Driven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry.

Although it is well known that "losses loom larger than gains," computational approaches to aversion-related decision making (ARDM) for psychiatric disorders is an underdeveloped area. Computational models of ARDM have been implemented primarily as state-dependent reinforcement learning models with bias parameters to quantify Pavlovian associations, and differential learning rates to quantify instrumental updating have been shown to depend on context, involve complex cost calculations, and include the consideration of counterfactual outcomes. Little is known about how individual differences influence these models relevant to anxiety-related conditions or addiction-related dysfunction. It is argued that model parameters reflecting 1) Pavlovian biases in the context of reinforcement learning or 2) hyperprecise prior beliefs in the context of active inference play an important role in the emergence of dysfunctional avoidance behaviors. The neural implementation of ARDM includes brain areas that are important for valuation (ventromedial prefrontal cortex) and positive reinforcement-related prediction errors (ventral striatum), but also aversive processing (insular cortex and cerebellum). Computational models of ARDM will help to establish a quantitative explanatory account of the development of anxiety disorders and addiction, but such models also face several challenges, including limited evidence for stability of individual differences, relatively low reliability of tasks, and disorder heterogeneity. Thus, it will be necessary to develop robust, reliable, and model-based experimental probes; recruit larger sample sizes; and use single case experimental designs for better pragmatic and explanatory biological models of psychiatric disorders.