Eye tracking scanpath analysis techniques on web pages: A survey, evaluation and comparison

Eye tracking has commonly been used to investigate how users interact with web pages, with the goal of improving their usability. This article comprehensively revisits the techniques that could be applicable to eye tracking data for analysing user scanpaths on web pages. It also uses a third-party eye tracking study to compare these techniques. This allows researchers to recognise existing techniques for their goals, understand how they work and know their strengths and limitations so that they can make an efficient choice for their studies. These techniques can mainly be used for calculating similarities/dissimilarities between scanpaths, computing transition probabilities between web page elements, detecting patterns in scanpaths and identifying common scanpaths. The scanpath analysis techniques are classified into four groups by their goals so that researchers can directly focus on the appropriate techniques for a sequential analysis of user scanpaths on web pages. This article also suggests dealing with the limitations of these techniques by pre-processing eye tracking data, considering cognitive processing and addressing their reductionist approach.

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The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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Understanding Eye Tracking Data for Re-engineering Web Pages

Current Trends in Web Engineering - Lecture Notes in Computer Science ◽

10.1007/978-3-319-04244-2_35 ◽

2013 ◽

pp. 345-349 ◽

Cited By ~ 2

Author(s):

Sukru Eraslan ◽

Yeliz Yes̨ilada ◽

Simon Harper

Keyword(s):

Eye Tracking ◽

Web Pages ◽

Tracking Data

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Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling

Journal of Mechanical Design ◽

10.1115/1.4048410 ◽

2020 ◽

Vol 142 (12) ◽

Cited By ~ 1

Author(s):

Priyesh Mehta ◽

Manoj Malviya ◽

Christopher McComb ◽

Guha Manogharan ◽

Catherine G. P. Berdanier

Keyword(s):

Additive Manufacturing ◽

Eye Tracking ◽

Transition Probabilities ◽

Hidden Markov ◽

Markov Modeling ◽

Tracking Data ◽

High Performing ◽

Hidden Markov Modeling ◽

Final Design ◽

Mining Design

Abstract In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities back and forth between multiple states.

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Reading patterns and cognitive processing in an eye-tracking study of note-reading in consecutive interpreting

Interpreting ◽

10.1075/intp.00050.che ◽

2020 ◽

Author(s):

Sijia Chen ◽

Jan-Louis Kruger ◽

Stephen Doherty

Keyword(s):

Eye Tracking ◽

Phase I ◽

Cognitive Load ◽

Cognitive Processing ◽

Phase Ii ◽

Preliminary Evidence ◽

Tracking Data ◽

Note Reading ◽

Reading Patterns ◽

Consecutive Interpreting

Abstract This article reports on the eye-tracking data collected from 18 professional interpreters while they performed consecutive interpreting with notes. It is a pioneering study in its visualisation of the way in which note-reading occurs. Preliminary evidence suggests that note-reading proceeds in a nonlinear manner. The data collected in this study also report on indicators of cognitive processing in consecutive interpreting, particularly during note-reading, which appears to be a cognitively demanding process. It differs from reading for comprehension in various ways, while staying closer to reading in sight translation. In addition, the data show that the note-taking choices made during Phase I of consecutive interpreting, in which interpreters listen to the source speech and write notes, affect the level of cognitive load in Phase II, in which interpreters read back their notes and produce a target speech.

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