Active Inference: A Process Theory

By Karl J. Friston, Thomas H. B. FitzGerald, Francesco Rigoli, Philipp Schwartenbeck, Giovanni Pezzulo. Neural Computation, 2017

1,004 citations recorded by the source corpus.

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence—or minimizing variational free energy—we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes’ optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton’s principle of least action.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0fdd66fcf6b9bc04428e337df66fd510a659eb2f

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In Cognitive Neuroscience

Citations in the corpus

A free energy principle for a particular physics (2019, methodology): “In cognitive neuroscience, this ultimately leads to ambiguity reducing, curious behaviour that underwrites the exploratory (i.e., epistemic) foraging that characterises self-evidencing (Baranes and Oudeyer, 2009; Berlyne, 1950; Friston et al., 2017b; Schmidhuber, 2006; Still and Precup, 2012).”
The free energy principle made simpler but not too simple (2022, neutral): “These beliefs are then used to evaluate the expected free energy of allowable policies, from which actions can be selected [123–125].”
Action and Perception as Divergence Minimization (2020, methodology): “Expressing preferences as models is inspired by the free energy principle and active inference (Friston, 2010; Friston et al., 2012; 2017), which we compare to in Appendix A. Divergence minimization inherits an interpretation of action and perception from active inference that we visualize in…”
The graphical brain: Belief propagation and active inference (2017, background): “…representations; for example, distinct (place coded) representations of the past and future are implicit in the delay period activity shown in prefrontal cortical units during delayed matching to sample (Kojima & Goldman-Rakic, 1982); see Friston, FitzGerald, et al. (2017) for a discussion.”
Active Inference, Curiosity and Insight (2017, background): “People who want a more detailed account of the basic active inference scheme can refer to Table 2 (for a full glossary of terms described in the appendix) and Friston, FitzGerald et al. (2016) and Friston et al. (2017).”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Deep Goal Hierarchies in Active Inference Active inference views the brain as a statistical organ that forms internal generative models of the (hidden) states and contingencies in the world, and uses these models to continuously generate predictions in the service of perception and adaptive behaviou”
The free energy principle for action and perception: A mathematical review (2017, methodology): “Examples of various generating functions have already been provided (Friston, 2008a; Friston et al., 2010, 2016; Pezzulo et al., 2015; Pio-Lopez, Nizard, Friston, & Pezzulo, 2016), to which we refer the reader.”
Bridging verbal coordination and neural dynamics (2025, unknown): “This also aligns with broader theories on the relationship between perception and action, such as predictive coding and active inference, which propose shared sensory prediction mechanism and neural computational architecture for both processes (Friston et al., 2017, 2020).”