Emily E Meyer¹, Lingqi Zhang¹, Nicolas P Cottaris¹, David H Brainard¹, Michael J Arcaro¹; ¹University of Pennsylvania

A hallmark of primate vision is the ability to quickly recognize objects despite considerable variations in how an object is projected onto the retina. However, the evolutionary origins of this behavior remains poorly understood. Among the closest relatives to primates, tree shrews (Tupaia belangeri) offer unique insights into the evolution of visual processing. Their extensive extrastriate cortex and visually guided behaviors represent key adaptations that may have supported advanced object recognition in primates. We trained three adult tree shrews on a match-to-sample task using stimuli previously used to demonstrate complex object recognition in humans, macaques, and marmosets. Like primates, tree shrews successfully identified objects across variations in position, size, and orientation, and when embedded in complex scenes. Moreover, behavioral performance was correlated across shrews, suggesting they utilize a common shape representation. To gain deeper insight into the representations driving their behavior, we compared tree shrew performance with predictions from visual processing models. We accounted for tree shrew optics using a front-end visual system model, ISETBio, then employed deep convolutional neural networks (DCNN) to probe the visual representations emerging from core features of the primate visual system—hierarchical connectivity and convolutional processing. We analyzed the correspondence between DCNN layer representations and tree shrew behavioral performance, finding that layers best predicting tree shrew performance varied with task complexity. While this provides insights into the depth of processing, it does not reveal which specific stimulus features drive tree shrew behavior. Moreover, the most diagnostic stimulus features for tree shrew behavior may not be captured by DCNNs. Therefore, we are testing models representing specific aspects of processing, including local texture (Gabor-jet model), structural shape (skeletal model), and visual saliency (SALICON). These findings help establish tree shrews as a model for high-level processing and offer insights not just about whether, but how they discriminate complex objects.