创造适应性技能:心理意象的非线性教学方法

IF 1.3 Q3 PSYCHOLOGY, APPLIED
R. Lindsay, J. Chow, P. Larkin, M. Spittle
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引用次数: 2

摘要

现有的心理意象(MI)指南强调需要复制身体练习的关键方面以获得最佳表现结果。然而,更多的研究应该探索将当代技能习得方法,如非线性教学法(NLP)应用于人工智能的技能发展。从NLP的角度来看,熟练的行为是基于感知信息和实际动作的耦合。这对MI等缺乏刺激的实践模式提出了挑战。最近的研究表明,尽管MI是缺乏刺激的,这表明它与NLP方法不相容,但它也可能对刺激敏感,可能允许与环境耦合。本文旨在为人工智能提供另一种生态动力学解释,以支持将NLP原则应用于人工智能。本文还概述了应用于人工智能的具体例子,并为从业者进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Creating Adaptable Skills: A Nonlinear Pedagogy Approach to Mental Imagery
Abstract Existing mental imagery (MI) guidelines emphasize the need to replicate critical aspects of physical practice for optimal performance outcomes. However, more research should explore applying contemporary skill acquisition approaches, such as Nonlinear Pedagogy (NLP), to MI for skill development. From a NLP perspective, skilled behavior is predicated on coupling perceptual information and actual movement. This presents a challenge for stimulus-absent modes of practice such as MI. Recent findings indicate that although MI is stimulus-absent, suggesting it is incompatible with a NLP approach, it may also be sensitive to stimuli, potentially allowing for coupling to the environment. This paper aims to provide an alternative ecological dynamics explanation of MI to support the application of NLP principles to MI. Specific examples of NLP applied to MI are also outlined and discussed for practitioners.
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来源期刊
CiteScore
3.60
自引率
11.10%
发文量
19
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