Evolvable psychology informed neural network for memory behavior modeling

Memory behavior modeling is a fundamental issue in the fields of cognitive psychology and education. Classical theoretical models of memory are characterized by insufficient accuracy and ongoing controversies, while data-driven memory modeling methods often require large amount of training data and lack interpretability, highlighting the need for new approaches to memory behavior modeling. This paper integrates classic psychological theories of memory to explore the feasibility of knowledge-driven neural networks in memory behavior modeling. It proposes the EPsyINN model, which combines temporal neural networks with sparse differential regression in a unified framework, enabling the joint optimization of neural networks and classical symbolic models. More specifically, to address the controversies in classical psychological theories and the ambiguity of descriptors, it proposes a descriptor evolution method based on differential operators to achieve precise descriptor characterization and advance the evolution of classical symbolic models. Additionally, it introduces a caching mechanism for regression coefficient matrices and an alternating iterative optimization method for multiple modules, effectively alleviating local optima in model optimization. On five large-scale real-world memory behavior datasets, the proposed method surpasses state-of-the-art memory modeling approaches in predictive accuracy, while the evolved classical symbolic models also achieve performance improvements. Ablation experiments validate the effectiveness of the proposed improvements, and application experiments demonstrate its potential to inspire psychological research. The code for the experiments is available at: https://github.com/hellowads/PsyINN.