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Virtual reality (VR) simulations have been adopted to provide controllable environments for running augmented reality (AR) experiments in diverse scenarios. However, insufficient research has explored the impact of AR applications on users, especially their attention patterns, and whether VR simulations accurately replicate these effects. In this work, we propose to analyze user attention patterns via eye tracking during XR usage. To represent applications that provide both helpful guidance and irrelevant information, we built a Sudoku Helper app that includes visual hints and potential distractions during the puzzle-solving period. We conducted two user studies with 19 different users each in AR and VR, in which we collected eye tracking data, conducted gaze-based analysis, and trained machine learning (ML) models to predict user attentional states and attention control ability. Our results show that the AR app had a statistically significant impact on enhancing attention by increasing the fixated proportion of time, while the VR app reduced fixated time and made the users less focused. Results indicate that there is a discrepancy between VR simulations and the AR experience. Our ML models achieve 99.3% and 96.3% accuracy in predicting user attention control ability in AR and VR, respectively. A noticeable performance drop when transferring models trained on one medium to the other further highlights the gap between the AR experience and the VR simulation of it.
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Integrating Real-World Distractions into Virtual Reality
With the proliferation of consumer-level virtual reality (VR) devices, users started experiencing VR in less controlled environments, such as in social gatherings and public areas. While the current VR hardware provides an increasingly immersive experience, it ignores stimuli originating from the physical surroundings that distract users from the VR experience. To block distractions from the outside world, many users wear noise-canceling headphones. However, this is insufficient to block loud or transient sounds (e.g., drilling or hammering) and, especially, multi-modal distractions (e.g., air drafts, temperature shifts from an A/C, construction vibrations, or food smells). To tackle this, we explore a new concept, where we directly integrate the distracting stimuli from the user's physical surroundings into their virtual reality experience to enhance presence. Using our approach, an otherwise distracting wind gust can be directly mapped to the sway of trees in a VR experience that already contains trees. Using our novel approach, we demonstrate how to integrate a range of distractive stimuli into the VR experience, such as haptics (temperature, vibrations, touch), sounds, and smells. To validate our approach, we conducted three user studies and a technical evaluation. First, to validate our key principle, we conducted a controlled study where participants were exposed to distractions while playing a VR game. We found that our approach improved users’ sense of presence, compared to wearing noise-canceling headphones. From these results, we engineered a sensing module that detects a set of simple distractive signals (e.g., sounds, winds, and temperature shifts). We validated our hardware in a technical evaluation and in an out-of-lab study where participants played VR games in an uncontrolled environment. Moreover, to gather the perspective of VR content creators that might one day utilize a system inspired by our findings, we invited game designers to use our approach and collected their feedback and VR designs. Finally, we present design considerations for mapping distracting external stimuli and discuss ethical considerations of integrating real-world stimuli into virtual reality.
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- Award ID(s):
- 2047189
- PAR ID:
- 10695321
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9781450393201
- Page Range / eLocation ID:
- 1 to 16
- Format(s):
- Medium: X
- Location:
- Bend OR USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3526113.3545682
