Abstract

Computational theories have fundamentally changed the scientific understanding of how the mind works for both healthy and pathological experiences and behaviours. In this context, the active inference framework has gained considerable attention within the scientific community (Heins et al., 2022; Smith et al., 2022). As a process theory, it integrates complex phenomena, such as perception, learning, and action under a unified theory of Bayesian inference (Da Costa et al., 2020; Friston et al., 2017). Active inference has proven useful in modelling data from heterogeneous fields ranging from cognitive neuroscience to biology and general psychology (e.g., Friston et al., 2016, 2017). Its com­ putational tractability and biological plausibility have also made it increasingly relevant to clinical psychology in recent years (e.g., Smith, Badcock, et al., 2021). In active inference and related, Bayesian neurocomputational theories, it is assumed that individuals do not have direct access to the circumstances in their surroundings. Instead, they have to infer the (probabilistic) properties of their environment through action and perception by integrating prior information about their environment with ambiguous sensory input in a rational (i.e., Bayes-optimal) manner (Friston et al., 2016; Hohwy et al., 2008). The resulting “internal model of the world” (i.e., the agent’s beliefs about how certain sensory information relates to environmental conditions) shapes future perception (Friston, 2010) and enables agents to leverage the past to predict the future in an ever-changing environment (Badcock et al., 2017). In accordance with this perspective, perception, action, and learning are all subject to inferential process­