Maij Femke, Wing Alan M, Medendorp W Pieter
Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands; and
School of Psychology, University of Birmingham, Birmingham, United Kingdom.
J Neurophysiol. 2017 Jul 1;118(1):187-193. doi: 10.1152/jn.00286.2016. Epub 2017 Mar 29.
People make systematic errors when localizing a brief tactile stimulus in the external space presented on the index finger while moving the arm. Although these errors likely arise in the spatiotemporal integration of the tactile input and information about arm position, the underlying arm position information used in this process is not known. In this study, we tested the contributions of afferent proprioceptive feedback and predictive arm position signals by comparing localization errors during passive vs. active arm movements. In the active trials, participants were instructed to localize a tactile stimulus in the external space that was presented to the index finger near the time of a self-generated arm movement. In the passive trials, each of the active trials was passively replayed in randomized order, using a robotic device. Our results provide evidence that the localization error patterns of the passive trials are similar to the active trials and, moreover, did not lag but rather led the active trials, which suggests that proprioceptive feedback makes an important contribution to tactile localization. To further test which kinematic property of this afferent feedback signal drives the underlying computations, we examined the localization errors with movements that had differently skewed velocity profiles but overall the same displacement. This revealed a difference in the localization patterns, which we explain by a probabilistic model in which temporal uncertainty about the stimulus is converted into a spatial likelihood, depending on the actual velocity of the arm rather than involving an efferent, preprogrammed movement. We show that proprioceptive feedback of arm motion rather than efferent motor signals contributes to tactile localization during an arm movement. Data further show that localization errors depend on arm velocity, not displacement per se, suggesting that instantaneous velocity feedback plays a role in the underlying computations. Model simulation using Bayesian inference suggests that these errors depend not only on spatial but also on temporal uncertainties of sensory and motor signals.
当人们在移动手臂时，对食指上呈现的外部空间中的短暂触觉刺激进行定位时，会出现系统性错误。尽管这些错误可能源于触觉输入与手臂位置信息的时空整合，但在此过程中所使用的潜在手臂位置信息尚不清楚。在本研究中，我们通过比较被动与主动手臂运动期间的定位误差，测试了传入本体感觉反馈和预测性手臂位置信号的作用。在主动试验中，参与者被要求在自身产生手臂运动的时刻附近，对呈现于食指的外部空间中的触觉刺激进行定位。在被动试验中，使用机器人设备以随机顺序对每个主动试验进行被动重放。我们的结果表明，被动试验的定位误差模式与主动试验相似，而且，被动试验并非滞后于而是领先于主动试验，这表明本体感觉反馈对触觉定位有重要贡献。为了进一步测试这种传入反馈信号的哪种运动学特性驱动了潜在的计算，我们检查了具有不同倾斜速度分布但总体位移相同的运动的定位误差。这揭示了定位模式的差异，我们通过一个概率模型来解释，在该模型中，刺激的时间不确定性根据手臂的实际速度被转换为空间可能性，而不是涉及传出的、预先编程的运动。我们表明，手臂运动的本体感觉反馈而非传出运动信号在手臂运动期间对触觉定位有贡献。数据进一步表明，定位误差取决于手臂速度，而非位移本身，这表明瞬时速度反馈在潜在计算中起作用。使用贝叶斯推理的模型模拟表明，这些误差不仅取决于感觉和运动信号的空间不确定性，还取决于时间不确定性。
