在恐惧条件化中对感知和概括的层次贝叶斯模型的计算协议
Kenny Yu1, Wolf Vanpaemel1, Francis Tuerlinckx1
1Quantitative Psychology and Individual Differences, KU Leuven, 3000 Leuven, Belgium.
STAR protocols
|February 27, 2026
概括
本研究引入了一个计算协议,通过结合贝叶斯感知和层次混合模型来分析恐惧概括. 它有助于区分人类概括行为的感知和学习机制.
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.
- 心理学 心理学 心理学
背景情况:
- 了解人类的概括是区分认知和感知机制的关键.
- 对恐惧概括的个体差异尚未完全理解.
- 现有的模型可能无法充分分离感知和学习贡献.
研究的目的:
- 介绍一种用于分析恐惧概括的个体差异的计算协议.
- 为此分析整合贝叶斯状态空间和层次混合模型.
- 要区分感知和基于学习的概括过程.
主要方法:
- 将贝叶斯状态空间模型应用于感知数据.
- 从概率的感知表示计算刺激距离.
- 采用层级混合物概括模型来解析概括组件.
主要成果:
- 该协议允许对恐惧概括的个体差异进行定量分析.
- 它为解开感知和学习贡献提供了一个框架.
- 刺激距离可以从概率学感知表示得出.
结论:
- 综合计算协议为研究恐惧概括提供了一种新的方法.
- 这种方法有助于更深入地了解概括的认知和感知基础.
- 它允许区分基于感知和基于学习的概括机制.
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