Qualitative tests of remote eyetracker recovery and performance during head rotation
- 578 Downloads
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.
KeywordsEyetracking Head movement Head orientation Developmental studies Data quality
We thank Kenneth Holmqvist, Fiona Mulvey, and the Eye-Tracking Group at the Lund University Humanities Lab for providing eyetracking setups in this study. We also thank Edwin Dalmaijer for help with the manuscript. This work was supported by a Netherlands Organization for Scientific Research (NWO) VICI Grant (45307004) and by an NWO Gravition Grant (024.001.003), both to C.K.
- Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., & van de Weijer, J. (2011). Eye tracking: A comprehensive guide to methods and measures. Oxford, UK: Oxford University Press.Google Scholar
- Saez de Urabain, I. R., Johnson, M. H., & Smith, T. J. (2014). GraFIX: A semiautomatic approach for parsing low- and high-quality eye-tracking data. Behavior Research Methods. Advance online publication. doi: 10.3758/s13428-014-0456-0