Scanpath comparisons for complex visual search in a naturalistic environment
Scanpath comparisons for complex visual search in a naturalistic environment
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DOI：
10.3758/s13428-018-1154-0
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发表时间：
2019-06-01
影响因子：
5.4
通讯作者：
Maresca, Anna M.
中科院分区：
文献类型：
--
作者：
Frame, Mary E.;Warren, Rik;Maresca, Anna M.
自然主义的监视任务提供了丰富的眼动追踪数据来源。由于感兴趣事件的时间不规则性，在监视研究中使用标准眼动跟踪分析技术（例如扫视频率或眨眼率）进行有意义的比较可能具有挑战性。自然主义的研究环境提出了独特的挑战，如需要专业或专家分析师，小样本量，长时间的数据收集会话。这些限制需要丰富的数据和复杂的分析，特别是在规定性的自然环境中，必须彻底了解问题，以实施有效和实用的解决方案。使用一个小样本的专家监测分析师和一个相等大小的样本的新手，我们计算扫描路径相似性的各种监测数据使用ScanMatch Matlab工具。ScanMatch实现了最初为DNA蛋白质序列比较开发的算法，并根据两个扫描路径的形态以及在感兴趣区域的持续时间（可选）为它们提供相似性得分。专家和新手表现出平等的停留时间的目标，无论识别精度和两个样本显示较高的扫描路径一致性作为一个功能的目标类型，而不是个别科目显示特定的扫描路径偏好的参与者。我们的研究结果表明，扫描路径分析可以利用作为一个非常有效的基于计算机的方法来表征监控识别错误，并指导实施的解决方案。相似性分数还可以提供对指导视觉搜索的过程的洞察。
Naturalistic surveillance tasks provide a rich source of eye-tracking data. It can be challenging to make meaningful comparisons using standard eye-tracking analysis techniques such as saccade frequency or blink rate in surveillance studies due to the temporal irregularity of events of interest. Naturalistic research environments present unique challenges, such as requiring specialized or expert analysts, small sample size, and long data collection sessions. These constraints demand rich data and sophisticated analyses, particularly in prescriptive naturalistic environments where problems must be thoroughly understood to implement effective and practical solutions. Using a small sample of expert surveillance analysts and an equal-sized sample of novices, we computed scanpath similarity on a variety of surveillance data using the ScanMatch Matlab tool. ScanMatch implements an algorithm initially developed for DNA protein sequence comparisons and provides a similarity score for two scanpaths based on their morphology and, optionally, duration in an area of interest. Both experts and novices showed equal dwell time on targets regardless of identification accuracy and both samples showed higher scanpath consistency across participants as a function of target type rather than individual subjects showing a particular scanpath preference. Our results show that scanpath analysis can be leveraged as a highly effective computer-based methodology to characterize surveillance identification errors and guide the implementation of solutions. Similarity scores can also provide insight into processes guiding visual search.