相关实验视频
Updated: Jul 18, 2025

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
Published on: January 9, 2016
使用扩散模型测量效用
Renato Berlinghieri1, Ian Krajbich2,3,4, Fabio Maccheroni5
1Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA.
我们开发了一种用于决策的漂移扩散模型 (DDM) 中估计参数的新方法. 这种方法直接从数据中推导出决策门和实用程序,简化了对经济选择的分析.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 决策科学 决策科学 决策科学
背景情况:
- 漂移扩散模型 (DDM) 是理解决策过程的领先框架.
- 许多决策涉及比较刺激,如经济价值,需要准确的参数估计.
研究的目的:
- 在比较决策任务中提出漂移扩散模型参数的一致估计器.
- 允许从实验数据中直接推导决策值,漂移率和主观效用.
主要方法:
- 为漂移扩散模型参数开发了一种新的一致估计器.
- 将估计器应用于涉及概率和奖励值比较的数据集.
- 验证了模型预测未观察到漂移速率的能力.
主要成果:
- 拟议的估计器成功地从数据中直接推导出决策值,漂移率和公用事业.
- 对两个数据集的分析证实了与漂移扩散模型的良好匹配.
- 发现主观公用事业与概率是线性的,与奖励值稍微凸起.
结论:
- 这种新方法提供了一种强大的方法,可以在漂移扩散模型框架内分析决策.
- 它消除了对决策参数功能形式的单独测量或假设的需求.
- 这些发现为管理经济选择的公用事业功能提供了洞察力.
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