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

I put an article with the title above on arXiv: https://arxiv.org/abs/2412.06839

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.

Additional remarks:

• It used the neural sequence memory mentioned in the previous post.
• It is based on rote sequence memory.  Though you may wonder a learning program must make generalization, most fluid intelligence tasks are one-shot and would not require generalization.
• To test more general fluid intelligence capabilities, it would be better testing it with visual analogy tasks such as Raven's progressive matrix tests or those found in ARC.