# Towards a Predictive Processing Implementation of the Common Model of Cognition

arXiv: https://arxiv.org/abs/2105.07308 · HTML: https://mlsanity.com/paper/2105.07308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 15 May 2021.

## Builds on (most cited first, 5 of 5)

- [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019)
- [The Neural Coding Framework for Learning Generative Models](https://mlsanity.com/paper/2012.03405/) (2020)
- [Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations](https://mlsanity.com/paper/1810.07411/) (2018)
- [Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation](https://mlsanity.com/paper/1701.00464/) (2017)
- [Environments for Lifelong Reinforcement Learning](https://mlsanity.com/paper/1811.10732/) (2018)

## Built on by (most cited first, 2 of 2)

- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization](https://mlsanity.com/paper/2310.15177/) (2023)

## 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.