LAUSR

ABSTRACT We recently proposed that attention control uses object-category representations consisting of category-consistent features (CCFs), those features occurring frequently and consistently across a category’s exemplars [Yu, C.-P., Maxfield, J. T., & Zelinsky, G. J. (2016). Searching for category-consistent features: A computational approach to understanding visual category representation. Psychological Science, 27(6), 870–884.] Here we extracted from a Convolutional Neural Network (CNN) designed after the primate ventral stream (VsNet) CCFs for 68 object categories spanning a three-level category hierarchy, and evaluated VsNet against the gaze behaviour of people searching for the same categorical targets. We also compared its success in predicting attention control to two other CNNs that differed in their degree and type of brain inspiration. VsNet not only replicated previous reports of stronger attention guidance to subordinate-level targets, but with its powerful CNN-CCFs it predicted attention control to individual target categories. Moreover, VsNet outperformed the other CNN models tested, despite these models having more trainable convolutional filters. We conclude that CCFs extracted from a brain-inspired CNN can predict goal-directed attention control.