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A predictive processing implementation of the common model of cognition

Authors
Dr. Alex Ororbia
Dr. Mary Kelly
Carleton University ~ Department of Cognitive Science
Abstract

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.

Tags

Keywords

cognitive architectures
artificial neural networks
holographic memory
deep learning
predictive processing
continual learning
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Cite this as:

Ororbia, A. G., & Kelly, M. A. (2021, July). A predictive processing implementation of the common model of cognition. Paper presented at Virtual MathPsych/ICCM 2021. Via mathpsych.org/presentation/626.