Control of fixational eye movements and posture
Final Report Abstract
Even during visual fixation, miniature (or fixational) eye movements are generated involuntarily. Fixational eye movements represent a dynamically rich phenomenon, including three key features: (i) A highly specific correlation pattern exists with persistence on a short and antipersistence on a long time scale, (ii) several components of fixation eye movements can be distinguished such as slow physiological drift and ballistic microsaccades, and (iii) both microsaccades and drift epochs are correlated, so that upcoming microsaccades can be predicted from epochs of slower drift than on average. In this project of Research Unit 868, our goals were to develop mathematical models for all of the three key features. We investigated an mathematical model based on a self-avoiding random walk on lattice that is driven by the combination of a self-generated activation field and a static potential. This framework could reproduce the correlation pattern in feature (i). Additionally, microsaccades are triggered dynamically, when the random walk hits a lattice position with activation above some critical threshold, which reproduced features (ii) and (iii). Taken together, proposed an integrated mathematical model of slow fixational eye movements and microsaccades that satisfied the key experimental findings. Next, we applied the model to a range of experimental paradigms including simple display changes and attentional cueing and to a task, in which human observers generated small voluntary saccades and microsaccades. Furthermore, we developed advanced statistical procedures based on Bayesian analysis to estimate scaling (Hurst) exponents from experimental data and to classify sequences of microsaccades. Since microsaccdes are often looked upon as a source of noise in EEG experiments, another important finding of our project is the demonstration of a physiological coupling between microsaccades and heartbeat. Finally, microsaccades can be used as a tool for the investigation of mental chronometry in auditory research. In summary, our project generated the first successful mathematical model for the generation of fixational eye movements and microsaccades, provided new techniques of analysis for fixational eye movements, proved the existence of unexpected physiological coupling to the fixational eyemovement system, and demonstrated how microsaccades can be used as a tool for investigation of cognitive processing even in non-visual tasks. Both the development of the basic mathematical model and the investigation of the coupling between microsaccades and heartbeat were based on cross-project collaborations within Research Unit 868.
Publications
- (2009). Noise-enhanced target discrimination under the influence of fixational eye movements and external noise. Chaos: An Interdisciplinary Journal of Nonlinear Science, 19(1):015112
Starzynski, C. and Engbert, R.
(See online at https://doi.org/10.1063/1.3098950) - (2010). Microsaccades are different from saccades in scene perception. Experimental Brain Research, 203(4):753–757
Mergenthaler, K. and Engbert, R.
(See online at https://doi.org/10.1007/s00221-010-2272-9) - (2011). An integrated model of fixational eye movements and microsaccades. Proceedings of the National Academy of Sciences U.S.A., 108(39):E765–E770
Engbert, R., Mergenthaler, K., Sinn, P., and Pikovsky, A.
(See online at https://doi.org/10.1073/pnas.1102730108) - (2011). Saccadic facilitation by modulation of microsaccades in natural backgrounds. Attention, Perception, & Psychophysics, 73(4):1029–1033
Sinn, P. and Engbert, R.
(See online at https://doi.org/10.3758/s13414-011-0107-9) - (2012). Bayesian estimation of the scaling parameter of fixational eye movements. EPL (Europhysics Letters), 100(4):40003
Makarava, N., Bettenbühl, M., Engbert, R., and Holschneider, M.
(See online at https://doi.org/10.1209/0295-5075/100/40003) - (2012). Bayesian selection of markov models for symbol sequences: Application to microsaccadic eye movements. PloS one, 7(9):e43388, 1–10
Bettenbühl, M., Rusconi, M., Engbert, R., and Holschneider, M.
(See online at https://doi.org/10.1371/journal.pone.0043388) - (2012). Computational modeling of collicular integration of perceptual responses and attention in microsaccades. The Journal of Neuroscience, 32(23):8035–8039
Engbert, R.
(See online at https://doi.org/10.1523/JNEUROSCI.0808-12.2012) - (2014). Microsaccadic responses indicate fast categorization of sounds: a novel approach to study auditory cognition. The Journal of Neuroscience, 34(33):11152–11158
Widmann, A., Engbert, R., and Schröger, E.
(See online at https://doi.org/10.1523/JNEUROSCI.1568-14.2014) - (2016). Microsaccades are coupled to heartbeat. The Journal of Neuroscience, 36(4):1237–1241
Ohl, S., Wohltat, C., Kliegl, R., Pollatos, O., and Engbert, R.
(See online at https://doi.org/10.1523/JNEUROSCI.2211-15.2016) - (2016). Small saccades versus microsaccades: Experimental distinction and modelbased unification. Vision Research, 118:132–143
Sinn, P. and Engbert, R.
(See online at https://doi.org/10.1016/j.visres.2015.05.012)
