September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
The visual coupling between neighbors in a virtual crowd
Author Affiliations
  • William Warren
    Dept. of Cognitive, Linguistic, & Psychological Sciences, Brown University
  • Kevin Rio
    Dept. of Cognitive, Linguistic, & Psychological Sciences, Brown University
Journal of Vision September 2015, Vol.15, 747. doi:10.1167/15.12.747
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      William Warren, Kevin Rio; The visual coupling between neighbors in a virtual crowd. Journal of Vision 2015;15(12):747. doi: 10.1167/15.12.747.

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

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Abstract

Pedestrians in a crowd use visual information to coordinate walking speed and heading direction with their neighbors. Previously, we characterized the strategies used to control these behaviors in pairs of pedestrians (Rio, Rhea, & Warren, 2014; Dacher & Warren, 2014). Here we investigate how a participant combines the influence of multiple neighbors, providing a bridge from individual behavior to crowd dynamics. In two experiments, a participant (N=10 per experiment) was instructed to “walk together” with a virtual crowd of 12 simulated humans presented within the FOV of a head-mounted display (90° H). On each trial, a subset of virtual neighbors changed their walking speed or heading direction mid-way through the trial. We manipulated the number of neighbors in the subset (0, 3, 6, 9, or 12, equivalent to 0%-100% of the crowd), their distance from the participant (1.5m or 3.5m), and the density of the crowd (interpersonal distance of 2.5m or 5.5m). Change in the participant’s walking speed and lateral position were measured relative to baseline control trials, when all neighbors maintained a constant speed and direction. The results support three main conclusions: First, neighbor influence is additive. Participant responses increased linearly with the number of neighbors in the subset (p< .001), for both speed and heading. Second, neighbor influence is weighted by distance. Responses were significantly weaker when the subset was far than near (p< .001), for both speed and heading. Third, the neighborhood structure appears to be metric (fixed radius) rather than topological (N nearest neighbors) (Ballerini et al., 2008). Responses depended on crowd density (p< .01), for both speed and heading, contrary to the topological hypothesis. Thus, a pedestrian in a crowd is visually coupled to at least 12 neighbors in the FOV and coupling strength decays rapidly with metric distance, placing strong constraints on models of collective behavior.

Meeting abstract presented at VSS 2015

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