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

Project Details

Description

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. PUBLIC HEALTH RELEVANCE: Language development is one of the hallmarks of the human species, yet it is difficult to study because of the huge variation in early exposure to different amounts of linguistic input. The use of artificial languages that are acquired in the lab over a few hours provides a window on the mechanisms of language development. We will study language learning in the lab to gain a unique perspective on how the infants and adults learn the patterns of words in streams of speech and contrast this with performance in nonhuman primates. These studies will not only reveal a basic mechanism of language learning, but also establish benchmarks against which language delay can be compared. Moreover, understanding the mechanisms that lead to successful acquisition in normal infants and adults can help to identify loci of language disorders and design methods for remediating disorders.
StatusFinished
Effective start/end date4/1/113/31/17

Funding

  • National Institutes of Health: $208,800.00
  • National Institutes of Health: $217,762.00
  • National Institutes of Health: $221,561.00
  • National Institutes of Health: $203,525.00
  • National Institutes of Health: $208,309.00

Fingerprint Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.