AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909791840894976 |
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| author | Baek, Sihun Qu, Zhehan Gorlatova, Maria |
| author_facet | Baek, Sihun Qu, Zhehan Gorlatova, Maria |
| contents | Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe's Guided Search model, and captured performance, gaze, motor behavior, and subjective load measures to analyze user attention and behavior. A user study with 34 participants revealed that top-down distraction degraded performance through semantic interference, while bottom-up distraction disrupted initial attentional engagement. Spatial distraction destabilized gaze behavior, leading to more scattered and less structured visual scanning patterns. We also found that performance was correlated with attention control ($R^2 = .20$--$.35$) under object-based distraction conditions, where distractors possessed task-relevant features. The study offers insights into distraction mechanisms and their impact on users, providing opportunities for generalization to ecologically relevant AR tasks while underscoring the need to address the unique demands of AR environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test Baek, Sihun Qu, Zhehan Gorlatova, Maria Human-Computer Interaction Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user behavior in AR environments. To address this gap, we present AR-TMT, an AR adaptation of the Trail Making Test that spatially renders targets for sequential selection on the Magic Leap 2. We implemented distractions in three categories: top-down, bottom-up, and spatial distraction based on Wolfe's Guided Search model, and captured performance, gaze, motor behavior, and subjective load measures to analyze user attention and behavior. A user study with 34 participants revealed that top-down distraction degraded performance through semantic interference, while bottom-up distraction disrupted initial attentional engagement. Spatial distraction destabilized gaze behavior, leading to more scattered and less structured visual scanning patterns. We also found that performance was correlated with attention control ($R^2 = .20$--$.35$) under object-based distraction conditions, where distractors possessed task-relevant features. The study offers insights into distraction mechanisms and their impact on users, providing opportunities for generalization to ecologically relevant AR tasks while underscoring the need to address the unique demands of AR environments. |
| title | AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.13468 |