OSCR

Effort produces after-effects costly for others but valued for self.

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  1. [1] § Materials and methods › Data analysis ↔ Scripts/Data_analysis.R, lines 298–369 · score 0.60 · button presses, response speed, lme4, scored, pairwise, prosocial decision

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R · 372 lines · 17 KB · no license · 1 match

  1. # load library packages
  2. if (!require("pacman")) install.packages("pacman")
  3. pacman::p_load(dplyr, MASS, psych, R.matlab, sjPlot, emmeans, lme4, lmerTest,
  4. ggplot2, tidyverse, readxl, ggeffects, rstatix, ggpubr)
  6. rm(list=ls()) # Clear environment
  7. ## specify your working directory ##
  8. setwd("/Users/yazheng/Documents/Papers/2_UnderReview/ProsocialEffort_EEG/SecondVersion/OSF/Data/")
  10. # Helper function: Check convergence (Recommended by Ben Bolker)
  11. didLmerConverge = function(lmerModel){
  12. relativeMaxGradient=signif(max(abs(with(lmerModel@optinfo$derivs,solve(Hessian,gradient)))),3)
  13. if (relativeMaxGradient < 0.001) {
  14. cat(sprintf("\tThe relative maximum gradient of %s is less than our 0.001 criterion.\n\tYou can safely ignore any warnings about a claimed convergence failure.\n\n", relativeMaxGradient))
  15. }
  16. else {
  17. cat(sprintf("The relative maximum gradient of %s exceeds our 0.001 criterion.\nThis looks like a real convergence failure; maybe try simplifying your model?\n\n", relativeMaxGradient))
  18. }
  19. }
  21. # Helper function: Calculate percentage of variance explained by random effects (SVD)
  22. svd_lmer = function(lmerModel){
  23. sv1_max <- svd(getME(lmerModel, "Tlist")[[1]])
  24. sv1_max$d
  25. svdValue = round(sv1_max$d^2/sum(sv1_max$d^2)*100, 2)
  26. cat("The percetage of explained variances by random effects are", svdValue)
  27. }
  29. ######################### load and clean all data #################################
  30. # Subjects to be excluded based on exclusion criteria desribed in the manuscript
  31. exclude_subs=c(129,143,111,119,154,163,134)
  33. # load data of the prosocial effort task----
  34. # load behavioral data
  35. a2= read_xlsx('Behavior/prosocial_effort_data.xlsx')
  37. # load EEG data
  38. channels <- c('FP1', 'FP2', 'F7', 'F3', 'FZ', 'F4', 'F8', 'FT7', 'FC3', 'FCZ',
  39. 'FC4', 'FT8', 'T7', 'C3', 'CZ', 'C4', 'T8', 'CP3', 'CPZ', 'CP4',
  40. 'P7', 'P3', 'PZ', 'P4', 'P8', 'O1', 'OZ', 'O2')
  41. # Performance markers
  42. Performmks = readMat('EEG/Perform_mks.mat')
  43. PstimType <- Performmks$Performmks %>% as.data.frame()
  44. colnames(PstimType) <- c('Subject','stimType')
  45. a2$Pfmmks<-PstimType$stimType
  47. # Feedback markers
  48. FBmks = readMat('EEG/FB_mks.mat')
  49. FBstimType <- FBmks$FBmks %>% as.data.frame()
  50. colnames(FBstimType) <- c('Subject','stimType')
  51. a2$FBmks<-FBstimType$stimType
  53. # Load performance-p3
  54. PerformP3 = as.data.frame(readMat('EEG/PerformP3300440.mat')$PerformP3)
  55. colnames(PerformP3) <- channels
  56. a2$PerformP3 <- apply(cbind (PerformP3$P3,PerformP3$PZ,PerformP3$P4),1, mean)
  58. # Load FRN (i.e., RewP) data
  59. FRN = as.data.frame(readMat('EEG/FRN300400.mat')$FRN)
  60. colnames(FRN) <- channels
  61. a2$FRN <- apply(cbind (FRN$FCZ,FRN$FC3,FRN$FC4),1, mean)
  63. # data preparation for the prosocial effort task
  64. # Delete seven subjects
  65. a2 <- a2[!a2$Subject%in%exclude_subs,]
  67. # Setup variables
  68. a2 <- a2 %>%
  69. mutate(
  70. IsSuccess = as.numeric(!Pfmmks %in% c(7, 8)),
  71. Feedback = ifelse(FB == "ü", "Good", "Bad"),
  72. Valence = case_when(RewardType %in% c(0.2,0.4,0.6,0.8,1) ~ "Gain",
  73. RewardType == 0 ~ "NoGain") ) %>%
  74. rename(Magnitude = PreReward,
  75. Num = Effort,
  76. Effort = EffortLevel) %>%
  77. mutate(
  78. Effort = case_when(Effort == 1 ~ 2,
  79. Effort == 2 ~ 3,
  80. Effort == 3 ~ 4,
  81. Effort == 4 ~ 5,
  82. Effort == 5 ~ 6),
  83. zEffort = as.vector(scale(Effort)),
  84. zMagnitude = as.vector(scale(Magnitude))
  85. )
  86. # Compute response speed: button presses per second
  87. a2 <- a2 %>% mutate( PressNum = case_when(
  88. Effort == 2 ~ MeanMaxNum*0.1,
  89. Effort == 3 ~ MeanMaxNum*0.3,
  90. Effort == 4 ~ MeanMaxNum*0.5,
  91. Effort == 5 ~ MeanMaxNum*0.7,
  92. Effort == 6 ~ MeanMaxNum*0.9),
  93. Speed = PressNum/ComRT )
  95. # Define contrasts
  96. a2$Subject <- as.factor(a2$Subject)
  97. a2$Effort <- as.factor(a2$Effort)
  98. a2$Magnitude <- as.factor(a2$Magnitude)
  100. a2$Valence <-factor(a2$Valence, levels = c( "Gain","NoGain"))
  101. contrasts(a2$Valence) <- contr.sdif(2)
  103. a2$Recipient <-factor(a2$Recipient, levels = c( "Self","Other"))
  104. contrasts(a2$Recipient) <- contr.sdif(2)
  106. a2$Feedback <-factor(a2$Feedback)
  107. contrasts(a2$Feedback) <- contr.sdif(2)
  109. a2$ComRT<-as.numeric(a2$ComRT)
  111. # Handle invalid ERP trials (failed responses)
  112. a2 <- a2 %>% mutate(PerformP3 = ifelse(Pfmmks == 7, NaN, PerformP3),
  113. FRN = ifelse(FBmks == 8, NaN, FRN) )
  116. # load data of the prosocial decision-making task----
  117. a3= read_xlsx('Behavior/prosocial_decision-making_data.xlsx')
  119. # data preparation for the prosocial decision-making task
  120. # Delete seven subjects
  121. a3 <- a3[!a3$Subject%in%exclude_subs,]
  123. a3 <- a3 %>%
  124. rename(Effort = EffortLevel, Recipient = Type, ChoiceRT = decide.RT,Magnitude = Cpoint ) %>%
  125. mutate(
  126. zEffort = as.numeric(scale(Effort)),
  127. zMagnitude = as.numeric(scale(Magnitude)),
  128. # Re-coding choice: 1=high-effort, 3=low-effort (0), 2=no response (NA)
  129. Choice = ifelse(Choice == 2, NA_real_,
  130. ifelse(Choice == 3, 0, 1)),
  131. ChoiceRT = ifelse(is.na(Choice), NA_real_, ChoiceRT)
  132. )
  134. # Define contrasts
  135. a3$Subject <- as.factor(a3$Subject)
  137. a3$Recipient <-factor(a3$Recipient, levels = c( "self","other"))
  138. contrasts(a3$Recipient) <- contr.sdif(2)
  140. a3$Effort <-factor(a3$Effort)
  141. a3$Magnitude <-factor(a3$Magnitude)
  143. # K (discounting rate) analysis----
  144. # load K of self and other
  145. Self <- read_xlsx('Behavior/Self_nlm.xlsx',sheet = 'self_para')
  146. Other <- read_xlsx('Behavior/Other_nlm.xlsx',sheet = 'other_para')
  148. Mdl <- cbind(Self$subj, Self$k, Other$k) %>% as.data.frame()
  149. colnames(Mdl)<-c("Subject","SelfK","OtherK")
  151. # Delete subject
  152. Mdl <- Mdl[!Mdl$Subject %in% exclude_subs,]
  153. # log-transformed K values
  154. Mdl$Self_logk <- log(Mdl$SelfK)
  155. Mdl$Other_logk <- log(Mdl$OtherK)
  157. # Normality tests
  158. shapiro.test(Mdl$Self_logk)
  159. shapiro.test(Mdl$Other_logk)
  161. # Paired-t test
  162. t.test(Mdl$Self_logk,Mdl$Other_logk,paired = TRUE)
  164. # Correlation between self-K and other K
  165. cor.test(Mdl$Self_logk,Mdl$Other_logk,method = "pearson")
  167. # Post-experimental rating data -------------------------------------------
  168. input_file = 'Rating/Rating.xlsx'
  169. # Read all Sheets and merge into one long dataframe
  170. Rat <- bind_rows(
  171. # Self data
  172. read_xlsx(input_file, sheet = 'SelfLike') %>% mutate(Recipient = "Self", Items = "Like"),
  173. read_xlsx(input_file, sheet = 'SelfDifficulity') %>% mutate(Recipient = "Self", Items = "Difficulity"),
  174. read_xlsx(input_file, sheet = 'SelfEffort') %>% mutate(Recipient = "Self", Items = "Effort"),
  175. # Other data
  176. read_xlsx(input_file, sheet = 'OtherLike') %>% mutate(Recipient = "Other", Items = "Like"),
  177. read_xlsx(input_file, sheet = 'OtherDifficulity')%>% mutate(Recipient = "Other", Items = "Difficulity"),
  178. read_xlsx(input_file, sheet = 'OtherEffort') %>% mutate(Recipient = "Other", Items = "Effort")
  179. ) %>%
  180. # Convert effort2-effort6 columns to one column
  181. pivot_longer(
  182. cols = matches("effort[0-9]+"),
  183. names_to = "Type",
  184. values_to = "Rating"
  185. ) %>%
  186. filter(!Subject %in% exclude_subs) %>%
  187. mutate(
  188. Subject = as.factor(Subject),
  189. Items = as.factor(Items),
  190. Recipient = factor(Recipient, levels = c("Self", "Other")),
  191. Effort = as.numeric(str_remove(Type, "effort")),
  192. zEffort = as.vector(scale(Effort, scale = TRUE, center = TRUE))
  193. )
  195. ############################################################ DATA ANALYSIS ####################################################################
  196. ###############################################################################################################################################
  198. ############################################################ The prosocial effort task #########################################################
  199. # Behavior analysis
  200. # Success rate
  201. print (summary(mod_suc <- glmer(IsSuccess~ Recipient*zEffort*zMagnitude + (Recipient + zEffort | Subject),
  202. data=a2,family=binomial, control = glmerControl(optimizer = 'bobyqa'))))
  203. tab_model(mod_suc,transform = NULL)
  205. # response speed
  206. print (summary(mod_speed <- lmer(Speed~ Recipient*zEffort*zMagnitude + (Recipient + zEffort | Subject),
  207. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  208. tab_model(mod_speed, p.val = "satterthwaite")
  210. # ERP analysis
  211. # Performance P3: the P3 locked to performance feedback
  212. print (summary(mod_PerformP3 <- lmer(PerformP3~ Recipient*zEffort*zMagnitude + (zEffort |Subject),
  213. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  214. tab_model(mod_PerformP3, p.val = "satterthwaite")
  216. # FRN (RewP)
  217. print (summary(mod_frn <- lmer(FRN~ Recipient*zEffort*zMagnitude*Valence + (Recipient+zEffort+zMagnitude | Subject),
  218. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  219. tab_model(mod_frn, p.val = "satterthwaite")
  220. # Slope analyses for the FRN
  221. # Recipient*Valence*zMagnitude
  222. mylist <- list(Recipient=c("Self","Other"),Valence=c("Gain","NoGain"))
  223. emtrends(mod_frn, pairwise~Recipient|Valence, var="zMagnitude",at=mylist) %>% summary(infer = c(TRUE,TRUE))
  224. # Recipient*zEffort*zMagnitude
  225. mylist <- list(zMagnitude=c(-1,1),Recipient=c("Self","Other"))
  226. emtrends(mod_frn, pairwise~Recipient|zMagnitude, var="zEffort",at=mylist) %>% summary(infer = c(TRUE,TRUE))
  228. ################################################## The prosocial decision-making task #########################################################
  229. # Behavior analysis: RT
  230. print (summary(mod_RT <- lmer(ChoiceRT~ Recipient*zMagnitude*(zEffort+I(zEffort^2)) + (Recipient+zEffort+I(zEffort^2)+zMagnitude | Subject),
  231. data=a3, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  232. tab_model(mod_RT, p.val = "satterthwaite")
  233. # Slope analyses for RT
  234. # recipient * effort
  235. emtrends(mod_RT, pairwise~Recipient, var="zEffort") %>% summary(infer = c(TRUE,TRUE))
  236. # recipient * magnitude
  237. emtrends(mod_RT, pairwise~Recipient, var="zMagnitude") %>% summary(infer = c(TRUE,TRUE))
  238. # linear effort * magnitude
  239. emtrends(mod_RT, pairwise~zMagnitude, var="zEffort",at=list(zMagnitude=c(1,-1))) %>% summary(infer = c(TRUE,TRUE))
  240. # the pure quadratic effort * magnitude
  241. eff_levels <- c(-1, 0, 1)
  242. mag_levels <- c(-1, 1)
  243. emm <- emmeans(mod_RT, ~ zEffort | zMagnitude, at = list(zEffort = eff_levels, zMagnitude = mag_levels))
  244. contrast(emm, method = "poly", max.degree = 2, by = "zMagnitude") %>% summary(infer = c(TRUE, TRUE))
  245. contrast(emm, interaction = c("poly", "pairwise"), max.degree = 2, by = NULL)
  247. # Behavior analysis: Choice
  248. print (summary(mod_choice <- glmer(Choice~ Recipient*zEffort*zMagnitude + (Recipient+zEffort+zMagnitude | Subject),
  249. data=a3, family=binomial, control = glmerControl(optimizer = 'bobyqa'))))
  250. tab_model(mod_choice,transform = NULL)
  251. # Slope analyses for choice
  252. # the interaction between recipient and effort
  253. emtrends(mod_choice, revpairwise ~ Recipient, var="zMagnitude")%>% summary( infer = c(TRUE, TRUE))
  254. # the interaction between effort and magnitude
  255. emtrends(mod_choice, revpairwise ~ zMagnitude, var="zEffort", at=list(zMagnitude=c(1,-1)))%>% summary(infer = c(TRUE, TRUE))
  258. ######################################################################### Rating data #########################################################
  259. ###############################################################################################################################################
  260. contrasts(Rat$Recipient) <- contr.sdif(2)
  261. # difficulty rating
  262. print (summary(mod_difficulty<- lmer(Rating~ Recipient*zEffort + (Recipient+zEffort | Subject),
  263. data=Rat[Rat$Items=='Difficulity',], control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  264. # effort rating
  265. print (summary(mod_effort <- lmer(Rating~ Recipient*zEffort + (Recipient+zEffort | Subject),
  266. data=Rat[Rat$Items=='Effort',], control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  267. # liking rating
  268. print (summary(mod_like <- lmer(Rating~ Recipient*zEffort + (Recipient+zEffort | Subject),
  269. data=Rat[Rat$Items=='Like',], control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  270. tab_model(mod_difficulty, mod_effort, mod_like, p.val = "satterthwaite")
  271. # interaction between recipient and effort for liking data
  272. emtrends(mod_like, ~ Recipient, var="zEffort") %>% summary(infer = c(TRUE,TRUE))
  274. # Correlations between self-reported data
  275. df_combined <- Rat %>%
  276. mutate(Items = recode(Items, "Effort" = "EffortRating")) %>%
  277. select(Subject, Items, Rating) %>%
  278. pivot_wider(names_from = Items, values_from = Rating, values_fn = mean)
  279. df_combined %>% cor_test(vars = c("Difficulity", "EffortRating", "Like")) %>% adjust_pvalue(method = "fdr")
  281. ############################################################### Other task parameters #########################################################
  282. # catch-trial data
  283. check <- read_xlsx('Behavior/catchtrial.xlsx') %>%
  284. filter(!is.na(effortchoice)) %>%
  285. mutate(
  286. effortcho = case_when(effortchoice == 1 ~ 2, effortchoice == 2 ~ 3, effortchoice == 3 ~ 4, effortchoice == 4 ~ 5, effortchoice == 5 ~ 6),
  287. rewardcho = case_when(rewardchoice == 0.1 ~ 0.2, rewardchoice == 0.2 ~ 0.4, rewardchoice == 0.3 ~ 0.6, rewardchoice == 0.4 ~ 0.8, rewardchoice == 0.5 ~ 1),
  288. EffACC = ifelse(effortcho == EffortLevel, 1, 0),
  289. RewACC = ifelse(rewardcho == Cpoint, 1, 0)
  290. ) %>%
  291. filter(!Subject %in% exclude_subs)
  293. checkACC <- check %>% group_by(Subject) %>%
  294. summarise(mEffACC = mean(EffACC, na.rm = TRUE),
  295. mRewACC = mean(RewACC, na.rm = TRUE) )
  296. print(checkACC)
  298. # averaged number of button presses across three trials ----
  299. subMPN <- a2 %>% group_by(Subject) %>% summarise(MPN = mean(MeanMaxNum, na.rm = TRUE))
  300. summary(subMPN$MPN)
  302. ################################################## Cross task analysis ##########################################################################
  303. # Whether k values estimated from the prosocial decision-making task influence the neural after-effects?
  304. # data preparation
  305. k_lookup <- Mdl %>%
  306. select(Subject, Self_logk, Other_logk) %>%
  307. mutate(Subject = as.factor(Subject)) %>%
  308. pivot_longer(cols = c(Self_logk, Other_logk),names_to = "Recipient",values_to = "logk") %>%
  309. mutate(Recipient = case_when(Recipient == "Self_logk" ~ "Self",Recipient == "Other_logk" ~ "Other"))
  310. a2 <- a2 %>%
  311. left_join(k_lookup, by = c("Subject", "Recipient")) %>%
  312. mutate(zlogk = as.numeric(scale(logk)))
  314. a3_summary <- a3 %>%
  315. group_by(Subject, Recipient) %>%
  316. summarise(p_choice = mean(Choice == 1, na.rm = TRUE), .groups = "drop") %>%
  317. mutate(Recipient = case_when(Recipient == "self" ~ "Self",Recipient == "other" ~ "Other"))
  318. a2 <- a2 %>%
  319. left_join(a3_summary, by = c("Subject", "Recipient")) %>%
  320. # group_by(Recipient) %>%
  321. mutate(zp_choice = as.numeric(scale(p_choice))) # %>% ungroup()
  323. a2$Recipient <-factor(a2$Recipient, levels = c( "Self","Other"))
  324. contrasts(a2$Recipient) <- contr.sdif(2)
  326. # log K
  327. print (summary(mod_frn_logk <- lmer(FRN~ Recipient*zEffort*zMagnitude*Valence*zlogk + (Recipient+zEffort+zMagnitude | Subject),
  328. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  329. tab_model(mod_frn_logk, p.val = "satterthwaite")
  331. mylist <- list(zMagnitude=c(-1,1),zlogk=c(-1,1),zEffort=c(-1,1))
  332. emtrends(mod_frn_logk,pairwise ~Recipient|zMagnitude + zlogk, var="zEffort",at=mylist) %>% summary(infer = c(TRUE, TRUE))
  334. # choice proportions
  335. print (summary(mod_frn_choice <- lmer(FRN~ Recipient*zEffort*zMagnitude*Valence*zp_choice + (Recipient+zEffort+zMagnitude | Subject),
  336. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  337. tab_model(mod_frn_choice, p.val = "satterthwaite")
  339. mylist <- list(zMagnitude=c(-1,1),zp_choice=c(-1,1),zEffort=c(-1,1))
  340. emtrends(mod_frn_choice,pairwise ~Recipient|zMagnitude + zp_choice, var="zEffort",at=mylist) %>% summary(infer = c(TRUE, TRUE))
  343. ################################################## Control analysis ############################################################
  344. # control for response speed and self-reported effort rating
  345. # z-scoring response speed
  346. a2<-a2%>%group_by(Subject)%>%mutate(zSpeed=as.numeric(scale(Speed)) )%>%ungroup()
  347. # control for effort rating
  348. effort_rating <- Rat[Rat$Items=='Effort',] %>% group_by(Subject) %>%
  349. mutate(ceffort_rating = as.vector(scale(Rating,scale = FALSE))) %>% ungroup() %>%
  350. select("Subject","Recipient","Effort", "Rating","ceffort_rating") %>%
  351. rename("effort_rating" = "Rating")
  352. a2 <- a2 %>%
  353. left_join(
  354. effort_rating %>%
  355. mutate(Effort = as.factor(Effort)) %>%
  356. select(Subject, Recipient, Effort, effort_rating, ceffort_rating),
  357. by = c("Subject", "Recipient", "Effort")
  358. )
  360. a2$Recipient <-factor(a2$Recipient, levels = c( "Self","Other"))
  361. contrasts(a2$Recipient) <- contr.sdif(2)
  363. # include response speed as a covariate
  364. print (summary(mod_frn_speed <- lmer(FRN~ Recipient*zEffort*zMagnitude*Valence+zSpeed + (Recipient+zEffort+zMagnitude | Subject),
  365. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  366. # include effort rating as a covariate
  367. print (summary(mod_frn_effrat <- lmer(FRN~ Recipient*zEffort*zMagnitude*Valence+effort_rating + (Recipient+zEffort+zMagnitude | Subject),
  368. data=a2, control = lmerControl(optimizer = 'bobyqa'), REML = FALSE)))
  369. tab_model(mod_frn_speed,mod_frn_effrat, p.val = "satterthwaite")

Data_analysis.R, no license · at the source

Overview

Authors: Ya Zheng1,2, Rumeng Tang3,4
ORCID iDs: Ya Zheng, Rumeng Tang
  1. Department of Psychology, Guangzhou University, Guangzhou, China
  2. Center for Reward and Social Cognition, School of Education, Guangzhou University, Guangzhou, China
  3. Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health and Department of Psychology, Sun Yat-sen University, Guangzhou, China
  4. Department of Psychology, Dalian Medical University, Dalian, China
Institutions: Guangzhou University (China); Sun Yat-sen University (China); Dalian Medical University (China)
Journal: eLife, volume 13, article RP103566
Dates: published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.103566 · PMID 42132118 · PMCID PMC13175574 · OpenAlex W4405526730
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials, Statistics
Keywords: Human
MeSH: Reward*, Social Behavior*, Electroencephalography, Female, Humans, Male (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (32571255, 31971027); 2024 Tertiary Education Scientific Research Project of Guangzhou Municipal Education Bureau (2024312195)
Citations: cited by 1 paper (Europe PMC); 56 references in the paper

Abstract

Engaging in prosocial behavior requires effort, yet people are often averse to exerting effort for others’ benefit. However, it remains unclear how effort exertion affects subsequent reward evaluation during prosocial acts. Here, we combined high-temporal-resolution electroencephalography with a paradigm that independently manipulated physical effort and monetary reward for self and others to elucidate the neural mechanisms underlying the reward after-effect of prosocial effort expenditure. We found dissociable reward after-effects for self-benefiting and other-benefiting effort. For self-benefiting rewards, the reward positivity (RewP) increased with effort demand, suggesting an effort-enhancement effect. In contrast, for other-benefiting rewards, the RewP decreased as effort increased, demonstrating an effort-discounting effect. Critically, this dissociation was contingent upon high reward magnitude and modulated by individual differences in effort discounting, yet remained distinct from performance evaluation. Our findings reveal distinct neural computations for self- and other-benefiting efforts, offering new insights into how prior effort expenditure shapes reward evaluation during prosocial 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 1 match between paragraphs and lines of code.

OSF bvpa2

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 14 files, 2 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (2 files), ggplot2 (2 files), ggpubr (2 files), lme4 (2 files), lmerTest (2 files), psych (2 files), rstatix (2 files), tidyverse (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
3 files
At the source: osf.io/bvpa2/

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

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;
  • 2 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.

Data availability

Data and code that support the findings of this study are available on Open Science Framework at https://osf.io/bvpa2/.

The following dataset was generated:

Zheng Y. 2026. Effort produces after-effects costly for others but valued for self. Open Science Framework. bvpa2

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 1 keyword, 6 MeSH terms, 2 funders, 54 references.

Cite

This paper

Zheng, Y., & Tang, R. (2026). Effort produces after-effects costly for others but valued for self. eLife, 13, RP103566. https://doi.org/10.7554/elife.103566

BibTeX

@article{zheng2026effort,
author = {Zheng, Ya and Tang, Rumeng},
title = {{Effort produces after-effects costly for others but valued for self}},
journal = {eLife},
year = {2026},
month = may,
volume = {13},
pages = {RP103566},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.103566},
url = {https://doi.org/10.7554/elife.103566},
pmid = {42132118},
pmcid = {PMC13175574}
}

RIS

TY - JOUR
AU - Zheng, Ya
AU - Tang, Rumeng
TI - Effort produces after-effects costly for others but valued for self
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/05/14
VL - 13
SP - RP103566
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.103566
UR - https://doi.org/10.7554/elife.103566
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.103566",
"type": "article-journal",
"title": "Effort produces after-effects costly for others but valued for self",
"container-title": "eLife",
"author": [
{
"family": "Zheng",
"given": "Ya"
},
{
"family": "Tang",
"given": "Rumeng"
}
],
"container-title-short": "Elife",
"volume": "13",
"page": "RP103566",
"DOI": "10.7554/elife.103566",
"PMID": "42132118",
"PMCID": "PMC13175574",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.103566",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
14
]
]
}
}

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

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