Toggle Main Menu Toggle Search

Open Access padlockePrints

Ensemble coding of crowd speed using biological motion

Lookup NU author(s): Dr Quoc Vuong


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 2020, The Psychonomic Society, Inc. The accurate perception of human crowds is integral to social understanding and interaction. Previous studies have shown that observers are sensitive to several crowd characteristics such as average facial expression, gender, identity, joint attention, and heading direction. In two experiments, we examined ensemble perception of crowd speed using standard point-light walkers (PLW). Participants were asked to estimate the average speed of a crowd consisting of 12 figures moving at different speeds. In Experiment 1, trials of intact PLWs alternated with trials of scrambled PLWs with a viewing duration of 3 seconds. We found that ensemble processing of crowd speed could rely on local motion alone, although a globally intact configuration enhanced performance. In Experiment 2, observers estimated the average speed of intact-PLW crowds that were displayed at reduced viewing durations across five blocks of trials (between 2500 ms and 500 ms). Estimation of fast crowds was precise and accurate regardless of viewing duration, and we estimated that three to four walkers could still be integrated at 500 ms. For slow crowds, we found a systematic deterioration in performance as viewing time reduced, and performance at 500 ms could not be distinguished from a single-walker response strategy. Overall, our results suggest that rapid and accurate ensemble perception of crowd speed is possible, although sensitive to the precise speed range examined.

Publication metadata

Author(s): Nguyen TTN, Vuong QC, Mather G, Thornton IM

Publication type: Article

Publication status: Published

Journal: Attention, Perception, and Psychophysics

Year: 2021

Volume: 83

Pages: 1014-1035

Print publication date: 01/04/2021

Online publication date: 09/11/2020

Acceptance date: 20/09/2020

ISSN (print): 1943-3921

ISSN (electronic): 1943-393X

Publisher: Springer


DOI: 10.3758/s13414-020-02163-3


Altmetrics provided by Altmetric