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Alternative TitleThe Dissociation and Cognitive Mechanism of Implicit and Explicit Probabilistic Category Learning
Thesis Advisor付秋芳
Degree Grantor中国科学院研究生院
Place of Conferral北京
Degree Discipline心理学
Keyword内隐学习 外显学习 概率类别学习 学习时间 工作记忆 意识知识
Abstract类别学习是学习者通过不断的练习,学会对刺激分类的过程。日常生活中人们对某些刺激的分类并不是非此即彼,非A即B的,而是有时属于类别A有时属于类别B,但属于类别A和B的概率或权重不同。在这种不确定的分类情况下,人们逐渐学会对新事物或新情境做出较准确的概率性分类的过程,称为概率类别学习(Probabilistic catgory learning)。基于观察的学习和基于反馈的学习是概率类别学习的两种学习方式。研究者一般认为,基于观察的概率类别学习是通过外显的言语系统进行;而基于反馈的概率类别学习是基于内隐的程序学习或知觉学习系统,还是基于外显的言语学习系统尚存在很大争议。
Other AbstractCategory learning is the process to categorize objects and events into separate classes. In an uncertain world, a certain object is not be classified either this class or that class, but sometimes in category A, sometimes belongs to the category B, depending on the different categorical weights. Probabilistic category learning involves a gradual learning that holding the probabilistic associations between available information and an outcome of interest to some degree and integrating this information into a singular judgment. There are two versions of probabilistic category learning task, known as “observation-based” and “feedback-based” version. The “observation-based” version is claimed to recruit the declarative systems. However, it is still controversial whether the “feedback-based” version recruits the implicit, procedural or perception learning system, or explicit, verbal learning system.
The purpose of this study is to investigate the dissociative characteristics and the cognitive mechanism of implicit and explicit probabilistic category learning based on multiple-systems theory, using the weather prediction task. This thesis is constructed with four parts and five experiments totally. Study 1 included three experiments investigating the differences between implicit and explicit probabilistic category learning by manipulating the total training time, stimulus presentation time and feedback time. The results shows that training time has different roles in implicit and explicit probabilistic category learning, which impacts the learning performance and the acquisition of the consciousness of the “observation-based” version but not the “feedback-based” version. Study 2 was composed of two experiments, exploring the role of working memory on implicit and explicit probabilistic category learning by using a concurrent task methodology. We found that both concurrent verbal and visuospatial working memory task impaired the explicit, “observation-based” probabilistic category learning, and only the visuospatial task but not the verbal task damaged the performance of the “feedback-based” probabilistic category learning, suggesting the different role of working memory on implicit and explicit probabilistic category learning.
Based on the findings of the research, we proposed that the effects of training time and work memory on implicit and explicit probabilistic category learning are quite different and the “feedback-based” probabilistic category learning is a process of implicit and nonverbal procedural or perception learning, while the “observation-based” version is an explicit, verbal learning.
Subject Area基础心理学
Document Type学位论文
Recommended Citation
GB/T 7714
李开云. 内隐与外显概率类别学习的分离及其认知机制[D]. 北京. 中国科学院研究生院,2013.
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