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Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement.

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R Markdown · 154 lines · 7.3 KB · no license

  1. ---
  2. title: "GLM_pic"
  3. author: "邱诗苇"
  4. date: "2024-07-11"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. ```
  12. ## GLM of parameter variables on 50 pairs
  14. ```{r prepare}
  15. library(tidyverse)
  16. library(ggpubr)
  17. library(lme4)
  19. dir <- getwd()
  20. data_path <- paste0(dir,"/rawdata/")
  21. code_path <- paste0(dir,"/code/")
  22. save_path <- paste0(dir,"/output/")
  24. df_raw_dyad <- readxl::read_xlsx(paste0(data_path,"dyad_data/excel/dyad_intersection.xlsx")) %>% select(subject,winornot,gainp,propp,briber,ph) %>% mutate(collusion=ifelse(briber==ph,1,0)) %>%
  25. mutate(context=ifelse(winornot==1,"control","bribery")) %>% select(-winornot)
  26. df_raw_PH <- readxl::read_xlsx(paste0(data_path,"ph_data/excel/Ph_intersection.xlsx"))
  27. df_raw_B <- readxl::read_xlsx(paste0(data_path,"b_data/excel/Briber_intersection.xlsx"))
  28. ```
  30. ```{r paired t test for context}
  31. df_ttest <- df_raw_dyad|> group_by(subject,context) |> summarise_each(mean)
  33. bruceR::TTEST(df_ttest |> select(subject,ph,context) |> spread(context,ph),c("bribery","control"),paired = T,digits = 3)
  35. bruceR::TTEST(df_ttest |> select(subject,briber,context) |> spread(context,briber),c("bribery","control"),paired = T,digits = 3)
  37. bruceR::TTEST(df_ttest |> select(subject,collusion,context) |> spread(context,collusion),c("bribery","control"),paired = T,digits = 3)
  38. ```
  41. ```{r GLM context*proportion*gain}
  42. df_glmer <- df_raw_dyad %>% mutate(Zprop=scale(propp),Zgainp=scale(gainp),Context=as.factor(ifelse(context=="control",0,1)))
  44. fit_PH <- glmer(ph~Context*Zprop*Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  45. summary(fit_PH)
  46. #简单斜率分析
  47. summary(emtrends(fit_PH,~Context,var="Zprop"), infer = c(TRUE, TRUE))
  48. contrast(emtrends(fit_PH,~Context,var="Zprop"), method = "pairwise")
  50. fit_B <- glmer(briber~Context*Zprop*Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  51. summary(fit_B)
  53. fit_Dyad <- glmer(collusion~Context*Zprop*Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  54. summary(fit_Dyad)
  55. #简单斜率分析
  56. summary(emtrends(fit_Dyad,~Context,var="Zprop"), infer = c(TRUE, TRUE))
  57. contrast(emtrends(fit_Dyad,~Context,var="Zprop"), method = "pairwise")
  58. ```
  60. ```{r GLM context+proportion+gain}
  61. df_glmer <- df_raw_dyad %>% mutate(Zprop=scale(propp),Zgainp=scale(gainp),Context=as.factor(ifelse(context=="control",0,1)))
  63. fit_PH <- glmer(ph~1+Context+Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  64. summary(fit_PH)
  66. fit_B <- glmer(briber~1+Context+Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  67. summary(fit_B)
  69. fit_Dyad <- glmer(collusion~1+Context+Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  70. summary(fit_Dyad)
  71. ```
  73. ```{r GLM context*proportion}
  74. df_glmer <- df_raw_dyad %>% mutate(Zprop=scale(propp),Zgainp=scale(gainp),Context=as.factor(ifelse(context=="control",0,1)))
  76. fit_PH <- glmer(ph~1+Context*Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  77. summary(fit_PH)
  79. fit_PH <- glmer(ph~1+Context*Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial")
  80. summary(fit_PH)
  82. fit_B <- glmer(briber~1+Context*Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  83. summary(fit_B)
  85. fit_Dyad <- glmer(collusion~1+Context*Zprop+Zgainp+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  86. summary(fit_Dyad)
  87. ```
  89. ```{r GLM context*gain_proposer}
  90. df_glmer <- df_raw_dyad %>% mutate(Zprop=scale(propp),Zgainp=scale(gainp),Context=as.factor(ifelse(context=="control",0,1)))
  92. fit_PH <- glmer(ph~1+Context*Zgainp+Zprop+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  93. summary(fit_PH)
  95. fit_B <- glmer(briber~1+Context*Zgainp+Zprop+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  96. summary(fit_B)
  98. fit_Dyad <- glmer(collusion~1+Context*Zgainp+Zprop+(1|subject),data=df_glmer,family = "binomial",contrasts = list(Context="contr.sum"))
  99. summary(fit_Dyad)
  100. ```
  102. ```{r plot}
  103. df_para_reg <- df_raw_dyad %>%
  104. group_by(gainp,propp,context) %>% summarise_each(briber,ph,collusion,fun="mean")
  107. # context effect, barplots
  108. df_context <- df_para_reg[,c(3:6)] |> mutate(Condition=context,Proposal=briber,Acceptance=ph,Success=collusion)|> select(-c(briber,ph,collusion,context))|> gather("Behavior","Rate",-Condition)
  109. df_context$Behavior <- factor(df_context$Behavior,levels = c("Proposal","Acceptance","Success"))
  111. ggbarplot(df_context,"Condition","Rate",facet.by = "Behavior",add = c("mean_se","dotplot"),add.params = list(size=0.5,fill="Condition"),width = 0.7,color = "Condition",fill = "Condition",palette = c("#9C27B0","#616161"),alpha=0.5,ggtheme = theme_pubclean(),legend = "none")+
  112. font("xylab",size = 22,face = "bold")+
  113. font("xy.text",size=14,face = "bold")+
  114. theme(strip.text = element_text(face = "bold", size = 20),axis.title.x = element_text(vjust=-0.8))+
  115. ylim(0,1)+yscale("percent",.format = TRUE)+
  116. stat_compare_means(comparisons = list(c("bribery", "control")), method = "t.test", paired = TRUE,
  117. label = "p.signif", label.y = 1, tip.length = 0.02,vjust = 0.7,bracket.size = 0.7,
  118. symnum.args = list(cutpoints = c(0, 0.001, 1), symbols = c("***", "ns")),
  119. fontface="bold",size=8)
  121. ggsave("output/BehavRate_barplot.png",height = 5,width = 10)
  124. # proportion effect, lineplots
  125. df_proportion <- df_para_reg |> ungroup() |> select(-gainp) |> mutate(Proportion=propp,Condition=context,Proposal=briber,Acceptance=ph,Success=collusion)|> select(-c(propp,briber,ph,collusion,context))|> gather("Behavior","Rate",-c(Condition,Proportion))
  126. df_proportion$Behavior <- factor(df_proportion$Behavior,levels = c("Proposal","Acceptance","Success"))
  128. ggline(df_proportion,"Proportion","Rate",facet.by = "Behavior",add = c("mean_se"),size = 1,color = "Condition",fill = "Condition",palette = c("#f16c23","#2b6a99"),alpha=0.5,ggtheme = theme_pubclean())+
  129. font("xylab",size = 22,face = "bold")+
  130. font("xy.text",size=14,face = "bold")+
  131. font("legend.title",size=16,face = "bold")+
  132. font("legend.text",size=14)+
  133. theme(strip.text = element_text(face = "bold", size = 20),legend.position="right")+
  134. yscale("percent",.format = TRUE)+ylim(0,1)
  136. ggsave("output/BehavRate_proportion_lineplot.png",height = 5,width = 12)
  139. # proposer's gain effect, lineplots
  140. df_progain <- df_para_reg |> ungroup() |> select(-propp) |> mutate(ProposerGain=gainp,Condition=context,Proposal=briber,Acceptance=ph,Success=collusion)|> select(-c(gainp,briber,ph,collusion,context))|> gather("Behavior","Rate",-c(Condition,ProposerGain))
  141. df_progain$Behavior <- factor(df_progain$Behavior,levels = c("Proposal","Acceptance","Success"))
  143. ggline(df_progain,"ProposerGain","Rate",facet.by = "Behavior",add = c("mean_se"),size = 1,color = "Condition",fill = "Condition",palette = c("#f16c23","#2b6a99"),alpha=0.5,ggtheme = theme_pubclean())+
  144. xlab("Player's Gain")+
  145. font("xylab",size = 22,face = "bold")+
  146. font("xy.text",size=14,face = "bold")+
  147. font("legend.title",size=16,face = "bold")+
  148. font("legend.text",size=14)+
  149. theme(strip.text = element_text(face = "bold", size = 20),legend.position="right")+
  150. ylim(0,1)+yscale("percent",.format = TRUE)
  152. ggsave("output/BehavRate_proposergain_lineplot.png",height = 5,width = 12)
  154. ```

behav_glm.Rmd, no license · at the source

Overview

Authors: Yixuan Lin1, Shiwei Qiu1, Yiyang Xu1, Yafeng Pan2, Yang Hu1,3, Xianchun Li1
ORCID iDs: Yixuan Lin
  1. Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center, Shanghai Key Laboratory of Mental Health and Psychological Crises Intervention, School of Psychology and Cognitive Science East China Normal University (ECNU) Shanghai China
  2. Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou China
  3. Center for Psychological Research on Anti‐Corruption, Institute of Discipline Inspection and Supervision ECNU Shanghai China
Institutions: East China Normal University (China); Zhejiang University (China)
Journal: Annals of the New York Academy of Sciences, volume 1561, issue 1, article e70327
Dates: received 8 December 2025; accepted 11 May 2026; published online 30 June 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/nyas.70327 · PMID 42376945 · PMCID PMC13316970 · OpenAlex W7166650443
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism)
Methods: Statistics, Connectivity, Machine learning
Keywords: cognitive computational modeling, fNIRS, inter‐brain synchronization, interpersonal corruption, representation similarity analysis
MeSH: Brain*, Decision Making*, Prefrontal Cortex*, Brain Mapping, Cognition, Cooperative Behavior, Female, Humans, Interpersonal Relations, Male, Morals, Reward, Spectroscopy, Near-Infrared (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: STI 2030-Major Projects (2021ZD0200500); Brain Science and Brain-like Intelligence Technology - National Science and Technology Major Project (2025ZD0215701); National Natural Science Foundation of China (32571256, 32200853); Fundamental Research Funds for the Central Universities (2022ECNUXWK‐XK003); National Science Foundation of Shanghai (23ZR1418400); Zhejiang Provincial Natural Science Foundation of China (LMS25C090002)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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OSF xvkft

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 72 files, 2 scripts
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Holds: documentation, 2 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration
Tools: ggpubr (2 files), lme4 (2 files), tidyverse (2 files), ggplot2 (1 file), Stan (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
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At the source: osf.io/xvkft/overview?view_only=dcdce8218217448ebf105ba921bafc4d

The paper's code and data availability statement is in the Data section.

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 13 MeSH terms, 6 funders, 37 references.

Cite

This paper

Lin, Y., Qiu, S., Xu, Y., Pan, Y., Hu, Y., & Li, X. (2026). Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement. Annals of the New York Academy of Sciences, 1561(1), e70327. https://doi.org/10.1111/nyas.70327

BibTeX

@article{lin2026wired,
author = {Lin, Yixuan and Qiu, Shiwei and Xu, Yiyang and Pan, Yafeng and Hu, Yang and Li, Xianchun},
title = {{Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement}},
journal = {Annals of the New York Academy of Sciences},
year = {2026},
month = jul,
volume = {1561},
number = {1},
pages = {e70327},
publisher = {Wiley},
issn = {0077-8923},
doi = {10.1111/nyas.70327},
url = {https://doi.org/10.1111/nyas.70327},
pmid = {42376945},
pmcid = {PMC13316970}
}

RIS

TY - JOUR
AU - Lin, Yixuan
AU - Qiu, Shiwei
AU - Xu, Yiyang
AU - Pan, Yafeng
AU - Hu, Yang
AU - Li, Xianchun
TI - Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement
T2 - Annals of the New York Academy of Sciences
J2 - Ann N Y Acad Sci
PY - 2026
DA - 2026/07/01
VL - 1561
IS - 1
SP - e70327
SN - 0077-8923
PB - Wiley
DO - 10.1111/nyas.70327
UR - https://doi.org/10.1111/nyas.70327
LA - en
ER -

CSL-JSON

{
"id": "10.1111/nyas.70327",
"type": "article-journal",
"title": "Wired for Corruption: Inter-Brain Synchrony Encodes Bribery-Related Value Information and Predicts Bribery Agreement",
"container-title": "Annals of the New York Academy of Sciences",
"author": [
{
"family": "Lin",
"given": "Yixuan"
},
{
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{
"family": "Xu",
"given": "Yiyang"
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{
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{
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}
],
"container-title-short": "Ann N Y Acad Sci",
"volume": "1561",
"issue": "1",
"page": "e70327",
"DOI": "10.1111/nyas.70327",
"PMID": "42376945",
"PMCID": "PMC13316970",
"ISSN": "0077-8923",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/nyas.70327",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

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