Properties of ocular fixations and saccades are highly stochastic during many experimental tasks, and their statistics are often used as proxies for various aspects of cognition. Although distinguishing saccades from fixations is not trivial, experimentalists generally use common ad-hoc thresholds in detection algorithms. This neglects inter-task and inter-individual variability in oculomotor dynamics, and potentially biases the resulting statistics. In this article, we introduce and evaluate an adaptive method based on a Markovian approximation of eye-gaze dynamics, using saccades and fixations as states such that the optimal threshold minimizes state transitions. Applying this to three common threshold-based algorithms (velocity, angular velocity, and dispersion), we evaluate the overall accuracy against a multi-threshold benchmark as well as robustness to noise. We find that a velocity threshold achieves the highest baseline accuracy (90-93\%) across both free-viewing and visual search tasks. However, velocity-based methods degrade rapidly under noise when thresholds remain fixed, with accuracy falling below 20% at high noise levels. Adaptive threshold optimization via K-ratio minimization substantially improves performance under noisy conditions for all algorithms. Adaptive dispersion thresholds demonstrate superior noise robustness, maintaining accuracy above 81% even at extreme noise levels (σ = 50 px), though a precision-recall trade-off emerges that favors fixation detection at the expense of saccade identification. In addition to demonstrating our parsimonious adaptive thresholding method, these findings provide practical guidance for selecting and tuning classification algorithms based on data quality and analytical priorities.

翻译：在许多实验任务中，视觉注视与眼跳的特性具有高度随机性，其统计特征常被用作认知过程多维度表征的代理指标。尽管区分眼跳与注视并非易事，实验人员通常使用通用经验阈值进行检测算法处理。这种做法忽视了任务间和个体间眼动动力学的差异性，可能导致统计结果出现偏差。本文提出并评估了一种基于眼球运动马尔可夫近似的自适应方法——以眼跳与注视作为隐状态，通过最小化状态转移次数确定最优阈值。将该方法应用于三种常见阈值算法（速度阈值、角速度阈值与离散度阈值）后，我们采用多阈值基准评估了整体准确率及噪声鲁棒性。研究发现，在自由观察与视觉搜索任务中，速度阈值实现了最高的基线准确率（90-93%）。然而，当阈值保持固定时，基于速度的方法在噪声干扰下性能急剧下降，高噪声水平下准确率低于20%。通过K比率最小化实现的自适应阈值优化显著提升了所有算法在噪声条件下的表现。自适应离散度阈值展现出优异的噪声鲁棒性，即使在极端噪声水平（σ=50像素）下仍能保持81%以上的准确率，但出现了以牺牲眼跳识别为代价优先检测注视的精度-召回率权衡现象。本研究不仅验证了简约型自适应阈值方法的有效性，更基于数据质量与分析优先级为分类算法的选择与调优提供了实践指导。