It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach

Dewhurst, Richard; Nyström, Maria; Jarodzka, Halszka; Foulsham, Tom; Johansson, Roger; Holmqvist, Kenneth

doi:10.3758/s13428-012-0212-2

Cited by 132 publications

(124 citation statements)

References 53 publications

(61 reference statements)

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“…Although several approaches have been proposed for the quantitative comparison of scan-paths [56,57], only 10% of selected studies use these quantitative metrics to measure and compare scan-paths, 1400 possibly because scan-paths are inherently complex and there are major computational challenges in scan-path modeling and comparison [57].…”

Section: Discussionmentioning

confidence: 99%

A systematic literature review on the usage of eye-tracking in software engineering

Sharafi

Soh

Guéhéneuc

2015

Information and Software Technology

169

105

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Section: Discussionmentioning

confidence: 99%

A systematic literature review on the usage of eye-tracking in software engineering

Sharafi

Soh

Guéhéneuc

2015

Information and Software Technology

169

105

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“…The MultiMatch algorithm (Dewhurst et al, 2012;Jarodzka et al, 2010) is a more sophisticated vector-based method. It produces scanpath distances in various dimensions, such as location and duration.…”

Section: Geometric Scanpath Comparisonmentioning

confidence: 99%

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

et al. 2016

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Our eye movements are driven by a continuous trade-off between the need for detailed examination of objects of interest and the necessity to keep an overview of our surrounding. In consequence, behavioral patterns that are characteristic for our actions and their planning are typically manifested in the way we move our eyes to interact with our environment. Identifying such patterns from individual eye movement measurements is however highly challenging. In this work, we tackle the challenge of quantifying the influence of experimental factors on eye movement sequences. We introduce an algorithm for extracting sequence-sensitive features from eye movements and for the classification of eye movements based on the frequencies of small subsequences. Our approach is evaluated against the state-of-the art on a novel and a very rich collection of eye movements data derived from four experimental settings, from static viewing tasks to highly dynamic outdoor settings. Our results show that the proposed method is able to classify eye movement sequences over a variety of experimental designs. The choice of parameters is discussed in detail with special focus on highlighting different aspects of general scanpath shape. Algorithms and evaluation data are available at: www.ti.uni-tuebingen.de/scanpathcomparison.html.

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“…Many works have addressed the problem of measuring the similarity of scan paths produced by different subjects -or the same subject in different trials -observing the same scene under the same task (a lively research line, see the discussion by Dewhurst et al [29]). In contrast, the problem of modelling the variability of visual scan paths produced by human observers has hitherto been overlooked by most computational accounts [89,14].…”

Section: Generation Of Gaze Shiftsmentioning

confidence: 99%

Modelling Task-Dependent Eye Guidance to Objects in Pictures

et al. 2014

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We introduce a model of attentional eye guidance based on the rationale that the deployment of gaze is to be considered in the context of a general actionperception loop relying on two strictly intertwined processes: sensory processing, depending on current gaze position, identifies sources of information that are most valuable under the given task; motor processing links such information with the oculomotor act by sampling the next gaze position and thus performing the gaze shift. In such a framework, the choice of where to look next is task-dependent and oriented to classes of objects embedded within pictures of complex scenes. The dependence on task is taken into account by exploiting the value and the payoff of gazing at certain image patches or proto-objects that provide a sparse representation of the scene objects. The different levels of the actionperception loop are represented in probabilistic form and eventually give rise to a stochastic process that generates the gaze sequence. This way the model also accounts for statistical properties of gaze shifts such as individual scan path variability. Results of the simulations are compared either with experimental data

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It depends on how you look at it: Scanpath comparison in multiple dimensions with MultiMatch, a vector-based approach

Abstract: Eye movement sequences-or scanpaths-vary depending on the stimulus characteristics and the task

Cited by 132 publications

References 53 publications

A systematic literature review on the usage of eye-tracking in software engineering

A systematic literature review on the usage of eye-tracking in software engineering

SubsMatch 2.0: Scanpath comparison and classification based on subsequence frequencies

Modelling Task-Dependent Eye Guidance to Objects in Pictures

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