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高技能国际象棋棋手的学习曲线:对练习幂律普遍性的检验。

Learning curves in highly skilled chess players: a test of the generality of the power law of practice.

作者信息

Howard Robert W

机构信息

University of New South Wales, Australia.

出版信息

Acta Psychol (Amst). 2014 Sep;151:16-23. doi: 10.1016/j.actpsy.2014.05.013. Epub 2014 Jun 11.

Abstract

The power law of practice holds that a power function best interrelates skill performance and amount of practice. However, the law's validity and generality are moot. Some researchers argue that it is an artifact of averaging individual exponential curves while others question whether the law generalizes to complex skills and to performance measures other than response time. The present study tested the power law's generality to development over many years of a very complex cognitive skill, chess playing, with 387 skilled participants, most of whom were grandmasters. A power or logarithmic function best fit grouped data but individuals showed much variability. An exponential function usually was the worst fit to individual data. Groups differing in chess talent were compared and a power function best fit the group curve for the more talented players while a quadratic function best fit that for the less talented. After extreme amounts of practice, a logarithmic function best fit grouped data but a quadratic function best fit most individual curves. Individual variability is great and the power law or an exponential law are not the best descriptions of individual chess skill development.

摘要

练习幂定律认为,幂函数能最好地将技能表现与练习量联系起来。然而,该定律的有效性和普遍性尚无定论。一些研究人员认为它是对个体指数曲线进行平均的产物,而另一些人则质疑该定律是否能推广到复杂技能以及反应时间以外的绩效指标。本研究通过387名熟练参与者(其中大多数是国际象棋特级大师),对练习幂定律在多年发展非常复杂的认知技能——国际象棋方面的普遍性进行了测试。幂函数或对数函数最适合分组数据,但个体表现出很大的变异性。指数函数通常最不适合个体数据。对不同国际象棋天赋的群体进行了比较,幂函数最适合天赋较高玩家的群体曲线,而二次函数最适合天赋较低玩家的群体曲线。在进行大量练习后,对数函数最适合分组数据,但二次函数最适合大多数个体曲线。个体变异性很大,幂定律或指数定律并不是对个体国际象棋技能发展的最佳描述。

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