ArticleComputational psychiatry (Cambridge, Mass.)2021
The Reward-Complexity Trade-off in Schizophrenia.
Article in Computational psychiatry (Cambridge, Mass.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Computationally Informed Insights Into Anhedonia and Treatment by Kappa Opioid Receptor Antagonism.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Trial
- NMDA receptor ablation in medial prefrontal cortex disrupts value updating and reward history integration.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Computational learning phenotypes are not related to individual differences in resting-state fMRI connectivity.Frontiers in neuroscience · 2026Article
- Time and memory costs jointly determine a speed-accuracy trade-off and set-size effects.Journal of experimental psychology. General · 2025Article
- Resource-rational psychopathology.Behavioral neuroscience · 2024Review
- Human decision making balances reward maximization and policy compression.PLoS computational biology · 2024Article
- Bayesian Reinforcement Learning With Limited Cognitive Load.Open mind : discoveries in cognitive science · 2024Article
- Undermatching Is a Consequence of Policy Compression.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Atypical meta-memory evaluation strategy in schizophrenia patients.Schizophrenia research. Cognition · 2022Article
- Reinforcement learning modeling reveals a reward-history-dependent strategy underlying reversal learning in squirrel monkeys.Behavioral neuroscience · 2022Article
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2 authors.
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Abstract
Action selection requires a policy that maps states of the world to a distribution over actions. The amount of memory needed to specify the policy (the policy complexity) increases with the state-dependence of the policy. If there is a capacity limit for policy complexity, then there will also be a trade-off between reward and complexity, since some reward will need to be sacrificed in order to satisfy the capacity constraint. This paper empirically characterizes the trade-off between reward and complexity for both schizophrenia patients and healthy controls. Schizophrenia patients adopt lower complexity policies on average, and these policies are more strongly biased away from the optimal reward-complexity trade-off curve compared to healthy controls. However, healthy controls are also biased away from the optimal trade-off curve, and both groups appear to lie on the same empirical trade-off curve. We explain these findings using a cost-sensitive actor-critic model. Our empirical and theoretical results shed new light on cognitive effort abnormalities in schizophrenia.
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