用人类遗忘规律优化神经网络，让模型学得更像人。
Human-like Forgetting Curves in Deep Neural Networks
- 基于记忆间隔原理设计保留率评估方法
- 多层感知机呈现类人遗忘曲线，复习后知识更稳固
- 适合持续学习与记忆模拟方向的研究者
本研究将认知科学与神经网络设计相融合，探究人工模型是否表现出类人遗忘曲线。基于艾宾浩斯的记忆衰退理论与间隔重复原则，我们提出一种量化框架，通过比较网络当前隐状态与先前存储的原型表示相似性，计算信息保留概率。该保留度量可指导复习时机调度，从而缓解部署中的灾难性遗忘，并提升训练效率。在多层感知机上的实验表明，知识随定期复习逐渐增强，呈现出类人遗忘曲线。这一发现揭示了神经网络天然具备模拟人类记忆衰退的能力，可为先进持续学习算法提供新思路。
原文摘要 · Abstract (English)
This study bridges cognitive science and neural network design by examining whether artificial models exhibit human-like forgetting curves. Drawing upon Ebbinghaus' seminal work on memory decay and principles of spaced repetition, we propose a quantitative framework to measure information retention in neural networks. Our approach computes the recall probability by evaluating the similarity between a network's current hidden state and previously stored prototype representations. This retention metric facilitates the scheduling of review sessions, thereby mitigating catastrophic forgetting during deployment and enhancing training efficiency by prompting targeted reviews. Our experiments with Multi-Layer Perceptrons reveal human-like forgetting curves, with knowledge becoming increasingly robust through scheduled reviews. This alignment between neural network forgetting curves and established human memory models identifies neural networks as an architecture that naturally emulates human memory decay and can inform state-of-the-art continual learning algorithms.
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