Hierarchical models of pain: Inference, information-seeking, and adaptive control

Computational models of pain consider how the brain processes nociceptive information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. Here, we consider how they may comprise a parallel hierarchical architecture that combines inference, information-seeking, and adaptive value-based control. This sheds light on the complex neural architecture of the pain system, and takes us closer to understanding from where pain 'arises' in the brain.