Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons
Figure 4
Overlap between k-means clusters in the IT neuronal space and object categories of the clustering hypotheses.
(A) Fifteen object clusters obtained by a typical run of the k-means algorithm over the IT neuronal representation space. The clusters' arrangement was determined by applying a hierarchical clustering algorithm to their centroids (see the dendrogram on the top; the same approach was used to arrange the shape-based categories shown in C, which resulted from the k-means object clustering in the output layer of an object recognition model [44]). (B–D) The semantic (B), shape-based (C) and low-level (D) categories that significantly overlapped with some of the neuronal-based clusters shown in A. Overlapping neuronal-based clusters and categories are indicated by matching names (e.g., faces) in A and B–D, with the objects in common between a cluster and a category enclosed by either a yellow (semantic), a red (shape-based) or a cyan (low-level) frame. (E) Average number of significant overlaps between neuronal-based clusters and semantic (first bar), shape-based (second bar) and low-level (third bar) categories across 1,000 runs of the k-means algorithm over both the neuronal representation space and the model representation space. The yellow, red and cyan striped portion of the first bar indicates the number of neuronal-based clusters that significantly overlapped with both a semantic category and either a shape-based or a low-level category.