F33. MODELLING THE PREDICTORS OF EFFORT-BASED DECISION-MAKING IN SCHIZOPHRENIA

Motivation deficits and reduced goal-directed behaviour are prominent in schizophrenia (SZ), and significantly contribute to poor functional and treatment outcomes. One of the critical components of the multi-faceted motivation system is effort valuation, which refers to the mental processes involved in computing how much effort one is willing to exert in order to obtain a desired outcome. These effort-cost computations are typically measured using effort-based decision-making (EBDM) paradigms, where individuals must choose between performing low- or high-effort tasks for varying reward magnitudes. Rather than demonstrating an overall unwillingness to expend effort, however, studies have shown that individuals with SZ inefficiently allocate effort across different probability and reward conditions. Thus, in order to better understand the underlying computations involved in effort-based decision-making, the present study sought to model the predictors of choice behaviour in SZ and healthy control (HC) participants. Fifty-one SZ patients and 51 demographically-matched HC participants completed the Effort Expenditure for Rewards Task (EEfRT) as a measure of EBDM. In addition, all participants underwent characterization of clinical amotivation severity and cognitive functioning using the Apathy Evaluation Scale (AES) and Brief Assessment of Cognition in Schizophrenia (BACS), respectively. Generalized Estimating Equations (GEE) were subsequently applied to the EEfRT data with a binary logistic distribution used to model the likelihood of choosing hard tasks. A number of models were tested with independent variables including reward magnitude, probability, expected value (EV), diagnostic group, AES, and BACS. GEE models revealed significant main effects for reward magnitude (b = .54, p < .001), probability (b = .02, p < .001), and EV (b = .46, p < .001), but no main effect of group. However, significant interaction terms were found between group and reward (b = -.33, p < .001), group and probability (b = -.01, p = .007), and group and EV (b = -.58, p = .001). While there were no AES or BACS main effects, there were significant AES x reward (b = -.02, p < .001) and AES x EV (b = -.02, p = .01) interactions, as well as BACS x reward (b = .11, p < .001), BACS x probability (b = .01, p < .001), and BACS x EV (b = .31, p < .001) interactions. While SZ and HC participants are similarly willing to exert effort in pursuit of a reward, patients with SZ are less likely to utilize important information regarding the magnitude, probability, and expected value associated with that reward in driving their effort-based decision-making. Moreover, reward magnitude and EV are less predictive of effortful choices for individuals with greater motivation and cognitive impairments, regardless of their diagnostic status. Taken together, these findings suggest a direct link between amotivation, cognition, and inefficient utilization of reward and probability information in the context of choice behaviour and effort-cost computations.

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