September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Recognition of static and dynamic social actions in the visual periphery
Author Affiliations
  • Laura Fademrecht
    Max Planck Institute for Biological Cybernetics, Tübingen, Germany
  • Isabelle Bülthoff
    Max Planck Institute for Biological Cybernetics, Tübingen, Germany
  • Stephan de la Rosa
    Max Planck Institute for Biological Cybernetics, Tübingen, Germany
Journal of Vision September 2015, Vol.15, 494. doi:10.1167/15.12.494
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      Laura Fademrecht, Isabelle Bülthoff, Stephan de la Rosa; Recognition of static and dynamic social actions in the visual periphery. Journal of Vision 2015;15(12):494. doi: 10.1167/15.12.494.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Although actions often appear in the visual periphery, little is known about action recognition outside of the fovea. Our previous results have shown that action recognition of moving life-size human stick figures is surprisingly accurate even in far periphery and declines non-linearly with eccentricity. Here, our aim was (1) to investigate the influence of motion information on action recognition in the periphery by comparing static and dynamic stimuli recognition and (2) to assess whether the observed non-linearity in our previous study was caused by the presence of motion because a linear decline of recognition performance with increasing eccentricity was reported with static presentations of objects and animals (Jebara et al. 2009; Thorpe et al. 2001). In our study, 16 participants saw life-size stick figure avatars that carried out six different social actions (three different greetings and three different aggressive actions). The avatars were shown dynamically and statically on a large screen at different positions in the visual field. In a 2AFC paradigm, participants performed 3 tasks with all actions: (a) They assessed their emotional valence; (b) they categorized each of them as greeting or attack and (c) they identified each of the six actions. We found better recognition performance for dynamic stimuli at all eccentricities. Thus motion information helps recognition in the fovea as well as in far periphery. (2) We observed a non-linear decrease of recognition performance for both static and dynamic stimuli. Power law functions with an exponent of 3.4 and 2.9 described the non-linearity observed for dynamic and static actions respectively. These non-linear functions describe the data significantly better (p=.002) than linear functions and suggest that human actions are processed differently from objects or animals.

Meeting abstract presented at VSS 2015

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