基于模型的算法塑造了自动评估处理
David E Melnikoff1, Benedek Kurdi2
1Graduate School of Business, Stanford University, Stanford, CA 94305.
概括
基于模型的算法影响了故意和自动的评估,挑战了现有的强化学习理论. 这表明自动处理在计算上比以前理解的更复杂.
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.
- 强化学习是一种强化学习.
背景情况:
- 强化学习算法分为基于模型的 (准确,昂贵) 和没有模型的 (廉价,容易出错).
- 主流理论将基于模型的算法与故意控制和无模型自动控制联系起来.
- 这一框架已经影响了各种心理学领域的研究.
研究的目的:
- 调查基于模型/无模型算法与故意/自动评估处理之间的一致性.
- 挑战一种普遍的假设,即基于模型的算法完全支持故意的响应.
主要方法:
- 三个预先注册的行为实验,有2572名成年参与者.
- 复制过去的发现,并引入新的实验来控制混.
- 多项式处理树建模用于分析自动评估措施.
主要成果:
- 实验1重复了之前的发现,但揭示了混因素.
- 在消除混后,实验2和实验3显示了基于模型的自动评估对自动评估的重大贡献.
- 基于模型的算法被证明可以塑造有意和自动的评估反应.
结论:
- 主导框架可能低估了基于模型的算法在决策中的普遍性.
- 自动评估处理在计算上似乎比以前理论化的更复杂.
- 自动和故意系统之间的区别可能不严格地映射到无模型和基于模型的算法.
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