Dataset Information

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

ABSTRACT:

Background

Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on "valueless" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.

Methods

Healthy volunteers (n = 38) and PSZ (n = 49) completed a learning paradigm consisting of four stimulus pairs. Reward magnitude (3, 2, 1, 0 points) and probability (90%, 80%, 20%, 10%) determined each stimulus's EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.

Results

PSZ with high motivational deficits made increasingly less optimal choices as the difference in reward value (probability × magnitude) between two competing stimuli increased. Using a previously validated computational hybrid model, PSZ relied less on EV ("Q-learning") and more on stimulus-response learning ("actor-critic"), which correlated with Scale for the Assessment of Negative Symptoms motivational deficit severity. PSZ specifically failed to represent reward magnitude, consistent with model demonstrations showing that response tendencies in the actor-critic were preferentially driven by reward probability.

Conclusions

Probability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on valueless stimulus-response associations. Confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work provides a first mechanistic explanation of complex EV-based learning deficits in PSZ with motivational deficits that arise from an inability to combine information from different reward modalities.

SUBMITTER: Hernaus D

PROVIDER: S-EPMC6399031 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Hernaus Dennis D Frank Michael J MJ Brown Elliot C EC Brown Jaime K JK Gold James M JM Waltz James A JA

Biological psychiatry. Cognitive neuroscience and neuroimaging 20181207 3

<h4>Background</h4>Motivational deficits in people with schizophrenia (PSZ) are associated with an inability to integrate the magnitude and probability of previous outcomes. The mechanisms that underlie probability-magnitude integration deficits, however, are poorly understood. We hypothesized that increased reliance on "valueless" stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficit ...[more]

PMID: 30683607

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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