AI 中文总结
本研究针对多变量模式分析中用于表征神经表征不相似度的交叉验证距离，提出了泛化交叉验证欧氏距离与交叉验证相关距离两种改进方案，提升了度量的可靠性与准确性。
AI 中文摘要
表征实验条件间神经表征的不相似度并随时间追踪该不相似度，是多变量模式分析的核心目标。Guggenmos等人（2018）在脑磁图（MEG）数据上评估了多种可用于该目的的度量的可靠性，并推荐使用交叉验证欧氏距离或类内校正皮尔逊距离。在本评论中，我们证明可对这些距离进行改进：首先，我们表明交叉验证欧氏距离等价于各划分间距离之和，且该等价关系可用于得到一种泛化变体，其可靠性与准确性更高；其次，我们利用欧氏距离与皮尔逊相关的关系，以类似方式定义交叉验证相关距离，所得距离比Guggenmos等人提出的形式更准确、更具可解释性；最后，我们讨论了泛化交叉验证与类内校正（另一种常用于提升可靠性的策略）之间的关系，并表明泛化交叉验证对相关距离而言准确性更高。
英文摘要
Characterizing the dissimilarity of neural representations between experimental conditions, and tracking it across time, is a central goal of multivariate pattern analysis. Guggenmos et al. (2018) assessed the reliability of many of the measures that can be used for that purpose on MEG data and recommended the use of either the cross-validated Euclidean distance or the within-class-corrected Pearson distance. In this commentary, we show that we can improve upon these distances. First, we show that the cross-validated Euclidean distance is equivalent to a sum of between-partition distances and that this equivalence can be leveraged to obtain a generalized variant, with increased reliability and accuracy. Second, we use the relationship between Euclidean distance and Pearson correlation to define a cross-validated correlation distance in a similar way. The resulting distance is more accurate and interpretable than a formulation proposed by Guggenmos and colleagues. Finally, we discuss the relationship between our generalized cross-validation and within-class correction, another strategy often used to increase reliability, and we show that generalized cross-validation results in higher accuracy for the correlation distance.
