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Action recognition in the visual periphery

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Fademrecht,  L
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  I
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Recognition & Categorization, Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons83877

de la Rosa,  S
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Project group: Social & Spatial Cognition, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Fademrecht, L., Bülthoff, I., & de la Rosa, S. (2016). Action recognition in the visual periphery. Journal of Vision, 16(3): 33, pp. 1-14. doi:10.1167/16.3.33.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7A1A-B
Abstract
Recognizing whether the gestures of somebody mean a greeting or a threat is crucial for social interactions. In real life, action recognition occurs over the entire visual field. In contrast, much of the previous research on action recognition has primarily focused on central vision. Here our goal is to examine what can be perceived about an action outside of foveal vision. Specifically, we probed the valence as well as first level and second level recognition of social actions (handshake, hugging, waving, punching, slapping, and kicking) at 0° (fovea/fixation), 15°, 30°, 45°, and 60° of eccentricity with dynamic (Experiment 1) and dynamic and static (Experiment 2) actions. To assess peripheral vision under conditions of good ecological validity, these actions were carried out by a life-size human stick figure on a large screen. In both experiments, recognition performance was surprisingly high (more than 66 correct) up to 30° of eccentricity for all recognition tasks and followed a nonlinear decline with increasing eccentricities.