In this paper, we propose a joint modeling approach to analyze dependency in parallel response data. We define two types of dependency: higher-level dependency and within-item conditional dependency. While higher-level dependency can be estimated with common latent variable modeling approaches, within-item conditional dependency is a unique kind of information that is often not captured with extant methods, despite its potential to shed new insights into the relationship between the two types of response data. We differentiate three ways of modeling within-item conditional dependency by conditioning on raw values, expected values, or residual values of the response data, which have different implications in terms of response processes. The proposed approach is illustrated with the example of analyzing parallel data on response accuracy and brain activations from a Theory of Mind assessment. The consequence of ignoring within-item conditional dependency is investigated with empirical and simulation studies in comparison to conventional dependency analysis that focuses exclusively on relationships between latent variables.

中文翻译：

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对测试响应和大脑激活的并行数据中的项目内依赖性进行建模


在本文中，我们提出了一种联合建模方法来分析并行响应数据中的依赖性。我们定义了两种类型的依赖关系：高级依赖关系和项内条件依赖关系。虽然可以使用常见的潜在变量建模方法来估计更高级别的依赖性，但项目内条件依赖性是一种独特的信息，通常无法通过现有方法捕获，尽管它有可能为两种类型之间的关系提供新的见解。响应数据。我们通过以响应数据的原始值、期望值或残差值为条件来区分项目内条件依赖性建模的三种方法，这在响应过程方面具有不同的含义。通过分析心理理论评估中的反应准确性和大脑激活的并行数据来说明所提出的方法。与仅关注潜在变量之间关系的传统依赖性分析相比，通过实证和模拟研究研究了忽略项目内条件依赖性的后果。