July 2013
Volume 13, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   July 2013
Speed-matching strategy used to regulate speed in side-by-side walking
Author Affiliations
  • Zachary Page
    Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
  • William Warren
    Department of Cognitive, Linguistic, and Psychological Sciences, Brown University
Journal of Vision July 2013, Vol.13, 950. doi:10.1167/13.9.950
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      Zachary Page, William Warren; Speed-matching strategy used to regulate speed in side-by-side walking. Journal of Vision 2013;13(9):950. doi: 10.1167/13.9.950.

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

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Abstract

The collective behavior of human crowds may result from visually-guided interactions between neighbors, such as walking side-by-side. By modeling these local interactions we aim to explain emergent crowd behavior. Previously we investigated behavioral strategies use to control speed in side-by-side walking (VSS 2012); here we report more extensive analyses and test additional models. A participant and a confederate walked together across an open 12m x 12m room, while their head positions were tracked. The confederate was instructed to slow down or speed up mid-trial, whereas the participant was instructed to walk next to the confederate. Cross-correlation of confederate and participant speed profiles revealed significant coordination (mean r=0.79, delay=650 ms) compared to randomly selected virtual pairs (mean r=0.18). Six speed-control models were compared, simulating the participant’s acceleration on each trial by using the confederate’s data as input: (a) distance model (median r=0.38), null the z-distance between the confederate and participant, (b) direction model (median r=0.21), null the angle between the visual direction of the confederate and the participant’s sagittal plane, (c) speed model (median r=0.80), null the difference in speed between the participant and the confederate, (d) constant distance model (median r=0.61), reduce the z-distance between neighbors to a constant value, (e) linear combination model (median r=0.80), a weighted sum of the speed and distance models, and (f) ratio model (median r=0.52), a ratio of the speed and distance models. Thus, a simple speed-matching model best accounts for the human data; adding a distance term does not explain further variance. This result is similar to that for speed control in pedestrian following (Rio 2010, 2012), suggesting a general principle for the visual coordination of speed among neighbors in a crowd. Future work will investigate the visual coordination of heading direction.

Meeting abstract presented at VSS 2013

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