Trends in Eye Tracking Scanpaths

Eraslan, Şükrü; Yeşi̇lada, Yeli̇z; Harper, Simon

doi:10.1145/2914586.2914591

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…This analysis provides a trending scanpath 2 of multiple users in terms of visual elements of a web page. The trending scanpath term here is referred to as the most commonly followed scanpath which is developed by multiple users [Eraslan et al 2016b]. The STA also differs from other existing clustering approaches in the context of sequential data clustering, such as discovering interesting places by analysing multiple sequences [Palma et al 2008], identifying elementary parts within a single sequence [Leiva and Vidal 2013], discovering common sub-sequences by analysing multiple sequences [Lee et al 2007; Anagnostopoulos et al 2006].…”

Section: Introductionmentioning

confidence: 99%

Scanpath Trend Analysis on Web Pages

2016

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Eye tracking studies have widely been used in improving the design and usability of web pages, and in the research of understanding how users navigate them. However, there is limited research in clustering users' eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist which means the resulting path is so short that is not useful. Moreover, there is little work on correlating users' scanpaths with visual elements of web pages and the underlying source code which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called Scanpath Trend Analysis (STA) which does not only consider the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically does this trend analysis to identify a trending scanpath of multiple web users in terms of visual elements of a web page. In contrast to existing research, the STA algorithm first analyses mostly visited visual elements in given scanpaths, then clusters the scanpaths by using these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study on six web pages for two different kinds of tasks, and therefore 12 cases in total. Our experimental results show that the STA algorithm generates a trending scanpath that addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all the cases. Based on the statistical tests, the STA algorithm also generates a trending scanpath which is significantly more similar to the inputted scanpaths compared to other existing work in 10 out of 12 cases. In the rest of the cases, the STA algorithm still performs significantly better than some of other existing work. This algorithm contributes to behaviour analysis research on the web which can be used for different purposes, for example re-engineering web pages guided by the trending scanpath to improve users' experience or guiding designers to improve their design.

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Section: Introductionmentioning

confidence: 99%

Scanpath Trend Analysis on Web Pages

2016

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“…The STA algorithm was evaluated by comparing its resultant paths with the resultant paths of other similar algorithms by using different AOI detection approaches [18,19]. The evaluation shows that the resultant path of the STA algorithm is the most similar one to individual scan paths, thus it discovers the most representative path.…”

Section: Discussionmentioning

confidence: 99%

Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web

Matthews

Eraslan²,

Yaneva

et al. 2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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People with autism often exhibit different visual behaviours from neurotypical users. To explore how these differences are exhibited on the Web, we model visual behaviour by combining pupillary response, which is an unobtrusive measure of physiological arousal, with eye-tracking scan paths that indicate visual attention. We evaluated our approach with two populations: 19 neurotypical users and 19 users with autism. We observe differences in their visual behaviours as, in certain instances, individuals with autism exhibit a lower arousal response to affective contents. While this is consistent with the literature on autism, we confirm this phenomenon on the Web. We discuss how our modelling method can be used to identify possible UX issues such as the presence of stress, cognitive load and differences in the perception of Web elements in relation to physiological arousal.

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“…Due to the high degree of viewing freedom, it is hard to define ground truth representative scanpaths. The only way to evaluate the rationality of the obtained scanpath is to compare it against each individual scanpath with the standard string-edit algorithm as suggested by Eraslan et al (2016bEraslan et al ( , 2016c. More sophisticated methods to compare scanpaths like ScanMatch (Cristino et al, 2010), Mul-tiMatch (Jarodzka, Holmqvist, & Nyström, 2010), and ScanGraph (Dolezalova & Popelka, 2016) are also be developed, facilitating the evaluation.…”

Section: Methodsmentioning

confidence: 99%

“…However, to get the group viewing pattern, we need to take into account all individual scanpaths rather than focus on a single one, like the identified scanpath in Figure 1(b), which we call representative scanpath. The surge of interest in dynamic vis-ual attention gives rise to various methods for representative scanpaths identification, most of which either stem from sequence mining algorithms or target a specific category of visual stimuli such as web pages (Eraslan, Yesilada & Harper, 2014, 2016a, 2016b, 2016c, 2017a, 2017b. So they have limitations when applied to analyze scanpaths.…”

Section: Related Workmentioning

confidence: 99%

Representative Scanpath Identification for Group Viewing Pattern Analysis

Chen

2018

JEMR

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Scanpaths are composed of fixations and saccades. Viewing trends reflected by scanpaths play an important role in scientific studies like saccadic model evaluation and real-life applications like artistic design. Several scanpath synthesis methods have been proposed to obtain a scanpath that is representative of the group viewing trend. But most of them either target a specific category of viewing materials like webpages or leave out some useful information like gaze duration. Our previous work defined the representative scanpath as the barycenter of a group of scanpaths, which actually shows the averaged shape of multiple scanpaths. In this paper, we extend our previous framework to take gaze duration into account, obtaining representative scanpaths that describe not only attention distribution and shift but also attention span. The extended framework consists of three steps: Eye-gaze data preprocessing, scanpath aggregation and gaze duration analysis. Experiments demonstrate that the framework can well serve the purpose of mining viewing patterns and “barycenter” based representative scanpaths can better characterize the pattern.

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Trends in Eye Tracking Scanpaths

Cited by 7 publications

References 20 publications

Scanpath Trend Analysis on Web Pages

Scanpath Trend Analysis on Web Pages

Combining Trending Scan Paths with Arousal to Model Visual Behaviour on the Web

Representative Scanpath Identification for Group Viewing Pattern Analysis

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