An active inference perspective for the amygdala complex.

We outline how a predictive processing framework based on active inference can help overcome the limitations of fragmented feed-forward processing theories and more comprehensively explain the role of the amygdala in anxiety, fear, and danger detection. This framework integrates theoretical predictions with empirical findings, suggesting that the central amygdala subnucleus acts as a Bayesian regulator of an interoceptive self-model, sending top-down predictions to the basolateral amygdala for efficient perception. Fear conditioning is discussed as an example of proactive homeostatic regulation, where exteroceptive cues are used to anticipate negative interoceptive experiences. By extension, other amygdala functions (e.g., its role in approach/avoidance behavior or attention) can be explained using the same computational principles within a hierarchical active inference model. The amygdala is a heterogeneous network of subcortical nuclei with central importance in cognitive and clinical neuroscience. Various experimental designs in human psychology and animal model research have mapped multiple conceptual frameworks (e.g., valence/salience and decision making) to ever more refined amygdala circuitry. However, these predominantly bottom up-driven accounts often rely on interpretations tailored to a specific phenomenon, thus preventing comprehensive and integrative theories. We argue here that an active inference model of amygdala function could unify these fractionated approaches into an overarching framework for clearer empirical predictions and mechanistic interpretations. This framework embeds top-down predictive models, informed by prior knowledge and belief updating, within a dynamical system distributed across amygdala circuits in which self-regulation is implemented by continuously tracking environmental and homeostatic demands. [ABSTRACT FROM AUTHOR]