Eye tracker data quality

Holmqvist, Kenneth; Nyström, Maria; Mulvey, Fiona

doi:10.1145/2168556.2168563

Cited by 299 publications

(204 citation statements)

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“…The quality of the raw data generated by the eye-tracker may vary depending on many different factors such as the eye-tracker model and manufacturer, the eye physiology, the calibration procedure, the position of the participant relative to the eye-tracker, the degree of head, or even ethnicity (Blignaut & Wium, 2014;Holmqvist et al, 2011;Holmqvist, Nyström, & Mulvey, 2012;Nyström, Andersson, Holmqvist, & van de Weijer, 2013;Saez de Urabain et al, 2015). Eye-tracking data coming from populations such as infants may contain considerably higher levels of noise than data from more compliant participants.…”

Section: Appendix a Fixation Detection And Codingmentioning

confidence: 99%

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

et al. 2017

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The goal of this article is to investigate the unexplored mechanisms underlying the development of saccadic control in infancy by determining the generalizability and potential limitations of extending the CRISP theoretical framework and computational model of fixation durations (FDs) in adult scene-viewing to infants. The CRISP model was used to investigate the underlying mechanisms modulating FDs in 6-month-olds by applying the model to empirical eye-movement data gathered from groups of infants and adults during free-viewing of naturalistic and semi-naturalistic videos. Participants also performed a gap-overlap task to measure their disengagement abilities. Results confirmed the CRISP model's applicability to infant data. Specifically, model simulations support the view that infant saccade programming is completed in two stages: an initial labile stage, followed by a non-labile stage. Moreover, results from the empirical data and simulation studies highlighted the influence of the material viewed on the FD distributions in infants and adults, as well as the impact that the developmental state of the oculomotor system can have on saccade programming and execution at 6 months. The present work suggests that infant FDs reflect on-line perceptual and cognitive activity in a similar way to adults, but that the individual developmental state of the oculomotor system affects this relationship at 6 months. Furthermore, computational modeling filled the gaps of psychophysical studies and allowed the effects of these two factors on FDs to be simulated in infant data providing greater insights into the development of oculomotor and attentional control than can be gained from behavioral results alone.

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Section: Appendix a Fixation Detection And Codingmentioning

confidence: 99%

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

et al. 2017

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“…The gimbal eye can rotate horizontally and vertically. A laser diode is attached on top of the inner ring (17.5 mm away from the pupil center) to indicate gaze direction successive data samples (x i , y i ) to (x i + 1 , y i + 1 ) (Holmqvist et al, 2012), of the form…”

Section: Precision Calculationmentioning

confidence: 99%

“…Longer periods increase the probability of fixational eye movements (tremor, microsaccades, and drift), which in turn will increase imprecision. Holmqvist, Nyström, and Mulvey (2012) showed that artificially increasing imprecision via the addition of Gaussian noise from 0.03°to 0.30°results in an increase of up to 200 ms in calculated fixation durations. If this range of noise, as measured by RMS(S2S) precision, is representative for current eye-trackers, it means that identical eye movements recorded on different eye-trackers, or with different levels of precision in recordings from the same eyetracker, can lead to different research results.…”

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confidence: 99%

A study of artificial eyes for the measurement of precision in eye-trackers

et al. 2016

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The precision of an eye-tracker is critical to the correct identification of eye movements and their properties. To measure a system's precision, artificial eyes (AEs) are often used, to exclude eye movements influencing the measurements. A possible issue, however, is that it is virtually impossible to construct AEs with sufficient complexity to fully represent the human eye. To examine the consequences of this limitation, we tested currently used AEs from three manufacturers of eye-trackers and compared them to a more complex model, using 12 commercial eye-trackers. Because precision can be measured in various ways, we compared different metrics in the spatial domain and analyzed the power-spectral densities in the frequency domain. To assess how precision measurements compare in artificial and human eyes, we also measured precision using human recordings on the same eyetrackers. Our results show that the modified eye model presented can cope with all eye-trackers tested and acts as a promising candidate for further development of a set of AEs with varying pupil size and pupil-iris contrast. The spectral analysis of both the AE and human data revealed that human eye data have different frequencies that likely reflect the physiological characteristics of human eye movements. We also report the effects of sample selection methods for precision calculations. This study is part of the EMRA/COGAIN Eye Data Quality Standardization Project.Keywords Eye movements . Artificial eye . Precision . Data quality . Eye-tracker noise . Power-spectral density High-quality eye movement data are a prerequisite for the valid measurement of fixation durations, saccade amplitudes and velocities, and many other behavioral measures in eye movement research. Spatial accuracy and precision are two of the most important aspects of eye data quality. Accuracy is defined as the difference between the tracker-estimated gaze position and the actual gaze position, whereas precision is defined as the ability to reliably reproduce a measurement, given a fixating eye (ideally, a stable eye)-see Fig. 1. Accuracy and precision are two independent measurements of eye-tracking data quality-that is, they can be both good or poor, or one good and the other poor. The most commonly used measures of precision in eye-trackers are the sample-tosample root mean square angular displacement [RMS(S2S)] and the standard deviation (SD) of samples in a given time window. These values will change not only dependent on the actual precision level of the tracker but also on the calculation used, and the samples or time period included in that calculation. Longer periods increase the probability of fixational eye movements (tremor, microsaccades, and drift), which in turn will increase imprecision. Holmqvist, Nyström, and Mulvey (2012) showed that artificially increasing imprecision via the addition of Gaussian noise from 0.03°to 0.30°results in an increase of up to 200 ms in calculated fixation durations. If this range of noise, as measured by RMS(S2S) precis...

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“…When doing area-of-interest analysis, even a change in accuracy of 0.5°can significantly alter the outcomes (Holmqvist, Nyström, & Mulvey, 2012). If one disregards the shifted orientations of one's participants, and assumes that the data are accurate, study outcomes might be invalid.…”

Section: Discussionmentioning

confidence: 99%

Qualitative tests of remote eyetracker recovery and performance during head rotation

et al. 2014

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What are the decision criteria for choosing an eyetracker? Often the choice is based on specifications by the manufacturer of the validity (accuracy) and reliability (precision) of measurements that can be achieved using a particular eyetracker. These specifications are mostly achieved under optimal conditions-for example, by using an artificial eye or trained participants fixed in a chinrest. Research, however, does not always take place in optimal conditions: For instance, when investigating eye movements in infants, school children, and patient groups with disorders such as attention-deficit hyperactivity disorder, it is practically impossible to restrict movement. We modeled movements often seen in infant research in two behaviors: (1) looking away from and back to the screen, to investigate eyetracker recovery, and (2) head orientations, to investigate eyetracker performance with nonoptimal orientations of the eyes. We investigated how eight eyetracking setups by three manufacturers (SMI, Tobii, and LC Technologies) coped with these modeled behaviors in adults. We report that the tested SMI eyetrackers dropped in sampling frequency when the eyes were not visible to the eyetracker, whereas the other systems did not, and discuss the potential consequences thereof. Furthermore, we report that the tested eyetrackers varied in their rates of data loss and systematic offsets during shifted head orientations. We conclude that (prospective) eye-movement researchers who cannot restrict movement or nonoptimal head orientations in their participants might benefit from testing their eyetracker in nonoptimal conditions. Additionally, researchers should be aware of the data loss and inaccuracies that might result from nonoptimal head orientations.

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Eye tracker data quality

Cited by 299 publications

References 15 publications

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

Disentangling the mechanisms underlying infant fixation durations in scene perception: A computational account

A study of artificial eyes for the measurement of precision in eye-trackers

Qualitative tests of remote eyetracker recovery and performance during head rotation

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