Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and behavioral patterns. Eye movement data offers a non-invasive diagnostic tool for ASD detection, as it is inherently discrete and exhibits short-term temporal dependencies, reflecting localized gaze focus between fixation points. These characteristics enable the data to provide deeper insights into subtle behavioral markers, distinguishing ASD-related patterns from typical development. Eye movement signals mainly contain short-term and localized dependencies. However, despite the widespread application of stacked attention layers in Transformer-based models for capturing long-range dependencies, our experimental results indicate that this approach yields only limited benefits when applied to eye movement data. This may be because discrete fixation points and short-term dependencies in gaze focus reduce the utility of global attention mechanisms, making them less efficient than architectures focusing on local temporal patterns. To efficiently capture subtle and complex eye movement patterns, distinguishing ASD from typically developing (TD) individuals, a discrete short-term sequential (DSTS) modeling framework is designed with Class-aware Representation and Imbalance-aware Mechanisms. Through extensive experiments on several eye movement datasets, DSTS outperforms both traditional machine learning techniques and more sophisticated deep learning models.

翻译：自闭症谱系障碍（ASD）是一种以社交沟通和行为模式缺陷为特征的神经发育障碍。眼动数据为ASD检测提供了一种非侵入性诊断工具，因其本质上是离散的，并呈现短期时间依赖性，反映了注视点之间的局部凝视焦点。这些特性使数据能够更深入地揭示细微的行为标记，从而区分ASD相关模式与典型发育模式。眼动信号主要包含短期和局部依赖性。然而，尽管基于Transformer的模型中堆叠注意力层在捕获长程依赖性方面得到广泛应用，但我们的实验结果表明，该方法应用于眼动数据时仅带来有限收益。这可能是因为离散的注视点及凝视焦点的短期依赖性降低了全局注意力机制的效用，使其效率低于专注于局部时间模式的架构。为有效捕捉细微且复杂的眼动模式以区分ASD与典型发育（TD）个体，本文设计了一种包含类别感知表征与不平衡感知机制的离散短期序列（DSTS）建模框架。通过在多个眼动数据集上的广泛实验，DSTS在性能上超越了传统机器学习技术和更复杂的深度学习模型。