CLARION模型：内隐与外显技能学习的整合

CLARION是一个与其它认知理论明显不同的计算认知模型，它可用来研究内隐和外显学习交互作用的各种定量数据。通过对经典内隐学习任务（如系列反应时任务、加工控制任务及其他高级认知加工任务）的模拟实验，在与人类实验数据的拟合上，CLARION展示出比以往认知模型更好的性能。它主要有两个特征：一是对内隐学习和外显学习的整合，二是对自下而上加工的强调。近几年，CLARION模型在理论和实践上都有较大进展。文章简要评述了CLARION模型，并围绕此模型介绍了学界有关内隐和外显加工交互作用的探索

CLARION: A Integrated Model in Studying the Relationship between Implicit and Explicit Skill Learning

CLARION is a computational cognitive model different from other ones. It aims at studying the data of researches about the interaction between implicit and explicit skill learning. By simulating of those classic experiments with human subject in implicit learning, such as SRT, DC, and other high-order cognitive task, CLARION demonstrated its better fit to human data. CLARION has its advantages at least in two aspect, one is its integration of implicit and explicit processing, and the other is its emphasizing on the importance of bottom-up processing. It has developed much during recent years, and this paper just made a brief review of its development and its main point.