Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E. MEDEA, Italy.
Move'n'Brains Lab, Department of Psychology, Università degli Studi di Torino, Italy.
Phys Life Rev. 2023 Dec;47:245-263. doi: 10.1016/j.plrev.2023.10.036. Epub 2023 Oct 31.
In the last decade, the source and the functional meaning of motor variability have attracted considerable attention in behavioral and brain sciences. This construct classically combined different levels of description, variable internal robustness or coherence, and multifaceted operational meanings. We provide here a comprehensive review of the literature with the primary aim of building a precise lexicon that goes beyond the generic and monolithic use of motor variability. In the pars destruens of the work, we model three domains of motor variability related to peculiar computational elements that influence fluctuations in motor outputs. Each domain is in turn characterized by multiple sub-domains. We begin with the domains of noise and differentiation. However, the main contribution of our model concerns the domain of adaptability, which refers to variation within the same exact motor representation. In particular, we use the terms learning and (social)fitting to specify the portions of motor variability that depend on our propensity to learn and on our largely constitutive propensity to be influenced by external factors. A particular focus is on motor variability in the context of the sub-domain named co-adaptability. Further groundbreaking challenges arise in the modeling of motor variability. Therefore, in a separate pars construens, we attempt to characterize these challenges, addressing both theoretical and experimental aspects as well as potential clinical implications for neurorehabilitation. All in all, our work suggests that motor variability is neither simply detrimental nor beneficial, and that studying its fluctuations can provide meaningful insights for future research.
在过去的十年中，运动可变性的来源和功能意义在行为和脑科学中引起了相当大的关注。这个结构经典地结合了不同的描述水平、内部稳健性或一致性的变量，以及多方面的操作意义。我们在这里提供了文献的全面综述，主要目的是构建一个精确的词汇表，超越运动可变性的通用和整体使用。在工作的破坏部分，我们对与影响运动输出波动的特殊计算元素相关的三种运动可变性领域进行建模。每个领域都有多个子领域。我们从噪声和分化领域开始。然而，我们模型的主要贡献涉及适应性领域，这是指同一精确运动表示内的变化。特别是，我们使用学习和（社会）拟合来指定依赖于我们学习倾向和我们在很大程度上受外部因素影响的固有倾向的运动可变性部分。特别关注名为共同适应性的子领域中的运动可变性。在运动可变性的建模中，还存在进一步的开创性挑战。因此，在另一个构建部分中，我们试图描述这些挑战，解决理论和实验方面以及神经康复的潜在临床意义。总之，我们的工作表明，运动可变性既不是简单的有害的，也不是有益的，研究其波动可以为未来的研究提供有意义的见解。
