Department of Psychology, University of Wisconsin-Madison, USA.
Neuropsychologia. 2012 Mar;50(4):470-8. doi: 10.1016/j.neuropsychologia.2011.11.006. Epub 2011 Nov 11.
The significance of the recent introduction to cognitive neuroscience of multivariate pattern analysis (MVPA) is that, unlike univariate approaches which are limited to identifying magnitudes of activity in localized parts of the brain, it affords the detection and characterization of patterns of activity distributed within and across multiple brain regions. This technique supports stronger inferences because it captures neural representations that have markedly higher selectivity than do univariate activation peaks. Recently, we used MVPA to assess the neural consequences of dissociating the internal focus of attention from short-term memory (STM), finding that the information represented in delay-period activity corresponds only to the former (Lewis-Peacock, Drysdale, Oberauer, & Postle, in press). Here we report several additional analyses of these data in which we directly compared the results generated by MVPA vs. those generated by univariate analyses. The sensitivity of MVPA to subtle variations in patterns of distributed brain activity revealed a novel insight: although overall activity remains elevated in category-selective brain regions corresponding to unattended STM items, the multivariate patterns of activity within these regions reflect the representation of a different category, i.e., the one that is currently being attended to. In addition, MVPA was able to dissociate attended from unattended STM items in brain regions whose univariate activity did not appear to be sensitive to the task. These findings highlight the fallacy of the assumption of homogeneity of representation within putative category-selective regions. They affirm the view that neural representations in STM are highly distributed and overlapping, and they demonstrate the necessity of multivariate analysis for dissociating such representations.
多变量模式分析（MVPA）在认知神经科学中的引入意义重大，与仅局限于识别大脑局部区域活动幅度的单变量方法不同，它可以检测和描述跨多个脑区分布的活动模式。该技术支持更强有力的推断，因为它捕捉到的神经表示具有明显高于单变量激活峰的选择性。最近，我们使用 MVPA 来评估将注意力的内部焦点与短期记忆（STM）分离的神经后果，发现延迟期活动中所代表的信息仅对应于前者（Lewis-Peacock、Drysdale、Oberauer 和 Postle，即将发表）。在这里，我们报告了对这些数据的几项额外分析，其中我们直接比较了 MVPA 与单变量分析生成的结果。MVPA 对分布式脑活动模式细微变化的敏感性揭示了一个新的见解：尽管与未注意的 STM 项目相对应的类别选择性脑区的整体活动仍然升高，但这些区域内的活动模式的多元模式反映了不同类别的表示，即当前正在注意的类别。此外，MVPA 能够在单变量活动似乎对任务不敏感的脑区中区分注意和未注意的 STM 项目。这些发现强调了在假定的类别选择性区域内表示的同质性假设的谬误。它们肯定了 STM 中的神经表示高度分布和重叠的观点，并证明了多元分析对于分离这些表示的必要性。
