Abstract
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.
Highlights
Advances in the technology of eye tracking (ET) have provided us with a deeper understanding of specific cognitive processes and the development of perceptual expertise
While the fixation duration seems to increase linearly for all participants, the standard deviation is shown to be in a similar order of magnitude, which indicates a high variation between individual participants in each group
This trend was shown for area of interest (AOI) building area (p = 0.081) and manual (p = 0.236), regardless of whether the task was learned on the simpler or the more complex stimulus, but showed significant differences for AOI bricks (p = 0.034)
Summary
Advances in the technology of eye tracking (ET) have provided us with a deeper understanding of specific cognitive processes and the development of perceptual expertise. Using traditional ET metrics, the majority of studies have focused on the analysis of visual expertise by investigating the eye movements of individuals in a wide range of domains, such as teaching (McIntyre & Foulsham, 2018a), medicine (Castner et al, 2020; Fox & Faulkne-Jones, 2017; van der Gijp et al, 2017) or aviation (Haslbeck & Zhang, 2017) These studies use ET as summary statistics, such as fixation duration, dwell time duration, fixation count, or time to first fixation to analyze expertise specific gaze behavior (Cristino et al, 2010; Kanan et al, 2015; Ooms et al., 2012). Studies using summary statistics have often generalized their findings, claiming that experts experience fewer fixations and shorter fixation durations, even though it has been established that expertise is highly domain-specific (Chi, 2006). Jarodzka and Boshuizen (2017) concluded that the reported measures are too reductionist to capture interesting insights into the nature of task- and stimuli-specific expert behaviors
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