September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Adaptation to the spatial smoothness of visual motion flow.
Author Affiliations
  • Kazushi Maruya
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
  • Takahiro Kawabe
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
  • Shin'ya Nishida
    NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation, Japan
Journal of Vision September 2015, Vol.15, 1011. doi:10.1167/15.12.1011
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      Kazushi Maruya, Takahiro Kawabe, Shin'ya Nishida; Adaptation to the spatial smoothness of visual motion flow.. Journal of Vision 2015;15(12):1011. doi: 10.1167/15.12.1011.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The spatiotemporal pattern of local motion vectors is a useful information source for visual recognition of moving objects, as demonstrated by our recent finding that human observers can recognize dynamic liquids and their viscosity only from the visual motion flow information (Kawabe et al., in press, Vision Research). Furthermore, the same study shows that the critical image parameter for the liquid-from-motion perception is the spatial smoothness of motion flow. However, it remains poorly understood how the brain processes the spatiotemporal pattern of motion flow, including the spatial smoothness. We have been analyzing the motion flow processing using the adaptation paradigm. Last year (Maruya & Nishida, 2014, VSS), we showed that the adaptation to a non-uniform motion field (a checkerboard pattern defined by two motion directions) significantly elevated the threshold (in terms of minimum direction difference) to detect a test motion-defined pattern. This time, we examined whether the adaptation to a specific range of spatial smoothness of motion flow would alter the apparent smoothness of the subsequently seen motion flows. The stimulus was an array of moving noise patches, each windowed by a stationary Gaussian. By rearranging patch positions of a motion field of a liquid flow, we generated motion flow fields with various levels of spatial smoothness — smooth and non-smooth fields for adaptation, and intermediate ones for test. To avoid adaptation to local motions and specific arrangements of motion flow, we updated the adaptation field to a new one every 1s. Observers’ task was to score the perceived smoothness of the motion flow in the test pattern, which was presented briefly (1s) after adaptation. The results show that perceived smoothness with test patterns is higher after adaptation to non-smooth flows than after adaptation to smooth flows. This indicates the existence of adaptable encoder of the spatial smoothness of motion flow.

Meeting abstract presented at VSS 2015

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