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

**Authors:** Gavindya Jayawardena, Anne Michalek, Sampath Jayarathna  
**arXiv:** [1906.07183](https://arxiv.org/abs/1906.07183)  
**Date:** 2019-06-17  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/1906.07183v1/

## Key points

- Growing recognition of ADHD as a diagnosis that can persist into adulthood
- Impact on economic status, occupational opportunities, and educational outcomes
- Increasing need for accurate diagnosis and effective interventions for adults with ADHD
- Importance of developing reliable and valid outcome measures reflecting core diagnostic criteria
- Adults with ADHD often exhibit reduced working memory capacity, indicating attentional control deficits
- Exploration of innovative approaches like using eye tracking technology to establish a relationship between ADHD and working memory capacity
- Study by Jayawardena, Michalek, and Jayarathna on using eye tracking technology to measure eye gaze metrics during working memory tasks for adults with and without ADHD
- Application of machine learning algorithms to generate unique feature set for accurate diagnosis based on eye gaze patterns
- Potential utility of eye tracking technology in assessing working memory capacity in relation to ADHD diagnosis
- Importance of incorporating innovative approaches into diagnostic practices for adult ADHD
- Identification of distinct patterns in eye gaze metrics associated with working memory tasks may lead to more precise diagnostic tools and tailored interventions for individuals with ADHD

## Summary

In recent years, there has been a growing recognition of ADHD as a diagnosis that can persist into adulthood. This significantly impacts various aspects of individuals' lives such as economic status, occupational opportunities, and educational outcomes. As a result, there is an increasing need for accurate diagnosis and effective interventions for adults with ADHD. One crucial aspect in addressing this need is the development and implementation of reliable and valid outcome measures that accurately reflect the core diagnostic criteria of ADHD. Research has shown that adults with ADHD often exhibit reduced working memory capacity compared to their non-ADHD peers (Michalek et al., 2014). This reduction in working memory capacity is indicative of attentional control deficits, which align with many of the symptoms outlined on behavioral checklists commonly used to diagnose ADHD. To further advance our understanding and treatment of ADHD in adults, it is essential to explore innovative approaches such as using computational methods like eye tracking technology to establish a relationship between ADHD and measures of working memory capacity. In a recent study conducted by Gavindya Jayawardena, Anne Michalek, and Sampath Jayarathna titled "Eye Gaze Metrics and Analysis of AOI for Indexing Working Memory towards Predicting ADHD," the researchers aimed to investigate the feasibility of using eye tracking technology to measure eye gaze metrics during a working memory capacity task for adults both with and without ADHD. By applying machine learning algorithms, they sought to generate a unique feature set that could potentially aid in the accurate diagnosis of ADHD based on eye gaze patterns. The study's findings not only shed light on the potential utility of eye tracking technology in assessing working memory capacity in relation to ADHD but also highlight the importance of incorporating such innovative approaches into diagnostic practices for adult ADHD. By identifying distinct patterns in eye gaze metrics associated with working memory tasks, researchers may be able to develop more precise diagnostic tools and interventions tailored specifically to individuals with ADHD. Overall, this research contributes valuable insights into the intersection between eye gaze metrics, working memory capacity, and ADHD diagnosis in adults. The implications of this study extend beyond theoretical considerations to practical applications in clinical settings where accurate diagnosis and targeted interventions are crucial for improving outcomes for individuals living with adult ADHD.