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.
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