Why Professional Athletes Need a Prolonged Period of Warm-Up and Other Peculiarities of Human Motor Learning

被引:34
作者
Ajemian, Robert [1 ]
D'Ausilio, Alessandro [2 ]
Moorman, Helene [3 ]
Bizzi, Emilio [1 ]
机构
[1] MIT, McGovern Inst Brain Res, Cambridge, MA 02139 USA
[2] Univ Roma La Sapienza, Dept Psychol, Rome, Italy
[3] Univ Calif Berkeley, Helen Wills Neurosci Inst, Berkeley, CA 94720 USA
关键词
expert performance; motor learning; negative transfer; neural network; savings; sensorimotor system; variability of practice;
D O I
10.1080/00222895.2010.528262
中图分类号
Q189 [神经科学];
学科分类号
071006 [神经生物学];
摘要
Professional athletes involved in sports that require the execution of fine motor skills must practice for a considerable length of time before competing in an event. Why is such practice necessary? Is it merely to warm-up the muscles, tendons, and ligaments, or does the athlete's sensorimotor network need to be constantly recalibrated? In this article, the authors present a point of view in which the human sensorimotor system is characterized by: (a) a high noise level and (b) a high learning rate at the synaptic level (which, because of the noise, does not equate to a high learning rate at the behavioral level). They argue that many heuristics of human skill learning, including the need for a prolonged period of warm-up in experts, follow from these assumptions.
引用
收藏
页码:381 / 388
页数:8
相关论文
共 13 条
[1]
THE 2ND FACET OF FORGETTING - A REVIEW OF WARM-UP DECREMENT [J].
ADAMS, JA .
PSYCHOLOGICAL BULLETIN, 1961, 58 (04) :257-273
[2]
[Anonymous], 1999, HUMAN KINETICS
[3]
[Anonymous], 1988, Parallel distributed processing
[4]
[Anonymous], STABLE ENCODIN UNPUB
[5]
[Anonymous], PLOS BIOL
[6]
A MASSIVELY PARALLEL ARCHITECTURE FOR A SELF-ORGANIZING NEURAL PATTERN-RECOGNITION MACHINE [J].
CARPENTER, GA ;
GROSSBERG, S .
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1987, 37 (01) :54-115
[7]
French R. M., 1992, Connection Science, V4, P365, DOI 10.1080/09540099208946624
[8]
Grossberg S, 1982, Studies of Mind and Brain: Neural Principles of Learning, Perception, Development, Cognition, and Motor Control
[9]
Haykin S., 1999, Neural Networks: A Comprehensive Foundation, DOI DOI 10.1017/S0269888998214044
[10]
Hertz J., 1991, Introduction to the theory of neural computation