The many roles of precision in action

Active inference describes (Bayes optimal) behaviour as motivated by the minimisation of surprise of one’s sensory observations, through optimisation of a generative model of the hidden causes of one’s sensory data in the brain. One of active inference’s key appeals is its conceptualisation of precision as biasing neuronal communication and thus inference within generative models. The importance of precision in perceptual inference is evident—many studies have demonstrated the importance of getting precision estimates right for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action; i.e., the key processes in action that rely on adequate estimates of precision—from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical “mixed” models—generative models spanning multiple levels of discrete (categorical) and continuous active inference. These kinds of models open up new perspectives on the unified description of hierarchical computation in action. We shall highlight how these models reflect the many roles of precision in action—from planning to execution—and the associated pathologies if precision estimation goes wrong. Thereby we shall also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision in action.