Impaired Expected Value Computations in Schizophrenia Are Associated With a Reduced Ability to Integrate Reward Probability and Magnitude of Recent Outcomes
| Autor: | Jaime K. Brown, James M. Gold, Elliot C. Brown, James A. Waltz, Michael J. Frank, Dennis Hernaus |
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| Přispěvatelé: | Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Anhedonia Dopamine Neuropsychological Tests Expected value DECISION-MAKING NEGATIVE SYMPTOMS Choice Behavior 0302 clinical medicine DEFICITS Healthy volunteers Reinforcement learning PERSPECTIVE 05 social sciences DEPRESSION ORBITOFRONTAL CORTEX Basal ganglia Female Schizophrenic Psychology medicine.symptom Psychology Hybrid model Cognitive psychology Adult Cognitive Neuroscience Reward value Motivational deficit Stimulus (physiology) 050105 experimental psychology Article 03 medical and health sciences Reward medicine Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Scale for the Assessment of Negative Symptoms Biological Psychiatry Probability Motivation PERFORMANCE 030227 psychiatry AMYGDALA Schizophrenia Orbitofrontal cortex PREDICTION ERROR Neurology (clinical) 030217 neurology & neurosurgery |
| Zdroj: | Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4(3), 280-290. Elsevier |
| ISSN: | 2451-9022 |
| DOI: | 10.1016/j.bpsc.2018.11.011 |
| Popis: | BackgroundMotivational 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 “value-less” stimulus-response associations, in lieu of expected value (EV)-based learning, could drive probability-magnitude integration deficits in PSZ with motivational deficits.MethodsHealthy volunteers (n= 38) and PSZ (n=49) completed a reinforcement learning paradigm consisting of four stimulus pairs. Reward magnitude (3/2/1/0 points) and probability (90%/80%/20%/10%) together determined each stimulus’ EV. Following a learning phase, new and familiar stimulus pairings were presented. Participants were asked to select stimuli with the highest reward value.ResultsPSZ 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 SANS 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. ConclusionsProbability-magnitude deficits in PSZ with motivational deficits arise from underutilization of EV in favor of reliance on value-less stimulus-response associations. Consistent with previous work and confirmed by our computational hybrid framework, probability-magnitude integration deficits were driven specifically by a failure to represent reward magnitude. This work reconfirms the importance of decreased Q-learning/increased actor-critic-type learning as an explanatory framework for a range of EV deficits in PSZ. |
| Databáze: | OpenAIRE |
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