Predicting Identity-Preserving Object Transformations across the Human Ventral Visual Stream

Correlations between the predicted and the corresponding true patterns for each transformation for the 75 most reliable voxels included in each ROI. The correlations are compared with two performance ceiling measurements (see Materials and Methods). A, Results plotted by ROI and transformation type showing pattern prediction for categories included in the training data (Trained Categories) and those that were not (Untrained Categories) as a function of the number of categories included in the training data. Significant values for pairwise t tests against averaged-run Ceiling and Single-run Ceiling for each condition and for the difference between the trained and untrained categories for each training set size are marked with asterisks at the top of each plot. All t tests were corrected for multiple comparisons using the Benjamini–Hochberg method across the ROIs and training set sizes within each transformation type, totaling 42 or 30 comparisons (6 ROIs × 7 or 5 training set sizes). B, Results normalized by dividing the correlations by Averaged-run Ceiling and then plotted by transformation type showing pattern prediction as a function of ROI including only the smallest and the largest training set sizes. The light ribbon around each plot line represents the between-subjects 95% confidence interval of the mean. ∼0.05 < p < 0.10, *p <0.05, **p < 0.01, ***p < 0. 001.

Schematic illustrations of the pattern predictability and pattern selectivity results when object identity and nonidentity features are represented in a near-orthogonal manner. A, Top, Predictability results of training only one category (e.g., cat). The mapping derived is specific to the one trained category. As such, the correlation between the true and predicted patterns would be higher for the category included in training than that not included in training. Bottom, Predictability results of training two categories. Because the mapping derived is not specific to any one trained category, the correlation between the true and predicted patterns could decrease for the category included in training. B, Predictability and selectivity results in lower and higher visual regions. Because objects are more correlated in lower than in higher visual regions to begin with, even when both regions could equally well predict an object category response pattern after training, category selectivity would be greater for higher than lower visual regions. Category selectivity is defined as the difference in the correlation of a predicted pattern with the true pattern of the matching category and the correlation of a predicted pattern with the true pattern of mismatching categories (i.e., Is the predicted pattern significantly closer to that of the matching category than mismatching categories?).

Category selectivity for the predicted patterns for the 75 most reliable voxels included in each ROI. Category selectivity was calculated as the correlation difference between the predicted and true patterns of the same category and that of different categories. A, Results plotted by ROI and transformation type showing category selectivity for categories included in the training data (Trained Categories) and those that were not (Untrained Categories) as a function of the number of categories included in the training data. Significant values for pairwise t tests against zero for each condition and for the difference between the trained and untrained categories for each training set size are marked with asterisks at the top of each plot. All t tests were corrected for multiple comparisons using the Benjamini–Hochberg method across the ROIs and training set sizes within each transformation type, totaling 42 or 30 comparisons (6 ROIs × 7 or 5 training set sizes). B, Results normalized by dividing the correlations by Averaged-run Ceiling and plotted by transformation type showing category selectivity as a function of the ROI including only the smallest and the largest training set sizes. The light ribbon around each plot line represents the between-subjects 95% confidence interval of the mean. ∼p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.

Category RDM correlation between the predicted and true patterns for the 75 most reliable voxels included in each ROI. The correlations are compared with two performance ceiling measurements (see above, Materials and Methods). A, Results plotted by ROI and transformation type showing category structure preservation for categories included in the training data (Trained Categories) and those that were not (Untrained Categories) as a function of the number of categories included in the training data. Significant values for pairwise t tests against Averaged-run Ceiling and Single-run Ceiling for each condition and for the difference between the trained and untrained categories for each training set size are marked with asterisks at the top of each plot. All t tests were corrected for multiple comparisons using the Benjamini–Hochberg method across the ROIs and training set sizes within each transformation type, totaling 42 or 30 comparisons (6 ROIs × 7 or 5 training set sizes). B, Results normalized by dividing the correlations by Averaged-run Ceiling and plotted by transformation type showing category structure preservation as a function of the ROI including only the smallest and the largest training set sizes. The light ribbon around each plot line represents the between-subjects 95% confidence interval of the mean. ∼p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001.