A computational understanding of zoomorphic perception in the human brain

Abstract

It is common to find objects that resemble animals on purpose (e.g., toys). While the perception of such objects as animal-like seems obvious to humans, such "Animal bias" for zoomorphic objects turned out to be a striking discrepancy between the human brain and artificial visual systems known as deep neural networks (DNNs). We provide a computational understanding of the human Animal bias. We successfully induced this bias in DNNs trained explicitly with zoomorphic objects. Alternative training schedules, focusing on previously identified differences between the brain and DNNs, failed to cause an Animal bias. Specifically, we considered the superordinate distinction between animate and inanimate classes, the sensitivity for faces and bodies, the bias for shape over texture, and the role of ecologically valid categories. These findings provide computational support that the Animal bias for zoomorphic objects is a unique property of human perception yet can be explained by human learning history.