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Decomposing the neurocomputational mechanisms of deontological moral preferences.

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  1. [1] § Methods › Computational modeling and model comparison ↔ code/modeling/model6_wrapper_code.R, lines 60–110 · score 0.57 · Model fitting, Chain, LOOIC, RStan

Paper

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The authors' code

R · 163 lines · 5.5 KB · no license · 1 match

  1. rm(list=ls())
  2. # use behav data
  3. library(dplyr)
  4. library(loo)
  5. library(rstan)
  8. ALLDATA = read.csv("all_behav_data.csv")
  9. ALLDATA$con[ALLDATA$con == "many_default"] = 1
  10. ALLDATA$con[ALLDATA$con == "no_default"] = -1
  11. ALLDATA$con[ALLDATA$con == "one_default"] = 0
  13. ALLDATA$outcome[ALLDATA$outcome == "many"] = 1
  14. ALLDATA$outcome[ALLDATA$outcome == "one"] = 0
  15. ALLDATA$outcome[ALLDATA$outcome == ""] = -1
  17. ALLDATA$con <- sapply(ALLDATA$con, as.numeric)
  18. ALLDATA$outcome <- sapply(ALLDATA$outcome, as.numeric)
  20. #complete_rows <- complete.cases(ALLDATA[, colnames])
  21. #ALLDATA <- ALLDATA[complete_rows, ]
  22. #ALLDATA = ALLDATA%>%filter(outcome != -1)
  25. allSubjs = unique(ALLDATA$subject)
  26. N = length(allSubjs)
  27. T = table(ALLDATA$subject)
  28. Tsubj = as.vector(T)
  29. maxTrials <- max(Tsubj)
  30. many_pain <- array(0, c(N, maxTrials))
  31. many_num <- array(0, c(N, maxTrials))
  32. one_pain <- array(0, c(N, maxTrials))
  33. default_op <- array(0, c(N, maxTrials))
  34. chosen_op <- array(-1, c(N, maxTrials))
  36. for ( i in 1:N) {
  37. curSubj <- allSubjs[i]
  38. useTrials <- Tsubj[i]
  39. tmp <- subset(ALLDATA, ALLDATA$sub == curSubj)
  40. many_pain[i, 1:useTrials] <- tmp$many_pain
  41. many_num[i, 1:useTrials] <- tmp$many_num
  42. one_pain[i, 1:useTrials] <- tmp$one_pain
  43. default_op[i, 1:useTrials] <- tmp$con
  44. chosen_op[i, 1:useTrials] <- tmp$outcome
  45. }
  47. dataList <- list(
  48. N = N,
  49. T = maxTrials,
  50. Tsubj = Tsubj,
  51. many_pain = many_pain,
  52. many_num = many_num,
  53. one_pain = one_pain,
  54. default_op = default_op,
  55. chosen_op = chosen_op
  56. )
  60. ############model fitting###########################
  62. Model6 = stan("./Rawlsian_project/code/modeling/Model6.stan", data = dataList, pars = c('alpha', 'phi','tau', 'mu_alpha', 'mu_phi','mu_tau',
  63. 'Many_utility', 'One_utility', 'Chosen_utility',
  64. 'Unchosen_utility', 'log_lik', 'y_pred'),
  65. iter = 2000, warmup = 1000, chains = 4, cores = 5)
  68. ####################################################
  69. lik_Model6 <- loo::extract_log_lik(Model6, parameter_name = 'log_lik')
  70. ######################
  71. LOOIC_6<- loo::loo(lik_Model6, cores = 5)
  74. LOOIC_comparison <- print(compare(x = list(LOOIC_6)))
  76. #save(Model6, LOOIC_comparison, file ='model6_fitting.RData')
  78. traceplot(Model6, "alpha")
  79. traceplot(Model6, "phi")
  80. traceplot(Model6, "tau")
  82. stan_plot(Model6, "alpha", show_density=T)
  83. stan_plot(Model6, "phi", show_density=T)
  84. stan_plot(Model6, "tau", show_density=T)
  87. parVals <- rstan::extract(Model6, permuted = TRUE)
  89. alpha_list = apply(parVals$alpha, 2, mean)
  90. phi_list = apply(parVals$phi, 2, mean)
  91. mean_outcome = ALLDATA %>%
  92. dplyr::group_by(subject) %>%
  93. dplyr::summarise(meanoutcome = mean(outcome, na.rm = TRUE))
  95. mean_outcome = mean_outcome$meanoutcome
  97. param = as.data.frame(cbind(subid, alpha_list, phi_list, mean_outcome))
  100. write.csv(param, file = '/home/zohyos7/project_cpt/modeling/paramlist_Model6.csv', row.names = TRUE)
  102. Many_utility = apply(parVals[['Many_utility']], c(2,3), mean)
  103. One_utility = apply(parVals[['One_utility']], c(2,3), mean)
  104. Chosen_utility = apply(parVals[['Chosen_utility']], c(2,3), mean)
  105. Unchosen_utility = apply(parVals[['Unchosen_utility']], c(2,3), mean)
  107. write.csv(Many_utility, file = 'many_utility.csv', row.names = FALSE)
  108. write.csv(One_utility, file = 'one_utility.csv', row.names = FALSE)
  109. write.csv(Chosen_utility, file = 'chosen_utility.csv', row.names = FALSE)
  110. write.csv(Unchosen_utility, file = 'unchosen_utility.csv', row.names = FALSE)
  112. #Many_utility
  113. #One_utility
  116. parameters <- rstan::extract(Model6)
  117. parameters$y_pred[parameters$y_pred == -1] = NA
  118. y_pred_mean = apply(parameters$y_pred, c(2,3), mean)
  119. dim(y_pred_mean)
  121. y_pred_mean_sub = apply(y_pred_mean, 1 , mean, na.rm = T)
  124. chosen_op <- chosen_op
  125. chosen_op[chosen_op == -1] <- NA
  127. chosen_op
  129. chosen_op_mean_sub = apply(chosen_op, 1, mean, na.rm = T)
  131. chosen_op_mean_sub
  134. cor.test(y_pred_mean_sub, chosen_op_mean_sub)
  136. plot(y_pred_mean_sub*100, chosen_op_mean_sub*100)
  138. real_pred = cbind(y_pred_mean_sub*100, chosen_op_mean_sub*100)
  139. colnames(real_pred) <- c('predicted_choice', 'actual_choice')
  141. real_pred = data.frame(real_pred)
  143. ggplot(data = real_pred, aes(x = actual_choice, y = predicted_choice)) + geom_point(shape= 21, size = 2.5, stroke = 1, color = "skyblue", fill = '#3CAEA3',alpha = 0.7) +
  144. scale_color_distiller(palette = "RdPu") + theme_classic()+
  145. scale_y_continuous(labels = function(x) paste0(x, "%"))+ # Add percent sign
  146. scale_x_continuous(labels = function(x) paste0(x, "%"))+ # Add percent sign
  147. labs(x = "actual choice (percent of choosing MANY)", y = "predicted choice (percent of choosing MANY)")+ #add labels
  148. theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())+
  149. theme(plot.title = element_blank())+
  150. theme(axis.text = element_text(size = 11, color="black"))+ # Axis text
  151. theme(axis.line = element_line(color="black", size = 0.5))+
  152. theme(axis.ticks = element_line(size = 0.3))+
  153. theme(axis.title = element_text(size = 11))+ # Axis title text
  154. theme(axis.title.y = element_text(margin = margin(t = 0, r = 5, b = 0, l = 0)))+
  155. theme(axis.title.x = element_text(margin = margin(t = 5, r = 0, b = 10, l = 0)))+
  156. theme(legend.position = "right")+ # Legend on right
  157. theme(legend.text = element_text(size = 11))+ # Increase legend text size.
  158. theme(legend.key.size = unit(0.7, "cm"))+ # Make legend box larger
  159. theme(legend.title = element_blank()) # Remove legend title
  162. y_pred_mean_sub*100
  163. chosen_op_mean_sub*100

model6_wrapper_code.R, no license · at the source

Overview

Authors: Yoonseo Zoh1, Soyeon Kim2, Hackjin Kim3, M J Crockett1,4, Woo-Young Ahn2,5,6
ORCID iDs: Yoonseo Zoh, Hackjin Kim, M J Crockett, Woo-Young Ahn
  1. Department of Psychology, Princeton University, Princeton, NJ, USA
  2. Department of Psychology, Seoul National University, Seoul 08826, Korea
  3. School of Psychology, Korea University, Seoul, Korea
  4. University Center for Human Values, Princeton University, Princeton, NJ, USA
  5. Department of Brain and Cognitive Sciences, Seoul National University, Seoul, Korea
  6. AI Institute, Seoul National University, Seoul, Korea
Institutions: Princeton University (United States); Seoul National University (South Korea); Korea University (South Korea)
Journal: PNAS nexus, volume 5, issue 4, article pgag074
Dates: received 20 October 2025; accepted 5 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/pnasnexus/pgag074 · PMID 41969575 · PMCID PMC13064635 · OpenAlex W4415823290
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/4nzqr_v1
Status: code verified
Categories: human (organism)
Methods: Statistics, fMRI & imaging, Machine learning
Topic: Psychology of Moral and Emotional Judgment (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation (BK21 FOUR); Seoul National University (RS2021-II211343); National Research Foundation of Korea (RS-2024-00435727, RS-2022-KH125035, 5199990314123, RS-2021-II211343, RS-2025-00516410); Korea Foundation for Advanced Studies
Citations: not cited yet (Europe PMC); 61 references in the paper
Research resources: which is based on “Nipype” 1.2.0 RRID:SCR_002502

Abstract

Research on the neurocomputational mechanisms of moral judgment has typically focused on contrasting “utilitarian” preferences to impartially maximize aggregate welfare and “deontological” preferences that judge the morality of actions based on rules. However, there has been little work to decompose the cognitive subcomponents of deontological preferences. Here, we investigated the neurocomputational mechanisms underlying two types of deontological preferences (Rawlsian and Kantian) and their contrast with utilitarian preferences in an incentivized moral dilemma task. Participants repeatedly decided how to allocate harm between a single individual (“the one”) and a group of three to four individuals (“the group”). The task distinguished preferences for Rawlsian, Kantian, and utilitarian strategies by quantifying trade-offs among active harm, concern for the worst-off individual, and overall utility. Behaviorally, participants favored the Rawlsian strategy, preferring to impose more harm overall rather than disproportionately harm the one individual. Computational modeling revealed two dissociable dimensions of individual variability in Rawlsian preferences: (i) minimizing the maximum amount of harm delivered to a single person and (ii) subjective threshold of acceptable amount of harm imposed on one person. The combination of univariate and multivariate functional MRI analyses revealed the engagement of distinct brain regions in these two dimensions of Rawlsian preferences, which respectively mapped onto activity in mentalizing and valuation networks. Our results reveal the neurocomputational mechanisms guiding trade-offs between the welfare of one versus a larger group and highlight distinct roles for the mentalizing and valuation networks in shaping Rawlsian moral preferences.

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 2huxq

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (3), Python (2), Stan (1)
Size: 25 files, 6 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Stan (3 files), NumPy (2 files), pandas (2 files), PsychoPy (2 files), tidyverse (2 files), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), rstatix (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
6 files
At the source: osf.io/2huxq/

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 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);
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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

Behavioral data, analysis scripts, computational modeling, task code, and unthresholded statistical maps of fMRI data have been deposited in the OSF repository and are publicly available at https://osf.io/2huxq/ (60) (https://doi.org/10.17605/osf.io/2huxq).

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 4 funders, 29 references, 1 RRID.

Cite

This paper

Zoh, Y., Kim, S., Kim, H., Crockett, M. J., & Ahn, W.-Y. (2026). Decomposing the neurocomputational mechanisms of deontological moral preferences. PNAS nexus, 5(4), pgag074. https://doi.org/10.1093/pnasnexus/pgag074

BibTeX

@article{zoh2026decomposing,
author = {Zoh, Yoonseo and Kim, Soyeon and Kim, Hackjin and Crockett, M J and Ahn, Woo-Young},
title = {{Decomposing the neurocomputational mechanisms of deontological moral preferences}},
journal = {PNAS nexus},
year = {2026},
month = apr,
volume = {5},
number = {4},
pages = {pgag074},
publisher = {Oxford University Press},
issn = {2752-6542},
doi = {10.1093/pnasnexus/pgag074},
url = {https://doi.org/10.1093/pnasnexus/pgag074},
pmid = {41969575},
pmcid = {PMC13064635}
}

RIS

TY - JOUR
AU - Zoh, Yoonseo
AU - Kim, Soyeon
AU - Kim, Hackjin
AU - Crockett, M J
AU - Ahn, Woo-Young
TI - Decomposing the neurocomputational mechanisms of deontological moral preferences
T2 - PNAS nexus
J2 - PNAS Nexus
PY - 2026
DA - 2026/04/07
VL - 5
IS - 4
SP - pgag074
SN - 2752-6542
PB - Oxford University Press
DO - 10.1093/pnasnexus/pgag074
UR - https://doi.org/10.1093/pnasnexus/pgag074
LA - en
ER -

CSL-JSON

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