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Comparing Brain Responses to Moral and Semantic Violations.

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The 5 matches
  1. [1] § 2. Materials and Methods › 2.4. EEG Acquisition and Analysis ↔ Analysis/Step3_ERP_lmer_Analysis/Step3_lmer analysis.zip/Step3_lmer analysis/P200/lmer_by_item_P200_forosf.RMD, lines 60 to 86 · score 0.66 · yielded singularity, initial model, lme4, slopes, ERP · Report a mismatch
  2. [2] § 3. Results › 3.1. By-Window ERP Analysis › 3.1.3. LPC ↔ Analysis/Step3_ERP_lmer_Analysis/Step3_lmer analysis.zip/Step3_lmer analysis/LPC/lmer_by_item_LPC_forOSF.Rmd, lines 228 to 251 · score 0.62 · scalp distribution, 500–900 ms, location, lmer, SE, CI · Report a mismatch
  3. [3] § 2. Materials and Methods › 2.2. Materials › 2.2.2. Norming ↔ Analysis/Additional analysis/Posttest scores analysis.zip/Posttest scores analysis/posttest scores analysis.Rmd, lines 28 to 43 · score 0.59 · emotionally moving, moral acceptance, moral scores, SD · Report a mismatch
  4. [4] § 2. Materials and Methods › 2.4. EEG Acquisition and Analysis ↔ Analysis/Step3_ERP_lmer_Analysis/Step3_lmer analysis.zip/Step3_lmer analysis/N400/lmer_byitem_N400_forOSF.Rmd, lines 78 to 103 · score 0.54 · random intercepts, lme4, singularity, slopes, model, ERP · Report a mismatch
  5. [5] § 2. Materials and Methods › 2.2. Materials › 2.2.2. Norming ↔ Analysis/Additional analysis/Norming_plausibility and moral accep scores ANOVA.zip/Norming_plausibility and moral accep scores ANOVA/norming plau and moral scores ANOVA.Rmd, lines 8 to 16 · score 0.50 · moral acceptance scores, norming, ANOVAs, plausibility · Report a mismatch

Paper

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The authors' code

R Markdown · 167 lines · 4 KB · no license · 1 match

  1. ---
  2. title: "lmer_by_item_P200"
  3. author: "Jian Meng"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  9. ```{r}
  11. library(readr)
  12. library(lme4)
  13. library(lmerTest)
  14. library(tidyverse)
  15. library(hypr)
  16. library(dplyr)
  17. library(sjPlot)
  18. EEG_norming_prepost <- read_csv("z_EEG_norming_prepost_P200.csv")
  20. View(EEG_norming_prepost)
  23. dum <- EEG_norming_prepost
  25. ```
  29. ```{r region definition and averaging}
  31. frontal_electrodes<-c("FCz", "FC1", "FC2", "FC3", "FC4", "Cz", "C3", "C4")
  33. frontal <- dum %>%
  34. filter(chlabel %in% frontal_electrodes ) %>%
  35. filter(!Subject %in% c("7", "8", "22", "28", "37")) %>%
  36. group_by(Subject, Item_number, ConditionLabel, z_accep, z_moving) %>%
  37. summarize(Ampl=mean(Ampl)) %>%
  38. filter(Ampl!= 0)
  42. #View(frontal)
  44. ```
  48. ```{r making the contrast}
  50. contrasts1 <- hypr(
  51. Viol = (Mor+Sem)/2 ~ Neu,
  52. MorSem = Mor ~ Sem,
  53. levels = c("Mor", "Neu", "Sem")
  54. )
  55. contrasts1
  57. ```
  60. ```{r lmer analysis for n=33}
  61. frontal$Subject<- as.factor(frontal$Subject)
  62. frontal$Item_number<- as.factor(frontal$Item_number)
  63. frontal$ConditionLabel<- as.factor(frontal$ConditionLabel)
  64. contrasts(frontal$ConditionLabel) <- contr.hypothesis(contrasts1)
  67. # initial model, did not converge
  69. #model_full <-lmer(Ampl ~ ConditionLabel+(1+ConditionLabel|Subject)+(1+ConditionLabel|Item_number), data=frontal)
  70. #summary(model_full)
  72. # dropping by-item slopes; yielded singularity warning
  74. #model_full <-lmer(Ampl ~ ConditionLabel+(1+ConditionLabel|Subject)+(1|Item_number), data=frontal)
  75. #summary(model_full)
  78. # dropping by-subject slopes
  80. model_rint_only_frontal <-lmer(Ampl ~ ConditionLabel+(1|Subject)+ (1|Item_number), data=frontal)
  82. summary(model_rint_only_frontal)
  84. confint(model_rint_only_frontal, level = 0.95, method = "Wald")
  86. ```
  88. ## assessing the effect of keeping participant m11
  90. first a dataset without m11
  91. ```{r a model without m11}
  93. frontal_no11 <- dum %>%
  94. filter(chlabel %in% frontal_electrodes ) %>%
  95. filter(!Subject %in% c("7", "8", "22", "28", "37", "11")) %>%
  96. group_by(Subject, Item_number, ConditionLabel) %>%
  97. summarize(Ampl=mean(Ampl)) %>%
  98. filter(Ampl!= 0)
  102. #View(frontal_no11)
  103. ```
  105. ```{r lmer without m11}
  106. frontal_no11$Subject<- as.factor(frontal_no11$Subject)
  107. frontal_no11$Item_number<- as.factor(frontal_no11$Item_number)
  108. frontal_no11$ConditionLabel<- as.factor(frontal_no11$ConditionLabel)
  109. contrasts(frontal_no11$ConditionLabel) <- contr.hypothesis(contrasts1)
  113. model_rint_only_11 <-lmer(Ampl ~ ConditionLabel+(1|Subject)+ (1|Item_number), data=frontal_no11)
  115. summary(model_rint_only_11) # no significant difference from the model that includes m11
  117. ```
  119. ```{r influence of participant 11}
  121. library(influence.ME)
  123. # Calculate influence measures
  124. infl <- influence(model_rint_only_frontal, group = "Subject")
  126. # View influence diagnostics (DFBETAs, Cook's distance, etc.)
  127. summary(infl)
  128. plot(infl, which = "cook") # to visualize leverage
  130. db <- dfbetas(infl)
  132. which(levels(frontal$Subject) == "11") #11 is in 9th position
  133. db[9,] # Extracts the specific influence values for the 9th subject in the dataset
  134. #DFBETAs Threshold: 2 sqrt{n} = 2 sqrt{33} approx= 0.348.
  135. # Since no value is larger than |0.354|, Subject 11 is not exerting a significant pull on that specific estimate.
  137. ```
  140. ```{r output to wordfile-1}
  142. model_rint_only_frontal_output <- sjPlot::tab_model(model_rint_only_frontal,transform=NULL,
  143. show.est = TRUE,
  144. show.se = TRUE,
  145. show.ci = 0.95,
  146. show.stat = TRUE,
  147. show.icc = FALSE,
  148. show.re.var = TRUE,
  149. p.style = "numeric_stars",
  150. p.val = "wald",
  151. string.ci = "95% CI",
  152. string.est = "Estimate",
  153. string.se = "SE",
  154. string.stat = "t",
  155. string.pred = "Parameters",
  156. col.order = c("est","ci","se","stat","p"),
  157. dv.labels = "",
  158. pred.labels = c("Intercept","Neutral.v. Violation","Moral.v.Semantic"),
  159. emph.p = FALSE,
  160. ci.hyphen = ",",
  161. title = "P200 analysis",
  162. file = "P200.doc")
  164. model_rint_only_frontal_output
  166. ```

lmer_by_item_P200_forosf.RMD, no license · at the source

Overview

Authors: Jian Meng1, Demi Zhang1, Yuling Zhong2, Xiaodong Xu2, Edith Kaan1
ORCID iDs: Jian Meng, Demi Zhang, Xiaodong Xu, Edith Kaan
  1. Department of Linguistics, University of Florida, Gainesville, FL 32611, USA; (J.M.); (D.Z.)
  2. School of Foreign Languages and Cultures, Nanjing Normal University, Nanjing 210098, (X.X.)
Institutions: University of Florida (United States); Nanjing Normal University (China)
Journal: Brain sciences, volume 16, issue 4, article 375
Dates: received 30 January 2026; accepted 27 March 2026; published online 30 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/brainsci16040375 · PMID 42041785 · PMCID PMC13115281 · OpenAlex W7143317388
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Physiology & signal measures, Machine learning
Keywords: moral judgment, LPC, ERP, semantic violation, language processing
Topic: Psychology of Moral and Emotional Judgment (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 49 references in the paper

Abstract

Highlights: What is the main finding?

Encountering a moral violation while reading short texts elicits an earlier posterior positivity in the event-related brain potential response compared with semantic violations, which showed a descriptively more frontal positivity.

What is the implication of the main finding?

The findings support the view that the processing of moral deviations involves mechanisms that are partially different in nature and timing from those recruited for the processing of semantic oddities.

Abstract: Background/Objectives: The processing and evaluation of behavior, actions or events that go against social (moral) norms can be assumed to operate on mental representations of the world and of how people typically behave. These mechanisms and representations may therefore be shared by the processing of meaning in general. The current study investigated whether the processing of deviations of morality can be distinguished from processing of semantic inconsistencies. Methods: Event-related brain potentials (ERPs) were recorded from English speakers while they read short written texts in English for comprehension. Texts contained words that constituted moral violations, semantic violations and neutral controls depending on the context, allowing for a direct comparison. Results: Using trial-based analyses, we found different ERP responses to semantic and moral violations: the moral violation elicited a long-lasting, posterior Late Positive Component (LPC) starting at around 300 ms, whereas the semantic violation elicited a positivity that started later and was descriptively more frontally distributed. Furthermore, the LPC amplitudes could be explained by the moral acceptance scores over and above plausibility scores, but not vice versa. Conclusions: The outcomes are compatible with the view that the processing of moral deviations engages at least some mechanisms that are different from the processing of semantic deviations.

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

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OSF gquje

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 22 files
Software Heritage: not checked
Found in: the text, “2.4. EEG Acquisition and Analysis”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (12 files), lme4 (5 files), lmerTest (5 files), ggplot2 (2 files), multcomp (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/gquje

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Data Availability Statement

The original data presented in the study are openly available on the Open Science Framework at https://osf.io/gquje/ (accessed on 15 March 2026).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 47 references.

Cite

This paper

Meng, J., Zhang, D., Zhong, Y., Xu, X., & Kaan, E. (2026). Comparing Brain Responses to Moral and Semantic Violations. Brain sciences, 16(4), 375. https://doi.org/10.3390/brainsci16040375

BibTeX

@article{meng2026comparing,
author = {Meng, Jian and Zhang, Demi and Zhong, Yuling and Xu, Xiaodong and Kaan, Edith},
title = {{Comparing Brain Responses to Moral and Semantic Violations}},
journal = {Brain sciences},
year = {2026},
month = mar,
volume = {16},
number = {4},
pages = {375},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2076-3425},
doi = {10.3390/brainsci16040375},
url = {https://doi.org/10.3390/brainsci16040375},
pmid = {42041785},
pmcid = {PMC13115281}
}

RIS

TY - JOUR
AU - Meng, Jian
AU - Zhang, Demi
AU - Zhong, Yuling
AU - Xu, Xiaodong
AU - Kaan, Edith
TI - Comparing Brain Responses to Moral and Semantic Violations
T2 - Brain sciences
J2 - Brain Sci
PY - 2026
DA - 2026/03/30
VL - 16
IS - 4
SP - 375
SN - 2076-3425
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/brainsci16040375
UR - https://doi.org/10.3390/brainsci16040375
LA - en
ER -

CSL-JSON

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