我们真的是贝叶斯主义者吗? 概率推理表明,知识转移的最佳程度还不够
Chin-Hsuan Sophie Lin1, Trang Thuy Do1, Lee Unsworth1
1Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia.
PLoS computational biology
|January 8, 2024
概括
人类学习和结合先前的知识与感官证据像贝叶斯学,但可能不会使用完整的贝叶斯计算所有行为. 这项研究探讨了人们如何整合新信息,揭示了贝叶斯低于最佳但适应性的策略.
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.
- 决策 决策 决策 决策
背景情况:
- 贝叶斯框架准确地模拟了人类如何整合先前的知识和感官证据.
- 关于人类行为是否反映了精确的,计算密集的贝叶斯计算存在争论.
研究的目的:
- 在整合新信息时,调查人类行为是否与完整的贝叶斯计算保持一致.
- 评估参与者如何将学到的先验与新的概率信息相结合.
主要方法:
- 参与者利用先前的知识和杂的感官证据 (概率) 估计了目标位置.
- 一个转移学习范式测试了训练有素的先验与新可能性的整合.
- 分析了行为数据,以量化贝叶斯最佳预测的偏差.
主要成果:
- 参与者学习了先验,并以贝叶斯式的方式结合了信息.
- 新的概率的整合在已学到的范围内 (插值) 比在外面 (外推) 更好.
- 观察到的整合在插入和抽取条件下,在定量上是贝叶斯次优的.
结论:
- 人类的行为可以像贝叶斯一样,而不需要使用完整的贝叶斯计算.
- 整合新信息的认知策略是适应性的,但并不总是最佳的.
- 该研究为研究各种任务中的决策机制提供了一个框架.
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