From gaze plots to eye fixation patterns using a clustering method based on Hausdorff distances

Drusch, Gautier; Bastien, J. M. Christian; Dinet, Jérôme

doi:10.1145/2044354.2044356

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In order to characterize and to compare scanpaths so as to uncover possible scan-patterns, Drusch and Bastien [7] has developed a clustering method based on the Hausdorff distance. The results have shown that scan-patterns could be identified: groups of users have been identified and their behaviors have been described with diverse eye-tracking metrics.…”

Section: 14mentioning

confidence: 99%

Analyzing Web pages visual scanpaths: between and within tasks variability

Drusch¹,

Bastien²

2012

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In this paper, we propose a new method for comparing scanpaths in a bottom-up approach, and a test of the scanpath theory. To do so, we conducted a laboratory experiment in which 113 participants were invited to accomplish a set of tasks on two different websites. For each site, they had to perform two tasks that had to be repeated ounce. The data were analyzed using a procedure similar to the one used by Duchowski et al. [8]. The first step was to automatically identify, then label, AOIs with the mean-shift clustering procedure [19]. Then, scanpaths were compared two by two with a modified version of the string-edit method, which take into account the order of AOIs visualizations [2]. Our results show that scanpaths variability between tasks but within participants seems to be lower than the variability within task for a given participant. In other words participants seem to be more coherent when they perform different tasks, than when they repeat the same tasks. In addition, participants view more of the same AOI when they perform a different task on the same Web page than when they repeated the same task. These results are quite different from what predicts the scanpath theory.

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Section: 14mentioning

confidence: 99%

Analyzing Web pages visual scanpaths: between and within tasks variability

Drusch¹,

Bastien²

2012

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“…The datasets generated and/or analyzed during the current study are available in the Mendeley repository titled 'Data for: Implementation of Real-Time Online Mouse Tracking Case Study in a Small Online Quiz' in 'Pre-processed Mouse Tracking Data' directory [61]: Tables 3, 5, 6 data are available in file mouse_tracking_mongolia_anonymous.ods [62]. Figures 8,9,10 and Table 4 are based on files: cpurammousetracking.txt [63], cpurampagetracking.txt [64], cpuramroitracking.txt [65], mousetrackingsimulationpcapng [66], pagetrackingsimulationpcapng [67], roitrackingsimulationpcapng [68].…”

Section: Abbreviationsmentioning

confidence: 99%

“…The fundamental strategy of mouse tracking is the recording of mouse clicks, mouse movements, and scrolls. Eye and mouse tracking have been implemented in the fields of education [8,9], reading patterns [10], search engines [11], visual navigation [12], web evaluation and usability [13,14]. Mouse tracking can be treated either independently [15] or as a correlation to eye tracking [16] in other words, as replacement.…”

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confidence: 99%

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Purnama

Usagawa

2020

J Big Data

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IntroductionWe are living in the age of information where information and communication technologies are becoming mainstream [1]. Computers and the Internet have become inseparable from our daily lives. It is now possible to do many things without the need to travel and wait; examples include, long-distance communication through online text, voice and video calls, social interaction via social media, online learning, ecommerce or online shopping, and many forms of entertainments such as online music, online videos, and online games. Most importantly, the Internet has become the primary source of information around the world.Online analytics has become a major area of interest because it enables the study of users' online behaviors. These include, users' attentions and, students' performance in online learning. Businesses also use online analytics to capture consumers' interests in AbstractPageview is the most popular webpage analytic metric in all sectors including blogs, business, e-commerce, education, entertainment, research, social media, and technology. To perform deeper analysis, additional methods are required such as mouse tracking, which can help researchers understand online user behavior on a single webpage. However, the geometrical data generated by mouse tracking are extremely large, and qualify as big data. A single swipe on a webpage from left to right can generate a megabyte (MB) of data. Fortunately, the geometrical data of each x and y point of the mouse trail are not always needed. Sometimes, analysts only need the heat map of a certain area or perhaps just a summary of the number of activities that occurred on a webpage. Therefore, recording all geometrical data is sometimes unnecessary. This work introduces preprocessing during real-time and online mouse tracking sessions. The preprocessing that is introduced converts the geometrical data from each x and y point to a region-of-interest concentration, in other words only heat map areas that the analyzer is interested in. Ultimately, the approach used here is able to greatly reduce the storage and transmission cost of real-time online mouse tracking.

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“…Josephson and Holmes [45] had mixed results in their effort to identify gaze patterns when subjects viewed a webpage. Drusch et al [46] also investigated scanpaths for webpages and identified two groups, from 91 subjects, with similar scanpaths using Hausdorff distance metrics. The empirical contribution of these studies is the documentation of a multi-stage gaze or search strategy that precedes purchase decision; however, none of these studies specified particular gaze sequences that guided this process and led to product choice.…”

Section: Plos Onementioning

confidence: 99%

Seeing through the forest: The gaze path to purchase

et al. 2020

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Eye tracking studies have analyzed the relationship between visual attention to point of purchase marketing elements (price, signage, etc.) and purchase intention. Our study is the first to investigate the relationship between the gaze sequence in which consumers view a display (including gaze aversion away from products) and the influence of consumer (top down) characteristics on product choice. We conducted an in-lab 3 (display size: large, moderate, small) X 2 (price: sale, non-sale) within-subject experiment with 92 persons. After viewing the displays, subjects completed an online survey to provide demographic data, self-reported and actual product knowledge, and past purchase information. We employed a random forest machine learning approach via R software to analyze all possible three-unit subsequences of gaze fixations. Models comparing multiclass F1-macro score and F1micro score of product choice were analyzed. Gaze sequence models that included gaze aversion more accurately predicted product choice in a lab setting for more complex displays. Inclusion of consumer characteristics generally improved model predictive F1-macro and F1-micro scores for less complex displays with fewer plant sizes Consumer attributes that helped improve model prediction performance were product expertise, ethnicity, and previous plant purchases.

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From gaze plots to eye fixation patterns using a clustering method based on Hausdorff distances

Cited by 10 publications

References 13 publications

Analyzing Web pages visual scanpaths: between and within tasks variability

Analyzing Web pages visual scanpaths: between and within tasks variability

Using real-time online preprocessed mouse tracking for lower storage and transmission costs

Seeing through the forest: The gaze path to purchase

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