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Object Motion Computation for the Initiation of Smooth Pursuit Eye Movements in Humans

¹Institut de Neurosciences Cognitives de la Méditerranée, Centre National de la Recherche Scientifique, Unité Mixte de Recherche 6193, Marseille France; ²Human Information Processing Research Branch, Human Factors Research and Technology Division, National Aeronautics and Space Administration, Ames Research Center, Moffett Field, California

Submitted 4 October 2004; accepted in final form 8 November 2004

Pursuing an object with smooth eye movements requires an accurate estimate of its two-dimensional (2D) trajectory. This 2D motion computation requires that different local motion measurements are extracted and combined to recover the global object-motion direction and speed. Several combination rules have been proposed such as vector averaging (VA), intersection of constraints (IOC), or 2D feature tracking (2DFT). To examine this computation, we investigated the time course of smooth pursuit eye movements driven by simple objects of different shapes. For type II diamond (where the direction of true object motion is dramatically different from the vector average of the 1-dimensional edge motions, i.e., VA IOC = 2DFT), the ocular tracking is initiated in the vector average direction. Over a period of less than 300 ms, the eye-tracking direction converges on the true object motion. The reduction of the tracking error starts before the closing of the oculomotor loop. For type I diamonds (where the direction of true object motion is identical to the vector average direction, i.e., VA = IOC = 2DFT), there is no such bias. We quantified this effect by calculating the direction error between responses to types I and II and measuring its maximum value and time constant. At low contrast and high speeds, the initial bias in tracking direction is larger and takes longer to converge onto the actual object-motion direction. This effect is attenuated with the introduction of more 2D information to the extent that it was totally obliterated with a texture-filled type II diamond. These results suggest a flexible 2D computation for motion integration, which combines all available one-dimensional (edge) and 2D (feature) motion information to refine the estimate of object-motion direction over time.

This article has been cited by other articles:

				A. Montagnini, M. Spering, and G. S. Masson Predicting 2D Target Velocity Cannot Help 2D Motion Integration for Smooth Pursuit Initiation J Neurophysiol, December 1, 2006; 96(6): 3545 - 3550. [Abstract] [Full Text] [PDF]