Joint modeling of decisions and neural activation poses the potential to provide significant advances in linking brain and behavior. However, methods of joint modeling have been limited by difficulties in estimation, often due to high dimensionality and simultaneous estimation challenges. In the current article, we propose a method of model estimation that draws on state-of-the-art Bayesian hierarchical modeling techniques and uses factor analysis as a means of dimensionality reduction and inference at the group level. This hierarchical factor approach can adopt any model for the individual and distill the relationships of its parameters across individuals through a factor structure. We demonstrate the significant dimensionality reduction gained by factor analysis and good parameter recovery, and illustrate a variety of factor loading constraints that can be used for different purposes and research questions, as well as three applications of the method to previously analyzed data. We conclude that this method provides a flexible and usable approach with interpretable outcomes that are primarily data-driven, in contrast to the largely hypothesis-driven methods often used in joint modeling. Although we focus on joint modeling methods, this model-based estimation approach could be used for any high dimensional modeling problem. We provide open-source code and accompanying tutorial documentation to make the method accessible to any researchers. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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在基于模型的抽样中使用组级因子模型解析高维数。


决策和神经激活的联合建模有可能在将大脑和行为联系起来方面提供重大进展。然而，联合建模方法受到估计困难的限制，通常是由于高维度和同步估计的挑战。在这篇文章中，我们提出了一种模型估计方法，该方法利用了最先进的贝叶斯分层建模技术，并使用因子分析作为群级降维和推理的手段。这种分层因子方法可以为个体采用任何模型，并通过因子结构提炼其参数在个体之间的关系。我们展示了因子分析和良好的参数恢复获得的显着降维，并说明了可用于不同目的和研究问题的各种因子载荷约束，以及该方法在先前分析数据中的三种应用。我们得出的结论是，与联合建模中经常使用的主要假设驱动方法相比，这种方法提供了一种灵活且可用的方法，其可解释的结果主要是数据驱动的。虽然我们专注于联合建模方法，但这种基于模型的估计方法可用于任何高维建模问题。我们提供开源代码和随附的教程文档，使任何研究人员都可以使用该方法。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。