The not-strongly-supervised models are less categorical than IT.

<p>Categoricality was measured using a categoricality index (vertical axis) for each model and brain RDM. The categoricality index is defined as the proportion of RDM variance explained by the category-cluster model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003915#pcbi.1003915.s005" target="_blank">Figure S5</a>), i.e. the squared correlation between the fitted category-cluster model and the RDM it is fitted to. Bars show the categoricality index for each of the not-strongly-supervised models. The blue (gray) line shows the categoricality index for hIT (mIT). Error bars show 95%-confidence intervals of the categoricality index estimates for the models. The 95%-confidence intervals for hIT and mIT are shown by the blue and gray shaded regions, respectively. Significant categoricality indices are marked by stars underneath the bars (* p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001). Error bars are based on bootstrapping of the stimulus set, and the p-values are obtained by category label randomization test. Significant differences between the categoricality indices of each model and hIT (inference by bootstrap resampling of the stimuli) are indicated by blue vertical arrows (p<0.05, Bonferroni-adjusted for 28 tests). The corresponding inferential comparisons for mIT are indicated by gray vertical arrows. Categoricality is significantly greater in hIT and mIT than in any of the 28 models. This analysis is based on equating the noise level in the models with that of hIT (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003915#s3" target="_blank">Materials and Methods</a>). Similar results obtain for a conservative inferential analysis comparing the categoricality of the noise-less models with that of the noisy estimates for hIT and mIT (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003915#pcbi.1003915.s009" target="_blank">Figure S9</a>).