ABSTRACT

Researchers interested in hemispheric dominance frequently aim to infer latent functional differences between the hemispheres from observed lateral behavioural or brain-activation differences. To be valid, these inferences may not only rely on the observed laterality measures but also need to account for the antecedent probabilities of the studied latent classes. This fact is frequently ignored in the literature, leading to misclassifications especially when considering low probability classes as, for example, “atypical” right hemispheric language dominance. In the present paper, we revisit this inference problem (a) by outlining a general Bayesian framework for the inferential process and (b) by exemplarily applying this framework on the inference of hemispheric dominance for speech processing from dichotic-listening laterality scores. Utilizing large-scale empirical data sets as well as simulation studies, we show that valid inferences also regarding low probable latent classes can be drawn applying the present framework, although within certain boundaries. We further illustrate that repeated laterality measures of the same person may be used to improve the classification outcome. The article additionally provides R package and Shiny app implementations of the suggested Bayesian framework, which allow to explore the boundaries of valid inference for the present and other examples.

摘要

对半球优势感兴趣的研究人员经常旨在根据观察到的横向行为或大脑激活差异来推断半球之间的潜在功能差异。为了有效，这些推论不仅可能依赖于所观察到的横向性测度，而且还需要考虑所研究的潜在类别的先验概率。这个事实在文献中经常被忽略，导致分类错误，尤其是在将低概率类别视为“非典型”右半球语言优势时。在本文中，我们通过（a）概述推理过程的通用贝叶斯框架，以及（b）通过示例性地将这种框架应用于从二分听法侧向得分进行语音处理的半球优势的推理，来重新审视此推理问题。利用大规模的经验数据集和模拟研究，我们表明，尽管存在一定的界限，但使用本框架也可以得出关于低可能潜伏类的有效推论。我们进一步说明，可以使用同一个人的反复偏侧测量来改善分类结果。本文还提供了建议的贝叶斯框架的R包和Shiny应用实现，它们允许探索本示例和其他示例的有效推论的边界。我们进一步说明，可以使用同一个人的反复偏侧测量来改善分类结果。本文还提供了建议的贝叶斯框架的R包和Shiny应用实现，它们允许探索本示例和其他示例的有效推论的边界。我们进一步说明，可以使用同一个人的重复侧向测量来改善分类结果。本文还提供了建议的贝叶斯框架的R包和Shiny应用实现，它们允许探索本示例和其他示例的有效推论的边界。