Ivanchenko Volodymyr, Jacobs Robert A
Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA.
Neural Comput. 2003 Sep;15(9):2051-65. doi: 10.1162/089976603322297287.
Bernstein (1967) suggested that people attempting to learn to perform a difficult motor task try to ameliorate the degrees-of-freedom problem through the use of a developmental progression. Early in training, people maintain a subset of their control parameters (e.g., joint positions) at constant settings and attempt to learn to perform the task by varying the values of the remaining parameters. With practice, people refine and improve this early-learned control strategy by also varying those parameters that were initially held constant. We evaluated Bernstein's proposed developmental progression using six neural network systems and found that a network whose training included developmental progressions of both its trajectory and its feedback gains outperformed all other systems. These progressions, however, yielded performance benefits only on motor tasks that were relatively difficult to learn. We conclude that development can indeed aid motor learning.
伯恩斯坦（1967年）提出，试图学习执行困难运动任务的人会尝试通过使用一种发展进程来缓解自由度问题。在训练初期，人们将其控制参数的一个子集（例如关节位置）保持在恒定设置，并试图通过改变其余参数的值来学习执行任务。随着练习，人们通过改变最初保持恒定的那些参数来完善和改进这种早期学到的控制策略。我们使用六个神经网络系统评估了伯恩斯坦提出的发展进程，发现一个其训练包括轨迹和反馈增益的发展进程的网络优于所有其他系统。然而，这些进程仅在相对难以学习的运动任务上产生了性能优势。我们得出结论，发展确实可以帮助运动学习。
