贝叶斯对最佳决策的信心
Joshua Calder-Travis1, Lucie Charles2, Rafal Bogacz3
1Department of Experimental Psychology, University of Oxford.
Psychological review
|July 18, 2024
概括
漂移扩散模型 (DDM) 可以扩展到准确预测决策信心. 信任度反映了积累的证据强度,而决策时间受到惩罚,支持单个积累器模型.
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.
背景情况:
- 漂移扩散模型 (DDM) 准确地模拟决策和响应时间.
- 目前基于DDM的信任模型有局限性,这促使人们探索扩展.
- 替代决策模型经常被用于信心,尽管DDM的成功.
研究的目的:
- 调查DDM的简单扩展,以更好地考虑决策信心.
- 确定一个单一的证据积累过程是否可以为决策和信心提供信息.
- 测试DDM框架是否可以调整以解释信任报告.
主要方法:
- 开发并比较了几种DDM变体,包括信任.
- 确保决策和信心依赖于相同的证据积累过程.
- 根据基准数据和新的预先注册研究验证的模型.
主要成果:
- 一个DDM变体的子集成功考虑了信心数据的定量方面.
- 信心似乎反映了证据的强度受到决策时间 (贝叶斯式读取) 的惩罚.
- 在信任报告中的时间罚款可能没有被完美校准.
结论:
- 可以扩展DDM框架,以提供关于决策信心的强有力的说明.
- 没有必要放弃DDM或单蓄电机模型进行信心研究.
- 结果支持贝叶斯的信心读数,包括决策时间.
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