Variability in the Vestibulo-Ocular Reflex and Vestibular Perception

Abstract

The vestibular system enables humans to estimate self-motion, stabilize gaze and maintain posture, but these behaviors are impacted by neural noise at all levels of processing (e.g., sensory, central, motor). Despite its essential importance, the behavioral impact of noise in human vestibular pathways is not completely understood. Here, we characterize the vestibular imprecision that results from neural noise by measuring trial-to-trial vestibulo-ocular reflex (VOR) variability and perceptual just-noticeable differences (JNDs) in the same human subjects as a function of stimulus intensity. We used head-centered yaw rotations about an Earth-vertical axis over a broad range of motion velocities (0-65°/s for VOR variability and 3-90°/s peak velocity for JNDs). We found that VOR variability increased from approximately 0.6°/s at a chair velocity of 1°/s to approximately 3°/s at 65°/s; it exhibited a stimulus-independent range below roughly 1°/s. Perceptual imprecision ("sigma") increased from 0.76°/s at 3°/s to 4.7°/s at 90°/s. Using stimuli that manipulated the relationship between velocity, displacement and acceleration, we found that velocity was the salient cue for VOR variability for our motion stimuli. VOR and perceptual imprecision both increased with stimulus intensity and were broadly similar over a range of stimulus velocities, consistent with a common noise source that affects motor and perceptual pathways. This contrasts with differing perceptual and motor stimulus-dependent imprecision in visual studies. Either stimulus-dependent noise or non-linear signal processing could explain our results, but we argue that afferent non-linearities alone are unlikely to be the source of the observed behavioral stimulus-dependent imprecision.

Keywords: just-noticeable difference; noise; vestibular; vestibulo-ocular reflex.

Figure 1:

Conceptual framework showing how shared, perceptual and motor noises result in perceptual and motor imprecision.

Figure 2:

Displacement, velocity and acceleration for the 4 motion conditions used. Each was a single sinusoidal cycle of acceleration, with the frequency and amplitude adjusted to change the relationship between displacement, velocity and acceleration. 1 Hz motions (black) lasted 1 s and had a peak velocity of 80°/s (solid black line) or 40°/s (dashed black line). 2 Hz motions (grey) lasted 0.5 s and had a peak velocity of 40°/s (dashed grey line) or 20°/s (solid grey line).

Figure 3:

Horizontal VOR responses from one subject in response to repeated yaw rotation stimuli. A: Dashed lines show chair velocity for trials in which the peak velocity was either 40°/s, 20°/s or 0°/s. Grey lines show resultant VOR responses. Motions lasted 0.5 s but only the first 0.2 s were analyzed. B: VOR variability was computed by taking the standard deviation across VOR responses at each time, with VOR variability calculated separately for trials at each chair velocity. Trials were normalized by the mean VOR gain. VOR variability is higher when chair velocity is higher, and relatively constant over time when no motion occurs (0°/s).

Figure 4:

The dependency of VOR variability on chair velocity. Each line shows VOR variability averaged across 6 subjects using the geometric mean, plotted as a function of chair velocity. They show that VOR variability increased with chair velocity. Furthermore, the relationship between VOR variability and chair velocity was similar for four different conditions despite them having different displacements and accelerations. The shading shows the standard error across subjects for the 80°/s condition.

Figure 5:

Decomposition of VOR variability using principal component analysis for 2 Hz motions. The black line shows the standard deviation across trials, where each trial was reconstituted from the first principal component. The dotted line shows the standard deviation across trials, where each trial was reconstituted from all principal components except for the first. For comparison, the grey line shows VOR variability for the no motion trials (0°/s).

Figure 6:

Mutual information between chair velocity and eye velocity as a function of instantaneous chair velocity. Curves show the geometric mean across subjects.

Figure 7:

Psychometric curve fit (black line) for a single condition and subject. Circles indicate the fraction of trials at each stimulus velocity in which the comparison motion was perceived faster than the reference motion, with the size of the circles indicate the number of trials at each stimulus velocity. The difference between the 50% and 76% levels (dashed lines) is perceptual sigma (grey triangle).

Figure 8:

Perceptual sigma and VOR variability have a similar dependency on velocity. For perceptual measurements, the x-axis is the peak velocity of the reference motion, while for VOR measurements it is the instantaneous chair velocity. Each symbol indicates the mean perceptual sigma across subjects for one reference peak velocity, calculated using the geometric mean. The error bars indicate standard error. The black line shows average VOR variability from the same subjects in response to stimuli with a peak velocity of 80°/s.

Figure 9:

Trial-to-trial motion device variability is small compared to behavioral variability. A: Total error for 40 trials with a peak velocity of 90°/s, showing the difference between actual and intended motion. B: Deterministic error is the average deviation between actual and intended motion. C: Stochastic noise is the total error for each trial less the deterministic error, and indicates the trial-to-trial variability in motion stimuli. D: The dependency of total error, deterministic error and stochastic noise on peak stimulus velocity.

Figure 10:

Exploring the possible physiologic origins of stimulus-dependent behavioral imprecision. A: VOR variability compared to the properties of vestibular afferent and thalamic neurons, and abducens neurons. The sensitivity of each has been arbitrarily scaled for display purposes, without changing the slope of the curves. B: VOR variability compared to the inverse of the properties of the same neurons. C: VOR variability compared to stimulus-independent noise, stimulus-dependent noise and a combination of the two.

Figure 11:

Artifacts arising from eye tracking. The black line shows velocity variability for tracking of an artificial eye image during repeated 1 Hz, 20°/s motions. For comparison, the variability of the VOR during no-motion trials is shown (geometric mean across 6 subjects).

Figure 12:

Sensitivity analysis to show the effects of filtering and normalization on VOR variability.

Figure 13:

Velocity storage predictions for the stimuli used for the VOR (A) and perception (B) tasks.