Best practices in eye tracking research

Carter, Benjamin T.; Luke, Steven G.

doi:10.1016/j.ijpsycho.2020.05.010

Cited by 344 publications

(223 citation statements)

References 192 publications

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“…2B). This time resolution is comparable to many in-lab eye-trackers currently on the market and in scientific use (Carter & Luke, 2020). Leeuw, 2015).…”

Section: Basic Setup and Data Qualitymentioning

confidence: 67%

Webcam-based online eye-tracking for behavioral research

Yang¹,

Krajbich²

2020

Preprint

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Experiments are increasingly moving online (especially during the COVID epidemic). This poses a major challenge for researchers who rely on in-lab process-tracing techniques such as eye-tracking. Researchers in computer science have developed a web-based eye-tracking application (WebGazer) (Papoutsaki et al., 2016) but it has yet to see use in behavioral research. This is likely due to the extensive calibration and validation procedure (~50% of the study time) and low/inconsistent temporal resolution (Semmelmann & Weigelt, 2018), as well as the challenge of integrating it into standard experimental software. Here, we incorporate WebGazer with the most widely used JavaScript library among behavioral researchers (jsPsych) and adjust the procedure and code to reduce calibration/validation and dramatically improve the temporal resolution (from 100-1000 ms to 20-30 ms or better). We test our WebGazer/jsPsych combination with a decision-making study on Amazon MTurk. We find little degradation in spatial or temporal resolution over the course of the ~30-minute experiment. We replicate previous in-lab findings on the relationship between gaze dwell time and value-based choice. In summary, we provide an open-source, accessible, software template and tutorial for web-based eye-tracking in behavioral research that is sufficient to replicate in-lab studies with just a modest number of participants (N=38), and that is orders of magnitude faster than in-lab data collection. Moreover, we highlight that web-based eye-tracking is a useful tool for all behavioral researchers, as it can be used to ensure that study participants are humans and not machines.

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“…2B). This time resolution is comparable to many in-lab eye-trackers currently on the market and in scientific use (Carter & Luke, 2020). Leeuw, 2015).…”

Section: Basic Setup and Data Qualitymentioning

confidence: 67%

Webcam-based online eye-tracking for behavioral research

Yang¹,

Krajbich²

2020

Preprint

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“…So, this total dwell time was the only indication of individuals' attention pattern viewing humorous information. Other eye-tracking metrics are now very valuable to allow researchers to provide information about how humorous content is prioritized and processed [e.g., ( 24 – 26 )].…”

Section: Introductionmentioning

confidence: 99%

Your Eyes Do Not Lie! Dissecting Humor Effects in Health Messages Using Eye Tracker Technology

Brigaud

Lafont

Blanc

2021

Front. Public Health

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In the past decade, humor in scientific research has become more and more popular providing an increase of data identifying the context in which humor is a promising communication strategy in preventive health messages. To avoid the limits of declarative responses usually recorded in past studies, eye tracker technology offers the possibility to assess and dissect the effects of humor on visual attention. In this brief report, we first attempt to extend the results of previous studies by recording eye movements while participants were exposed to humorous and nonhumorous print health ads dealing with tobacco and alcohol consumption. A secondary purpose is specifically to test the visual attention French women devoted to humorous tobacco preventive ads, the worrying results of recent studies urging to find a way to improve tobacco preventive campaigns. Based on three complementary eye-tracking measures (i.e., total dwell time, fixation count, and revisits), the results showed that humorous health messages were scanned longer and more frequently and revisited more often compared to nonhumorous ones. In addition, humor appeared to reduce smokers' avoidance of preventive tobacco messages. The different pattern of visual exploration confirms that humor is a good strategy to grab attention even of individuals who are involved in the health topic addressed. In short, this paper argues for introducing lightness into a very serious subject, health communication, based on the analysis of eye movement evidence.

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“…The usability tests were conducted individually. Six participants wore Tobii eye tracker glasses for gaze analysis [7]. Prior to the test, the nature and purpose of the study was explained and permission for participation was obtained through an informed consent form that was signed by the participants.…”

Section: Participantsmentioning

confidence: 99%

“…These glasses can provide objective data regarding gaze behavior in ambulant situations. Our goal in using them is to obtain insight into navigation behavior, search behavior, and the division of attention over text and visual aspects of the notification app [7].…”

Section: Eyetrackingmentioning

confidence: 99%

First Eyetracking Results of Dutch CoronaMelder Contact Tracing and Notification App

Klooster

Slijkhuis

Gend

et al. 2021

Lecture Notes in Computer Science

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The coronavirus disease (COVID-19) has led to a global pandemic. AQ1 Many countries are using contact tracing as one of the interventions to control virus transmission. In conventional contact tracing, index cases with positive test results are asked to provide contact information of close contacts who were at risk of acquiring infection from the index case, within a given time period before the AQ2 positive test result. Mobile contact tracing apps can augment traditional contact tracing, as they exchange contact information wirelessly and semi-automatically, and allow for quicker warning of at-risk contacts. AQ3 However, little is known about user acceptance and perception of such contact tracing apps, or how people perceive the advice presented therein. In July 2020, the beta version of the official Dutch corona notification app was usability tested with participants from various age groups. This paper presents the results of the objective eye tracking measurements executed during these usability tests. The results show that both health and process related information is easily overlooked in the app by participants. A closed-loop contact with the builders of the app allowed for a direct feedback channel and quick improvements during the development stage.

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Best practices in eye tracking research

Cited by 344 publications

References 192 publications

Webcam-based online eye-tracking for behavioral research

Webcam-based online eye-tracking for behavioral research

Your Eyes Do Not Lie! Dissecting Humor Effects in Health Messages Using Eye Tracker Technology

First Eyetracking Results of Dutch CoronaMelder Contact Tracing and Notification App

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