人类和神经网络在持续学习过程中表现出类似的转移和干扰模式
Eleanor Holton1,2, Lukas Braun3,4, Jessica Af Thompson3
1Department of Experimental Psychology, University of Oxford, Oxford, UK. eleanor.holton@princeton.edu.
Nature human behaviour
|October 31, 2025
概括
人工神经网络和人类在学习新任务时表现出类似的知识干扰. 两者都从先前的知识中受益,但难以保留旧的信息,揭示了共享学习的权衡.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 人工神经网络 (ANN) 经常在灾难性遗忘中扎,新学习会覆盖旧知识.
- 人们普遍认为人类拥有超级机制来克服这种干扰,从而促进持续的学习.
- 了解这些差异对于开发更强大的AI和理解人类学习至关重要.
研究的目的:
- 在基于规则的顺序学习过程中,研究和比较人类和ANN的知识干扰模式.
- 确定基础学习,知识转移和干扰生物和人工系统的共享计算原则.
- 探索人类学习中的个体差异及其在ANN中的潜在并行.
主要方法:
- 对人类参与者和ANN进行了A-B-A连续的基于规则的学习任务.
- 来自人类的行为数据和来自ANN的代表性分析被用于量化学习,转移和干扰.
- 在不同的制度下 ("富有"和"") 接受了ANN的培训,以模拟不同的代表策略.
主要成果:
- 人类和ANN都表现出类似的干扰模式:先前的知识有助于类似的新任务,但在重新测试时干扰增加.
- 在ANN中重复使用表示加速了新学习,但导致覆盖了先前的知识.
- 人类参与者表现出个体差异",lumpers"表现出更多的干扰但更好的传输",splitters"表现出更少的干扰但更差的传输.
- 这些人类行为特征被训练有素的ANN所反映,分别是"富有" (像Lumper一样) 和"惰" (像Splitter一样) 的制度.
结论:
- 人类和ANN在知识传输和干扰避免之间有着共同的基本计算权衡.
- 在ANN中干扰和转移的机制为理解人类学习动态提供了一个计算模型.
- 人类学习策略的个体差异 ("lumpers"与"splitters") 在ANN表示策略中具有直接的平行.
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