ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2022
Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Effort-Cost Decision-making Among Individuals With Schizophrenia: A Systematic Review and Meta-analysis.JAMA psychiatry · 2023Pooled it
- Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder - personalized medicine using computational psychiatry in a randomized clinical trial.Journal of clinical and experimental neuropsychology · 2023Trial
- Reliability of computational versus noncomputational metrics of working memory and episodic memory in serious mental illness.Journal of psychopathology and clinical science · 2026Article
- Value representation in youth psychopathology: evidence of a transdiagnostic risk mechanism for psychosis.Translational psychiatry · 2026Article
- HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neurocognitive Modeling.bioRxiv : the preprint server for biology · 2026Article
- 16p11.2 duplication and NMDA receptor antagonism have distinct impacts on reward learning and decision making.Psychopharmacology · 2026Article
- From symptom-based heterogeneity to mechanism-based profiling in youth ADHD: the promise of computational psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 2025Article
- Evidence for shallow cognitive maps in schizophrenia.Cognitive, affective & behavioral neuroscience · 2025Article
- 16p11.2 duplication shows early male-biased impacts on reward learning, but NMDA receptor antagonism reduces optimal choice selection in both wildtypes and 16p11.2 duplication.bioRxiv : the preprint server for biology · 2025Article
- Developing algorithmic psychiatry via multi-level spanning computational models.Cell reports. Medicine · 2025Review
- Beyond reward learning deficits: Exploration-exploitation instability reveals computational heterogeneity in value-based decision making in early psychosis.medRxiv : the preprint server for health sciences · 2025Article
- A prefrontal thalamocortical readout for conflict-related executive dysfunction in schizophrenia.Cell reports. Medicine · 2024Article
- Review
- The challenge of learning adaptive mental behavior.Journal of psychopathology and clinical science · 2024Article
- Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Understanding Cognitive Behavioral Therapy for Psychosis Through the Predictive Coding Framework.Biological psychiatry global open science · 2024Review
- Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making.Current biology : CB · 2024Article
- Cognitive Control in Schizophrenia: Advances in Computational Approaches.Current directions in psychological science · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundPsychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.
methodsWe used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72).
resultsUsing accuracy, there was a main effect of group (F
conclusionsBoth medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.