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Updated: May 28, 2026

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
Published on: May 10, 2012
Contribution of execution noise to arm movement variability in three-dimensional space
Gregory A Apker1, Christopher A Buneo
1School of Biological and Health Systems Engineering, Arizona State Univ., P.O. Box 879709, Tempe, AZ 85287-9709, USA.
Abstract:
Reaching movements are subject to noise associated with planning and execution, but precisely how these noise sources interact to determine patterns of endpoint variability in three-dimensional space is not well understood. For frontal plane movements, variability is largest along the depth axis (the axis along which visual planning noise is greatest), with execution noise contributing to this variability along the movement direction. Here we tested whether these noise sources interact in a similar way for movements directed in depth. Subjects performed sequences of two movements from a single starting position to targets that were either both contained within a frontal plane ("frontal sequences") or where the first was within the frontal plane and the second was directed in depth ("depth sequences"). For both sequence types, movements were performed with or without visual feedback of the hand. When visual feedback was available, endpoint distributions for frontal and depth sequences were generally anisotropic, with the principal axes of variability being strongly aligned with the depth axis. Without visual feedback, endpoint distributions for frontal sequences were relatively isotropic and movement direction dependent, while those for depth sequences were similar to those with visual feedback. Overall, the results suggest that in the presence of visual feedback, endpoint variability is dominated by uncertainty associated with planning and updating visually guided movements. In addition, the results suggest that without visual feedback, increased uncertainty in hand position estimation effectively unmasks the effect of execution-related noise, resulting in patterns of endpoint variability that are highly movement direction dependent.
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