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Over the last few years, large neural generative models, capable ofsynthesizing semantically rich passages of text or producing complex images,have recently emerged as a popular representation of what has come to be knownas ``generative artificial intelligence'' (generative AI). Beyond opening thedoor to new opportunities as well as challenges for the domain of statisticalmachine learning, the rising popularity of generative AI brings with itinteresting questions for Cognitive Science, which seeks to discover the natureof the processes that underpin minds and brains as well as to understand howsuch functionality might be acquired and instantianted in biological (orartificial) substrate. With this goal in mind, we argue that a promisingresearch program lies in the crafting of cognitive architectures, along-standing tradition of the field, cast fundamentally in terms ofneuro-mimetic generative building blocks. Concretely, we discuss the COGnitiveNeural GENerative system, such an architecture that casts the Common Model ofCognition in terms of Hebbian adaptation operating in service of optimizing avariational free energy functional.

A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization