scholarly journals Comparing Preferences towards Multiracial Advertising in Sweden and the US-Exploration through Eye-Tracking

Genealogy ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 109
Author(s):  
Sayaka Osanami Törngren ◽  
Emi Moriuchi ◽  
Caroline Adolfsson ◽  
Marcus Nyström ◽  
Sofia Ulver
Keyword(s):  
Eye Tracking ◽  
Dwell Time ◽  
Reaction Times ◽  
Racial Diversity ◽  
Tracking Data ◽  
Eye Tracker ◽  
Limited Sample ◽  
Limited Sample Size ◽  
American Students ◽  
The Us

This article examined and compared the US-born and Swedish-born college students’ preferences towards monoracial or multiracial advertisement. We showed four fashion advertisements, tracked their eye movements with a stationary eye-tracker, and asked questions through survey and debriefing to understand how students see and perceive advertisements with and without racial diversity. We found that both Swedish and American students exhibited higher preference in monoracial advertisements. We also found that Swedish and American students’ preferences towards advertisements were quite similar, but there were some variations in the reported level of attractiveness of the advertisements, reaction times, and dwell time between the Swedish and American students. Even though we did not find any statistically significant results from the eye-tracking data due to the limited sample size, the results point to interesting trends and tendencies that need to be addressed in further studies.

The Use of Eye Tracking to Discern the Threshold at Which Metopic Orbitofrontal Deformity Attracts Attention

2020 ◽  
Vol 57 (12) ◽  
pp. 1392-1401
Author(s):  
Mark P. Pressler ◽  
Emily L. Geisler ◽  
Rami R. Hallac ◽  
James R. Seaward ◽  
Alex A. Kane
Keyword(s):  
Surgical Treatment ◽  
Graduate Students ◽  
Eye Tracking ◽  
Dwell Time ◽  
Surgical Intervention ◽  
Three Dimensional ◽  
Average Dwell Time ◽  
Eye Tracker ◽  
Gaze Patterns ◽  
Areas Of Interest

Introduction and Objectives: Surgical treatment for trigonocephaly aims to eliminate a stigmatizing deformity, yet the severity that captures unwanted attention is unknown. Surgeons intervene at different points of severity, eliciting controversy. This study used eye tracking to investigate when deformity is perceived. Material and Methods: Three-dimensional photogrammetric images of a normal child and a child with trigonocephaly were mathematically deformed, in 10% increments, to create a spectrum of 11 images. These images were shown to participants using an eye tracker. Participants’ gaze patterns were analyzed, and participants were asked if each image looked “normal” or “abnormal.” Results: Sixty-six graduate students were recruited. Average dwell time toward pathologic areas of interest (AOIs) increased proportionally, from 0.77 ± 0.33 seconds at 0% deformity to 1.08 ± 0.75 seconds at 100% deformity ( P < .0001). A majority of participants did not agree an image looked “abnormal” until 90% deformity from any angle. Conclusion: Eye tracking can be used as a proxy for attention threshold toward orbitofrontal deformity. The amount of attention toward orbitofrontal AOIs increased proportionally with severity. Participants did not generally agree there was “abnormality” until deformity was severe. This study supports the assertion that surgical intervention may be best reserved for more severe deformity.

scholarly journals The Power of Eye Tracking in Economics Experiments

2016 ◽  
Vol 106 (5) ◽  
pp. 309-313 ◽  
Author(s):  
Joanna N. Lahey ◽  
Douglas Oxley
Keyword(s):  
Economic Theory ◽  
Data Analysis ◽  
Eye Tracking ◽  
Data Management ◽  
Tracking Data ◽  
Eye Tracker ◽  
Hiring Decisions ◽  
Economics Experiments ◽  
Set Up ◽  
Tracking Technology

Eye tracking is a technology that tracks eye activity including how long and where a participant is looking. As eye tracking technology has improved and become more affordable its use has expanded. We discuss how to design, implement, and analyze an experiment using this technology to study economic theory. Using our experience fielding an experiment to study hiring decisions we guide the reader through how to choose an eye tracker, concerns with participants and set-up, types of outputs, limitations of eye tracking, data management and data analysis. We conclude with suggestions for combining eye tracking with other measurements.

Measuring dwell time percentage from head-mounted eye-tracking data – comparison of a frame-by-frame and a fixation-by-fixation analysis

Ergonomics ◽  
2014 ◽  
Vol 58 (5) ◽  
pp. 712-721 ◽  
Author(s):  
Pieter Vansteenkiste ◽  
Greet Cardon ◽  
Renaat Philippaerts ◽  
Matthieu Lenoir
Keyword(s):  
Eye Tracking ◽  
Dwell Time ◽  
Tracking Data ◽  
Data Comparison

scholarly journals Algorithms for the automated correction of vertical drift in eye-tracking data

2021 ◽  
Author(s):  
Jon W. Carr ◽  
Valentina N. Pescuma ◽  
Michele Furlan ◽  
Maria Ktori ◽  
Davide Crepaldi
Keyword(s):  
Eye Tracking ◽  
Vertical Axis ◽  
Great Promise ◽  
Tracking Data ◽  
Eye Tracker ◽  
Algorithm Selection ◽  
Vertical Drift ◽  
Gradual Loss ◽  
Adjacent Line ◽  
Dynamic Time

AbstractA common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection.

scholarly journals Investigating the Use of Eye Fixation Data for Emotion Classification in VR

2021 ◽  
Vol 12 (3) ◽  
pp. 1852-1857
Author(s):  
Lim Jia Zheng Et.al
Keyword(s):  
Eye Tracking ◽  
Classification Accuracy ◽  
Preliminary Investigation ◽  
Support Vector ◽  
Tracking Data ◽  
Eye Tracker ◽  
Emotion Classification ◽  
Learning Classifier ◽  
Rbf Kernel ◽  
Tracking Technology

Eye-tracking technology has become popular recently and widely used in research on emotion recognition since its usability. In this paper, we presented a preliminary investigation on a novelty approach for detecting emotions using eye-tracking data in virtual reality (VR) to classify 4-quadrant of emotions according to russell’scircumplex model of affects. A presentation of 3600 videos is used as the experiment stimuli to evoke the emotions of the user in VR. An add-on eye-tracker within the VR headset is used for the recording and collecting device of eye-tracking data. Fixation data is extracted and chosen as the eye feature used in this investigation. The machine learning classifier is support vector machine (SVM) with radial basis function (RBF) kernel. The best classification accuracy achieved is 69.23%. The findings showed that emotion classification using fixation data has promising results in the prediction accuracy from a four-class random classification.

scholarly journals An Exploratory Application of Eye-Tracking Methods in a Discrete Choice Experiment

2018 ◽  
Vol 38 (6) ◽  
pp. 658-672 ◽  
Author(s):  
Caroline Vass ◽  
Dan Rigby ◽  
Kelly Tate ◽  
Andrew Stewart ◽  
Katherine Payne
Keyword(s):  
Decision Making ◽  
Eye Tracking ◽  
Risk Communication ◽  
Discrete Choice ◽  
Dwell Time ◽  
Ordinary Least Squares ◽  
Discrete Choice Experiments ◽  
Tracking Data ◽  
Significant Difference ◽  
Areas Of Interest

Background. Discrete choice experiments (DCEs) are increasingly used to elicit preferences for benefit-risk tradeoffs. The primary aim of this study was to explore how eye-tracking methods can be used to understand DCE respondents’ decision-making strategies. A secondary aim was to explore if the presentation and communication of risk affected respondents’ choices. Method. Two versions of a DCE were designed to understand the preferences of female members of the public for breast screening that varied in how risk attributes were presented. Risk was communicated as either 1) percentages or 2) icon arrays and percentages. Eye-tracking equipment recorded eye movements 1000 times a second. A debriefing survey collected sociodemographics and self-reported attribute nonattendance (ANA) data. A heteroskedastic conditional logit model analyzed DCE data. Eye-tracking data on pupil size, direction of motion, and total visual attention (dwell time) to predefined areas of interest were analyzed using ordinary least squares regressions. Results. Forty women completed the DCE with eye-tracking. There was no statistically significant difference in attention (fixations) to attributes between the risk communication formats. Respondents completing either version of the DCE with the alternatives presented in columns made more horizontal (left-right) saccades than vertical (up-down). Eye-tracking data confirmed self-reported ANA to the risk attributes with a 40% reduction in mean dwell time to the “probability of detecting a cancer” ( P = 0.001) and a 25% reduction to the “risk of unnecessary follow-up” ( P = 0.008). Conclusion. This study is one of the first to show how eye-tracking can be used to understand responses to a health care DCE and highlighted the potential impact of risk communication on respondents’ decision-making strategies. The results suggested self-reported ANA to cost attributes may not be reliable.

scholarly journals Algorithms for the automated correction of vertical drift in eye tracking data

2020 ◽  
Author(s):  
Jon W Carr ◽  
Valentina Nicole Pescuma ◽  
Michele Furlan ◽  
Maria Ktori ◽  
Davide Crepaldi
Keyword(s):  
Eye Tracking ◽  
Vertical Axis ◽  
Great Promise ◽  
Tracking Data ◽  
Eye Tracker ◽  
Algorithm Selection ◽  
Vertical Drift ◽  
Gradual Loss ◽  
Adjacent Line ◽  
Dynamic Time

A common problem in eye tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post-hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection.

scholarly journals Predicting Video Saliency Using Crowdsourced Mouse-Tracking Data

2019 ◽  
Author(s):  
Виталий Людвиченко ◽  
Vitaliy Lyudvichenko ◽  
Дмитрий Ватолин ◽  
Dmitriy Vatolin
Keyword(s):  
Eye Tracking ◽  
Large Scale ◽  
Peripheral Vision ◽  
Tracking Data ◽  
Eye Tracker ◽  
Mouse Tracking ◽  
Saliency Maps ◽  
Video Viewing ◽  
Computer Mouse ◽  
Neural Network Algorithm

This paper presents a new way of getting high-quality saliency maps for video, using a cheaper alternative to eye-tracking data. We designed a mouse-contingent video viewing system which simulates the viewers’ peripheral vision based on the position of the mouse cursor. The system enables the use of mouse-tracking data recorded from an ordinary computer mouse as an alternative to real gaze fixations recorded by a more expensive eye-tracker. We developed a crowdsourcing system that enables the collection of such mouse-tracking data at large scale. Using the collected mouse-tracking data we showed that it can serve as an approximation of eye-tracking data. Moreover, trying to increase the efficiency of collected mouse-tracking data we proposed a novel deep neural network algorithm that improves the quality of mouse-tracking saliency maps.

scholarly journals Product Recommendation Based on Eye Tracking Data Using Fixation Duration

2021 ◽  
Vol 5 (4) ◽  
pp. 109
Author(s):  
Juni Nurma Sari ◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
Ridi Ferdiana
Keyword(s):  
Eye Tracking ◽  
Collaborative Filtering ◽  
Fixation Duration ◽  
Product Information ◽  
Sensor Technology ◽  
Tracking Data ◽  
Eye Tracker ◽  
Product Recommendation ◽  
User Attention ◽  
Product Recommendations

E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

scholarly journals TO THE QUESTION OF PECULIARITIES OF EYE MOVEMENT ANALYSIS IN THE PROCESS OF FACE PERCEPTION

2021 ◽  
pp. 196-219
Author(s):  
Galina Ya. Menshikova ◽  
Anna O. Pichugina
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Face Perception ◽  
Dwell Time ◽  
Holistic Processing ◽  
Facial Features ◽  
Tracking Data ◽  
Individual Strategies ◽  
Areas Of Interest

Background. The article is devoted to the study of the mechanisms of face perception when using the technology of eye-tracking. In the scientific literature, two processes are distinguished - analytical (perception of individual facial features) and holistic (perception of a general configuration of facial features). It is assumed that each of the mechanisms can be specifically manifested in patterns of eye movements during face perception. However, there is disagreement among the authors concerning the eye movements patterns which reflect the dominance of the holistic or analytic processing. We hypothesized that the contradictions in the interpretation of eye movement indicators in the studies of face perception may be associated with the features of the eye-tracker data processing, namely, with the specifics of identifying areas of interest (eyes, nose, bridge of the nose, lips), as well as with individual strategies of eye movements. Objective. Revealing the features of eye movements analysis in the process of facial perception. Method. A method for studying analytical and holistic processing in the task of assessing the attractiveness of upright and inverted faces using eye-tracking technology has been developed and tested. The eye-tracking data were analyzed for the entire sample using three types of processing, differing in the marking of the areas of interest (AOIs), and separately for two groups differing in eye movement strategies. The distinction of strategies was considered based on differences in the mean values of the fixation duration and the amplitude of saccades. Results. It was shown that: the presence of statistically significant differences of the dwell time in the AOIs between the condition of upright and inverted faces depended on the method of identifying these AOIs. It was shown that the distribution of the dwell time by zones is closely related to individual strategies of eye movements. Analysis of the data separately by groups showed significant differences in the distribution of the dwell time in the AOIs. Conclusion. When processing eye-tracking data obtained in the studies of face perception, it is necessary to consider individual strategies of eye movements, as well as the features associated with identifying AOIs. The absence of a single standard for identifying these areas can be the reason for inconsistency of the data about the holistic or analytical processing dominance. According to our data, the most effective for the analysis of holistic processing is a more detailed type of marking the AOIs, in which not only the main features (eyes, nose, mouth) are distinguished, but also the area of the nose bridge and nose.

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