ABSTRACT
In this paper we evaluate several of the most popular algorithms for segmenting fixations from saccades by testing these algorithms on the scanning patterns of toddlers. We show that by changing the parameters of these algorithms we change the reported fixation durations in a systematic fashion. However, we also show how choices in analysis can lead to very different interpretations of the same eye-tracking data. Methods for reconciling the disparate results of different algorithms as well as suggestions for the use of fixation identification algorithms in analysis, are presented.
- Anliker, J. 1976. Eye movements- On-line measurement, analysis, and control. Eye movements and psychological processes, 185--199.Google Scholar
- Brockmann, D., and Geisel, T. 1999. Are human scanpaths Lévy flights? Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470) 1.Google Scholar
- Duchowski, A. 2003. Eye Tracking Methodology: Theory and Practice. Springer. Google ScholarDigital Library
- Gordon, H. 1981. Errors in Computer Packages. Least Squares Regression Through the Origin. The Statistician 30, 1, 23--29.Google ScholarCross Ref
- Inhoff, A., and Radach, R. 1998. Definition and computation of oculomotor measures in the study of cognitive processes. Eye guidance in reading and scene perception, 1--28.Google Scholar
- Jacob, R., and Karn, K. 2003. Eye tracking in human-computer interaction and usability research: Ready to deliver the promises (Section commentary). The Mind's Eyes: Cognitive and Applied Aspects of Eye Movements. Oxford: Elsevier Science.Google Scholar
- Karsh, R., and Breitenbach, F. 1983. Looking at looking: The amorphous fixation measure. Eye Movements and Psychological Functions: International Views, 53--64.Google Scholar
- Lundqvist, D., Flykt, A., and Óhman, A. 1998. The Karolinska Directed Emotional Faces. Pictoral face set available from the Department of Neurosciences, Karolinska Hospital, Stockholm, Sweden.Google Scholar
- Privitera, C., and Stark, L. 2000. Algorithms for defining visual regions-of-interest: comparison with eye fixations. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 9, 970--982. Google ScholarDigital Library
- Salvucci, D., and Goldberg, J. 2000. Identifying fixations and saccades in eye-tracking protocols. Proceedings of the symposium on Eye tracking research & applications, 71--78. Google ScholarDigital Library
- Santella, A., and DeCarlo, D. 2004. Robust clustering of eye movement recordings for quantification of visual interest. Eye Tracking Research & Applications (ETRA) Symposium. Google ScholarDigital Library
- Widdel, H. 1984. Operational problems in analysing eye movements. Theoretical and Applied Aspects of Eye Movement Research, 21--29.Google Scholar
Index Terms
- The incomplete fixation measure
Recommendations
Identifying fixations and saccades in eye-tracking protocols
ETRA '00: Proceedings of the 2000 symposium on Eye tracking research & applicationsThe process of fixation identification—separating and labeling fixations and saccades in eye-tracking protocols—is an essential part of eye-movement data analysis and can have a dramatic impact on higher-level analyses. However, algorithms for ...
Measuring Focused Attention Using Fixation Inner-Density
Augmented Cognition: Users and ContextsAbstractExamining user reactions via the unobtrusive method of eye tracking is becoming increasingly popular in user experience studies. A major focus of this type of research is accurately capturing user attention to stimuli, which is typically ...
Identifying Fixations in Gaze Data via Inner Density and Optimization
Eye tracking is an increasingly common technology with a variety of practical uses. Eye-tracking data, or gaze data, can be categorized into two main events: fixations represent focused eye movement, indicative of awareness and attention, whereas saccades ...
Comments