The Memory Tesseract: Developing A Unified Framework for Modelling Memory and Cognition
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".