August 2012
Volume 12, Issue 9
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2012
   Visual control of speed in side-by-side walking
Author Affiliations
  • Zachary Page
    Dept. of Cognitive, Linguistic, and Psychological Sciences, Brown University
  • William Warren
    Dept. of Cognitive, Linguistic, and Psychological Sciences, Brown University
Journal of Vision August 2012, Vol.12, 188. doi:10.1167/12.9.188
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Zachary Page, William Warren;    Visual control of speed in side-by-side walking. Journal of Vision 2012;12(9):188. doi: 10.1167/12.9.188.

      Download citation file:


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

      ×
  • Supplements
Abstract

Side-by-side walking is a common locomotor interaction in which two mutually visually-coupled pedestrians adopt a common speed and direction of locomotion. Understanding this interaction may help to explain the collective behavior of human crowds. In this study, we tested three behavioral strategies for speed control in side-by-side walking: (1) The Distance Model nulls the distance along the travel axis between a walker and their partner; (2) the Direction Model nulls the angle between the visual direction of the partner and the walker’s medial-lateral axis; (3) the Speed Model nulls the difference in speed between the walker and the partner. Two participants were asked to walk next to one another to a goal in an open 12m x 12m room while their head positions were recorded (60 Hz). In the Initial Speed condition, each participant was instructed to begin walking at one of three speeds (slow, normal or fast) and then to walk together to the goal. In the Mid-trial Speed condition, one participant was instructed to change their speed (slow down or speed up) mid trial. The models were fit to the time series of acceleration for each walker, treating the partner’s data as input, for all trials in a condition (two free parameters: a coefficient and a time delay). The speed-matching model yielded the highest correlations between simulated and participant data, whereas the distance and direction model correlations were near zero. In addition, participants maintained their initial relative positions along the travel axis throughout a trial, consistent with the speed model. Further tests of models that combine distance and speed are ongoing. Previously, Rio & Warren (VSS 2010) similarly found that a speed model also characterized pedestrian following. Ultimately, components for side-by-side walking and following could be combined to model the emergent behavior of crowds.

Meeting abstract presented at VSS 2012

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×