使用眼球追踪数据和机器学习方法预测图形位置
1Department of Cartography and Geomatics, Adam Mickiewicz University Poznan, 61-712 Poznan, Poland; p.cybulski@amu.edu.pl; Tel.: +48-61-829-6251.
Journal of eye movement research
|August 29, 2025
概括
眼睛跟踪数据和机器学习可以预测地图符号是中央还是外围的. 视线行为,特别是视线垂直分散, 是视觉搜索焦点的一个关键指标.
科学领域:
- 地图学
- 人与计算机的交互
- 认知科学
背景情况:
- 视觉搜索对于地图阅读至关重要,受地图设计和人类感知的影响.
- 了解符号位置 (中心与外围) 是有效地使用地图的关键.
- 眼睛追踪提供了在地图交互期间视觉注意力的客观数据.
研究的目的:
- 确定眼睛跟踪数据和机器学习是否可以预测地图符号的位置 (中心或外围).
- 确定哪些眼动特征最能预测视觉搜索焦点.
- 探索视觉感知地图界面的潜力.
主要方法:
- 从地图上的视觉搜索任务中分析两个眼睛跟踪数据集.
- 提取和标准化眼睛运动特征 (固定时间,动幅度,视线分散).
- 应用特征选择和机器学习分类算法 (例如,随机森林,梯度提升,AdaBoost).
主要成果:
- 在第一个数据集上,模型获得了高精度 (例如0.822) 和ROC-AUC (>0.86),其中AdaBoost和梯度提升表现最好.
- 第二个数据集提出了更多的分类挑战,尽管一些模型显示了高回忆率.
- 固定标准偏差 (垂直轴) 是最能预测视线分散和搜索焦点的特征.
结论:
- 视角行为,特别是垂直视角分散,可靠地表明在地图上视觉搜索的空间焦点.
- 机器学习模型可以有效地从眼睛跟踪数据中预测图形符号的位置.
- 这些发现支持适应性地图界面的开发,以提高可用性.
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