Tests of invariant and configural brain representation.

A: Varying degrees of change in neural encoding as a function of a change in context. With a change in context, context-invariant representations do not change at all, whereas context-specific representations change completely, with a continuum between both extremes. Tests in the literature focus on evidence against one of the two extremes. B: Tests of context invariance and specificity. Steps 1 and 2 are common to all tests. Different tests differ on how invariance/specificity is evaluated in step 3. The figure depicts distributions of classifier decision variables and the areas of these distributions on which each test focuses (in gray). C: Representations are transformed from the space of neural activities to the space of voxel measurements. Context-invariant representations (top) cannot be transformed to decrease their invariance and increase their specificity, whereas context-specific representations (bottom) can be transformed to increase their invariance and decrease their specificity. D: Example highlighting the differences between spatially smooth versus fine-grained encoding schemes, and a particular combination of the two schemes that produces false-positives in a voxelwise analysis. Each column represents a voxel containing neurons (small circles), each with selectivity for one of two values of the target property (red and yellow). The multivoxel pattern of activity is the same for both levels of the context dimension (spatially smooth encoding), but completely different populations of neurons encode each level (fine-grained encoding). This figure includes public domain clipart and all other parts are original: https://commons.wikimedia.org/wiki/File:Gaussian_distribution.svg https://www.wpclipart.com/signs_symbol/arrows/BW_arrows/arrow_BW_thick_left.png.html.