Detection and Localization of Changes in Immersive Virtual Reality

Immersive visualization, i.e. the presentation of stimuli, data, and information with head-mounted displays and virtual reality (VR) techniques, is nowadays common in several application contexts. For effective use of such setups, it is worth studying if the attentional mechanisms are affected (improved or worsened) in any way, or if human performances in detecting changes are similar to what happens in the real world. Here, we focus on assessing the Visual Working Memory (VWM) in VR by using a change localization task, and on developing a computational model to account for experiment outcomes. In the change localization experiment, we have four factors: set size, spatial layout, visual angle, and observation time. The results show that there is a limit of the VWM capacity around \(7\pm 2\) items, as reported in the literature. The localization precision is affected by visual angle and observation time (p \(\,<\,\) 0.0001), only. The proposed model shows high agreement with the human data (r \(\,>\,\) 0.91 and p \(\,<\,\) 0.05).