Evolvable Psychology Informed Neural Network for Memory Behavior Modeling
<|reference_start|>Evolvable Psychology Informed Neural Network for Memory Behavior Modeling: 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.<|reference_end|>
@article{shen2024evolvable,
    title={Evolvable Psychology Informed Neural Network for Memory Behavior
  Modeling},
    author={Xiaoxuan Shen and Zhihai Hu and Qirong Chen and Shengyingjie Liu and
  Ruxia Liang and Jianwen Sun},
    journal={arXiv preprint arXiv:2408.14492},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14492},
    primaryClass={cs.LG}
}