How hierarchical models improve point estimates of model parameters at the individual level
How hierarchical models improve point estimates of model parameters at the individual level
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DOI：
10.1016/j.jmp.2016.03.007
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发表时间：
2016-08-01
影响因子：
1.8
通讯作者：
Katahira, Kentaro
中科院分区：
文献类型：
--
作者：
Katahira, Kentaro
计算模型已被用来分析行为实验的数据。使用计算模型的一个目的是从行为数据估计个体受试者的模型参数或内部变量。这些估计通常与表征受试者的其他变量相关，以调查哪些计算过程与特定的个人或生理特征相关。虽然估计的准确性对于这些目的很重要，但从个体受试者数据获得的参数估计值通常不可靠。为了解决这个问题，研究人员已经开始使用分层建模方法来估计计算模型的参数，从多个主题的数据。人们普遍认为，与其他非分层方法相比，分层模型提供了可靠的估计。然而，如何以及在什么条件下，层次模型提供更好的估计比其他方法还有待系统地研究。本研究试图调查这些问题，集中在两个措施的估计精度：个人参数和受试者特质变量的估计和绝对测量误差（均方根误差，RMSE）的估计之间的相关性。一个简单的高斯模型的基础上的分析计算阐明了分层模型如何提高这两个措施的点估计。我们还进行了模拟研究，采用几个现实的计算模型的基础上合成的数据，以确认理论属性在现实情况下举行。(C)2016作者（S）爱思唯尔公司出版
Computational models have been used to analyze the data from behavioral experiments. One objective of the use of computational models is to estimate model parameters or internal variables for individual subjects from behavioral data. The estimates are often correlated with other variables that characterize subjects in order to investigate which computational processes are associated with specific personal or physiological traits. Although the accuracy of the estimates is important for these purposes, the parameter estimates obtained from individual subject data are often unreliable. To solve this problem, researchers have begun to use hierarchical modeling approaches to estimate parameters of computational models from multiple-subject data. It is widely accepted that the hierarchical model provides reliable estimates compared to other non-hierarchical approaches. However, how and under what conditions the hierarchical models provide better estimates than other approaches has yet to be systematically investigated. This study attempts to investigate these issues, focusing on two measures of estimation accuracy: the correlation between estimates of individual parameters and subject trait variables and the absolute measures of error (root mean squared error, RMSE) of the estimates. An analytical calculation based on a simple Gaussian model clarifies how the hierarchical model improves the point estimates of these two measures. We also performed simulation studies employing several realistic computational models based on the synthesized data to confirm that the theoretical properties hold in realistic situations. (C) 2016 The Author(s). Published by Elsevier Inc.