Action prevents error : predictive processing without active inference

Abstract

According to predictive processing, minds relentlessly aim at a single goal: prediction error minimization. Prediction error minimization is said to explain everything the mind does, from perception to cognition to action. Here I focus on action. ‘Active inference’ is the standard approach to action in predictive processing. According to active inference, as it has been developed by Friston and collaborators, action ensues when proprioceptive predictions generate prediction error at the motor periphery, and classical reflex arcs engage to quash the error. I raise a series of problems for active inference. I then offer an alternative approach on which action prevents error (APE), rather than quash it. I argue that the action prevents error approach solves all the problems raised for active inference. In addition, I show how the alternative approach can be independently motivated by further commitments of predictive processing and that it is compatible with other prominent approaches to sensorimotor psychology, such as optimal feedback control.