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Competing value signals impair reward-learning via dopaminergic mechanisms and increase exploration.

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The authors' code

Stan · 138 lines · 3.9 KB · CC-BY-4.0

  1. functions {
  2. real partial_log_lik(array[,] int choice_slice,
  3. int start, int end,
  4. array[,] int reward,
  5. array[] real persev,
  6. array[] real alpha,
  7. array[] real beta,
  8. int nTrials) {
  9. real lp = 0;
  10. for (s in start:end) {
  11. vector[2] v = rep_vector(0.0, 2);
  12. vector[2] pb;
  14. for (t in 1:nTrials) {
  15. pb = rep_vector(0.0, 2);
  16. if (t > 1) {
  17. pb[ choice_slice[s, t-1] ] = persev[s];
  18. }
  20. lp += categorical_logit_lpmf(
  21. choice_slice[s, t] |
  22. beta[s] * (v + pb)
  23. );
  25. real pe = reward[s, t] - v[ choice_slice[s, t] ];
  26. v[ choice_slice[s, t] ] += alpha[s] * pe;
  27. }
  28. }
  29. return lp;
  30. }
  31. }
  33. data {
  34. int<lower=1> nSubjects;
  35. int<lower=1> nTrials;
  36. int<lower=1> nConditions;
  37. int<lower=1> nSessions;
  39. array[nSessions, nConditions, nSubjects, nTrials] int<lower=1, upper=2> choice;
  40. array[nSessions, nConditions, nSubjects, nTrials] int<lower=-1, upper=1> reward;
  41. }
  43. parameters {
  44. array[nSessions, nConditions] real persev_mu;
  45. array[nSessions, nConditions] real<lower=0> persev_sd;
  46. array[nSessions, nConditions] real<lower=0, upper=1> alpha_mu;
  47. array[nSessions, nConditions] real<lower=0> alpha_kappa;
  48. array[nSessions, nConditions] real beta_mu_log;
  49. array[nSessions, nConditions] real<lower=0> beta_sd_log;
  51. array[nSessions, nConditions, nSubjects] real persev_z;
  52. array[nSessions, nConditions, nSubjects] real beta_z;
  53. array[nSessions, nConditions, nSubjects] real<lower=0, upper=1> alpha;
  54. }
  56. transformed parameters {
  57. array[nSessions, nConditions, nSubjects] real persev;
  58. array[nSessions, nConditions, nSubjects] real<lower=0> beta;
  59. array[nSessions, nConditions] real<lower=0> alpha_a;
  60. array[nSessions, nConditions] real<lower=0> alpha_b;
  62. for (m in 1:nSessions) {
  63. for (c in 1:nConditions) {
  65. alpha_a[m, c] = alpha_mu[m, c] * alpha_kappa[m, c];
  66. alpha_b[m, c] = (1 - alpha_mu[m, c]) * alpha_kappa[m, c];
  68. for (s in 1:nSubjects) {
  69. persev[m, c, s] = persev_mu[m, c] + persev_sd[m, c] * persev_z[m, c, s];
  70. real log_beta = beta_mu_log[m, c] + beta_sd_log[m, c] * beta_z[m, c, s];
  71. beta[m, c, s] = exp(log_beta);
  72. }
  73. }
  74. }
  75. }
  77. model {
  78. for (m in 1:nSessions) {
  79. for (c in 1:nConditions) {
  80. alpha_mu[m, c] ~ beta(2, 2);
  81. alpha_kappa[m, c] ~ lognormal(log(10), 0.25);
  82. beta_mu_log[m, c] ~ normal(log(3), 0.25);
  83. beta_sd_log[m, c] ~ normal(0.2, 0.1) T[0, ];
  84. persev_mu[m, c] ~ normal(0.0, 0.5);
  85. persev_sd[m, c] ~ normal(0.5, 0.25) T[0, ];
  87. for (s in 1:nSubjects) {
  88. persev_z[m, c, s] ~ normal(0, 1);
  89. beta_z[m, c, s] ~ normal(0, 1);
  90. alpha[m, c, s] ~ beta(alpha_a[m, c], alpha_b[m, c]);
  91. }
  93. target += reduce_sum(
  94. partial_log_lik,
  95. choice[m, c],
  96. 8,
  97. reward[m, c],
  98. persev[m, c],
  99. alpha[m, c],
  100. beta[m, c],
  101. nTrials
  102. );
  103. }
  104. }
  105. }
  107. generated quantities {
  108. array[nSessions, nConditions, nSubjects, nTrials] real log_lik;
  109. array[nSessions, nConditions, nSubjects, nTrials, 2] real ev;
  110. array[nSessions, nConditions, nSubjects, nTrials] real pe;
  112. for (m in 1:nSessions) {
  113. for (c in 1:nConditions) {
  114. for (s in 1:nSubjects) {
  115. vector[2] v = rep_vector(0.0, 2);
  116. vector[2] pb;
  118. for (t in 1:nTrials) {
  119. pb = rep_vector(0.0, 2);
  120. if (t > 1) {
  121. pb[choice[m, c, s, t-1]] = persev[m, c, s];
  122. }
  124. log_lik[m, c, s, t] = categorical_logit_lpmf(
  125. choice[m, c, s, t] |
  126. beta[m, c, s] * (v + pb)
  127. );
  129. ev[m, c, s, t, 1] = v[1];
  130. ev[m, c, s, t, 2] = v[2];
  132. pe[m, c, s, t] = reward[m, c, s, t] - v[ choice[m, c, s, t] ];
  133. v[ choice[m, c, s, t] ] += alpha[m, c, s] * pe[m, c, s, t];
  134. }
  135. }
  136. }
  137. }
  138. }

pers.stan, under CC-BY-4.0 · at the source

Overview

Authors: Wen-Wei Lin1, Pei-Yu Lee1, Hsin-Yun Tsai2, Yi-Hsuan Lin2, Min-Min Lin1, Zheng-Liang Lu3, Mei-Yu Yeh1, Ming-Tsung Tseng1
ORCID iDs: Ming-Tsung Tseng
  1. Graduate Institute of Brain and Mind Sciences, National Taiwan University College of Medicine, Taipei, Taiwan
  2. Taiwan International Graduate Program in Interdisciplinary Neuroscience, National Taiwan University and Academia Sinica, Taipei, Taiwan
  3. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
Institutions: National Taiwan University (Taiwan); Academia Sinica (Taiwan)
Journal: PLoS biology, volume 24, issue 7, article e3003922
Dates: received 9 February 2026; accepted 13 July 2026; published online 24 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003922 · PMID 42497225 · PMCID PMC13427004 · OpenAlex W7170303932
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cognitive (subfield)
Methods: Statistics, Machine learning, Connectivity, fMRI & imaging
MeSH: Dopamine*, Learning*, Reward*, Amisulpride, Animals, Choice Behavior, Conditioning, Operant, Female, Humans, Magnetic Resonance Imaging, Male, Punishment, Reinforcement Machine Learning, Reinforcement, Psychology, Sulpiride (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science and Technology Council (NSTC 113-2314-B-002-273-MY3); Ministry of Science and Technology of Taiwan (MOST 107-2314-B-002-073 and MOST 108-2410-H-002-110-MY2)
Citations: not cited yet (Europe PMC); 105 references in the paper

Abstract

Effective reinforcement learning requires balancing exploration of uncertain options with exploitation of known outcomes. In real-world contexts, the same action may yield rewards in some situations and punishments in others, yet how these learning processes influence each other remains unclear. Here, we examine the neural mechanisms underlying how reward and punishment learning interact to guide adaptive behavior. We conducted four experiments (N = 159) using an instrumental learning task with binary choices, some of which were exclusive to reward or punishment learning trials, while others appeared in both, allowing assessment of their interaction. When choices were tied to a single learning type, reward learning engages less exploration (i.e., fewer choices of lower-value options) than punishment learning. Critically, when both learning processes were concurrently engaged, reward learning was selectively impaired, accompanied by enhanced exploration and greater activation in exploration-related prefrontal regions revealed by fMRI. Computational modeling showed that impaired reward learning was best explained by sensitivity to prior punishment history associated with reward-learning options, while individual differences in loss aversion predicted the degree of increased exploration. Finally, pharmacological attenuation of dopaminergic signaling via the D2/3 receptor antagonist amisulpride abolished both the increased exploration and the interference with reward learning. These findings suggest that punishment-history interference during reward learning is dopamine-modulated and associated with increased exploration, with individual differences in this exploration linked to loss aversion, providing a mechanistic account of how the brain resolves competing value signals and informing dopamine-related learning disturbances in neuropsychiatric conditions.

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

Repositories

Its files are read in the Code ↔ Paper reader above.

Zenodo 21261694

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Stan (6 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
8 files
At the source: doi.org/10.5281/zenodo.21261694

linwenwei6915/reward-punishment-interference-stan-models

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0f3f66dff9fbfc00e9ce123d723e970293af7dab, 8 July 2026
Languages: Stan (6)
Size: 8 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Stan (6 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/linwenwei6915/reward-punishment-interference-stan-models/tree/v1.0.0

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

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Datasets cited

Data Availability

De-identified behavioral data, the numerical data underlying the figure plots (S1 Data), and unthresholded fMRI statistical maps are available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/T7YWA). The dataset and analysis scripts are available on Zenodo (https://doi.org/10.5281/zenodo.21261694), with a README describing the repository contents.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 15 MeSH terms, 2 funders, 92 references.

Cite

This paper

Lin, W.-W., Lee, P.-Y., Tsai, H.-Y., Lin, Y.-H., Lin, M.-M., Lu, Z.-L., Yeh, M.-Y., & Tseng, M.-T. (2026). Competing value signals impair reward-learning via dopaminergic mechanisms and increase exploration. PLoS biology, 24(7), e3003922. https://doi.org/10.1371/journal.pbio.3003922

BibTeX

@article{lin2026competing,
author = {Lin, Wen-Wei and Lee, Pei-Yu and Tsai, Hsin-Yun and Lin, Yi-Hsuan and Lin, Min-Min and Lu, Zheng-Liang and Yeh, Mei-Yu and Tseng, Ming-Tsung},
title = {{Competing value signals impair reward-learning via dopaminergic mechanisms and increase exploration}},
journal = {PLoS biology},
year = {2026},
month = jul,
volume = {24},
number = {7},
pages = {e3003922},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003922},
url = {https://doi.org/10.1371/journal.pbio.3003922},
pmid = {42497225},
pmcid = {PMC13427004}
}

RIS

TY - JOUR
AU - Lin, Wen-Wei
AU - Lee, Pei-Yu
AU - Tsai, Hsin-Yun
AU - Lin, Yi-Hsuan
AU - Lin, Min-Min
AU - Lu, Zheng-Liang
AU - Yeh, Mei-Yu
AU - Tseng, Ming-Tsung
TI - Competing value signals impair reward-learning via dopaminergic mechanisms and increase exploration
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/07/24
VL - 24
IS - 7
SP - e3003922
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003922
UR - https://doi.org/10.1371/journal.pbio.3003922
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pbio.3003922",
"type": "article-journal",
"title": "Competing value signals impair reward-learning via dopaminergic mechanisms and increase exploration",
"container-title": "PLoS biology",
"author": [
{
"family": "Lin",
"given": "Wen-Wei"
},
{
"family": "Lee",
"given": "Pei-Yu"
},
{
"family": "Tsai",
"given": "Hsin-Yun"
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{
"family": "Lin",
"given": "Yi-Hsuan"
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{
"family": "Lin",
"given": "Min-Min"
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{
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"given": "Zheng-Liang"
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{
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"given": "Mei-Yu"
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{
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"given": "Ming-Tsung"
}
],
"container-title-short": "PLoS Biol",
"volume": "24",
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"PMID": "42497225",
"PMCID": "PMC13427004",
"ISSN": "1544-9173",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pbio.3003922",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
24
]
]
}
}

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