关于相关表示和诱导偏见的危险性
Thomas L Griffiths1, Sreejan Kumar2, R Thomas McCoy3
1Departments of Psychology and Computer Science, Princeton University, Princeton, NJ, USA tomg@princeton.edu http://cocosci.princeton.edu/tom/.
The Behavioral and brain sciences
|September 28, 2023
概括
使用贝叶斯推理的人类行为模型表明"思想语言". 然而,这项研究认为,从计算诱导偏差推断认知表征是一种有缺陷的方法.
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.
- 思想的哲学 思想的哲学
背景情况:
- 贝叶斯推理模型越来越多地用于解释人类行为.
- 这些模型的成功通常被解释为人类离散的,组成的"思维语言"的证据.
- 这种解释将计算层面的理论与算法层面的机制联系起来.
研究的目的:
- 从贝叶斯模型的成功中批判性地评估"思想语言"的推断.
- 检查在认知科学中弥合计算和算法分析水平的方法学挑战.
- 为理解认知架构和行为数据之间的关系提出更严格的框架.
主要方法:
- 对计算和算法解释层次之间的关系的概念分析.
- 批评从诱导偏见到表示结构的推断性跳跃.
- 关于贝叶斯对人类行为和认知架构的建模现有文献的综述.
主要成果:
- 贝叶斯模型的成功并不需要具有离散的构成结构的"思维语言".
- 将感应偏差 (计算层面) 与表示格式 (算法层面) 混,会导致不合理的结论.
- 对行为模式的替代解释可能存在,不依赖于特定的代表性假设.
结论:
- 基于贝叶斯成功模型,人类拥有"思维语言"的结论在方法上是不合理的.
- 在认知科学中,需要在计算目标和算法机制之间进行更清晰的区别.
- 未来的研究应该专注于开发可以更直接测试关于认知表征的假设的方法.
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