Evaluating Preattentive Features for Detecting Changes in Virtual Environments

TL;DR

This study investigates how preattentive visual features influence change detection in immersive VR under varying visual complexity and spatial separation. Using a fully crossed within-subjects design across Feature Type (Depth, Size, Angle), Number of Features (Single/Double/Triple), and Separation (Isolated vs Grouped), it demonstrates that depth cues yield the most robust and rapid detection, especially as scene complexity grows. Increasing the number of features degrades performance and raises workload, while spatial isolation consistently improves detection accuracy and speed, particularly in cluttered scenes. The findings yield practical guidelines for VR and immersive analytics, emphasizing depth-based encodings, reduced clutter, and strategic spatial separation to optimize user performance and reduce cognitive load.

Abstract

Visual perception plays a critical role in detecting changes within immersive Virtual Reality (VR) environments. However, as visual complexity increases, perceptual performance declines, making it more difficult to detect changes quickly and accurately. This study examines how visual features, known for facilitating preattentive processing, impact a change detection task in immersive 3D environments, with a focus on visual complexity, object attributes, and spatial proximity. Our results demonstrate that preattentive processing enhances change detection, particularly when the altered object is spatially isolated and not perceptually grouped with similar surrounding objects. Changes to isolated objects were detected more reliably, suggesting that perceptual isolation reduces cognitive load and draws more attention. Conversely, when a changed object was surrounded by visually similar elements, participants were less likely to detect the change, indicating that perceptual grouping hinders individual object recognition in complex scenes. These results provide guidelines for designing VR applications that strategically utilize spatial isolation and visual features to improve the user experience.

Figures (8)

• Figure 1: A) Illustration of the experimental environment. A 6 $\times$ 6 grid-patterned board is placed 3.4 m or 4 m (depending on the depth condition) in front of each participant, with its height (at the center of the board) adjusted to match each participant's eye level. B) One of the objects is changed after a participant turns their head. Participants have to find and select one that has changed.
• Figure 2: Figure 3: The procedure for generating object composition. Starting with a grid of identical objects (1), one object was selected for the Isolated position (2), and a grouping region was defined away from it (3). Within this region, approximately half of the objects were randomly selected (4) and assigned different visual features from the non-selected objects (5). To ensure grouping, layouts were discarded and regenerated if any selected object lacked a neighboring selected object (4)-1.
• Figure 3: Detection time results show a significant interaction effect between Feature Type and Number of Features. Error bars represent standard errors of the mean. The two graphs visualize the same interaction effect using different axis mappings.
• Figure 4: Detection time results with 95% confidence interval. (A) Feature Type, (B) Number of Features, and (C) Separation. Pairwise comparisons of the main effects are indicated with brackets and asterisks: * ($p<.05$), ** ($p<.01$), and *** ($p<.001$).
• Figure 5: The timeout rate results in a 95% confidence interval. (A) Feature Type, (B) Number of Features, and (C) Separation. Pairwise comparisons of the main effects are indicated with brackets and asterisks: * ($p<.05$), ** ($p<.01$), and *** ($p<.001$).