 62
Dates: received 31 July 2025; accepted 10 March 2026; published online 27 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44271-026-00446-3 · PMID 41896335 · PMCID PMC13046855 · OpenAlex W7141222894
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Machine learning, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Statistics, fMRI & imaging
Keywords: Cognitive control, Human behaviour, Motivation
Topic: Behavioral Health and Interventions (Applied Psychology, Psychology), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (CRC 940, CRC/TRR 265)
Citations: cited by 1 paper (Europe PMC); 107 references in the paper

Abstract

Impaired self-control has been linked to deficits in performance monitoring and is associated with impulsive and compulsive behaviors. Although altered error-related negativity (ERN) amplitudes have been observed in disorders characterized by these traits, it remains unclear how such alterations relate to the translation of monitoring signals into behavior. In a sample of 221 participants, we combined electroencephalography with ecological momentary assessment to examine how self-reported impulsivity and compulsivity relate to daily self-control and moderate the association between ERN amplitude and daily self-control. High compulsivity was associated with increased desire enactment and more frequent self-control failures. Critically, ERN amplitude was associated with better self-control only at low levels of both impulsivity and compulsivity. These findings indicate that the relationship between performance monitoring and daily self-control varies as a function of trait-level impulsivity and compulsivity. While the present findings are correlational in nature, they suggest that considering trait interactions may inform cognitive and neural models of self-control and our understanding of how performance monitoring translates into adaptive behavior.

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 7 matches between paragraphs and lines of code.

OSF 4gpnd

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (5)
Size: 29 files, 5 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), emmeans (2 files), ggplot2 (2 files), car (1 file), easystats (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), nlme (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
5 files
At the source:

Code availability

The code that supports the findings of this study is available in the Open Science Framework with the identifier 10.17605/OSF.IO/4GPND. This includes the scripts used for analyzing the dataset reported in the manuscript.

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

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;
  • 5 scripts, each with its path and the digest of its content;
  • 7 matches 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

Datasets cited

Data availability

The data that support the findings of this study are available in the Open Science Framework with the identifier 10.17605/OSF.IO/4GPND.

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 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 1 funder, 92 references.

Cite

This paper

Overmeyer, R., Kräplin, A., Goschke, T., & Endrass, T. (2026). The association between the error-related negativity and self-control is moderated by impulsivity and compulsivity. Communications psychology, 4(1), 62. https://doi.org/10.1038/s44271-026-00446-3

BibTeX

@article{overmeyer2026association,
author = {Overmeyer, Rebecca and Kräplin, Anja and Goschke, Thomas and Endrass, Tanja},
title = {{The association between the error-related negativity and self-control is moderated by impulsivity and compulsivity}},
journal = {Communications psychology},
year = {2026},
month = mar,
volume = {4},
number = {1},
pages = {62},
publisher = {Springer Science+Business Media},
issn = {2731-9121},
doi = {10.1038/s44271-026-00446-3},
url = {https://doi.org/10.1038/s44271-026-00446-3},
pmid = {41896335},
pmcid = {PMC13046855}
}

RIS

TY - JOUR
AU - Overmeyer, Rebecca
AU - Kräplin, Anja
AU - Goschke, Thomas
AU - Endrass, Tanja
TI - The association between the error-related negativity and self-control is moderated by impulsivity and compulsivity
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/03/27
VL - 4
IS - 1
SP - 62
SN - 2731-9121
PB - Springer Science+Business Media
DO - 10.1038/s44271-026-00446-3
UR - https://doi.org/10.1038/s44271-026-00446-3
LA - en
ER -

CSL-JSON

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"author": [
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"page": "62",
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"PMID": "41896335",
"PMCID": "PMC13046855",
"ISSN": "2731-9121",
"publisher": "Springer Science+Business Media",
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"language": "en",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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In common: easystats, emmeans, car, 5 other tools, EEG, cognitive, 2 references

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