Evolvable Psychology Informed Neural Network for Memory Behavior Modeling

TL;DR

This paper introduces PsyINN, a neural network framework informed by psychological theories, that models memory behavior more accurately and interpretably than traditional methods, using novel descriptor evolution and optimization techniques.

Contribution

It develops a psychology theory informed neural network with descriptor evolution and optimization strategies, improving interpretability and accuracy in memory behavior modeling.

Findings

Outperforms state-of-the-art methods on four large-scale datasets

Enhances interpretability of memory models through descriptor evolution

Demonstrates potential to inspire psychological research

Abstract

Memory behavior modeling is a core issue in cognitive psychology and education. Classical psychological theories typically use memory equations to describe memory behavior, which exhibits insufficient accuracy and controversy, while data-driven memory modeling methods often require large amounts of training data and lack interpretability. Knowledge-informed neural network models have shown excellent performance in fields like physics, but there have been few attempts in the domain of behavior modeling. This paper proposed a psychology theory informed neural networks for memory behavior modeling named PsyINN, where it constructs a framework that combines neural network with differentiating sparse regression, achieving joint optimization. Specifically, to address the controversies and ambiguity of descriptors in memory equations, a descriptor evolution method based on differentiating operators is proposed to achieve precise characterization of descriptors and the evolution of memory theoretical equations. Additionally, a buffering mechanism for the sparse regression and a multi-module alternating iterative optimization method are proposed, effectively mitigating gradient instability and local optima issues. On four large-scale real-world memory behavior datasets, the proposed method surpasses the state-of-the-art methods in prediction accuracy. Ablation study demonstrates the effectiveness of the proposed refinements, and application experiments showcase its potential in inspiring psychological research.