  It is commonly believed that the central visual field is important for
recognizing objects and faces, and the peripheral region is useful for scene
recognition. However, the relative importance of central versus peripheral
information for object, scene, and face recognition is unclear. In a behavioral
study, Larson and Loschky (2009) investigated this question by measuring the
scene recognition accuracy as a function of visual angle, and demonstrated that
peripheral vision was indeed more useful in recognizing scenes than central
vision. In this work, we modeled and replicated the result of Larson and
Loschky (2009), using deep convolutional neural networks. Having fit the data
for scenes, we used the model to predict future data for large-scale scene
recognition as well as for objects and faces. Our results suggest that the
relative order of importance of using central visual field information is face
recognition>object recognition>scene recognition, and vice-versa for peripheral
information.
