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

It is commonly believed that the central visual field (fovea and parafovea) 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. Larson and Loschky (2009) investigated this question in the context of scene processing using experimental conditions where a circular region only reveals the central visual field and blocks peripheral information (”Window”), and in a ”Scotoma” condition, where only the peripheral region is available. They measured 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 terms of achieving maximum recognition accuracy. In this work, we modeled and replicated the result of Larson and Loschky (2009), using deep convolutional neural networks (CNNs). 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 viceversa for peripheral information. Furthermore, our results predict that central information is more efficient than peripheral information on a per-pixel basis across all categories, which is consistent with Larson and Loschky’s data.