Active Inference emerged from the field of theoretical neurobiology (Friston, 2005), where it was “first used to model the function, structure, and dynamics of the human brain” (Ramstead, 2024). It built upon foundational work in predictive coding (Rao and Ballard, 1999) and the Helmholtzian concept of perception as “unconscious inference” (Helmholtz, 1867).
Active Inference’s central premise that “all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence — or minimizing variational free energy” (Friston 2017) provides a unifying theory to explain and predict myriad aspects of brain function and behavior (Friston, 2010). As such, it has been applied to many areas of neuroscientific research.
Active Inference models are used to provide parsimonious explanations for neural mechanisms and motifs, such as canonical microcircuits and neural networks (Bastos et al 201200959-2), Isomura et al 2022).
Researchers have furnished Active Inference models for phenomena including motivated control (Pezzulo et al 201830022-6)), sense of agency (Friston et al 2013), modulation of uncertainty by the dopaminergic system (Friston et al 2012), and the computational relationship between interoceptive and exteroceptive neural systems (Allen, 2022).
Active Inference frameworks have also been used to explain the dynamics of a variety of neurological and psychiatric conditions, including depression (Barrett et al 2016) and schizophrenia (Limongi et al 2023).
Recent studies have shown that in-vitro neuronal networks self-organize in response to stimuli in ways that are consistent with, and predicted by, the Free Energy Principle (Isomura et al 2023). The FEP also provides theoretical commitments towards testable theories of consciousness (Whyte et al, 2024).
As a multi-scale theory, Active Inference aims to ground neurobiology in physics-as-information-processing, and links it to other domains of inquiry, including diverse intelligence (Levin 2023) and artificial intelligence (Friston et al 2024).