Using Computational Phenotyping to Identify Divergent Strategies for Effort Allocation Across the Psychosis Spectrum

Abstract

Background and hypothesis: Disturbances in effort-cost decision-making have been highlighted as a potential transdiagnostic process underpinning negative symptoms in individuals with schizophrenia. However, recent studies using computational phenotyping show that individuals employ a range of strategies to allocate effort, and use of different strategies is associated with unique clinical and cognitive characteristics. Building on prior work in schizophrenia, this study evaluated whether effort allocation strategies differed in individuals with distinct psychotic disorders.

Study design: We applied computational modeling to effort-cost decision-making data obtained from individuals with psychotic disorders (n = 190) who performed the Effort Expenditure for Rewards Task. The sample included 91 individuals with schizophrenia/schizoaffective disorder, 90 individuals with psychotic bipolar disorder, and 52 controls.

Study results: Different effort allocation strategies were observed both across and within different disorders. Relative to individuals with psychotic bipolar disorder, a greater proportion of individuals with schizophrenia/schizoaffective disorder did not use reward value or probability information to guide effort allocation. Furthermore, across disorders, different effort allocation strategies were associated with specific clinical and cognitive features. Those who did not use reward value or probability information to guide effort allocation had more severe positive and negative symptoms, and poorer cognitive and community functioning. In contrast, those who only used reward value information showed a trend toward more severe positive symptoms.

Conclusions: These findings indicate that similar deficits in effort-cost decision-making may arise from different computational mechanisms across the psychosis spectrum.

Keywords: computational modeling; negative symptoms; reward.