This is the official code repository for the paper presented at ACM VRST 2025, titled "AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test", authored by Sihun Baek, Zhehan Qu, Maria Gorlatova. video link, paper link.
Despite the growing use of AR in safety-critical domains, the field lacks a systematic understanding of how different types of distraction affect user attention 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 attention and behavior.
Check the video below for demoonstration of AR-TMT. It includes all the stages of AR-TMT that we implmeneted. The link
The source code of AR-TMT is available here. AR-TMT containts the following scripts:
Scripts/AR-TMT
└───SharedInfomanager.cs ## Main script to manage the overall AR-TMT flow.
└───ShootingAction_controler.cs ## shooting action controller
└───TargetGenerator.cs ## Targets and Distractor generator
└───EyeTrackerLogger.cs ## Eye tracking data logger
└───DataTranmission.cs ## Data transmission function to a local computer
└───MarkerDetection.cs ## Aruco Marker Detector to initiate and locate the AR-TMT test
└───NoticeHandler.cs ## Stage Description UI handler
└───SelectionNoticeHandler.cs ## Main Selection UI handler
└───PlaneDetectionMarker.cs ## Aruco Marker Detector for panel detection
└───MotorSpeed Test ## VisuoMotorSpeed Test
└───MLcameraTest ## Egocentric Video Recorder
└───Questionnaire ## After-stage ratings
If you use this work, please cite:
@inproceedings{baek2025ar,
title={AR-TMT: Investigating the Impact of Distraction Types on Attention and Behavior in AR-based Trail Making Test},
author={Baek, Sihun and Qu, Zhehan and Gorlatova, Maria},
booktitle={Proceedings of the 2025 31st ACM Symposium on Virtual Reality Software and Technology},
pages={1--11},
year={2025}
}
For questions or collaboration, contact:
Sihun Baek
sihun.baek@duke.edu
Department of Electrical and Computer Engineering, Duke University
We thank the study's participants for their time in the data collection. This study was done in the Intelligent Interactive Internet of Things Lab at Duke University, and was approved by our institution's Institutional Review Board.
The authors of this repository are Sihun Baek, Zhehan Qu, and Maria Gorlatova. Contact Information of the authors:
- Sihun Baek (sihun.baek AT duke.edu)
- Zhehan Qu (zhehan.qu AT duke.edu)
- Maria Gorlatova (maria.gorlatova AT duke.edu)
This work was supported in part by NSF grants CSR-2312760, CNS-2112562, and IIS-2231975, NSF CAREER Award IIS-2046072, NSF NAIAD Award 2332744, a Cisco Research Award, a Meta Research Award, Defense Advanced Research Projects Agency Young Faculty Award HR0011-24-1-0001, and the Army Research Laboratory under Cooperative Agreement Number W911NF-23-2-0224. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency, the Army Research Laboratory, or the U.S. Government. This paper has been approved for public release; distribution is unlimited. No official endorsement should be inferred. The U.S.~Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.