Apathy and decision-making in non-clinical individuals: A computational model of effort, reward and probability

Apathy, a transdiagnostic syndrome characterized by diminished motivation, is increasingly observed in non-clinical individuals, yet its cognitive mechanisms in non-clinical populations remain poorly understood. This study investigated the association between trait apathy and effort-based decision-making (EBDM), focusing on the critical roles of reward, probability and frame (gain vs. loss), and aimed to uncover the underlying computational processes of subjective value (SV) calculation. Participants included 84 non-clinical young adults (45 females, aged 18-25). They completed a key-pressing task where they decided to accept or reject offers that combined varying levels of effort demand, potential gain/loss frame, and probability. We analyzed behavioral measures including task accept rate and response time. Computational modeling was further employed to fit and compare different SV discounting models to participants' choices, estimating key parameters such as effort sensitivity and probability sensitivity. Compared to participants with low trait apathy, those with high trait apathy showed a significantly lower accept rate. Computational modeling indicated that this deficit was driven by the increased effort sensitivity in the gain frame and heightened probability sensitivity in the loss frame. These findings revealed that trait apathy is associated with altered effort-based decision-making, likely driven by an imbalance in how effort costs and probability-related risks are integrated into SV computation. Computational modeling provided an integrated perspective to jointly evaluate effort, reward, and probability, highlighting that apathetic individuals may require higher rewards or lower effort to act.