9 found
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  1.  20
    Lila R. Gleitman, Anna Papafragou & John C. Trueswell, Hard Words.
    How do children acquire the meaning of words? And why are words such as know harder for learners to acquire than words such as dog or jump? We suggest that the chief limiting factor in acquiring the vocabulary of natural languages consists not in overcoming conceptual difficulties with abstract word meanings but rather in mapping these meanings onto their corresponding lexical forms. This opening premise of our position, while controversial, is shared with some prior approaches. The present discussion moves forward (...)
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  2. Jeffrey Lidz & Lila R. Gleitman (2004). Yes, We Still Need Universal Grammar. Cognition 94 (1):85-93.
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  3.  10
    Andrew C. Connolly, Jerry A. Fodor, Lila R. Gleitman & Henry Gleitman (2007). Why Stereotypes Don’T Even Make Good Defaults. Cognition 103 (1):1-22.
  4. Cynthia Fisher & Lila R. Gleitman (2002). Language Acquisition. In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley
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  5.  12
    Lila R. Gleitman, Henry Gleitman, Carol Miller & Ruth Ostrin (1996). Similar, and Similar Concepts. Cognition 58 (3):321-376.
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  6.  18
    Jeffrey Lidz & Lila R. Gleitman (2004). Argument Structure and the Child's Contribution to Language Learning. Trends in Cognitive Sciences 8 (4):157-161.
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  7.  1
    Lila R. Gleitman, Henry Gleitman & Elizabeth F. Shipley (1972). The Emergence of the Child as Grammarian. Cognition 1 (2-3):137-164.
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  8.  1
    John C. Trueswell, Yi Lin, Benjamin Armstrong, Erica A. Cartmill, Susan Goldin-Meadow & Lila R. Gleitman (2016). Perceiving Referential Intent: Dynamics of Reference in Natural Parent–Child Interactions. Cognition 148:117-135.
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  9. Jon Scott Stevens, Lila R. Gleitman, John C. Trueswell & Charles Yang (2016). The Pursuit of Word Meanings. Cognitive Science 40 (7):n/a-n/a.
    We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed Pursuit, uses an associative learning mechanism to estimate word-referent probability but pursues and tests the best referent-meaning at any given time. Pursuit is found to perform as well as global models under many conditions extracted from (...)
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