Eye tracking (ET) technology is increasingly utilized to quantify visual behavior in the study of the development of domain-specific expertise. However, the identification and measurement of distinct gaze patterns using traditional ET metrics has been challenging, and the insights gained shown to be inconclusive about the nature of expert gaze behavior. In this article, we introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. We demonstrate the algorithm in a study where novice (n = 28) and expert (n = 2) eye movements were recorded in successive trials (n = 8), allowing us to verify whether similar patterns develop with increasing expertise. In the proposed approach, AOI sequences were transformed to string representation and processed using the k-mer method, a well-known method from the field of computational biology. Our results for expertise development suggest that basic tendencies are visible in traditional ET metrics, such as the fixation duration, but are much more evident for k-mers of k > 2. With increased on-task experience, the appearance of expert k-mer patterns in novice gaze sequences was shown to increase significantly (p < 0.001). The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.

眼动追踪 (ET) 技术越来越多地用于量化视觉行为，以研究特定领域的专业知识的发展。然而，使用传统的 ET 指标识别和测量不同的注视模式一直具有挑战性，并且所获得的见解对于专家注视行为的性质尚无定论。在本文中，我们介绍了一种用于提取与对象相关的凝视序列的算法方法，并通过在简化飞机装配任务的多次试验研究期间调查凝视序列模式的发展来确定与任务相关的专业知识。我们在一项研究中演示了该算法，其中新手 ( n  = 28) 和专家 ( n  = 2) 眼球运动在连续试验 ( n = 8），使我们能够验证类似的模式是否随着专业知识的增加而发展。在所提出的方法中，AOI 序列被转换为字符串表示，并使用计算生物学领域的著名方法k -mer 方法进行处理。我们的专业发展结果表明，基本趋势在传统的 ET 指标中是可见的，例如固定持续时间，但对于k > 2 的k - mer更为明显。随着任务经验的增加，专家k -mer的出现新手注视序列的模式显着增加（p  < 0.001）。结果说明多试k-mer 方法适用于揭示特定的认知过程，并且可以使用包含空间和时间信息的注视模式来量化学习进度，这可以为新手培训和专家评估提供有价值的工具。