一个算法解释人类如何有效地学习,转移和构建层次结构化的决策政策
Jing-Jing Li1, Anne G E Collins2
1Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America.
Cognition
|October 5, 2024
概括
人类通过构建层次政策来学习复杂的决策,从压缩表示开始,并使用元学习和贝叶斯推理来开发灵活的智能.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 人类智能通过层次决策政策表现出了显著的灵活性.
- 了解学习的计算机制和构建这些策略至关重要.
研究的目的:
- 调查人类层次化的政策制定背后的学习过程.
- 开发一种解释观察到的人类决策策略的计算模型.
主要方法:
- 进行了一项大规模的决策实验,有1026名参与者做出超过100万个选择.
- 开发了一种新的算法账户,集成了强化学习,政策压缩,元学习和贝叶斯推理.
主要成果:
- 人类参与者展示了学习,转移和重组等级政策的能力.
- 行为数据支持一个模型,在这个模型中,最初的压缩策略逐渐展开成等级结构.
- 算法建模表明,学习等级政策的时间落后结构.
结论:
- 人类的决策依赖于强化学习,政策压缩,元学习和工作记忆的动态相互作用.
- 这一过程支持了资源理性,构成性决策和政策抽象.
- 这些发现为灵活的人类智能的计算基础提供了洞察力.
关键词:
抽象是一种抽象.复合性 复合性是指复合性.计算认知建模计算认知建模决策方式 决策方式层次结构 层次结构超级学习 (meta-learning) 是一种学习方式.强化学习是一种强化学习.转移学习转移学习更多相关视频
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