Figure 5.

Human dissimilarity judgments emphasize additional categorical divisions not present in human IT. (A) We decomposed the dissimilarity matrices for hIT and judgments into two additive components, reflecting the category-related dissimilarity variance and non-category-related dissimilarity variance (i.e., within-category dissimilarities and noise). (B) The decomposition was performed by fitting a linear model with multiple predictor dissimilarity matrices, each reflecting a categorical division (red, magenta, cyan, blue) or an imbalance between average within-category dissimilarities of two categories (e.g., average within-animate dissimilarity < average within-inanimate dissimilarity). We fitted the model to the RDMs for hIT and judgments using ordinary-least-squares and estimated the ratio of category-related dissimilarity variance (captured by the model) and non-category-related dissimilarity variance (residuals). We then equated the proportion of residual variance by adding noise to the RDM with smaller proportion residual variance. The judgments had a smaller proportion of residual variance. The judgments matrix shown in A contains the added noise. Equating the residual variance is necessary for valid statistical inference (for details on the noise model and inference, see Materials and Methods). (C) We then fitted the model to the residual-equated RDMs and compared hIT and judgments in terms of the percentage of category variance explained by each category division. The animate/inanimate and face/body divisions explained significantly more variance in hIT than in the judgments. The human/non-human and natural/artificial divisions explained significantly more variance in the judgments than in hIT.