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，眼动注视与扫视的特性具有高度随机性，其统计量常被用作认知多方面的代理指标。尽管区分扫视与注视并非易事，实验者通常在检测算法中使用通用的经验阈值。这种做法忽略了眼动动力学在任务间与个体间的变异性，并可能使所得统计量产生偏差。本文提出并评估了一种基于眼动动态马尔可夫近似的自适应方法，该方法将扫视与注视视为状态，并通过最小化状态转移来优化阈值。我们将此方法应用于三种常见的基于阈值的算法（速度、角速度与分散度），并以多阈值基准评估其整体准确性及对噪声的鲁棒性。研究发现，在自由观看与视觉搜索任务中，速度阈值获得了最高的基线准确率（90–93%）。然而，当阈值固定时，基于速度的方法在噪声下性能迅速下降，在高噪声水平下准确率低于20%。通过K比率最小化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。自适应分散度阈值表现出更优的噪声鲁棒性，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，但出现了精度-召回权衡，即以牺牲扫视识别为代价提升了注视检测的性能。除了展示我们简洁的自适应阈值方法外，这些发现还为根据数据质量和分析重点选择与调优分类算法提供了实用指导。