# A Neural Model of Rule Discovery with Relatively Short-Term Sequence Memory

arXiv: https://arxiv.org/abs/2412.06839 · HTML: https://mlsanity.com/paper/2412.06839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 7 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Dense Associative Memory for Pattern Recognition](https://mlsanity.com/paper/1606.01164/) (2016)
- [Inductive logic programming at 30: a new introduction](https://mlsanity.com/paper/2008.07912/) (2020)
- [Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven's Progressive Matrices](https://mlsanity.com/paper/2201.12382/) (2022)
- [Biological learning in key-value memory networks](https://mlsanity.com/paper/2110.13976/) (2021)
- [Long Sequence Hopfield Memory](https://mlsanity.com/paper/2306.04532/) (2023)

## Abstract (arXiv)

This report proposes a neural cognitive model for discovering regularities in event sequences. In a fluid intelligence task, the subject is required to discover regularities from relatively short-term memory of the first-seen task. Some fluid intelligence tasks require discovering regularities in event sequences. Thus, a neural network model was constructed to explain fluid intelligence or regularity discovery in event sequences with relatively short-term memory. The model was implemented and tested with delayed match-to-sample tasks.