# Human-like Forgetting Curves in Deep Neural Networks

arXiv: https://arxiv.org/abs/2506.12034 · HTML: https://mlsanity.com/paper/2506.12034/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 5 of 5)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Replay in Deep Learning: Current Approaches and Missing Biological Elements](https://mlsanity.com/paper/2104.04132/) (2021)
- [Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing](https://mlsanity.com/paper/2404.12127/) (2024)

## Built on by (most cited first, 1 of 1)

- [Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers](https://mlsanity.com/paper/2604.11508/) (2026)

## Abstract (arXiv)

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.