Human optokinetic nystagmus: A stochastic analysis

Abstract

Optokinetic nystagmus (OKN) is a fundamental gaze-stabilizing response found in almost all vertebrates, in which eye movements attempt to compensate for the optic flow caused by self-motion. It is an alternating sequence of slow compensatory eye movements made in the direction of stimulus motion and fast eye movements made predominantly in the opposite direction. The timing and amplitude of these slow phases (SPs) and quick phases (QPs) are remarkably variable, and the cause of this variability is poorly understood. In this study principal components analysis was performed on OKN data to illustrate that the variability in correlation matrices across individuals and recording sessions reflected changes in the noise in the system while the linear relationships between variables remained predominantly the same. Three components were found that could explain the variance in OKN data, and only variables from within a single cycle contributed highly to any given component. A linear stochastic model of OKN was developed based on these results that describes OKN as a triple first order Markov process, with three sources of noise affecting SP velocity, the QP trigger, and QP amplitude. This model was used to predict the degree of signal dependent noise in the system, the duration of the transient state of SP velocity, and an apparent undershoot bias to the QP target location.