A computational theory of evaluation processes in apathy

Abstract

Computational modelling can offer valuable insight on mental illness. However, this approach has rarely been adopted to investigate apathy, a condition characterising a variety of psychiatric and neurological syndromes. This paper proposes a computational model of apathy and tests key model predictions in the healthy adult population. Building upon recent reference-dependent theories of evaluation, the model interprets apathy as arising from an excessive uncertainty about the distribution of incentives in the environment. This predicts that high-apathy individuals appraise the value of stimuli as less extreme and as more similar to one another. These predictions were assessed in two online studies where healthy adults rated the value of pictures characterised by varying levels of emotional salience. In line with the model, we observed that high-apathy individuals perceive negative stimuli as less negative, positive stimuli as less positive, and discriminate less among stimuli characterised by different salience. The contribution of this paper is twofold. On a more specific level, it sheds light on the precise mechanisms underlying evaluation processes in apathy. On a more general level, it highlights the insight offered by models of reference-dependent evaluation for understanding psychopathology.

A formal theory of mood instability

“…In particular, when uncertainty is high (σ=40 compared to the optimal σ=20), emotional experience is less intense: less rewarding, if positive; less punishing, if negative. This situation has been argued to parallel the clinical condition of apathy, where an individual experiences all stimuli as equally rewarding and punishing, resulting in lower motivation to pursue any one of them (Rigoli & Martinelli, 2022). Conversely, when uncertainty is low (σ=10 rather than σ=20), intense emotionality (or emotional reactivity) emerges.…”