OSCR

Mood modulations of emotional word processing: A predictive perspective view of EEG data.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Method › Analysis › ERP ↔ Scripts/ERP_LMM.R, lines 84 to 135 · score 0.50 · singular fit, Treatment, permutation, clusters, predictors, model · Report a mismatch

Paper

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

The paper is loaded when this pane is shown.

The authors' code

R · 315 lines · 9.4 KB · no license · 1 match

  1. # ERP Mood-Valence study_words
  2. # ERP_LMM
  4. # libraries
  5. {
  6. library(rstatix)
  7. library(ggplot2)
  8. library(ggpubr)
  9. library(car)
  10. library(plyr)
  11. library(dplyr)
  12. library(psych)
  13. library(hrbrthemes)
  14. library(devtools)
  15. library(emmeans)
  16. library(tidyverse)
  17. library(hrbrthemes)
  18. library(viridis)
  19. library(effects)
  20. library(afex) # for 'allFit' on lmer models
  21. library(xtable) # for printing latex-tables
  22. library(sjPlot) # for plotting interactions in LMMs
  23. library(sjmisc) # for plotting interactions in LMMs
  24. }
  27. setwd("D:/Desktop/...")
  28. # Mac:
  29. setwd("/Users/el1445ko/Desktop/...")
  31. # ==========================================================================
  33. # datasets:
  34. # permutation cluster 1, N1 parietal ((Hi-Neu)Happy-Ctrl), electrodes CP3P3P5P1PO3PO7
  35. erp <- read.csv2(file = "N1_cluster1_trials.csv", fileEncoding = "UTF-8-BOM")
  37. # permutation cluster 2, N1 frontal ((Hi-Neu)Happy-Ctrl), electrodes F8FT8T8AF8C6FT10
  38. erp <- read.csv2(file = "N1_cluster2_trials.csv", fileEncoding = "UTF-8-BOM")
  40. # permutation cluster 3, N1 parietal ((Hi-Low)Happy-Ctrl), electrodes P1P2PzO1O2OzPO4POz
  41. erp <- read.csv2(file = "N1_cluster3_trials.csv", fileEncoding = "UTF-8-BOM")
  43. # permutation cluster 4, P2 frontal ((Hi-Low)Happy-Ctrl), electrodes F7F5F3FT7FC5AF7AF3
  44. erp <- read.csv2(file = "P2_cluster1_trials.csv", fileEncoding = "UTF-8-BOM")
  46. # permutation cluster 5, P2 frontal ((Neu-Low)Happy-Ctrl), electrodes F7F5F3F1FzFT7FC5FC3FC1AF3C3
  47. erp <- read.csv2(file = "P2_cluster2_trials.csv", fileEncoding = "UTF-8-BOM")
  49. # permutation cluster 6, EPN parietal ((Hi-Neu)Happy-Sad), electrodes CP5CP3P7P3PO7PO3
  50. erp <- read.csv2(file = "EPN_cluster1_trials.csv", fileEncoding = "UTF-8-BOM")
  52. # factors
  53. erp$participant = as.factor(erp$participant)
  54. erp$mood = as.factor(erp$mood)
  55. erp$valence = as.factor(erp$valence)
  56. erp$score = as.numeric(erp$score)
  57. erp$item = as.factor(erp$item)
  58. erp$condition <- NULL
  59. erp$itemID <- NULL
  61. # reorder levels of mood and valence
  62. erp$mood <- factor(erp$mood, levels = c("base", "happy", "sad"))
  63. erp$valence <- factor(erp$valence, levels = c("neut", "high", "low"))
  66. # head(erp, 3)
  67. summary(erp)
  68. str(erp)
  69. describe(erp)
  70. # View(erp)
  72. ## sample balance:
  73. table(erp$valence, erp$mood)
  75. # visual inspection:
  76. (QQ <- ggqqplot(erp, "score", ggtheme = theme_bw()) +
  77. facet_grid(mood ~ valence, labeller = "label_both"))
  79. (Density <- ggplot(erp, aes(x=score))+
  80. geom_density(alpha = 0.5, fill = '#E69F00')+
  81. facet_grid(mood ~ valence))
  84. ## boxplot
  85. erp %>%
  86. ggplot(aes(x=mood, y=score, fill=valence)) +
  87. geom_boxplot() +
  88. scale_fill_viridis(discrete = TRUE, alpha=0.6) +
  89. # geom_jitter(aes(color=valence), width = 0.25, size=3, alpha=0.9) +
  90. theme_ipsum() +
  91. theme(
  92. legend.position="bottom",
  93. plot.title = element_text(size=20)
  94. ) +
  95. ggtitle("N1") +
  96. xlab("")
  98. ## boxplot by predictor
  99. par(mfrow=c(1, 2))
  100. plot(erp$mood, erp$score, pch=16); lines(lowess(erp$score ~ erp$mood))
  101. plot(x=erp$valence, y=erp$score, pch=16); lines(lowess(erp$score ~ erp$valence))
  102. par(mfrow=c(1, 1))
  105. # ==========================================================================
  107. # models
  108. # treatment coding for both predictors, since reflects the permutation contrasts
  109. summary(model1 <- lmer(score ~ valence * mood + (1 + mood | participant) + (1 | item),
  110. contrasts = list(mood = contr.treatment(3, base = 1),
  111. valence = contr.treatment(3, base = 1)),
  112. data = erp,REML = FALSE))
  113. # N1_1: singular fit
  114. # N1_2: singular fit
  115. # N1_3: failed to converge
  116. # P2_1: singular fit
  117. # P2_2: singular fit
  118. # EPN_1: converged
  119. performance::r2(model1)
  122. summary(model2 <- lmer(score ~ valence * mood + (1 | participant) + (1 | item),
  123. contrasts = list(mood = contr.treatment(3, base = 1),
  124. valence = contr.treatment(3, base = 1)),
  125. data = erp, REML = FALSE))
  126. # converged for all clusters
  127. performance::r2(model2)
  128. anova(model2)
  129. # summary(rePCA(model2))
  130. # Confint(model2) # confidence intervals
  132. plot(model2)
  133. plot(residuals(model2))
  134. hist(residuals(model2))
  135. qqnorm(residuals(model2))
  137. # ! list of models, run and saved:
  138. # model2_N1_1
  139. # model2_N1_2
  140. # model2_N1_3
  141. # model2_P2_1
  142. # model2_P2_2
  143. # model2_EPN_1
  146. # ==========================================================================
  148. # CONRASTS AND INTERACTIONS
  149. emm_options(pbkrtest.limit = 7659) # observations = 7659
  150. # If lags, limit = 5964
  151. # emm_options(pbkrtest.limit = 5964)
  154. # N1_1. valence (high, neutral) × mood (positive, control)
  155. (N1_1_emmp_means <- emmeans(model2_N1_1, ~ mood | valence, adjust = "holm")) # means
  156. (N1_1_emmp_contrasts <- contrast(N1_1_emmp_means, "pairwise"))
  158. N1_1_emmp_means
  159. N1_1_emmp_contrasts
  162. # N1_2. valence (high, neutral) × mood (positive, control)
  163. (N1_2_emmp_means <- emmeans(model2_N1_2, ~ mood | valence, adjust = "holm")) # means
  164. (N1_2_emmp_contrasts <- contrast(N1_2_emmp_means, "pairwise"))
  166. N1_2_emmp_means
  167. N1_2_emmp_contrasts
  170. # N1_3. valence (high, low) × mood (positive, control)
  171. (N1_3_emmp_means <- emmeans(model2_N1_3, ~ mood | valence, adjust = "holm")) # means
  172. (N1_3_emmp_contrasts <- contrast(N1_3_emmp_means, "pairwise"))
  174. N1_3_emmp_means
  175. N1_3_emmp_contrasts
  178. # P2_1. valence (high, low) × mood (positive, control)
  179. (P2_1_emmp_means <- emmeans(model2_P2_1, ~ mood | valence, adjust = "holm")) # means
  180. (P2_1_emmp_contrasts <- contrast(P2_1_emmp_means, "pairwise"))
  182. P2_1_emmp_means
  183. P2_1_emmp_contrasts
  187. # interaction terms for ((HiVal-LoVal)Pos minus Ctrl):
  188. emm_options(pbkrtest.limit = 5964)
  189. (P2_1_emmp_means_new <- emmeans(model2_P2_1, ~ valence | mood, adjust = "holm")) # means
  190. val23_diffs <- contrast(P2_1_emmp_means_new, method = list("Val2 - Val3" = c(0, 1, -1)), # Valence2 - Valence3
  191. by = "mood")
  192. interaction_contrast <- contrast(val23_diffs, method = list("Mood2 vs Mood1 on (Val2-Val3)" = c(-1, 1, 0)), by = NULL)
  193. summary(interaction_contrast, infer = c(TRUE, TRUE))
  197. # P2_2
  198. (P2_2_emmp_means <- emmeans(model2_P2_2, ~ mood | valence, adjust = "holm")) # means
  199. (P2_2_emmp_contrasts <- contrast(P2_2_emmp_means, "pairwise"))
  201. P2_2_emmp_means
  202. P2_2_emmp_contrasts
  206. # interaction terms for ((HiVal-LoVal)Pos minus Ctrl):
  207. emm_options(pbkrtest.limit = 5964)
  208. (P2_2_emmp_means_new <- emmeans(model2_P2_2, ~ valence | mood, adjust = "holm")) # means
  209. val13_diffs <- contrast(P2_2_emmp_means_new, method = list("Val1 - Val3" = c(1, 0, -1)), # Valence1 - Valence3
  210. by = "mood")
  211. interaction_contrast <- contrast(val13_diffs, method = list("Mood2 vs Mood1 on (Val1-Val3)" = c(-1, 1, 0)), by = NULL)
  212. summary(interaction_contrast, infer = c(TRUE, TRUE))
  215. # EPN
  216. (EPN_1_emmp_means <- emmeans(model2_EPN_1, ~ mood | valence, adjust = "holm")) # means
  217. (EPN_1_emmp_contrasts <- contrast(EPN_1_emmp_means, "pairwise"))
  219. EPN_1_emmp_means
  220. EPN_1_emmp_contrasts
  223. # interaction terms for ((HiVal-NeuVal)Pos-Neg):
  224. (EPN_1_emmp_means_new <- emmeans(model2_EPN_1, ~ valence | mood, adjust = "holm"))
  225. val21_diffs <- contrast(EPN_1_emmp_means_new, method = list("Val2 - Val1" = c(-1, 1, 0)), # Valence2 - Valence1
  226. by = "mood")
  227. interaction_contrast <- contrast(val21_diffs, method = list("Mood2 vs Mood3 on (Val2-Val1)" = c(0, 1, -1)),
  228. by = NULL)
  229. summary(interaction_contrast, infer = c(TRUE, TRUE))
  233. # ==========================================================================
  235. # PLOT:
  237. # NB ! change data frame, ylim and labels for each clusters as necessary
  239. # N1_2 = right anterior
  240. # N1_1 = left parietal
  241. # N1_3 = central parietal
  242. # P2_1: smaller
  243. # P2_2: larger
  245. plot <- as.data.frame(EPN_1_emmp_means) # change for other clusters accordingly
  246. ggplot(plot, aes(x = mood, y = emmean, color = valence, group = valence)) +
  247. geom_point(size = 2, position = position_dodge(0.2)) +
  248. geom_line(size = 1, position = position_dodge(0.2)) +
  249. geom_errorbar(aes(ymin = emmean-SE, ymax = emmean+SE), width = 0.1, position = position_dodge(0.2)) +
  250. scale_y_reverse() +
  251. scale_color_manual(values = c("#619CFF", "#F8766D", "#00BA38")) +
  252. # ylim(0, -1.2) + # change appr as follows for clusters:
  253. # for N1_1 and N1_2: ylim(0,-1.2);
  254. # for N1_3: ylim(1,-0.2);
  255. # for P2_1: ylim(1.25, 0.05) or as above;
  256. # for P2_2: ylim(1.75, 0.55);
  257. # for EPN: ylim(0, -1.2)
  258. labs(title = "EPN, Left Parietal", x = "Mood", y = "Amplitude (microV)", color = "Valence") +
  259. # change title as needed
  260. scale_x_discrete(labels = c("Control", "Positive", "Negative")) +
  261. theme_minimal(base_size = 14) +
  262. theme(text = element_text(size = 20), legend.position="top",
  263. axis.text.x = element_text(size=18), axis.title.x = element_text(size=18, face="bold"),
  264. axis.text.y = element_text(size=18), axis.title.y = element_text(size=18, face="bold"))
  267. # ==========================================================================
  268. # summary
  270. # N1_1
  271. summary(model2_N1_1)
  272. anova(model2_N1_1)
  273. performance::r2(model2_N1_1)
  274. N1_1_emmp_means
  275. N1_1_emmp_contrasts
  278. # N1_2
  279. summary(model2_N1_2)
  280. anova(model2_N1_2)
  281. performance::r2(model2_N1_2)
  282. N1_2_emmp_means
  283. N1_2_emmp_contrasts
  286. # N1_3
  287. summary(model2_N1_3)
  288. anova(model2_N1_3)
  289. performance::r2(model2_N1_3)
  290. N1_3_emmp_means
  291. N1_3_emmp_contrasts
  294. # P2_1
  295. summary(model2_P2_1)
  296. anova(model2_P2_1)
  297. performance::r2(model2_P2_1)
  298. P2_1_emmp_means
  299. P2_1_emmp_contrasts
  302. # P2_2
  303. summary(model2_P2_2)
  304. anova(model2_P2_2)
  305. performance::r2(model2_P2_2)
  306. P2_2_emmp_means
  307. P2_2_emmp_contrasts
  310. # EPN_1
  311. summary(model2_EPN_1)
  312. anova(model2_EPN_1)
  313. performance::r2(model2_EPN_1)
  314. EPN_1_emmp_means
  315. EPN_1_emmp_contrasts

ERP_LMM.R, no license · at the source

Overview

Authors: Ekaterina Kopaeva1, Johan Blomberg1, Mikael Roll1
ORCID iDs: Ekaterina Kopaeva, Johan Blomberg, Mikael Roll
  1. Center for Languages and Literature, Lund University,PO Box 201, 22100 Lund, Sweden
Institutions: Lund University (Sweden)
Journal: Cognitive, affective & behavioral neuroscience, volume 26, issue 3, pages 1101-1122
Dates: received 20 May 2025; accepted 23 December 2025; published online 25 March 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3758/s13415-025-01394-x · PMID 41882306 · PMCID PMC13260159 · OpenAlex W7140322260
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging
Keywords: Mood, Affective state, Emotional language processing, Event-related potential, Language, Semantics
MeSH: Affect*, Brain*, Emotions*, Evoked Potentials*, Adult, Electroencephalography, Female, Humans, Male, Reaction Time, Semantics, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Crafoordska Stiftelsen (2017.0006); Swedish Research Council (2018.00632); Knut och Alice Wallenbergs Stiftelse (2018.0454); Lund University
Citations: not cited yet (Europe PMC); 107 references in the paper

Abstract

Mood is a pervasive affective state that influences cognition, including language processing, where it functions as part of the pragmatic context. When linguistic expressions carry emotional valence, mood may amplify or attenuate their perceived emotional nature. Motivated by inconsistent findings on the timing and nature of mood and valence interactions, we conducted an EEG study to investigate whether induced moods alter the temporal profile of emotional word processing. Participants rated words of high, low, and neutral valence in a control condition and after positive and negative mood induction. Event-related potentials were analysed across three early processing windows using cluster-based permutation tests. Results showed that mood modulated early neural activity: in the positive mood, high-valence words reduced N1 amplitudes compared with control, reflecting diminished prediction error. In the subsequent P2 and EPN windows, reduced amplitudes for high- and neutral-valence words indicated a decreased need for model updating under mood-congruent expectations. By contrast, the negative mood exerted weaker and later effects. Behavioural responses corroborated these findings, with faster response times to valenced words. Within predictive coding frameworks, mood can be understood as a hyperprior that shapes precision weighting and error dynamics. Our results suggest that induced positive mood facilitates the processing of congruent words and broadens the range of input treated as predictable, whereas induced negative mood primarily reduces positive affect. These findings highlight the role of mood in dynamically tuning early language processing through the regulation of prediction error and model update.

Supplementary Information: The online version contains supplementary material available at 10.3758/s13415-025-01394-x.

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 j2enh

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (4)
Size: 16 files, 4 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), ggpubr (4 files), rstatix (4 files), tidyverse (4 files), afex (3 files), car (3 files), easystats (3 files), emmeans (3 files), psych (3 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
4 files
At the source: osf.io/j2enh/

Code availability

R scripts are available at https://osf.io/j2enh/.

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;
  • 4 scripts, 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.

Availability of data and materials

The data and materials for this experiment are available at https://osf.io/j2enh/. This experiment was not preregistered.

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, 3 authors, 6 keywords, 12 MeSH terms, 4 funders, 97 references.

Cite

This paper

Kopaeva, E., Blomberg, J., & Roll, M. (2026). Mood modulations of emotional word processing: A predictive perspective view of EEG data. Cognitive, affective & behavioral neuroscience, 26(3), 1101-1122. https://doi.org/10.3758/s13415-025-01394-x

BibTeX

@article{kopaeva2026mood,
author = {Kopaeva, Ekaterina and Blomberg, Johan and Roll, Mikael},
title = {{Mood modulations of emotional word processing: A predictive perspective view of EEG data}},
journal = {Cognitive, affective \& behavioral neuroscience},
year = {2026},
month = mar,
volume = {26},
number = {3},
pages = {1101--1122},
publisher = {Springer Science+Business Media},
issn = {1530-7026},
doi = {10.3758/s13415-025-01394-x},
url = {https://doi.org/10.3758/s13415-025-01394-x},
pmid = {41882306},
pmcid = {PMC13260159}
}

RIS

TY - JOUR
AU - Kopaeva, Ekaterina
AU - Blomberg, Johan
AU - Roll, Mikael
TI - Mood modulations of emotional word processing: A predictive perspective view of EEG data
T2 - Cognitive, affective & behavioral neuroscience
J2 - Cogn Affect Behav Neurosci
PY - 2026
DA - 2026/03/25
VL - 26
IS - 3
SP - 1101
EP - 1122
SN - 1530-7026
PB - Springer Science+Business Media
DO - 10.3758/s13415-025-01394-x
UR - https://doi.org/10.3758/s13415-025-01394-x
LA - en
ER -

CSL-JSON

{
"id": "10.3758/s13415-025-01394-x",
"type": "article-journal",
"title": "Mood modulations of emotional word processing: A predictive perspective view of EEG data",
"container-title": "Cognitive, affective & behavioral neuroscience",
"author": [
{
"family": "Kopaeva",
"given": "Ekaterina"
},
{
"family": "Blomberg",
"given": "Johan"
},
{
"family": "Roll",
"given": "Mikael"
}
],
"container-title-short": "Cogn Affect Behav Neurosci",
"volume": "26",
"issue": "3",
"page": "1101-1122",
"DOI": "10.3758/s13415-025-01394-x",
"PMID": "41882306",
"PMCID": "PMC13260159",
"ISSN": "1530-7026",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.3758/s13415-025-01394-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
25
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41598-023-43794-4
Dissociating different temporal stages of emotional word processing by feature-based attention.
Journal: Scientific reports
In common: EEG, cognitive, 19 references
[2] doi:10.1371/journal.pone.0353990 [code]
Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language.
Journal: PloS one
In common: afex, psych, emmeans, 4 other tools, EEG, cognitive, 15 references
[3] doi:10.1371/journal.pone.0295148
Neural correlates of word processing influenced by painful primes.
Journal: PloS one
In common: EEG, 15 references
[4] doi:10.1111/psyp.70318
EEG Based Decoding of the Perception and Regulation of Taboo Words.
Journal: Psychophysiology
In common: EEG, cognitive, 13 references
[5] doi:10.1162/imag_a_00162 [code]
Relevance acquisition through motivational incentives: Modeling the time-course of associative learning and the role of visual features.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: EEG, cognitive, 12 references
[6] doi:10.1038/s41598-025-34117-w [code]
The impact of awareness on affective emoji priming in visual word recognition.
Journal: Scientific reports
In common: afex, easystats, emmeans, 3 other tools, EEG, cognitive, 6 references
[7] doi:10.1111/psyp.14696 [code]
Linking brain-heart interactions to emotional arousal in immersive virtual reality.
Journal: Psychophysiology
In common: afex, easystats, rstatix, 4 other tools, EEG, cognitive, 5 references
[8] doi:10.1016/j.dcn.2023.101251 [code]
Age and vocabulary knowledge differentially influence the N400 and theta responses during semantic retrieval.
Journal: Developmental cognitive neuroscience
In common: afex, easystats, rstatix, 5 other tools, EEG, 4 references
[9] doi:10.3758/s13415-025-01303-2 [code]
Impaired reinforcement learning and coding of prediction errors in patients with cerebellar degeneration - a study with EEG and voxel-based morphometry.
Journal: Cognitive, affective & behavioral neuroscience
In common: afex, easystats, rstatix, 5 other tools, EEG, cognitive, 3 references
[10] doi:10.1111/nyas.15407 [code]
Foreign cultural norms are better accepted in the second language.
Journal: Annals of the New York Academy of Sciences
In common: afex, easystats, psych, 4 other tools, EEG, cognitive, 4 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Add the badge to the code's README

OSCR: the badge of this paper

If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x.svg)](https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x.svg
   :target: https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x"><img src="https://openscicode.org/badge/doi/10.3758/s13415-025-01394-x.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.