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Proprioceptive Guidance of Saccades in Eye–Hand Coordination

¹Centre for Vision Research and ²Departments of Kinesiology and Health Science, ³Psychology, and ⁴Biology, York University, Toronto; and ⁵ Canadian Institutes of Health Research Group for Action and Perception, Ontario, Canada

Submitted 26 September 2005; accepted in final form 12 May 2006

The saccade generator updates memorized target representations for saccades during eye and head movements. Here, we tested if proprioceptive feedback from the arm can also update handheld object locations for saccades, and what intrinsic coordinate system(s) is used in this transformation. We measured radial saccades beginning from a central light-emitting diode to 16 target locations arranged peripherally in eight directions and two eccentricities on a horizontal plane in front of subjects. Target locations were either indicated 1) by a visual flash, 2) by the subject actively moving the handheld central target to a peripheral location, 3) by the experimenter passively moving the subject’s hand, or 4) through a combination of the above proprioceptive and visual stimuli. Saccade direction was relatively accurate, but subjects showed task-dependent systematic overshoots and variable errors in radial amplitude. Visually guided saccades showed the smallest overshoot, followed by saccades guided by both vision and proprioception, whereas proprioceptively guided saccades showed the largest overshoot. In most tasks, the overall distribution of saccade endpoints was shifted and expanded in a gaze- or head-centered cardinal coordinate system. However, the active proprioception task produced a tilted pattern of errors, apparently weighted toward a limb-centered coordinate system. This suggests the saccade generator receives an efference copy of the arm movement command but fails to compensate for the arm’s inertia-related directional anisotropy. Thus the saccade system is able to transform hand-centered somatosensory signals into oculomotor coordinates and combine somatosensory signals with visual inputs, but it seems to have a poorly calibrated internal model of limb properties.

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