通过顺序抽样进行价值构建,解释了决策中的序列依赖性
Ariel Zylberberg1, Akram Bakkour1,2,3, Daphna Shohamy1,4,5
1Mortimer B Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States.
bioRxiv : the preprint server for biology
|October 17, 2024
概括
主观价值在决策过程中发生变化. 一个新的算法,Reval,显示这些价值变化比稳定值更好地解释选择和大脑活动,挑战现有模型.
科学领域:
- 决策神经科学 决策神经科学
- 认知心理学 认知心理学
- 神经经济学 神经经济学
背景情况:
- 决策通常假定稳定的项目值,不一致性归因于随机噪音.
- 有限证据积累 (BEA) 模型使用暂时不相关的噪音来进行感知决策.
- 这种假设可能不适用于内部状态波动的基于价值的决策.
研究的目的:
- 调查主观项目值在决策任务期间是否发生变化.
- 开发和应用一种新的算法 (Reval) 来检测和量化价值变化.
- 评估动态值是否改善选择和响应时间的模型.
主要方法:
- 使用Reval算法重新分析现有的零食选择数据.
- 将Reval衍生值与明确声明值进行比较.
- 模拟选择和响应时间,使用动态与静态值假设.
- 价值变化与大脑活动 (大胆信号) 在中腹前额叶皮层的相关性.
主要成果:
- 发现主观项目值在短时间的实验会话中发生显著变化.
- 来自Reval的动态值比静态值更好地解释了选择和响应时间.
- 动态价值变化也更好地解释了与价值相关的大脑区域的神经活动.
- 一个修改后的BEA模型包含了非独立的证据样本,支持重新估值的概念.
结论:
- 主观价值不是静态的,而是在偏好选择过程中动态地重新评估的.
- 重估是基于价值的决策的关键因素,影响选择和神经表示.
- 现有的决策模式可能需要纳入动态价值构建以获得更高的准确性.
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