七参数扩散模型:在Stan中实现贝叶斯分析的实现
Franziska Henrich1, Raphael Hartmann2, Valentin Pratz3
1Department of Psychology, University of Freiburg, Engelbergerstraße 41, D-79106, Freiburg, Germany. franziska.henrich@psychologie.uni-freiburg.de.
我们在Stan中实施了一种灵活的七参数扩散模型,用于认知过程分析. 这种贝叶斯式方法准确地恢复参数,并验证响应时间数据的算法.
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.
- 心理测量 心理测量 心理测量
背景情况:
- 扩散模型对于理解使用响应和响应时间数据的认知过程至关重要.
- 现有的模型往往缺乏灵活性来捕捉试验间的变化.
研究的目的:
- 在Stan概率编程语言中实现一个全面的七参数扩散模型.
- 为了纳入漂移率,非决策时间和相对起点的试验间变化.
- 为认知建模提供灵活的贝叶斯框架.
主要方法:
- 在Stan环境中实施七参数扩散模型.
- 使用贝叶斯框架,具有灵活的先前和模型结构定义.
- 通过模拟研究进行性能评估,重点关注参数恢复和校准.
主要成果:
- 模拟研究表明,扩散模型参数的恢复总体上很好.
- 基于模拟的校准证实了在Stan.中实现的贝叶斯算法的有效性.
- 这种实现为认知建模研究提供了更大的灵活性.
结论:
- 斯坦实施的七参数扩散模型为认知科学研究提供了强大而灵活的工具.
- 这种方法验证了在Stan中使用贝叶斯方法来分析复杂的认知数据.
- 该模型处理试验间变化的能力提高了其用于详细认知过程分析的实用性.
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