从综合意见中学习
Kerem Oktar1, Tania Lombrozo1, Thomas L Griffiths1,2
1Department of Psychology, Princeton University.
Psychological science
|July 24, 2024
概括
人们经常使用聚合意见,就像群众的智慧,来做决定. 研究表明,贝叶斯模型最好预测个人如何将这些社会信息与他们自己的信仰相结合.
科学领域:
- 认知科学 认知科学
- 社会心理学 社会心理学
- 决策科学 决策科学 决策科学
背景情况:
- 人类的认知依赖于从他人的意见中获取信息.
- 之前的研究重点是从直接证词中学习.
- 综合意见是影响判断和决策的越来越重要的社会信息来源.
研究的目的:
- 调查个人如何从聚合的意见中学习.
- 为了比较不同的社会学习计算模型的预测准确度.
- 在使用聚合意见时,确定人类判断是否与贝叶斯原则保持一致.
主要方法:
- 在美国对886名参与者进行了三项在线实验.
- 将人类判断与来自三个计算模型的预测进行了比较:贝叶斯模型和来自认识论和经济学的两个替代方案.
- 贝叶斯模型代表了一种将比例与先前信念相结合的策略.
主要成果:
- 在所有研究中,参与者的判断与贝叶斯模型预测的一致性最强.
- 一些参与者的判断更好地通过替代计算策略来解释.
- 系统推断是从汇总的意见中得出的,通常与贝叶斯的解决方案保持一致.
结论:
- 个人从汇总的意见中系统地推断出信息.
- 贝叶斯模型为理解人们如何从聚合的社会信息中学习提供了一个强大的框架.
- 未来的研究可以在这些发现的基础上,探索社会学习策略中的细微差别.
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