# Detection of ADHD based on Eye Movements during Natural Viewing

arXiv: https://arxiv.org/abs/2207.01377 · HTML: https://mlsanity.com/paper/2207.01377/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Multimodal Integration of Human-Like Attention in Visual Question Answering](https://mlsanity.com/paper/2109.13139/) (2021)
- [An unexpected unity among methods for interpreting model predictions](https://mlsanity.com/paper/1611.07478/) (2016)

## Built on by (most cited first, 5 of 5)

- [Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading](https://mlsanity.com/paper/2304.10784/) (2023)
- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models](https://mlsanity.com/paper/2304.13536/) (2023)
- [Visual Attention Graph](https://mlsanity.com/paper/2503.08531/) (2025)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)

## Abstract (arXiv)

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that is highly prevalent and requires clinical specialists to diagnose. It is known that an individual's viewing behavior, reflected in their eye movements, is directly related to attentional mechanisms and higher-order cognitive processes. We therefore explore whether ADHD can be detected based on recorded eye movements together with information about the video stimulus in a free-viewing task. To this end, we develop an end-to-end deep learning-based sequence model which we pre-train on a related task for which more data are available. We find that the method is in fact able to detect ADHD and outperforms relevant baselines. We investigate the relevance of the input features in an ablation study. Interestingly, we find that the model's performance is closely related to the content of the video, which provides insights for future experimental designs.