Visual crowding in driving

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.

The Practical Dilemma and Path Optimization of Big Data Integration into Ideological and Political Teaching in Private Colleges and Universities

“…Hidden increased the difficulty of follow-up education [20][21][22][23]. ird, From the perspective of big data's elimination of useless information and retention of useful information, the whole process of education should be controlled.…”

[Retracted] Analysis of Different College Music Education Management Modes Using Big Data Platform and Grey Theoretical Model

“…It is a severe form, but only adopts the method of external teachers for temporary guidance and completes the tasks of college students' cultural performances arranged by the superiors every year [ 13 ]. Based on educational data mining technology, Xia et al use the deterministic factor method and sequential pattern mining in association rule mining to mine the minimum association rules for students' course selection and students' temporary interest learning patterns, so as to analyze students' behavior [ 14 ]. Chen and University completes the analysis of student behavior through data mining of students' behavior characteristic data.…”

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

“…These previous implementations of Bubble Magnification have been either head contingent 15,18–20 or gaze contingent 16 . In our pilot testing, two experimenters with NV found that a gaze‐contingent bubble did not seem to work well when watching videos because: (1) objects of interest that were obscured by the magnification scotoma 21 were missed; (2) there were delays in determining a new object of interest, which would probably be exacerbated by using a preferred retinal locus (PRL) 22,23 and (3) there may be difficulties making accurate saccades in the presence of crowding 24,25 . For these reasons, in the current study, we developed a novel Bubble Magnification technique that magnified the region or the centre of interest (COI) in a video clip.…”

“…16 In our pilot testing, two experimenters with NV found that a gaze-contingent bubble did not seem to work well when watching videos because: (1) objects of interest that were obscured by the magnification scotoma 21 were missed; (2) there were delays in determining a new object of interest, which would probably be exacerbated by using a preferred retinal locus (PRL) 22,23 and (3) there may be difficulties making accurate saccades in the presence of crowding. 24,25 For these reasons, in the current study, we developed a novel Bubble Magnification technique that magnified the region or the centre of interest (COI) in a video clip. This intelligent magnification approach employed gaze information from viewers with NV to determine a temporally and spatially dynamic magnification centre of a video that we call the democratic COI (in the sense that the location comes from the decisions of a population sample).…”