Assessing visual search performance using a novel dynamic naturalistic scene

Abstract

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.

Figure 1.

(A) Photograph of the apparatus, showing an individual viewing the virtual hallway simulation. (B) Visual target selection (the principal of the school) from four possible choices (balanced for gender and race).

Figure 2.

(Top row) Screen shots showing examples of the three levels of crowd density (low, medium, and high). (Bottom left) No-clutter condition in which task-irrelevant features and objects are absent. (Bottom right) Top–down view of the hallway showing the possible target walking paths. Black lines represent the initial start of a target's path into the hallway, dashed red lines are paths taken on the same side as the target entered, and solid yellow lines represent a path crossing in from of the viewer. Distance 1 represents the closest location and distance 4 the farthest, with an equivalent spacing between each of the four distances.

Figure 3.

Heat maps illustrating the extent of eye movement search patterns overlaid on top of representative examples of low, medium, and high crowd density levels of visual distractors. The color scheme of the heat maps represents differing levels of gaze data density across spatial regions of the screen space (yellow indicates more time looking in an area, and blue indicates less time). There is consistent clustering of eye movements across the levels of distractor density, reflecting a tight flame shape around the target as they locate and pursue. Note further dispersion of gaze data from the target as crowd density increases.

Figure 4.

Group mean (black solid lines) and individual mean (gray dashed lines) data are shown for the four performance outcomes of interest and across the three levels of crowd density. (A) Ellipse area, (B) gaze error, (C) reaction time, and (D) pursuit/saccade ratio. Standard errors are shown for the group means (some not visible).

Figure 5.

Group mean data are shown for the four performance outcomes of interest and are displayed across the three levels of crowd density and two levels of clutter presence. (A) Ellipse area, (B) gaze error, (C) reaction time, and (D) pursuit/saccade ratio. Standard errors are shown for group means.

Figure 6.

Group mean reaction time data are shown across the three levels of crowd density and broken down among the four distances at which the target can begin the walking path in the hallway. Standard errors are shown (some not visible).