scholarly journals Peer Review #1 of "A new comprehensive eye-tracking test battery concurrently evaluating the Pupil Labs glasses and the EyeLink 1000 (v0.1)"

2019 ◽  
Author(s):  
Benedikt V. Ehinger ◽  
Katharina Groß ◽  
Inga Ibs ◽  
Peter König

ABSTRACTEye-tracking experiments rely heavily on good data quality of eye-trackers. Unfortunately, it is often that only the spatial accuracy and precision values are available from the manufacturers. These two values alone are not sufficient enough to serve as a benchmark for an eye-tracker: Eye-tracking quality deteriorates during an experimental session due to head movements, changing illumination or calibration decay. Additionally, different experimental paradigms require the analysis of different types of eye movements, for instance smooth pursuit movements, blinks or microsaccades, which themselves cannot readily be evaluated by using spatial accuracy or precision alone. To obtain a more comprehensive description of properties, we developed an extensive eye-tracking test battery. In 10 different tasks, we evaluated eye-tracking related measures such as: the decay of accuracy, fixation durations, pupil dilation, smooth pursuit movement, microsaccade detection, blink detection, or the influence of head motion. For some measures, true theoretical values exist. For others, a relative comparison to a gold standard eye-tracker is needed. Therefore, we collected our gaze data simultaneously from a gold standard remote EyeLink 1000 eye-tracker and compared it with the mobile Pupil Labs glasses.As expected, the average spatial accuracy of 0.57° for the EyeLink 1000 eye-tracker was better than the 0.82° for the Pupil Labs glasses (N=15). Furthermore, we detected less fixations and shorter saccade durations for the Pupil Labs glasses. Similarly, we found fewer microsaccades using the Pupil Labs glasses. The accuracy over time decayed only slightly for the EyeLink 1000, but strongly for the Pupil Labs glasses. Finally we observed that the measured pupil diameters differed between eye-trackers on the individual subject level but not the group level.To conclude, our eye-tracking test battery offers 10 tasks that allow us to benchmark the many parameters of interest in stereotypical eye-tracking situations, or addresses a common source of confounds in measurement errors (e.g. yaw and roll head movements).All recorded eye-tracking data (including Pupil Labs’ eye video files), the stimulus code for the test battery and the modular analysis pipeline are available (https://github.com/behinger/etcomp).BVE, KG, IIandPKconceived the experiment.IIandBVEcreated the experiment and recorded the gaze data.BVEandKGperformed the analysis.BVE, KGandPKreviewed the manuscript critically.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7086 ◽  
Author(s):  
Benedikt V. Ehinger ◽  
Katharina Groß ◽  
Inga Ibs ◽  
Peter König

Eye-tracking experiments rely heavily on good data quality of eye-trackers. Unfortunately, it is often the case that only the spatial accuracy and precision values are available from the manufacturers. These two values alone are not sufficient to serve as a benchmark for an eye-tracker: Eye-tracking quality deteriorates during an experimental session due to head movements, changing illumination or calibration decay. Additionally, different experimental paradigms require the analysis of different types of eye movements; for instance, smooth pursuit movements, blinks or microsaccades, which themselves cannot readily be evaluated by using spatial accuracy or precision alone. To obtain a more comprehensive description of properties, we developed an extensive eye-tracking test battery. In 10 different tasks, we evaluated eye-tracking related measures such as: the decay of accuracy, fixation durations, pupil dilation, smooth pursuit movement, microsaccade classification, blink classification, or the influence of head motion. For some measures, true theoretical values exist. For others, a relative comparison to a reference eye-tracker is needed. Therefore, we collected our gaze data simultaneously from a remote EyeLink 1000 eye-tracker as the reference and compared it with the mobile Pupil Labs glasses. As expected, the average spatial accuracy of 0.57° for the EyeLink 1000 eye-tracker was better than the 0.82° for the Pupil Labs glasses (N= 15). Furthermore, we classified less fixations and shorter saccade durations for the Pupil Labs glasses. Similarly, we found fewer microsaccades using the Pupil Labs glasses. The accuracy over time decayed only slightly for the EyeLink 1000, but strongly for the Pupil Labs glasses. Finally, we observed that the measured pupil diameters differed between eye-trackers on the individual subject level but not on the group level. To conclude, our eye-tracking test battery offers 10 tasks that allow us to benchmark the many parameters of interest in stereotypical eye-tracking situations and addresses a common source of confounds in measurement errors (e.g., yaw and roll head movements). All recorded eye-tracking data (including Pupil Labs’ eye videos), the stimulus code for the test battery, and the modular analysis pipeline are freely available (https://github.com/behinger/etcomp).


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sue Yeon Syn ◽  
JungWon Yoon

PurposeThis study aims to understand how college students' personal and health-related characteristics are related to their reading behaviors and cognitive outcomes of Facebook health information through eye tracking data and cognitive outcomes.Design/methodology/approachThis study analyzed users' gaze movement data and results of recall and recognition tests to investigate users' reading patterns and their consequences with cognitive outcomes. The gaze movements are analyzed with eye tracking data including the average fixation count and time to first fixation.FindingsThe results of reading patterns show that Texts and Images are highly viewed and viewed immediately by participants when the posts were presented. There was no clear pattern with fixation counts to determine cognitive outcomes. However, the findings of study suggest that there is a clear pattern of reading Facebook posts with areas of interest (AOIs). Among five AOIs observed, participants viewed Images first and then Texts when a Facebook post is presented. On the other hand, they read Texts more carefully than Images. The findings of this study suggest that while images contribute to gaining users' attention, a clear and precise message needs to be delivered in text message to ensure readers' correct understanding and application of health information.Originality/valueThe user-centered evidence on reading behaviors and cognitive outcomes will make contributions to how health professionals and health organizations can make optimal use of Facebook for effective health information communication.Peer reviewThe peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2020-0177


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