Statistical learning of multiple patterns in infants, adults, and monkeys

描述(由申请人提供)：本资助申请的总体目标是了解天真的学习者如何从环境中收集分布信息，并隐含地决定输入语料库包含单一结构还是多个结构。成熟的学习者非常擅长以特定于语境的方式解释信息，从而将输入划分为两个或更多的子结构。我们将通过研究两种类型的NAOVE学习者-人类婴儿和金丝猴-以及成熟的成年人来研究这个特定于背景的统计学习问题。这项拟议研究的具体目标是确定婴儿是否以及如何了解到语流中嵌入了多种信息模式，或者有多个词指向同一对象，并确定特定于背景的统计学习是否具有特定于物种的偏见。两种类型的实验设计将被用来研究特定背景的统计学习。第一种是在底层结构中使用单一的变化。各种语境线索将被引入，以表明潜在结构已经经历了变化，依赖的衡量标准是学习者是否已经习得了第一个、第二个、第一个和第二个结构，或者两个结构都没有。第二种设计使用两个交替的结构，它们由各种刺激线索发出信号，以划分两个潜在的结构。值得注意的是，在这两种设计中，如果学习者聚合整个语料库的结构信息，而不是将语料库划分为两个子集，则不可能进行学习。因此，这些设计测试了学习者提取将输入划分为子集的上下文线索的能力。拟议研究的意义对任何学习理论都是基本的，尤其是对内隐(被动暴露)统计学习的影响，这种学习被认为是许多领域早期人类发展的特征。婴儿必须学习--通过对分布模式的敏感性和天生的偏见--信息的模式是特定于背景的，就像双语一样。我们拟议的实验将扩展我们最近对人类成年人的研究，确定(A)婴儿是否表现出相同的学习偏见(首要效应)和语境敏感性(对说话者的声音)，(B)塔玛琳猴子是否表现出同样的偏见和语境效应，以及(C)在分词和参照任务中，语境特定的统计学习在人类成年人和婴儿中的限制是什么。

DESCRIPTION (provided by applicant): The overall goal of the present grant application is to understand how a naive learner collects distributional information from the environment and makes an implicit decision that the corpus of input contains either a single structure or multiple structures. Mature learners are incredibly facile at interpreting information in a context-specific manner, thereby partitioning the input into two or more sub-structures. We will investigate this question of context-specific statistical learning by studying two types of naove learners - human infants and tamarin monkeys - as well as mature adults. The specific objective of the proposed research is to determine whether and how infants learn that there are multiple patterns of information embedded in streams of speech, or that there are multiple words that refer to the same object, and to determine whether context-specific statistical learning has species-specific biases. Two types of experimental designs will be used to study context-specific statistical learning. The first uses a single change in the underlying structure. A variety of contextual cues will be introduced to signal that the underlying structure has undergone a change, and the dependent measure is whether the learner has acquired the first, the second, both the first and the second, or neither structures. The second design uses two alternating structures that are signaled by a variety of stimulus cues to partition the two underlying structures. It is important to note that in both of these designs, if the learner aggregates the structural information across the entire corpus, rather than partitioning the corpus into two subsets, no learning is possible. Thus, these designs test the ability of the learner to extract the contextual cues that partition the input into subsets. The implications of the proposed studies are fundamental to any theory of learning, but particularly to the kind of implicit (passive exposure) statistical learning that is thought to characterize much of early human development in many domains. Infants must learn - by a combination of sensitivity to distributional patterns and innate biases - that patterns of information are context-specific, as in the case of bilingualism. Our proposed experiments will extend our recent studies of human adults by determining (a) whether infants show the same pattern of learning biases (primacy effects) and context-sensitivity (to talker voice), (b) whether tamarin monkeys show these same biases and context effects, and (c) what the limits of context-specific statistical learning are in human adults and infants in both word segmentation and referential tasks.