自相关贝叶斯抽样器:用于概率判断,估计,置信区间,选择,置信判断和响应时间的理性过程
Jian-Qiao Zhu1, Joakim Sundh2, Jake Spicer1
1Department of Psychology, University of Warwick.
Psychological review
|June 8, 2023
概括
人类决策的变化源于抽样假设,而不是感官噪音. 自相关贝叶斯采样器 (ABS) 模型通过整合随机假设以获得更好的规范模型来解释这一点.
科学领域:
- 认知神经科学 认知神经科学
- 计算心理学 计算心理学
- 决策理论 决策理论
背景情况:
- 规范性决策模型往往无法与人类行为相匹配.
- 现有的计算模型需要特定任务的非规范性假设.
研究的目的:
- 提出贝叶斯的方法,决策的变化源于假设采样,而不是感官噪音.
- 介绍一个新的计算过程,即自相关贝叶斯样本采集器 (ABS).
主要方法:
- 开发了一个贝叶斯框架,假设大脑样本假设来自后部分布.
- 介绍了自相关贝叶斯样本采集器 (ABS) 算法,以建模自相关假设生成.
- 分析了这个框架如何解释各种经验决策现象.
主要成果:
- 人类反应的变化主要归因于后置假设采样.
- 该ABS模型考虑了概率判断,估计,信心,选择和响应时间.
- 证明了自相关假设采样解释了经验效应.
结论:
- 将重点转向整合随机假设,为规范性决策模型提供了一个统一的视角.
- "贝叶斯大脑"可能运行在样本上,而不是概率上.
- 行为变化很可能反映的是计算,而不是感官噪音.
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