Evolvable Psychology Informed Neural Network for Memory Behavior Modeling

• URL: http://arxiv.org/abs/2408.14492v1
• Date: Fri, 23 Aug 2024 01:35:32 GMT
• Title: Evolvable Psychology Informed Neural Network for Memory Behavior Modeling
• Authors: Xiaoxuan Shen, Zhihai Hu, Qirong Chen, Shengyingjie Liu, Ruxia Liang, Jianwen Sun,
• Abstract summary: This paper proposes a theory informed neural networks for memory behavior modeling named PsyINN. It constructs a framework that combines neural network with differentiating sparse regression, achieving joint optimization. On four large-scale real-world memory behavior datasets, the proposed method surpasses the state-of-the-art methods in prediction accuracy.
• Score: 2.5258264040936305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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