Latash Mark L
Department of Kinesiology, The Pennsylvania State University, University Park, PA, United States.
Moscow Institute of Physics and Technology, Dolgoprudnyj, Russia.
Front Syst Neurosci. 2021 Nov 18;15:735406. doi: 10.3389/fnsys.2021.735406. eCollection 2021.
Biological systems differ from the inanimate world in their behaviors ranging from simple movements to coordinated purposeful actions by large groups of muscles, to perception of the world based on signals of different modalities, to cognitive acts, and to the role of self-imposed constraints such as laws of ethics. Respectively, depending on the behavior of interest, studies of biological objects based on laws of nature (physics) have to deal with different salient sets of variables and parameters. Understanding is a high-level concept, and its analysis has been linked to other high-level concepts such as "mental model" and "meaning". Attempts to analyze understanding based on laws of nature are an example of the top-down approach. Studies of the neural control of movements represent an opposite, bottom-up approach, which starts at the interface with classical physics of the inanimate world and operates with traditional concepts such as forces, coordinates, etc. There are common features shared by the two approaches. In particular, both assume organizations of large groups of elements into task-specific groups, which can be described with only a handful of salient variables. Both assume optimality criteria that allow the emergence of families of solutions to typical tasks. Both assume predictive processes reflected in anticipatory adjustments to actions (motor and non-motor). Both recognize the importance of generating dynamically stable solutions. The recent progress in studies of the neural control of movements has led to a theory of hierarchical control with spatial referent coordinates for the effectors. This theory, in combination with the uncontrolled manifold hypothesis, allows quantifying the stability of actions with respect to salient variables. This approach has been used in the analysis of motor learning, changes in movements with typical and atypical development and with aging, and impaired actions by patients with various neurological disorders. It has been developed to address issues of kinesthetic perception. There seems to be hope that the two counter-directional approaches will meet and result in a single theoretical scheme encompassing biological phenomena from figuring out the best next move in a chess position to activating motor units appropriate for implementing that move on the chessboard.
生物系统与无生命世界的不同之处在于其行为，这些行为涵盖从简单的运动到大量肌肉的协调有目的行动，再到基于不同模态信号对世界的感知、认知行为，以及诸如伦理法则等自我施加约束的作用。相应地，根据所关注的行为，基于自然法则（物理学）对生物对象的研究必须处理不同的显著变量和参数集。理解是一个高层次概念，其分析已与其他高层次概念如“心理模型”和“意义”联系起来。基于自然法则分析理解的尝试是自上而下方法的一个例子。对运动神经控制的研究代表了一种相反的自下而上方法，它从与无生命世界的经典物理学的接口开始，并运用诸如力、坐标等传统概念。这两种方法有共同特征。特别是，两者都假设将大量元素组织成特定任务组，这些任务组只用少数几个显著变量就能描述。两者都假设有最优性标准，这使得针对典型任务能出现一系列解决方案。两者都假设预测过程体现在对行动（运动和非运动）的预期调整中。两者都认识到生成动态稳定解决方案的重要性。运动神经控制研究的最新进展导致了一种具有效应器空间参照坐标的分层控制理论。该理论与非受控流形假设相结合，使得能够相对于显著变量量化行动的稳定性。这种方法已用于运动学习分析、典型和非典型发育及衰老过程中运动的变化分析，以及各种神经疾病患者的行动障碍分析。它已被发展用于解决动觉感知问题。似乎有希望这两种相反方向的方法能够结合，形成一个单一的理论框架，涵盖从在国际象棋局面中找出最佳下一步行动到激活适合在棋盘上执行该行动的运动单位等生物现象。
