Precision of speed discrimination and smooth pursuit eye movements

Abstract

Several studies have shown that the precision of smooth pursuit eye speed can match perceptual speed discrimination thresholds during the steady-state phase of pursuit [Kowler, E., & McKee, S. (1987). Sensitivity of smooth eye movement to small differences in target velocity. Vision Research, 27, 993-1015; Gegenfurtner, K., Xing, D., Scott, B., & Hawken, M. (2003). A comparison of pursuit eye movement and perceptual performance in speed discrimination. Journal of Vision, 3, 865-876]. Recently, Osborne et al. [Osborne, L. C., Lisberger, S. G., & Bialek, W. (2005). A sensory source for motor variation. Nature, 437, 412-416; Osborne, L. C., Hohl, S. S., Bialek, W., & Lisberger S. G. (2007). Time course of precision in smooth-pursuit eye movements of monkeys. Journal of Neuroscience, 27, 2987-2998] claimed that pursuit precision during the initiation phase of pursuit also matches the sensory variability, implying that there is no motor noise added during pursuit initiation. However, these results were derived from a comparison of monkey pursuit data to human perceptual data from the literature, which were obtained with different stimuli. To directly compare precision for perception and pursuit, we measured pursuit and perceptual variability in the same human observers using the same stimuli. Subjects had to pursue a Gaussian blob in a step-ramp paradigm and give speed judgments on the same or in different trials. Speed discrimination thresholds were determined for different presentation durations. The analysis of pursuit precision was performed for short intervals containing the initiation period only and also for longer intervals including steady-state pursuit. In agreement with published studies, we found that the Weber fractions for psychophysical speed discrimination were fairly constant for different presentation durations, even for the shortest presentation duration of 150ms. Pursuit variability was 3-4 times as high for the analysis interval (300ms) containing the open-loop phase only. For pursuit analysis intervals of 400-500ms, pursuit variability approached perceptual variability. Our results show that, for the stimuli we used, the motor system contributes at least 50% to the total variability of smooth pursuit eye movements during the initiation phase.

Frequency dependence of vestibuloocular reflex thresholds

“…Our findings appear qualitatively consistent with some earlier smooth pursuit findings that suggested that sensory noise is a substantive contributor to smooth pursuit noise (Kowler and McKee 1987;Osborne et al 2005Osborne et al , 2007Rasche and Gegenfurtner 2009;Stone and Krauzlis 2003;Watamaniuk and Heinen 1999). Just as smooth pursuit provides an easily measureable signal that has been reported to reveal characteristics associated with sensory processing noise associated with vision (e.g., Beutter and Stone 2000; Osborne et al 2007), the VOR may eventually provide a readily measureable signal that reveals characteristics of vestibular sensory noise, though this remains to be shown.…”

“…Data analysis. Like earlier similar analyses of eye movement thresholds (e.g., Rasche and Gegenfurtner 2009;Stone and Krauzlis 2003), data analysis was automated with operator intervention limited to trials that had saccades, blinks, etc. For each trial, an automated MATLAB program plotted the eye position data before, during, and after the motion and then found the average eye position just before the motion and subtracted this from the average position just after the motion to calculate the eye displacement.…”

The brain uses adaptive internal models of scene statistics for sensorimotor estimation and planning

“…First, the close match between the true variances within a stimulus set and the fitted models' estimates of speed variance shows that subjects internalize accurate estimates of speed variance. Second, the fitted levels of noise on sensory speed signals closely approximate the Weber fractions measured in an experiment using similar viewing conditions (in which subjects were asked to track targets) (22). This pair of observations lends credence to the hypothesis that subjects use adaptive internal models of speed statistics to estimate object speed to plan motor actions.…”

“…The best-fitting sensory noise SD was 0.14 in log units (95% credible interval = 0.12, 0.17), equivalent to the same Weber fraction. This value is higher than most psychophysical estimates of the Weber fraction for speed discrimination derived from experiments using fixation (between 0.05 and 0.1 for similar stimulus conditions) (18); however, it is very close to the Weber fraction estimated from one study (22) that had subjects track moving targets (mean Weber fraction = 0.15), which subjects are likely to have done in the current experiment. The SD of memory noise fit to subjects' data was significantly greater than zero (95% credible interval = 0.06, 0.16), justifying its inclusion in the model.…”

Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics

“…This is despite very noisy motion information provided to the pursuit system by motion-sensitive areas (Osborne et al, 2005(Osborne et al, , 2007. To overcome this noise, the brain integrates stimulus information over time across large populations of neurons (Treue et al, 2000), which improves the reliability of sensory information (Snowden and Braddick, 1991;Perrett et al, 1998).…”