动态决策的Tweedledum和Tweedledee:在扩散决策和累积模型之间进行区分
1The Ohio State University, Columbus, OH, USA. kvam.4@osu.edu.
Psychonomic bulletin & review
|October 1, 2024
概括
本研究为动态决策中的证据积累模型提供了一个通用的框架. 包括机器学习在内的四种新方法区分了绝对证据和相对证据的决策规则.
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.
- 决策理论 决策理论
背景情况:
- 动态决策理论通常依赖于证据积累模型,如赛车积累器或扩散模型.
- 这些模型代表了随着时间的推移而变化的支平衡.
- 现有的模型是更一般的证据积累过程的特殊情况.
研究的目的:
- 引入一个通用的证据积累框架.
- 确定区分绝对证据和相对证据模型的方法.
- 为比较各种决策理论提供工具.
主要方法:
- 分析决策者所考虑的信息,以过接近零的证据样本.
- 操纵刺激的可区分性来解开漂移率组件.
- 采用机器学习用于基于生成模型的数据分类.
- 使用机器学习来估计选择选项之间的几何关系.
主要成果:
- 开发了四种不同的方法来区分证据积累模型.
- 适用于导向歧视任务的应用证明了证据的趋同.
- 调查结果支持在决策过程中基于积累器的证据表示.
结论:
- 提出的方法有效地区分绝对证据和相对证据模型.
- 一般化框架为理解动态决策提供了一个强有力的方法.
- 这些工具对决策理论的实证和理论比较有价值.
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