人类认知和神经网络的并行权衡:在语境和权重学习之间的动态相互作用
Jacob Russin1,2, Ellie Pavlick1, Michael J Frank2,3
1Department of Computer Science, Brown University, Providence, RI 02906.
概括
人类学习有两种模式:快速的规则推断和缓慢的试错. 这项研究通过展示神经网络中的上下文学习 (ICL) 如何补充重量学习 (IWL),解释双重学习过程来整合这些.
科学领域:
- 认知科学
- 神经科学
- 人工智能
背景情况:
- 人类的学习表现为双重:基于规则的快速学习和缓慢的增量学习.
- 心理学理论为这些学习模式提出了不同的系统.
- 这些双重系统与主要使用增量更新的神经网络的协调是具有挑战性的.
研究的目的:
- 将人类学习的双过程理论与神经网络模型相协调.
- 研究神经网络中的上下文学习 (ICL) 与人类学习现象的关系.
- 提供认知学习机制的整合性视角.
主要方法:
- 研究了metalearning神经网络和大型语言模型.
- 研究了在语境学习 (ICL) 和权重学习 (IWL) 之间的相互作用.
- 分析了课程效应,组合性和灵活性-保留权衡.
主要成果:
- 证明ICL和IWL之间的相互作用自然解释了人类的各种学习现象.
- 显示神经网络中的新兴ICL提供了与IWL不同的学习特性.
- 发现ICL与基于激活的动态有关,
结论:
- ICL和IWL的整合为理解人类认知中的双过程理论提供了一个统一的框架.
- 神经网络中的新兴ICL功能可以与传统IWL共存.
- 这项研究将学习的计算模型与人类认知的心理理论结合起来.
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