远距离规则的内隐学习及其限制

Implicit Learning of Nonlocal Dependencies and the Limitations

人们能够无意识地获得外部世界结构化规则的知识,这种学习机制被称为内隐学习。关于内隐学习获得的是具体的表面组块还是抽象的深层规则一直存在争议。远距离规则的内隐学习为组块与规则学习之争提供了一个崭新的研究视角。本研究从人工语言学习、音乐学习和知觉-动作序列学习这三个领域梳理了远距离规则内隐学习的研究进展,探讨了远距离规则内隐学习的限制,并从迁移效应、限制因素等研究内容,以及功能磁共振成像和人工神经网络模拟等研究技术上对未来研究提出了展望。

People are capable of acquiring unconscious knowledge of structural regularities from the environment. This form of learning is known as implicit learning. In the implicit learning literature, there remains controversy over what contents can be implicitly learned, i.e., chunks or abstract rules. Implicit learning of nonlocal dependencies provtoes a new perspective for the artificial language learning, music learning and perceptual motor sequences learning, this paper reviews the recent literature on implicit learning of nonlocal dependencies, discusses the limitations of implicit learning,and puts forward some new issues and approaches in terms of the transfer effects, limitations, functional magnetic resonance imaging and artificial neural networks simulations for future studies.