Archive | 2019
Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity
Abstract
ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accu~ately diagnose this population through the development of reliable and vah~ outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used :o diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be ~seful to advancing our understanding and treatment of ADHD in adults. Thzs chapter will outline a feasibility study in which eye tracking was us_ed to measure eye gaze metrics during a WMC task for adults with and wzthout ADHD_and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study. DOI: I 0.4018/978-l-5225-7467-5.ch003 Copyrigh t © 2019, IOI Global. Copying or distributing in pri nt or elec tronic forms without written permi ssion of IGI Glohal is prohibited. predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity