Does display type matter for change detection? comparing immersive and non-immersive displays under low and high semantic availability

Change detection is a cognitively challenging process that involves three stages: spotting (becoming aware of a change); localising (establishing the specific location of the change); and identifying (recognising the nature of the change). Each of these stages has the potential to be influenced by both the way the data is presented (e.g., display type) and the fidelity of that data. To explore these issues, we conducted two studies, both of which looked at the effects of display type (immersive virtual reality (VR) or desktop monitor (DM)), and the semantic availability of the scene (low or high realism). Study 1 ( N = 38 ) explored the VR–DM differences in a broad scope, which examined six change types spanning both spatial and non-spatial changes— disappear , appear , translation , rotation , replacement , and colour . However, there were no significant differences between VR and DM in spotting, localising, and identifying at either level of (semantic) realism. Study 2 ( N = 20 ) followed this up by exploring only two types of spatial change ( translation and rotation ) at a much finer degree of granularity while retaining the same experimental paradigm with necessary refinement. Study 2 showed a significant VR advantage over DM, with different patterns across realism conditions: In low-realism scenes, VR significantly outperformed DM on localisation and change-type identification overall, with the largest VR–DM contrasts observed for the smallest translations . In high-realism scenes, the only significant effect was a display-by-magnitude interaction for change-type identification at the smallest translations . Taken both studies together, VR benefits are most likely for subtle spatial changes, particularly small translations , when the semantic availability is limited. Questionnaire ratings also suggested that reliance on visual features varies with semantic availability. Semantic cues were rated significantly higher than other features in high realism scenes only. Finally, there is no significant difference between VR and DM in terms of workload, motion sickness and self-confidence, suggesting that the perceptual advantages of VR come with no additional physical or cognitive costs for change detection. • Examined change detection in VR vs desktop under different semantic availability. • Covered spotting, localisation, and identification comprehensively across two studies. • VR improved localisation and change-type identification in low-realism scenes. • VR advantage is most pronounced for small spatial-translation changes. • Visual feature priority adapts; semantics dominate when available.