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

TL;DR

This paper introduces a neural cognitive model designed to discover regularities in event sequences using short-term memory, aiming to explain fluid intelligence tasks involving pattern recognition in sequences.

Contribution

The paper presents a novel neural model specifically tailored for discovering regularities in short-term sequence memory within fluid intelligence tasks.

Findings

Model successfully explains regularity discovery in event sequences

Effective in delayed match-to-sample tasks

Provides insights into neural mechanisms of fluid intelligence

Abstract

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.