Action and behavior: a free-energy formulation

Abstract

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz's agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptiveThe free-energy principle is an attempt to explain the structure and function of the brain, starting from the fact that we exist: This fact places constraints on our interactions with the world, which have been studied for years in evolutionary biology and systems theory. However, recent advances in statistical physics and machine learning point to a simple scheme that enables biological systems to comply with these constraints. If one looks at the brain as implementing this scheme (minimizing a free-energy bound on disorder), then many aspects of its anatomy and physiology start to make sense. In this article, we show that free-energy can be reduced by selectively sampling sensory inputs. This leads to adaptive responses and provides a new view of how movement control might work in the brain. The main conclusion is that we only need to have expectations about the sensory consequences of moving in order to elicit movement. This means we that can replace the notion of desired movements with expected movements and understand action in terms of perceptual expectations.The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG, sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.

Predicting the sensory consequences of one’s own action: First evidence for multisensory facilitation

“…An important distinction with the forward-model theory is that in the free energy formulation, movement is not driven by motor commands, but by predictions about the proprioceptive consequences of that movement. Thus, prediction errors are not there to refine motor behavior, but themselves are the motor signals (Friston, Daunizeau, Kilner, & Kiebel, 2010). In both theories, the efference copy plays an important role.…”

A social Bayesian brain: How social knowledge can shape visual perception

“…The predictive processing framework provides an interesting perspective on human action (Friston, Daunizeau, Kilner, & Kiebel, 2010): actions are generated through the fulfilment of internal (proprioceptive) predictions, and can be deployed to minimize sensory prediction errors (e.g., if you expect to see John, move your eyes until John appears). Minimization of prediction error through action is called 'active inference' .…”

A nice surprise? Predictive processing and the active pursuit of novelty

“…That process ('active inference' in the sense of Friston et al 2010) aims at the progressive reduction of organism-salient prediction error signals. The computational basis of perception, cognition, and action (if this ambitious story is on track) involves only three 'basic elements' -predictions (flowing from a long-term multi-level 'generative model'), prediction error signals (calculated relative to active predictions), and the estimated, context-varying 'precision' of those prediction error signals.…”