9 found
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  1.  23
    Hard Words.Lila R. Gleitman, Anna Papafragou & John C. Trueswell - unknown
    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.  15
    Why Stereotypes Don’T Even Make Good Defaults.Andrew C. Connolly, Jerry A. Fodor, Lila R. Gleitman & Henry Gleitman - 2007 - Cognition 103 (1):1-22.
  3. Yes, We Still Need Universal Grammar.Jeffrey Lidz & Lila R. Gleitman - 2004 - Cognition 94 (1):85-93.
  4. Language Acquisition.Cynthia Fisher & Lila R. Gleitman - 2002 - In J. Wixted & H. Pashler (eds.), Stevens' Handbook of Experimental Psychology. Wiley.
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  5.  13
    Similar, and Similar Concepts.Lila R. Gleitman, Henry Gleitman, Carol Miller & Ruth Ostrin - 1996 - Cognition 58 (3):321-376.
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  6.  36
    Argument Structure and the Child's Contribution to Language Learning.Jeffrey Lidz & Lila R. Gleitman - 2004 - Trends in Cognitive Sciences 8 (4):157-161.
  7.  1
    The Emergence of the Child as Grammarian.Lila R. Gleitman, Henry Gleitman & Elizabeth F. Shipley - 1972 - Cognition 1 (2-3):137-164.
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  8.  1
    Perceiving Referential Intent: Dynamics of Reference in Natural Parent–Child Interactions.John C. Trueswell, Yi Lin, Benjamin Armstrong, Erica A. Cartmill, Susan Goldin-Meadow & Lila R. Gleitman - 2016 - Cognition 148:117-135.
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  9. The Pursuit of Word Meanings.Stevens Jon Scott, R. Gleitman Lila, C. Trueswell John & Yang Charles - 2016 - Cognitive Science 41 (8).
    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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