研究眼动类型与个体差异对注视预测的影响，发现不同人、不同眼动模式下预测效果差异显著。
Gaze Prediction as a Function of Eye Movement Type and Individual Differences
- 对比三种模型在不同眼动类型上的表现，识别个体差异影响
- 固定注视时噪声越大，预测越差；快速扫视速度越高，预测越弱
- 建议未来研究报告个体差异数据，模型设计应降低人与人之间的差异
眼动预测是提升眼动追踪系统性能与用户体验的有前景方向。本研究分析了个体差异对注视预测性能的影响。采用三种不同机制的模型：轻量级长短期记忆网络（LSTM）、基于变换器的多变量时间序列表征学习模型（TST），以及嵌入卡尔曼滤波框架的视动植物数学模型（OPKF）。各模型在不同眼动类型上进行评估。结果显示，所有模型在不同眼动类型下均存在显著个体差异。固定注视时，注视噪声与预测性能下降相关；扫视时，速度越高，预测表现越差。我们认为这些个体差异至关重要，建议未来研究报告跨被试变异统计量，并提出未来模型设计应致力于减少个体间差异。
原文摘要 · Abstract (English)
Eye movement prediction is a promising area of research with the potential to improve performance and the user experience of systems based on eye-tracking technology. In this study, we analyze individual differences in gaze prediction performance. We use three fundamentally different models within the analysis: the lightweight Long Short-Term Memory network (LSTM), the transformer-based network for multivariate time series representation learning (TST), and the Oculomotor Plant Mathematical Model wrapped in the Kalman Filter framework (OPKF). Each solution was assessed on different eye-movement types. We show important subject-to-subject variation for all models and eye-movement types. We found that fixation noise is associated with poorer gaze prediction in fixation. For saccades, higher velocities are associated with poorer gaze prediction performance. We think these individual differences are important and propose that future research should report statistics related to inter-subject variation. We also propose that future models should be designed to reduce subject-to-subject variation.
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