The Memory Tesseract: Developing A Unified Framework for Modelling Memory and Cognition

Computational memory models can explain the behaviour of human memory in diverseexperimental paradigms—whether it be recall or recognition, short-term or long-term retention,implicit or explicit learning. Simulation has led to parsimonious theories of memory, but at a costof a profusion of competing models. As different models focus on different phenomena, there isno best model. However, the models share many characteristics, indicating wide agreement onthe mathematics of how memory works in the brain.On the basis of an analysis of computational memory models, we argue that these modelscan be understood in terms of a single neurally-plausible computational and theoreticalframework. We present a proof of concept neural implementation, integration with the ACT-Rcognitive architecture, and demonstrate model performance on procedural, declarative, episodic,and semantic learning tasks.This research aims to advance cognitive psychology towards a single integrated,computational model of human memory that can account for human performance on diverseexperimental tasks, that can be implemented at a neural level of detail, and can be scaled tomodelling arbitrarily long-term learning.