Literature DB >> 32492098

Visual crowding in driving.

Ye Xia1, Mauro Manassi1,1, Ken Nakayama1, Karl Zipser1, David Whitney1,1,1.   

Abstract

Visual crowding-the deleterious influence of nearby objects on object recognition-is considered to be a major bottleneck for object recognition in cluttered environments. Although crowding has been studied for decades with static and artificial stimuli, it is still unclear how crowding operates when viewing natural dynamic scenes in real-life situations. For example, driving is a frequent and potentially fatal real-life situation where crowding may play a critical role. In order to investigate the role of crowding in this kind of situation, we presented observers with naturalistic driving videos and recorded their eye movements while they performed a simulated driving task. We found that the saccade localization on pedestrians was impacted by visual clutter, in a manner consistent with the diagnostic criteria of crowding (Bouma's rule of thumb, flanker similarity tuning, and the radial-tangential anisotropy). In order to further confirm that altered saccadic localization is a behavioral consequence of crowding, we also showed that crowding occurs in the recognition of cluttered pedestrians in a more conventional crowding paradigm. We asked participants to discriminate the gender of pedestrians in static video frames and found that the altered saccadic localization correlated with the degree of crowding of the saccade targets. Taken together, our results provide strong evidence that crowding impacts both recognition and goal-directed actions in natural driving situations.

Entities:  

Year:  2020        PMID: 32492098     DOI: 10.1167/jov.20.6.1

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  3 in total

1.  An implementation of Bubble Magnification did not improve the video comprehension of individuals with central vision loss.

Authors:  Francisco M Costela; Stephanie M Reeves; Russell L Woods
Journal:  Ophthalmic Physiol Opt       Date:  2021-03-28       Impact factor: 3.992

2.  Global and high-level effects in crowding cannot be predicted by either high-dimensional pooling or target cueing.

Authors:  Alban Bornet; Oh-Hyeon Choung; Adrien Doerig; David Whitney; Michael H Herzog; Mauro Manassi
Journal:  J Vis       Date:  2021-11-01       Impact factor: 2.240

3.  Analysis of Different College Music Education Management Modes Using Big Data Platform and Grey Theoretical Model.

Authors:  Ying Guo; Shanshan Xu
Journal:  J Environ Public Health       Date:  2022-08-16
  3 in total

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