Psychology Department, Rutgers Center for Cognitive Science, Rutgers Computational Biomedicine Imaging and Modeling Center, Rutgers University, 152 Frelinghuysen Rd, Piscataway, NJ 08854, USA.
Behav Brain Funct. 2013 Mar 6;9:10. doi: 10.1186/1744-9081-9-10.
Complex movement sequences are composed of segments with different levels of functionality: intended segments towards a goal and segments that spontaneously occur largely beneath our awareness. It is not known if these spontaneously-occurring segments could be informative of the learning progression in naïve subjects trying to skillfully master a new sport routine.
To address this question we asked if the hand speed variability could be modeled as a stochastic process where each trial speed depended on the speed of the previous trial. We specifically asked if the hand speed maximum from a previous trial could accurately predict the maximum speed of a sub-sequent trial in both intended and spontaneous movement segments. We further asked whether experts and novices manifested similar models, despite different kinematic dynamics and assessed the predictive power of the spontaneous fluctuations in the incidental motions.
We found a simple power rule to parameterize speed variability for expert and novices with accurate predictive value despite randomly instructed speed levels and training contexts. This rule on average tended to yield similar exponent across speed levels for intended motion segments. Yet for the spontaneous segments the speed fluctuations had exponents that changed as a function of speed level and training context. Two conditions highlighted the expert performance: broad bandwidth of velocity-dependent parameter values and low noise-to-signal ratios that unambiguously distinguished between training regimes. Neither of these was yet manifested in the novices.
We suggest that the statistics of intended motions may be a predictor of overall expertise level, whereas those of spontaneously occurring incidental motions may serve to track learning progression in different training contexts. These spontaneous fluctuations may help the central systems to kinesthetically discriminate the peripheral re-afferent patterns of movement variability associated with changes in movement speed and training context. We further propose that during learning the acquisition of both broad bandwidth of speeds and low noise-to-signal ratios may be critical to build a verifiable kinesthetic (movement) percept and reach the type of automaticity that an expert acquires.
复杂的运动序列由具有不同功能水平的片段组成：有意向的片段朝着目标，以及在很大程度上自发出现的片段，这些片段在我们的意识之下。目前还不清楚这些自发出现的片段是否可以为试图熟练掌握新运动常规的新手提供有关学习进展的信息。
为了解决这个问题，我们询问手速变异性是否可以建模为一个随机过程，其中每个试验速度取决于前一个试验的速度。我们特别询问前一个试验的手速最大值是否可以准确预测后续试验的最大速度，无论是有意向的运动片段还是自发的运动片段。我们进一步询问专家和新手是否表现出相似的模型，尽管运动动力学不同，并评估了意外运动中的自发波动的预测能力。
我们发现，尽管随机指示速度水平和训练背景不同，但对于专家和新手来说，一种简单的幂律规则可以很好地描述速度变异性，具有准确的预测值。这个规则平均倾向于在有意向的运动片段中产生相似的速度水平。然而，对于自发的运动片段，速度波动的指数随着速度水平和训练背景的变化而变化。两种情况突出了专家的表现：速度相关参数值的带宽较宽，噪声与信号比低，这可以明确区分训练模式。这些在新手身上都尚未表现出来。
我们建议，有意向运动的统计数据可能是整体专业水平的预测指标，而自发出现的意外运动的统计数据可能有助于跟踪不同训练背景下的学习进展。这些自发的波动可能有助于中央系统通过运动速度和训练背景的变化来进行动觉区分运动变异性的外周再传入模式。我们进一步提出，在学习过程中，获得较宽的速度带宽和较低的噪声与信号比可能是建立可验证的动觉（运动）感知并达到专家所获得的那种自动性的关键。
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