Research on predictive processing models has focused largely on two specific algorithmic
theories: Predictive Coding for perception and Active Inference for decision-making. While …

The free energy principle, and its corollary active inference, constitute a bio-inspired theory
that assumes biological agents act to remain in a restricted set of preferred states of the …

The active inference framework, and in particular its recent formulation as a partially
observable Markov decision process (POMDP), has gained increasing popularity in recent …

The aim of this paper is to introduce a field of study that has emerged over the last decade,
called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising …

Active inference is a mathematical framework which originated in computational
neuroscience as a theory of how the brain implements action, perception and learning …

Active inference is a first principle account of how autonomous agents operate in dynamic,
nonstationary environments. This problem is also considered in reinforcement learning, but …

The search for the neural correlates of consciousness is in need of a systematic, principled
foundation that can endow putative neural correlates with greater predictive and explanatory …

Active inference is an account of cognition and behavior in complex systems which brings
together action, perception, and learning under the theoretical mantle of Bayesian inference …

Recent research has demonstrated the potential of psychedelic therapy for mental health
care. However, the psychological experience underlying its therapeutic effects remains …

Active Inference (AIF) is an emerging framework in the brain sciences which suggests that
biological agents act to minimise a variational bound on model evidence. Control-as …