Eye Movement Traits in Differentiating Experts and Laymen

Harężlak, Katarzyna; Kasprowski, Paweł; Kasprowska, Sabina

doi:10.1007/978-3-319-67792-7_9

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…The following division rule was applied: in each classifier run, N pp entropy maps-calculated for one participant and for one session-were always left for defining a test set. The number of samples in the training and test sets (N trs and N tss , respectively) may be expressed by means of formulas defined in Equations (6) and 7.…”

Section: Eye-movement Events Detectionmentioning

confidence: 99%

“…As a consequence, substantially more institutions have obtained a new tool for dealing with their quotidian tasks. For example, eye-movement technology may be used for things such as ascertaining the usability of various interface types [1,2], recognizing visual patterns when reading [3,4] or searching for interesting information, and differentiating between experts and novices [5,6]. Usage of eye-tracking solutions also plays an important role in medicine [7], psychology [8], and cognitive studies [9].…”

Section: Introductionmentioning

confidence: 99%

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An Analysis of Entropy-Based Eye Movement Events Detection

Harężlak

Kasprowski

2019

Entropy

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Analysis of eye movement has attracted a lot of attention recently in terms of exploring areas of people’s interest, cognitive ability, and skills. The basis for eye movement usage in these applications is the detection of its main components—namely, fixations and saccades, which facilitate understanding of the spatiotemporal processing of a visual scene. In the presented research, a novel approach for the detection of eye movement events is proposed, based on the concept of approximate entropy. By using the multiresolution time-domain scheme, a structure entitled the Multilevel Entropy Map was developed for this purpose. The dataset was collected during an experiment utilizing the “jumping point” paradigm. Eye positions were registered with a 1000 Hz sampling rate. For event detection, the knn classifier was applied. The best classification efficiency in recognizing the saccadic period ranged from 83% to 94%, depending on the sample size used. These promising outcomes suggest that the proposed solution may be used as a potential method for describing eye movement dynamics.

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Section: Eye-movement Events Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Analysis of Entropy-Based Eye Movement Events Detection

Harężlak

Kasprowski

2019

Entropy

Self Cite

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“…Exploring gaze behavior is a popular research method in many domains, since it can tell us how we are filtering information and how we might differ in our perception. Examples of between-group comparisons include differences in gaze behavior of experts and laymen (Bernal et al, 2014;Giovinco et al, 2014;Harezlak, Kasprowski, & Kasprowska, 2018), differences between elderly and younger people (Fontana et al, 2017) or differences in visual exploration due to native language (Stutterheim, Andermann, Carroll, Flecken, & Mertins, 2012). Besides comparisons of natural groups, differences in gaze behavior are studied subject to different experimental conditions like manual driving and highly automated driving (Navarro, Mara Stadler mara.stadler@helmholtz-muenchen.de Extended author information available on the last page of the article.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of dynamic eye-tracking experiments: Relative importance of visual stimulus elements for gaze behavior in the multi-group case

et al. 2021

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This paper presents a model that allows group comparisons of gaze behavior while watching dynamic video stimuli. The model is based on the approach of Coutrot and Guyader (2017) and allows linear combinations of feature maps to form a master saliency map. The feature maps in the model are, for example, the dynamically salient contents of a video stimulus or predetermined areas of interest. The model takes into account temporal aspects of the stimuli, which is a crucial difference to other common models. The multi-group extension of the model introduced here allows to obtain relative importance plots, which visualize the effect of a specific feature of a stimulus on the attention and visual behavior for two or more experimental groups. These plots are interpretable summaries of data with high spatial and temporal resolution. This approach differs from many common methods for comparing gaze behavior between natural groups, which usually only include single-dimensional features such as the duration of fixation on a particular part of the stimulus. The method is illustrated by contrasting a sample of a group of persons with particularly high cognitive abilities (high achievement on IQ tests) with a control group on a psycholinguistic task on the conceptualization of motion events. In the example, we find no substantive differences in relative importance, but more exploratory gaze behavior in the highly gifted group. The code, videos, and eye-tracking data we used for this study are available online.

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Eye Movement Traits in Differentiating Experts and Laymen

Cited by 2 publications

References 14 publications

An Analysis of Entropy-Based Eye Movement Events Detection

An Analysis of Entropy-Based Eye Movement Events Detection

Statistical modeling of dynamic eye-tracking experiments: Relative importance of visual stimulus elements for gaze behavior in the multi-group case

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