Christopher R. Bennett; Peter J. Bex; Lotfi B. Merabet

Assessing visual search performance using a novel dynamic naturalistic scene

publication date
January 2021
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Daily activities require the constant searching and tracking of visual targets in dynamic and complex scenes. Classic work assessing visual search performance has been dominated by the use of simple geometric shapes, patterns, and static backgrounds. Recently, there has been a shift toward investigating visual search in more naturalistic dynamic scenes using virtual reality (VR)-based paradigms. In this direction, we have developed a first-person perspective VR environment combined with eye tracking for the capture of a variety of objective measures. Participants were instructed to search for a preselected human target walking in a crowded hallway setting. Performance was quantified based on saccade and smooth pursuit ocular motor behavior. To assess the effect of task difficulty, we manipulated factors of the visual scene, including crowd density (i.e., number of surrounding distractors) and the presence of environmental clutter. In general, results showed a pattern of worsening performance with increasing crowd density. In contrast, the presence of visual clutter had no effect. These results demonstrate how visual search performance can be investigated using VR-based naturalistic dynamic scenes and with high behavioral relevance. This engaging platform may also have utility in assessing visual search in a variety of clinical populations of interest.


The present work investigates dynamic visual search performance using a VR-based task combined with eye tracking in a sample of participants with neurotypical development. Our approach incorporates control of task features that are easy to understand and implement. Furthermore, their effects on search performance can be characterized through an analysis of ocular motor behavior in a naturalistic dynamic visual scene. The high behavioral relevance of the task may also find utility in assessing functional visual performance in other clinical populations of interest (Parsons, 2015; Parsons et al., 2017).

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