- Jarrod A Lewis-Peacock: Department of Psychology, University of Wisconsin-Madison, USA. jalewpea@princeton.edu
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.