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The development of predictive processes in children’s discourse understanding ...
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46 |
Social and Discourse Contributions to the Determination of Reference in Cross-Situational Word Learning
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In: Stanford University web domain (2013)
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Parsing entire discourses as very long strings : capturing topic continuity in grounded language learning
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48 |
Learning and Long-Term Retention of Large-Scale Artificial Languages
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49 |
Learning and Long-Term Retention of Large-Scale Artificial Languages
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In: PLoS (2012)
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51 |
Verbal interference suppresses exact numerical representation
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In: Prof. Gibson (2011)
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52 |
Overcoming Memory Limitations in Rule Learning
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In: MIT Web Domain (2011)
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Contributions of Prosodic and Distributional Features of Caregivers' Speech in Early Word Learning
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In: Soroush Vosoughi (2010)
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Early word learning through communicative inference
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Abstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010. ; Cataloged from PDF version of thesis. ; Includes bibliographical references (p. 109-122). ; How do children learn their first words? Do they do it by gradually accumulating information about the co-occurrence of words and their referents over time, or are words learned via quick social inferences linking what speakers are looking at, pointing to, and talking about? Both of these conceptions of early word learning are supported by empirical data. This thesis presents a computational and theoretical framework for unifying these two different ideas by suggesting that early word learning can best be described as a process of joint inferences about speakers' referential intentions and the meanings of words. Chapter 1 describes previous empirical and computational research on "statistical learning"--the ability of learners to use distributional patterns in their language input to learn about the elements and structure of language-and argues that capturing this abifity requires models of learning that describe inferences over structured representations, not just simple statistics. Chapter 2 argues that social signals of speakers' intentions, even eye-gaze and pointing, are at best noisy markers of reference and that in order to take advantage of these signals fully, learners must integrate information across time. Chapter 3 describes the kinds of inferences that learners can make by assuming that speakers are informative with respect to their intended meaning, introducing and testing a formalization of how Grice's pragmatic maxims can be used for word learning. Chapter 4 presents a model of cross-situational intentional word learning that both learns words and infers speakers' referential intentions from labeled corpus data. ; by Michael C. Frank. ; Ph.D.
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Keyword:
Brain and Cognitive Sciences
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URL: http://hdl.handle.net/1721.1/62045
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Effects of Caregiver Prosody on Child Language Acquisition
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In: Soroush Vosoughi (2010)
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Learning words and their meanings from unsegmented child-directed speech
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Exploring word learning in a high-density longitudinal corpus
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In: MIT web domain (2009)
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