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剖析贝叶斯:使用影响测量来测试从样本中获得的概率密度信息的规范性使用
Keiji Ota1,2,3,4, Laurence T Maloney1,2
1Department of Psychology, New York University, New York, New York, United States.
PLoS computational biology
|May 1, 2024
概括
人类的决策偏离贝叶斯决策理论 (BDT) 的预测,特别是在如何权衡样本信息方面. 替代模型,比如那些使用极端样本点的模型,更好地解释了认知任务中观察到的行为.
科学领域:
- 认知心理学 认知心理学
- 计算神经科学是一种神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 贝叶斯决策理论 (BDT) 在涉及不确定性和价值的决策任务中建模了规范性绩效.
- 规范模型规定了最佳的信息编码和组合,以最大限度地提高预期的奖励.
- 标准的BDT计算涉及概率,但实际任务通常使用来自样本的概率密度函数 (PDF).
研究的目的:
- 调查人类在BDT框架内进行视觉认知任务的个人计算的能力.
- 在使用样本衍生PDF时,评估人类遵守准确性,附加性和影响性的规范性原则.
- 将人类决策策略与规范的BDT预测进行比较,并探索替代模型.
主要方法:
- 将贝叶斯决策理论 (BDT) 解构为用于孤立测试的顺序计算.
- 评估人类在需要使用从样本中得出的概率密度函数 (PDF) 的任务中的表现.
- 测量影响力,以量化个人样本点在决策中的权重,并将其与规范标准进行比较.
主要成果:
- 参与者在基于PDF的决策任务中系统地违反了规范性准确性和附加性原则.
- 虽然准确性和添加性偏差具有轻微影响,但样本点影响权重与BDT预测有显著差异.
- 人类决策者未能利用PDF的几何对称性,与规范的BDT模型不同.
结论:
- 在使用样本衍生PDF的任务中,人类的决策与规范贝叶斯决策理论 (BDT) 的预测有所不同.
- 规范性BDT模型使用几何对称性的假设并不反映在人类行为中.
- 一个替代模型,优先考虑基于单个极端样本点的决策,提供了对观察到的人类数据的更准确的解释.
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