A predictive processing implementation of the common model of cognition

We present how a cognitive architecture can be built from the neural circuit models proposed under the frameworks of holographic memory and neural generative coding. Specifically, we draw inspiration from well-known cognitive architectures such as ACT-R, Soar, Leabra, and Nengo, as well as the common model of cognition, to propose the kernel that might drive a complex, modular system that would prove useful for developing intelligent agents that tackle statistical learning tasks, as well as for answering questions and testing hypotheses in cognitive science and computational neuroscience.