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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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Nonlinear relationships between eye gaze and recognition accuracy for ethnic ingroup and outgroup faces.
Researchers have used eye-tracking measures to explore the relationship between face encoding and recognition, including the impact of ethnicity on this relationship. Previous studies offer a variety of conflicting conclusions. This confusion may stem from misestimation of the relationship between encoding and recognition. First, most previous models fail to account for the structure of eye-tracking data, potentially falling prey to Simpson’s paradox. Second, previous models assume a linear relationship between attention (e.g., the number of fixations to a to-be-remembered face) and recognition accuracy. Two eye-tracking studies (Ns = 41, 59), one online experiment that manipulates exposure (N = 150), and a mega-analysis examine the effects of ethnicity using what we believe to be more appropriate analytical models. Across studies and measures, we document a novel, critical pattern: The relationship between attention and recognition is nonlinear and negatively accelerating. At low levels of baseline attention, a small increment in attention improves recognition. However, as attention increases further, increments yield smaller and smaller benefits. This finding parallels work in learning and memory. In models that allow for nonlinearity, we find evidence that central features (eyes, nose, and mouth) generally contribute to recognition accuracy, potentially resolving disagreements in the field. We also find that the effects of attention on recognition are similar for ingroup and outgroup faces, which have important implications for theories of perceptual expertise.
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- PAR ID:
- 10595582
- Publisher / Repository:
- American Psychological Association
- Date Published:
- Journal Name:
- Journal of Personality and Social Psychology
- ISSN:
- 0022-3514
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1037/pspa0000395
