Eye Gaze Metrics and Analysis of AOI for Indexing Working Memory towards Predicting ADHD

TL;DR

This study explores the use of eye gaze metrics during a working memory task to differentiate adults with ADHD from those without, aiming to improve diagnosis and understanding of attentional deficits.

Contribution

It introduces a novel approach combining eye tracking and machine learning to identify ADHD-specific features during working memory tasks.

Findings

Eye gaze metrics differ significantly between ADHD and non-ADHD adults.

Machine learning models can classify ADHD status based on gaze features.

Feasibility of using eye tracking for ADHD assessment is demonstrated.

Abstract

ADHD is being recognized as a diagnosis which persists into adulthood impacting economic, occupational, and educational outcomes. There is an increased need to accurately diagnose and recommend interventions for this population. One consideration is the development and implementation of reliable and valid outcome measures which reflect core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity when compared to their peers (Michalek et al., 2014). A reduction in working memory capacity indicates attentional control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as eye tracking technology, to generate a relationship between ADHD and measures of working memory capacity would be useful to advancing our understanding and treatment of the diagnosis in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a working memory capacity task for adults with and without 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.