{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:47:21Z","timestamp":1791917241365,"version":"4.3.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014796","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000}}],"reference-count":80,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"publisher","award":["R01 MH084840"],"award-info":[{"award-number":["R01 MH084840"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["S10OD036341"],"award-info":[{"award-number":["S10OD036341"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Various neurocognitive processes contribute to reinforcement learning (RL) and decision making, requiring careful task designs and computational modeling to disentangle them. People rely on capacity-limited working memory (WM) to rapidly and flexibly adapt behavior during learning, together with slower incremental RL processes that support robust long-term retention. The RLWM paradigm was developed to isolate these contributions by manipulating WM demands during learning. Empirical and computational advances support the separability of such processes, but only imperfectly, because a given choice can often equally be attributed to use of WM vs. RL. Here, we show that jointly modeling decision dynamics \u2013 accounting not only for choices but also response time distributions, together with hierarchical Bayesian parameter estimation \u2013 improves the ability to disentangle separable learning and cognitive control processes. Despite added complexity, models with decision dynamics improved parameter recovery and yielded accurate out-of-sample prediction for choices in a held-out test phase after learning. In contrast, models fit only to choices produced an inflation of RL learning rates and failed the out-of-sample test. Moreover, joint modeling also revealed a novel neurocognitive process by which participants proactively widen decision boundaries to increase response caution under increased WM load. Applying our model to patients with schizophrenia revealed a deficit in proactive control mechanisms as well as slowed incremental RL, which were masked by previous choice-only models, while replicating previously established WM deficits. In summary, we show how joint modeling enables accurate, model-based and mechanism-oriented computational phenotyping of psychopathologies.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014796","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:46:56Z","timestamp":1790790416000},"page":"e1014796","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0356-4659","authenticated-orcid":true,"given":"Krishn","family":"Bera","sequence":"first","affiliation":[{"name":"Carney Institute for Brain Science, Brown University","place":["Providence, Rhode Island, United States of America"],"department":["Department of Cognitive & Psychological Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Fengler","sequence":"additional","affiliation":[{"name":"Carney Institute for Brain Science, Brown University","place":["Providence, Rhode Island, United States of America"],"department":["Department of Cognitive & Psychological Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan A.","family":"Boudewyn","sequence":"additional","affiliation":[{"name":"University of California","place":["Santa Cruz, California, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cameron S.","family":"Carter","sequence":"additional","affiliation":[{"name":"University of California","place":["Irvine, California, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Molly A.","family":"Erickson","sequence":"additional","affiliation":[{"name":"University of Chicago","place":["Chicago, Illinois, United States of America"],"department":["Department of Psychiatry & Behavioral Neuroscience"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James M.","family":"Gold","sequence":"additional","affiliation":[{"name":"University of Maryland School of Medicine","place":["Baltimore, Maryland, United States of America"],"department":["Department of Psychiatry"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven J.","family":"Luck","sequence":"additional","affiliation":[{"name":"University of California","place":["Davis, California, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Daniel","family":"Ragland","sequence":"additional","affiliation":[{"name":"University of California","place":["Davis, California, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew P.","family":"Yonelinas","sequence":"additional","affiliation":[{"name":"University of California","place":["Davis, California, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angus W.","family":"MacDonald III","sequence":"additional","affiliation":[{"name":"University of Minnesota","place":["Minneapolis, Minnesota, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deanna M.","family":"Barch","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis","place":["St. Louis, Missouri, United States of America"],"department":["Department of Psychology"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J.","family":"Frank","sequence":"additional","affiliation":[{"name":"Carney Institute for Brain Science, Brown University","place":["Providence, Rhode Island, United States of America"],"department":["Department of Cognitive & Psychological Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"pcbi.1014796.ref001","volume-title":"Reinforcement learning: an introduction","author":"RS Sutton","year":"2018"},{"issue":"4","key":"pcbi.1014796.ref002","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.tics.2019.01.010","article-title":"Holistic reinforcement learning: the role of structure and attention","volume":"23","author":"A Radulescu","year":"2019","journal-title":"Trends Cogn Sci"},{"issue":"6992","key":"pcbi.1014796.ref003","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1038\/nature02581","article-title":"Temporal difference models describe higher-order learning in humans","volume":"429","author":"B Seymour","year":"2004","journal-title":"Nature"},{"issue":"3","key":"pcbi.1014796.ref004","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.cognition.2008.08.011","article-title":"Hierarchically organized behavior and its neural foundations: a reinforcement learning perspective","volume":"113","author":"MM Botvinick","year":"2009","journal-title":"Cognition"},{"issue":"5","key":"pcbi.1014796.ref005","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1523\/JNEUROSCI.16-05-01936.1996","article-title":"A framework for mesencephalic dopamine systems based on predictive Hebbian learning","volume":"16","author":"PR Montague","year":"1996","journal-title":"J Neurosci"},{"issue":"5306","key":"pcbi.1014796.ref006","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1126\/science.275.5306.1593","article-title":"A neural substrate of prediction and reward","volume":"275","author":"W Schultz","year":"1997","journal-title":"Science"},{"issue":"1","key":"pcbi.1014796.ref007","first-page":"1","article-title":"Adaptive cost-benefit control fueled by striatal dopamine","volume":"48","author":"MJ Frank","year":"2025","journal-title":"Annu\u00a0Rev Neurosci"},{"issue":"3","key":"pcbi.1014796.ref008","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1037\/a0037015","article-title":"Opponent actor learning (OpAL): modeling interactive effects of striatal dopamine on reinforcement learning and choice incentive","volume":"121","author":"AGE Collins","year":"2014","journal-title":"Psychol Rev"},{"key":"pcbi.1014796.ref009","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.85107","article-title":"On the normative advantages of dopamine and striatal opponency for learning and choice","volume":"12","author":"A Jaskir","year":"2023","journal-title":"Elife"},{"issue":"2","key":"pcbi.1014796.ref010","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1162\/089976606775093909","article-title":"Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia","volume":"18","author":"RC O\u2019Reilly","year":"2006","journal-title":"Neural Comput"},{"key":"pcbi.1014796.ref011","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cobeha.2020.10.003","article-title":"The role of executive function in shaping reinforcement learning","volume":"38","author":"M Rmus","year":"2021","journal-title":"Curr Opin Behav Sci"},{"issue":"7","key":"pcbi.1014796.ref012","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1111\/j.1460-9568.2011.07980.x","article-title":"How much of reinforcement learning is working memory, not reinforcement learning? 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HSSM: A widely applicable toolbox for hierarchical Bayesian neurocognitive modeling. 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Sequential neural likelihood: fast likelihood-free inference with autoregressive flows. In: Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, 2019. 837\u201348."},{"key":"pcbi.1014796.ref066","article-title":"Likelihood-free inference with emulator networks. ArXiv","author":"J-M Lueckmann","year":"2018"},{"issue":"1","key":"pcbi.1014796.ref067","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1111\/bmsp.12159","article-title":"Towards end-to-end likelihood-free inference with convolutional neural networks","volume":"73","author":"ST Radev","year":"2020","journal-title":"Br J Math Stat Psychol"},{"issue":"5","key":"pcbi.1014796.ref068","doi-asserted-by":"crossref","first-page":"911","DOI":"10.3758\/s13423-020-01719-6","article-title":"Sequential sampling models without random between-trial variability: the racing diffusion model of speeded decision making","volume":"27","author":"G Tillman","year":"2020","journal-title":"Psychon Bull Rev"},{"issue":"3","key":"pcbi.1014796.ref069","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1037\/0033-295X.108.3.550","article-title":"The time course of perceptual choice: the leaky, competing accumulator model","volume":"108","author":"M Usher","year":"2001","journal-title":"Psychol Rev"},{"issue":"4","key":"pcbi.1014796.ref070","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.3758\/s13423-022-02237-3","article-title":"\u201cReliable organisms from unreliable components\u201d revisited: the linear drift, linear infinitesimal variance model of decision making","volume":"30","author":"PL Smith","year":"2023","journal-title":"Psychon Bull Rev"},{"issue":"3","key":"pcbi.1014796.ref071","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.cogpsych.2009.12.001","article-title":"Bayesian hypothesis testing for psychologists: a tutorial on the Savage-Dickey method","volume":"60","author":"E-J Wagenmakers","year":"2010","journal-title":"Cogn Psychol"},{"issue":"518","key":"pcbi.1014796.ref072","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: a review for statisticians","volume":"112","author":"DM Blei","year":"2017","journal-title":"J Am Stat Assoc"},{"key":"pcbi.1014796.ref073","unstructured":"Kingma DP, Welling M. Auto-encoding variational Bayes. arXiv. 2013. doi: 10.48550\/ARXIV.1312.6114"},{"issue":"14","key":"pcbi.1014796.ref074","first-page":"1","article-title":"Automatic differentiation variational inference","volume":"18","author":"A Kucukelbir","year":"2017","journal-title":"J\u00a0Mach Learn Res"},{"key":"pcbi.1014796.ref075","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.1516","article-title":"PyMC: a modern, and comprehensive probabilistic programming framework in Python","volume":"9","author":"O Abril-Pla","year":"2023","journal-title":"PeerJ Comput Sci"},{"key":"pcbi.1014796.ref076","unstructured":"Bradbury J, Frostig R, Hawkins P, Johnson MJ, Leary C, Maclaurin D, et al. JAX: composable transformations of Python NumPy programs. http:\/\/github.com\/jax-ml\/jax 2018."},{"key":"pcbi.1014796.ref077","unstructured":"PyTensor Developers. Pytensor. 2024. URL https:\/\/github.com\/pymc-devs\/pytensor"},{"key":"pcbi.1014796.ref078","author":"A Fengler","year":"2026"},{"key":"pcbi.1014796.ref079","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst. 2019;32."},{"issue":"3","key":"pcbi.1014796.ref080","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1037\/dec0000005","article-title":"Absolute performance of reinforcement-learning models for the Iowa Gambling Task","volume":"1","author":"H Steingroever","year":"2014","journal-title":"Decision"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1014796","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014796","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:56:32Z","timestamp":1791914192000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014796"}},"subtitle":[],"editor":[{"given":"Jean","family":"Daunizeau","sequence":"first","affiliation":[{"name":"Brain and Spine Institute (ICM)","place":["FRANCE"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,30]]},"references-count":80,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,30]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1014796","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,30]]}}}