Literature DB >> 29735404

Impaired Expected Value Computations Coupled With Overreliance on Stimulus-Response Learning in Schizophrenia.

Dennis Hernaus1, James M Gold2, James A Waltz2, Michael J Frank3.   

Abstract

BACKGROUND: While many have emphasized impaired reward prediction error signaling in schizophrenia, multiple studies suggest that some decision-making deficits may arise from overreliance on stimulus-response systems together with a compromised ability to represent expected value. Guided by computational frameworks, we formulated and tested two scenarios in which maladaptive representations of expected value should be most evident, thereby delineating conditions that may evoke decision-making impairments in schizophrenia.
METHODS: In a modified reinforcement learning paradigm, 42 medicated people with schizophrenia and 36 healthy volunteers learned to select the most frequently rewarded option in a 75-25 pair: once when presented with a more deterministic (90-10) pair and once when presented with a more probabilistic (60-40) pair. Novel and old combinations of choice options were presented in a subsequent transfer phase. Computational modeling was employed to elucidate contributions from stimulus-response systems (actor-critic) and expected value (Q-learning).
RESULTS: People with schizophrenia showed robust performance impairments with increasing value difference between two competing options, which strongly correlated with decreased contributions from expected value-based learning (Q-learning). Moreover, a subtle yet consistent contextual choice bias for the probabilistic 75 option was present in people with schizophrenia, which could be accounted for by a context-dependent reward prediction error in the actor-critic.
CONCLUSIONS: We provide evidence that decision-making impairments in schizophrenia increase monotonically with demands placed on expected value computations. A contextual choice bias is consistent with overreliance on stimulus-response learning, which may signify a deficit secondary to the maladaptive representation of expected value. These results shed new light on conditions under which decision-making impairments may arise.
Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Computational psychiatry; Decision making; Expected value; Motivational deficits; Reinforcement learning; Schizophrenia

Mesh:

Year:  2018        PMID: 29735404      PMCID: PMC8984835          DOI: 10.1016/j.bpsc.2018.03.014

Source DB:  PubMed          Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging        ISSN: 2451-9022


  37 in total

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  7 in total

1.  Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes.

Authors:  Dennis Hernaus; Michael J Frank; Elliot C Brown; Jaime K Brown; James M Gold; James A Waltz
Journal:  Biol Psychiatry Cogn Neurosci Neuroimaging       Date:  2018-12-07

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3.  Using Computational Modeling to Capture Schizophrenia-Specific Reinforcement Learning Differences and Their Implications on Patient Classification.

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5.  Reduced learning bias towards the reward context in medication-naive first-episode schizophrenia patients.

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6.  Retention of Value Representations Across Time in People With Schizophrenia and Healthy Control Subjects.

Authors:  Adam J Culbreth; James A Waltz; Michael J Frank; James M Gold
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  7 in total

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