Constraining Cognitive Abstractions Through Bayesian Modeling
Constraining Cognitive Abstractions Through Bayesian Modeling
复制标题
通过贝叶斯建模约束认知抽象
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Brandon M. Turner
中科院分区：
文献类型：
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作者：
Brandon M. Turner
有很多方法可以将神经和行为测量联合收割机结合起来研究认知。有些方法是理论性的，有些方法是统计性的。主要的统计方法将两个数据来源视为独立的，并通过（事后）回归分析推断出两种措施之间的关系。在本章中，我们将回顾另一种方法，该方法允许同时对两种度量进行灵活建模。然后，我们探讨和阐述了这种建模方法的几个最重要的好处，并关闭与线性弹道累加器模型和漂移扩散模型的神经和行为数据的模型比较。
There are many ways to combine neural and behavioral measures to study cognition. Some ways are theoretical, and other ways are statistical. The predominant statistical approach treats both sources of data as independent and the relationship between the two measures is inferred by way of a (post hoc) regression analysis. In this chapter, we review an alternative approach that allows for flexible modeling of both measures simultaneously. We then explore and elaborate on several of the most important benefits of this modeling approach, and close with a model comparison of the Linear Ballistic Accumulator model and a drift diffusion model on neural and behavioral data.
影响因子：
19.9
作者：
Forstmann, Birte U.;Wagenmakers, Eric-Jan;Eichele, Tom;Brown, Scott;Serences, John T.
通讯作者：
Serences, John T.