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

Modeling the Contribution of Central Versus Peripheral Vision in Scene, Object, and Face Recognition