Towards a Predictive Processing Implementation of the Common Model of Cognition
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Abstract (arXiv)
In this article, we present a cognitive architecture that is built from powerful yet simple neural models. Specifically, we describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory. The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales than what is possible with existant cognitive architectures.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 39 references that are in the map. First posted on arXiv 15 May 2021.
Builds on
5 of 39 references.
References · 5
- 2019Superposition of many models into onecited by 64
- 2020The Neural Coding Framework for Learning Generative Modelscited by 44
- 2018Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representationscited by 32
- 2017Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representationcited by 6
- 2018Environments for Lifelong Reinforcement Learningcited by 4
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Citing papers per year: 2023: 2.
2 papers.