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.

杏仁核是皮质下核的异质网络，在认知和临床神经科学中具有核心重要性。人类心理学和动物模型研究中的各种实验设计已将多个概念框架（例如效价/显着性和决策）映射到更加精细的杏仁核电路。然而，这些主要是自下而上驱动的解释往往依赖于针对特定现象的解释，从而阻碍了全面和综合的理论。我们在这里认为，杏仁核功能的主动推理模型可以将这些分散的方法统一成一个总体框架，以实现更清晰的经验预测和机械解释。该框架在分布于杏仁核回路的动态系统中嵌入了自上而下的预测模型，通过先验知识和信念更新提供信息，其中通过持续跟踪环境和稳态需求来实现自我调节。