Action and behavior: a free-energy formulation

By Karl J. Friston, Jean Daunizeau, James M. Kilner, Stefan J. Kiebel. 2010

746 citations recorded by the source corpus.

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 proprioceptive 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.

Subfields: Cognitive Neuroscience, Social Psychology, Neuroscience, Cognitive Science & Psychology

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Citations in the corpus

On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting (2022, background): “The idea of posing control problems as distribution matching has resurfaced numerous times in the RL literature (Kappen et al., 2012; Friston et al., 2010; Levine, 2018; Hafner et al., 2020; Buckley et al., 2017).”
RL with KL penalties is better viewed as Bayesian inference (2022, background): “More-over, there are frameworks such as active inference (Friston et al., 2010; Buckley et al., 2017) or action and perception as divergence minimisation (Hafner et al., 2020) that further generalise control as inference to a principle of minimising the KL divergence from a probability distribution…”
Perceptions as Hypotheses: Saccades as Experiments (2012, background): “In short, active inference can be regarded as equipping a generalized predictive coding scheme with classical reﬂex arcs: see (Friston et al., 2009, 2010a) for details.”
A nice surprise? Predictive processing and the active pursuit of novelty (2018, background): “For discussion, see (Engel et al. 2001; Feldman and Friston 2010).”
Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) (2010, methodology): “[19] (2010) also propose an approach which, in many ways, seems similar to ours, based on free energy minimization and predictive coding.”
Free Energy, Value, and Attractors (2011, background): “We have shown recently that adaptive behaviour can be prescribed by prior expectations about sensory inputs, which action tries to fulfill [6].”
Intrinsically Motivated Learning in Natural and Artificial Systems (2013, background): “…the pervasive influence of brain reward systems on behavior (e.g., Linden 2011), other theoretical principles— not discussed here—have been proposed that can account for aspects of intrinsic motivation (e.g., Andry et al. 2004; Baranes and Oudeyer 2010; Friston et al. 2010; Hesse et al. 2009).”
The free energy principle made simpler but not too simple (2022, neutral): “In the neurosciences, this is known as active inference [60–62].”
A Free Energy Principle for Biological Systems (2012, background): “Domain Process or paradigm Perception • Perceptual categorisation (bird songs) [18] • Novelty and omission-related responses [18] • Perceptual inference (speech) [19] Sensory learning • Perceptual learning (mismatch negativity) [33] Attention • Attention and the Posner paradigm [20] • Attention and ”
The role of prediction in social neuroscience (2012, methodology): “…scheme has been applied to perceptual inference (Kiebel et al., 2009), perceptual learning (Friston, 2008), reinforcement learning (Friston et al., 2009), active inference (Friston et al., 2010), attentional processing (Feldman and Friston, 2010), and sensorimotor control (Wolpert and Miall, 1996).”
Affective Value in the Predictive Mind (2017, background): “More specifically, they are induced by their expected exteroceptive and proprioceptive consequences (Friston et al. 2010), much in line with James’ “anticipatory image” (James 1890) and with the ideomotor principle (Hoffmann 2003).”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, methodology): “Action control and recognition using this idea have been implemented for simple examples with a two-degree of freedom arm (Friston et al., 2010, 2011).”
An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity (2017, background): “It has been shown in the past how networks in the predictive coding framework can perform unsupervised learning, attentional modulations, and action selection (Rao & Ballard, 1999; Feldman & Friston, 2010; Friston, Daunizeau, Kilner, & Kiebel, 2010).”