Sober Samuel J, Sabes Philip N
Department of Physiology, W. M. Keck Foundation Center for Integrative Neuroscience, and Neuroscience Graduate Program, University of California San Francisco, San Francisco, California 94143-0444, USA.
J Neurosci. 2003 Aug 6;23(18):6982-92. doi: 10.1523/JNEUROSCI.23-18-06982.2003.
When planning goal-directed reaches, subjects must estimate the position of the arm by integrating visual and proprioceptive signals from the sensory periphery. These integrated position estimates are required at two stages of motor planning: first to determine the desired movement vector, and second to transform the movement vector into a joint-based motor command. We quantified the contributions of each sensory modality to the position estimate formed at each planning stage. Subjects made reaches in a virtual reality environment in which vision and proprioception were dissociated by shifting the location of visual feedback. The relative weighting of vision and proprioception at each stage was then determined using computational models of feedforward motor control. We found that the position estimate used for movement vector planning relies mostly on visual input, whereas the estimate used to compute the joint-based motor command relies more on proprioceptive signals. This suggests that when estimating the position of the arm, the brain selects different combinations of sensory input based on the computation in which the resulting estimate will be used.
在规划目标导向的伸手动作时，受试者必须通过整合来自感觉外周的视觉和本体感觉信号来估计手臂的位置。在运动规划的两个阶段都需要这些整合后的位置估计：首先是确定期望的运动矢量，其次是将运动矢量转换为基于关节的运动指令。我们量化了每种感觉模态对每个规划阶段形成的位置估计的贡献。受试者在虚拟现实环境中进行伸手动作，在该环境中通过移动视觉反馈的位置来分离视觉和本体感觉。然后使用前馈运动控制的计算模型确定每个阶段视觉和本体感觉的相对权重。我们发现，用于运动矢量规划的位置估计主要依赖视觉输入，而用于计算基于关节的运动指令的估计则更多地依赖本体感觉信号。这表明，在估计手臂位置时，大脑会根据最终估计结果所用于的计算来选择不同的感觉输入组合。
