Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21218-2685, USA.
Neuroimage. 2011 Feb 14;54(4):2822-7. doi: 10.1016/j.neuroimage.2010.09.091. Epub 2010 Oct 23.
The premise of Multi-Voxel Pattern Analysis (MVPA) of functional Magnetic Resonance Image (fMRI) data is that mental encodings or states give rise to patterns of neural activation, which in turn, give rise to patterns of blood-oxygen level dependent (BOLD) responses distributed across sets of voxels. Statistical learning algorithms can then be used to detect relationships between mental encodings and BOLD responses, typically through pattern classification. Amongst many other applications, this technique has been used to evidence abstract category representation in an assortment of brain areas and across a range of cognitive domains. In this commentary, we address a critical domain-general caveat to inferring abstract category representation from MVPA that has been partly overlooked in the recent literature: specifically, the distinction between representing specific exemplars within categories, and representing the abstract categories themselves. Using a simulation, we demonstrate that certain forms of MVPA training and testing do not constitute sufficient evidence of category representation, and illustrate prospective and novel retrospective resolutions for this issue.
多体素模式分析（MVPA）的前提是功能磁共振成像（fMRI）数据的精神编码或状态产生神经激活模式，进而产生血氧水平依赖（BOLD）反应的模式分布在体素集中。然后可以使用统计学习算法来检测精神编码和 BOLD 反应之间的关系，通常是通过模式分类。除了许多其他应用之外，该技术还被用于证明在各种大脑区域和一系列认知领域中存在抽象类别表示。在这篇评论中，我们讨论了从 MVPA 推断抽象类别表示的一个关键的领域普遍性警告，该警告在最近的文献中部分被忽视：具体来说，就是在表示类别中的特定示例和表示抽象类别本身之间的区别。我们使用模拟演示了某些形式的 MVPA 训练和测试并不能构成类别表示的充分证据，并说明了针对该问题的前瞻性和新颖的回顾性解决方案。
