Conference proceeding
Eye Tracking-Based Workload and Performance Assessment for Skill Acquisition
Advances in Neuroergonomics and Cognitive Engineering, v 953, pp 129-141
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The result of training to improve in a given skill is most often demonstrated by an increase in the relevant performance measures. However, a complementary and at times more informative measure is the mental workload imposed on the performer when doing the task. While a number of varied methods exist for measuring workload, we have chosen to explore physiological and neurological correlates for their low amount of impact and interference on subjects during an experiment. In this study, participants trained on a six-task cognitive battery over four weeks while being simultaneously recorded with remote eye tracking and a host of other neurophysiological instruments. In this preliminary analysis, we found that measures of saccades, fixations, and pupil diameters significantly correlated with task performance over time and at different difficulties, indicating the validity of our task battery as well as the specificity of workload-related eye tracking measures.
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Details
- Title
- Eye Tracking-Based Workload and Performance Assessment for Skill Acquisition
- Creators
- Jesse Mark - Drexel University, School of Biomedical Engineering, Science, and Health SystemsAdrian Curtin - Drexel University, School of Biomedical Engineering, Science, and Health SystemsAmanda Kraft - Lockheed Martin (United States)Trevor Sands - Lockheed Martin (United States)William D. Casebeer - Lockheed Martin (United States)Matthias Ziegler - Lockheed Martin (United States)Hasan Ayaz - Drexel University, School of Biomedical Engineering, Science, and Health Systems
- Contributors
- H Ayaz (Editor) - Drexel University
- Publication Details
- Advances in Neuroergonomics and Cognitive Engineering, v 953, pp 129-141
- Series
- Advances in Intelligent Systems and Computing
- Publisher
- Springer Nature
- Number of pages
- 13
- Grant note
- FA8650-16-c-6764 / Air Force Research Laboratory
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000502759200014
- Scopus ID
- 2-s2.0-85067696340
- Other Identifier
- 991019168631204721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Neurosciences