在人类和变压器网络学习期间对数据分布的共享敏感性
Jacques Pesnot Lerousseau1,2,3, Christopher Summerfield4
1Department of Experimental Psychology, University of Oxford, Oxford, UK. jacques.pesnot-lerousseau@univ-amu.fr.
Nature human behaviour
|December 23, 2025
概括
人类和变压器网络通过平衡示例多样性和冗余性来类似地学习. 然而,只有人类才能从动态的培训计划中受益,强调早期学习中的各种例子.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 了解人类学习机制对于推进人工智能至关重要.
- 变压器网络是最先进的深度学习模型.
- 比较人类和人工智能学习可以揭示基本原则.
研究的目的:
- 为了调查人类是否能像变压器网络一样学习.
- 为了比较人类和人工智能的"上下文"和"权重"学习.
- 检查培训数据分布对学习策略的影响.
主要方法:
- 在规则学习任务中训练了530名人类和变压器网络.
- 操纵的培训数据多样性和冗余性.
- 对新的查询 ("上下文"学习) 和记忆回忆 ("权重"学习) 进行测量概括.
主要成果:
- 人类和变压器对训练数据操纵的反应类似.
- 冗余性和多样性在推动"权重"和"上下文"学习方面产生了影响.
- 多样性和冗余性的平衡组合使得两者都能够实现并联学习策略.
结论:
- 影响学习的数据分布性质在人类和变压器之间得到保护.
- 人类与变形机不同,受益于动态的培训课程,强调早期的多样性.
- 虽然核心学习机制有相似之处,但人类学习表现出独特的课程敏感性.
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