Department of English, University of Amsterdam.
Department of Psychology, Lancaster University.
Top Cogn Sci. 2019 Jul;11(3):536-554. doi: 10.1111/tops.12439.
Implicit learning generally refers to the acquisition of structures that, like knowledge of natural language grammar, are not available to awareness. In contrast, statistical learning has frequently been related to learning language structures that are explicitly available, such as vocabulary. In this paper, we report an experimental paradigm that enables testing of both classic implicit and statistical learning in language. The paradigm employs an artificial language comprising sentences that accompany visual scenes that they represent, thus combining artificial grammar learning with cross-situational statistical learning of vocabulary. We show that this methodology enables a comparison between acquisition of grammar and vocabulary, and the influences on their learning. We show that both grammar and vocabulary are promoted by explicit information about the language structure, that awareness of structure affects acquisition during learning, and awareness precedes learning, but is not distinctive at the endpoint of learning. The two traditions of learning-implicit and statistical-can be conjoined in a single paradigm to explore both the phenomenological and learning consequences of statistical structural knowledge.
内隐学习通常是指结构的习得，这些结构像自然语言语法知识一样，无法被意识到。相比之下，统计学习经常与学习明确可获得的语言结构有关，例如词汇。在本文中，我们报告了一个实验范式，可以在语言中同时测试经典的内隐学习和统计学习。该范式采用一种人工语言，其中包含伴随代表它们的视觉场景的句子，从而将人工语法学习与词汇的跨情境统计学习结合起来。我们表明，这种方法可以比较语法和词汇的习得，以及它们学习的影响。我们表明，语法和词汇都受到语言结构的显式信息的促进，结构意识影响学习过程中的习得，并且意识先于学习，但在学习终点不具有独特性。这两种学习传统——内隐学习和统计学习——可以在一个单一的范式中结合起来，以探索统计结构知识的现象学和学习后果。
