For evaluating whether an eye-tracker is suitable for measuring microsaccades, Poletti & Rucci (2016) propose that a measure called 'resolution' could be better than the more established root-mean-square of the sample-to-sample distances (RMS-S2S). Many open questions exist around the resolution measure, however. Resolution needs to be calculated using data from an artificial eye that can be turned in very small steps. Furthermore, resolution has an unclear and uninvestigated relationship to the RMS-S2S and STD (standard deviation) measures of precision (Holmqvist & Andersson, 2017, p. 159-190), and there is another metric by the same name (Clarke, Ditterich, Drüen, Schönfeld, and Steineke 2002), which instead quantifies the errors of amplitude measurements. In this paper, we present a mechanism, the Stepperbox, for rotating artificial eyes in arbitrary angles from 1' (arcmin) and upward. We then use the Stepperbox to find the minimum reliably detectable rotations in 11 video-based eye-trackers (VOGs) and the Dual Purkinje Imaging (DPI) tracker. We find that resolution correlates significantly with RMS-S2S and, to a lesser extent, with STD. In addition, we find that although most eye-trackers can detect some small rotations of an artificial eye, the rotations of amplitudes up to 2∘ are frequently erroneously measured by video-based eye-trackers. We show evidence that the corneal reflection (CR) feature of these eye-trackers is a major cause of erroneous measurements of small rotations of artificial eyes. Our data strengthen the existing body of evidence that video-based eye-trackers produce errors that may require that we reconsider some results from research on reading, microsaccades, and vergence, where the amplitude of small eye movements have been measured with past or current video-based eye-trackers. In contrast, the DPI reports correct rotation amplitudes down to 1'.

Department of Computer Science and Informatics University of the Free State Bloemfontein South Africa

Department of Psychology Regensburg University Regensburg Germany

Faculty of Arts Masaryk University Brno Czech Republic

Institute of Psychology Nicolaus Copernicus University in Torun Toruń Poland

Zobrazit více v PubMed

Blignaut, P. (2014). Mapping the pupil-glint vector to gaze coordinates in a simple video-based eye tracker. Journal of Eye Movement Research, 7(1), 1–11.

Blignaut, P., Holmqvist, K., Nyström, M., & Dewhurst, R. (2014). Improving the accuracy of video-based eye tracking in real time through post-calibration regression. In Current Trends in Eye Tracking Research (pp. 77–100) Cham: Springer.

Buswell, G. T. (1935). How people look at pictures. Chicago: University of Chicago Press.

Cerrolaza, J. J., Villanueva, A., & Cabeza, R. (2012). Study of polynomial mapping functions in video-oculography eye trackers. ACM Transactions on Computer-Human Interaction (TOCHI), 19(2), 10.

Clarke, A. H., Ditterich, J., Drüen, K., Schönfeld, U., & Steineke, C. (2002). Using high frame rate cmos sensors for three-dimensional eye tracking. Behavior Research Methods, Instruments, & Computers, 34(4), 549–560. PubMed

Crane, H., D. & Steele, C. M. (1985). Generation-V dual-Purkinje-image eyetracker. Applied Optics, 24(4), 527–537. PubMed

Ditchburn, R., & Ginsborg, B. (1952). Vision with a stabilized retinal image. Nature, 170, 36–37. PubMed

Dodge, R.. & Cline, T. S. (1901). The angle velocity of eye movements. Psychological Review, 8(2), 145–157.

Drewes, J. (2014). Smaller is better: Drift in gaze measurements due to pupil dynamics. PLoS ONE, 9(10), e111197. PubMed PMC

Drewes, J., Masson, G. S., & Montagnini, A. (2012). Shifts in reported gaze position due to changes in pupil size: Ground truth and compensation. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 209–212). New York: ACM.

Drewes, J., Montagnini, A., & Masson, G. S. (2011). Effects of pupil size on recorded gaze position: A live comparison of two eye tracking systems. Journal of Vision, 11(11), 494–494.

Engbert, R.. & Kliegl, R. (2003). Binocular coordination in microsaccades. In The mind’s eye, (pp. 103–117).Oxford: Elsevier.

Fang, Y., Gill, C., Poletti, M., & Rucci, M. (2018). Monocular microsaccades: Do they really occur? Journal of Vision, 18(3), 18–18. PubMed PMC

Gautier, J., Bedell, H. E., Siderov, J., & Waugh, S. J. (2016). Monocular microsaccades are visual-task related. Journal of Vision, 16(3), 37–37. PubMed

Holmqvist, K. (2015). Common predictors of accuracy, precision and data loss in 12 eye-trackers. Available at ResearchGate.

Holmqvist, K.. & Andersson, R. (2017). Eye tracking: A comprehensive guide to methods, paradigms and measures. Lund: Lund Eye-Tracking Research Institute.

Holmqvist, K., Nyström, M., & Mulvey, F. (2012). Eye tracker data quality: What it is and how to measure it. In Proceedings of the Symposium on Eye Tracking Research and Applications (pp. 45–52). New York: ACM.

Hooge, I., Holmqvist, K., & Nyström, M. (2016). The pupil is faster than the corneal reflection (cr): Are video based pupil-cr eye trackers suitable for studying detailed dynamics of eye movements? Vision Research, 128, 6–18. PubMed

Hooge, I. T., Hessels, R. S., & Nyström, M. (2019). Do pupil-based binocular video eye trackers reliably measure vergence? Vision Research, 156, 1–9. PubMed

Hooge, I. T., Niehorster, D. C., Nyström, M., Andersson, R., & Hessels, R. S. (2018). Is human classification by experienced untrained observers a gold standard in fixation detection? Behavior Research Methods, 50(5), 1864–1881. PubMed PMC

Ko, H.-k., Snodderly, D. M., & Poletti, M. (2016). Eye movements between saccades: Measuring ocular drift and tremor. Vision Research, 122, 93–104. PubMed PMC

Kowler, E. (2011). Eye movements: The past 25 years. Vision Research, 51, 1457–1483. PubMed PMC

Martinez-Conde, S., Macknik, S. L., Troncoso, X. G., & Dyar, T. A. (2006). Microsaccades counteract visual fading during fixation. Neuron, 49(2), 297–305. PubMed

Martinez-Conde, S., Macknik, S. L., Troncoso, X. G., & Hubel, D. H. (2009). Microsaccades: A neurophysiological analysis. Trends in Neurosciences, 32(9), 463–475. PubMed

McCamy, M. B., Otero-Millan, J., Leigh, R. J., King, S. A., Schneider, R. M., Macknik, S. L., & Martinez-Conde, S. (2015). Simultaneous recordings of human microsaccades and drifts with a contemporary video eye tracker and the search coil technique. PLOS ONE, 10(6), 1–20. PubMed PMC

McConkie, G. (1981). Evaluating and reporting data quality in eye movement research. Behavior Research Methods, 13(2), 97–106.

Niehorster, D. C., Cornelissen, T. H., Holmqvist, K., Hooge, I. T., & Hessels, R. S. (2017). What to expect from your remote eye-tracker when participants are unrestrained. Behavior Research Methods, 50, 213–227. PubMed PMC

Orquin, J. L.. & Holmqvist, K. (2017). Threats to the validity of eye-movement research in psychology. Behavior Research Methods, 50(4), 1645–1656. PubMed

Otero-Millan, J., Castro, J. L. A., Macknik, S. L., and Martinez-Conde, S. (2014). Unsupervised clustering method to detect microsaccades. Journal of Vision, 14(2), 18. PubMed

Poletti, M.. & Rucci, M. (2016). A compact field guide to the study of microsaccades: challenges and functions. Vision Research, 118, 83–97. PubMed PMC

Rayner, K. (1998). Eye movements in reading and information processing: 20 years of research. Psychological Bulletin, 124(3), 372. PubMed

Reingold, E. M. (2014). Eye tracking research and technology: Towards objective measurement of data quality. Visual Cognition, 22(3), 635–652. PubMed PMC

Wang, X., Holmqvist, K., ∧ Alexa, M. (2019). The recorded mean point of vergence is biased. Journal of Eye Movement Research, 12(4), 2. PubMed PMC

Yarbus, A. L. (1967). Eye movements and vision. New York Plenum Press.

Zemblys, R., Niehorster, D. C., & Holmqvist, K. (2018a). gazenet: End-to-end eye-movement event detection with deep neural networks. Behavior Research Methods, 51(2), 840–864. PubMed

Zemblys, R., Niehorster, D. C., Komogortsev, O., & Holmqvist, K. (2018b). Using machine learning to detect events in eye-tracking data. Behavior Research Methods, 50(1), 840–181. PubMed

Eye tracking: empirical foundations for a minimal reporting guideline