A computational approach to understanding effort-based decision-making in depression

Motivational dysfunction is a core feature of depression and can have debilitating effects on everyday function. However, it is unclear which cognitive processes underlie impaired motivation and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools.

Effort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N = 67), before applying it in a case-control study including current (N = 41) and remitted (N = 46) unmedicated depressed individuals and healthy volunteers with (N = 36) and without (N = 57) a family history of depression.

Four fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort-acceptance bias, but not altered effort or reward sensitivity.

This work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms and a fruitful target for treatment and prevention.