{ "abstract": "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 – accounting not only for choices but also response time distributions, together with hierarchical Bayesian parameter estimation – 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. Author summary: Humans learn from feedback using multiple brain systems in parallel: reinforcement learning, which gradually strengthens successful behaviors through trial and error, and working memory, which supports rapid learning by holding recent information in mind. These systems often drive the same outward behavior, making their contributions notoriously difficult to disentangle. Here, we show that analyzing not only what people choose but how quickly they respond fundamentally changes what we can learn about these processes. We introduce a computational framework that captures both learning and decision dynamics in a single model, and discover that people proactively slow down their responses when they anticipate greater memory demands, a cognitive control strategy not previously identified in this context. As an independent check, we used the model to predict a later memory-free phase it had never been fit to; it succeeded, while choice-only predictions diverged from people’s behavior. This model also reveals that individuals with schizophrenia show both a blunting of gradual learning and a failure of this proactive adjustment, deficits that were entirely hidden from prior analyses based on choices alone. Our approach is broadly extensible, allowing researchers to formulate and compare theories that jointly characterize learning processes and decision dynamics within a unified model.", "abstract_shown": true, "affiliations": [ "Department of Cognitive & Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America", "Department of Psychology, University of California, Santa Cruz, California, United States of America", "Department of Psychology, University of California, Irvine, California, United States of America", "Department of Psychiatry & Behavioral Neuroscience, University of Chicago, Chicago, Illinois, United States of America", "Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States of America", "Department of Psychology, University of California, Davis, California, United States of America", "Department of Psychology, University of Minnesota, Minneapolis, Minnesota, United States of America", "Department of Psychology, Washington University in St. Louis, St. Louis, Missouri, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 12, "authors": [ { "affs": [ 0 ], "name": "Krishn Bera" }, { "affs": [ 1 ], "name": "Alexander Fengler" }, { "affs": [ 2 ], "name": "Megan A. Boudewyn" }, { "affs": [ 3 ], "name": "Cameron S. Carter" }, { "affs": [ 4 ], "name": "Molly A. Erickson" }, { "affs": [ 5 ], "name": "James M. Gold" }, { "affs": [ 6 ], "name": "Steven J. Luck" }, { "affs": [ 7 ], "name": "J. Daniel Ragland" }, { "name": "Andrew P. Yonelinas" }, { "name": "Angus W. MacDonald III" }, { "name": "Deanna M. Barch" }, { "name": "Michael J. Frank" } ], "bibtex": "https://research.draconitic.com/paper/e7d0qjw4tt21s.bib", "cited_by": 0, "doi": "10.1371/journal.pcbi.1014796", "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014796", "id": "e7d0qjw4tt21s", "issns": [ "1553-7358" ], "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014796", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2805|Neural and Behavioral Psychology Studies" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive neuroscience/Working memory" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Decision making" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognition/Memory/Working memory" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive neuroscience/Working memory" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Decision making" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning" ], [ "plos", "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Learning/Human learning" ], [ "plos", "/Biology and life sciences/Neuroscience/Learning and memory/Learning" ], [ "plos", "/Biology and life sciences/Neuroscience/Learning and memory/Learning/Human learning" ], [ "plos", "/Biology and life sciences/Neuroscience/Learning and memory/Memory/Working memory" ], [ "plos", "/Biology and life sciences/Psychology/Behavior" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Decision making" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Learning" ], [ "plos", "/Biology and life sciences/Psychology/Cognitive psychology/Learning/Human learning" ], [ "plos", "/Medicine and health sciences/Mental health and psychiatry/Schizophrenia" ], [ "plos", "/Research and analysis methods/Simulation and modeling" ], [ "plos", "/Social sciences/Psychology/Behavior" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Decision making" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Learning" ], [ "plos", "/Social sciences/Psychology/Cognitive psychology/Learning/Human learning" ] ], "openalex": "W7214687488", "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014796&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-30", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/e7d0qjw4tt21s.ris", "sources": [ { "first_seen": "2026-10-30T12:17:37Z", "native_id": "10.1371/journal.pcbi.1014796", "source": "plos", "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014796" } ], "subjects": [ { "field": "biology", "origin": "plos:cognitive neuroscience", "subfield": "neuroscience" } ], "title": "Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences", "title_text": "Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:37Z", "url": "https://research.draconitic.com/paper/e7d0qjw4tt21s", "venue": "PLOS Computational Biology", "versions": [] }