PT  - JOURNAL ARTICLE
AU  - Samuel J. Gershman
AU  - Lucy Lai
TI  - The reward-complexity trade-off in schizophrenia
AID  - 10.1101/2020.11.16.385013
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.11.16.385013
4099  - http://biorxiv.org/content/early/2020/11/17/2020.11.16.385013.short
4100  - http://biorxiv.org/content/early/2020/11/17/2020.11.16.385013.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.