September 2015
Volume 15, Issue 12
Free
Vision Sciences Society Annual Meeting Abstract  |   September 2015
Characterizing ocular drift and tremor: contributions to the retinal input
Author Affiliations
  • Hee-kyoung Ko
    Department of Neuroscience, Institute for Neuroscience, and Center for Perceptual Systems, University of Texas at Austin
  • Donald Snodderly
    Department of Neuroscience, Institute for Neuroscience, and Center for Perceptual Systems, University of Texas at Austin
  • Murat Aytekin
    Department of Psychological and Brain Sciences, Boston University
  • Martina Poletti
    Department of Psychological and Brain Sciences, Boston University
Journal of Vision September 2015, Vol.15, 214. doi:10.1167/15.12.214
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      Hee-kyoung Ko, Donald Snodderly, Murat Aytekin, Martina Poletti; Characterizing ocular drift and tremor: contributions to the retinal input. Journal of Vision 2015;15(12):214. doi: 10.1167/15.12.214.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

When we view a scene, saccades separate brief periods of fixation during which the information is acquired and processed. Mounting evidence indicates that the retinal motion introduced by fixational drift enhances fine detail, but physiological studies have not analyzed its effect. Responses of visual neurons to natural images are generally studied as if the eye is stationary during fixation. One reason for this situation is the challenge of measuring ocular drift with high accuracy and precision. Here we assess precision and resolution of two eyetracking systems, an eye coil (Remmel labs EM6) and a dual-Purkinje image eyetracker, (DPI v.6), to support neural and psychophysical studies of natural images with high precision during drift periods. We examined these systems noise with a model eye and eye coil mimicking the signal from a real eye. The impact of system noise on the measurements was characterized. The optimized eye coil system with a bandpass of 0-320 Hz had an RMS noise level of 0.18’~0.27’, and the slow drift over a period of 7 min was 0.52’ ± 0.16’ (N=10). The RMS noise level of a DPI eyetracker was of 0.35’ for both the horizontal and the vertical axes, and the slow drift over a period of 10 min was 0.7’ ± 0.20’ (N=6). By comparing the power spectrum of the system noise with ocular drift recorded from human subjects and a monkey, we determined the optimal filter to characterize drift speed. We also identified a high frequency tremor that varied between 50 and 100 Hz. Our results across the humans and the monkey consistently showed that ocular speed during fixation is much faster than previously thought, and tremor is sometimes larger than expected. These results can facilitate the development of standard procedures for optimizing study of the dynamics of microscopic ocular motion.

Meeting abstract presented at VSS 2015

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