AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test

2025-09-16

AI Summary

Prior research lacks a systematic understanding of how multimodal interference affects user behavior in augmented reality (AR), particularly in safety-critical contexts. Method: We developed an AR version of the Trail Making Test (AR-TMT) on Magic Leap 2, the first AR paradigm to classify interference into top-down, bottom-up, and spatial types based on guided search theory. Integrating eye-tracking, motion capture, and subjective workload assessment, we quantified their impacts on cognitive load, visual scanning, and motor performance. Results: Top-down interference induced semantic conflict; bottom-up interference delayed initial attentional orienting; spatial interference degraded oculomotor stability. Under object-related interference, individual differences in attentional control accounted for 20–35% of behavioral variance. This work establishes a theoretical foundation and methodological framework for human factors design and interference resilience evaluation in AR systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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.

Problem

• Investigating distraction types' impact on AR user attention
• Analyzing how distractions affect behavior in AR environments
• Understanding distraction mechanisms in safety-critical AR applications

Innovation

• AR adaptation of Trail Making Test
• Three distraction categories implementation
• Performance and behavior correlation analysis