AI Article Synopsis
- The ongoing debate around accurately modeling gaze patterns poses challenges for analyzing eye-tracking data, but a Lévy flight model may offer a solution due to its scale-invariant characteristics.
- Our research indicates that children with ADHD show distinct differences in the characteristics of their eye-gaze patterns compared to non-ADHD children, reflecting a less optimal search strategy.
- Combining eye-tracking data with traditional cognitive tests could enhance ADHD diagnosis, as it allows for seamless data collection without additional time commitments.
Article Abstract
The precise mathematical description of gaze patterns remains a topic of ongoing debate, impacting the practical analysis of eye-tracking data. In this context, we present evidence supporting the appropriateness of a Lévy flight description for eye-gaze trajectories, emphasizing its beneficial scale-invariant properties. Our study focuses on utilizing these properties to aid in diagnosing Attention-Deficit and Hyperactivity Disorder (ADHD) in children, in conjunction with standard cognitive tests. Using this method, we found that the distribution of the characteristic exponent of Lévy flights statistically is different in children with ADHD. Furthermore, we observed that these children deviate from a strategy that is considered optimal for searching processes, in contrast to non-ADHD children. We focused on the case where both eye-tracking data and data from a cognitive test are present and show that the study of gaze patterns in children with ADHD can help in identifying this condition. Since eye-tracking data can be gathered during cognitive tests without needing extra time-consuming specific tasks, we argue that it is in a prime position to provide assistance in the arduous task of diagnosing ADHD.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11120544 | PMC |
| http://dx.doi.org/10.3390/e26050392 | DOI Listing |
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