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  1. The Dynamics of Lexical Competition During Spoken Word Recognition.James S. Magnuson, James A. Dixon, Michael K. Tanenhaus & Richard N. Aslin - 2007 - Cognitive Science: A Multidisciplinary Journal 30 (1):133-156.
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  • The Dynamics of Lexical Competition During Spoken Word Recognition.James S. Magnuson, James A. Dixon, Michael K. Tanenhaus & Richard N. Aslin - 2007 - Cognitive Science 31 (1):133-156.
    The sounds that make up spoken words are heard in a series and must be mapped rapidly onto words in memory because their elements, unlike those of visual words, cannot simultaneously exist or persist in time. Although theories agree that the dynamics of spoken word recognition are important, they differ in how they treat the nature of the competitor set—precisely which words are activated as an auditory word form unfolds in real time. This study used eye tracking to measure the (...)
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  • The role of prosodic boundaries in the resolution of lexical embedding in speech comprehension.Anne Pier Salverda, Delphine Dahan & James M. McQueen - 2003 - Cognition 90 (1):51-89.
  • The Dynamics of Lexical Competition During Spoken Word Recognition.James S. Magnuson, James A. Dixon, Michael K. Tanenhaus & Richard N. Aslin - 2007 - Cognitive Science 31 (1):133-156.
    The sounds that make up spoken words are heard in a series and must be mapped rapidly onto words in memory because their elements, unlike those of visual words, cannot simultaneously exist or persist in time. Although theories agree that the dynamics of spoken word recognition are important, they differ in how they treat the nature of the competitor set—precisely which words are activated as an auditory word form unfolds in real time. This study used eye tracking to measure the (...)
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  • The Dynamics of Lexical Competition During Spoken Word Recognition.James S. Magnuson, James A. Dixon, Michael K. Tanenhaus & Richard N. Aslin - 2007 - Cognitive Science 31 (1):133-156.
    The sounds that make up spoken words are heard in a series and must be mapped rapidly onto words in memory because their elements, unlike those of visual words, cannot simultaneously exist or persist in time. Although theories agree that the dynamics of spoken word recognition are important, they differ in how they treat the nature of the competitor set—precisely which words are activated as an auditory word form unfolds in real time. This study used eye tracking to measure the (...)
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  • Lexical competition and the acquisition of novel words.M. Gareth Gaskell & Nicolas Dumay - 2003 - Cognition 89 (2):105-132.
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  • Inhibitory priming effects in auditory word recognition: when the target's competitors conflict with the prime word.Sophie Dufour & Ronald Peereman - 2003 - Cognition 88 (3):B33-B44.
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  • Feature Statistics Modulate the Activation of Meaning During Spoken Word Processing.Barry J. Devereux, Kirsten I. Taylor, Billi Randall, Jeroen Geertzen & Lorraine K. Tyler - 2016 - Cognitive Science 40 (2):325-350.
    Understanding spoken words involves a rapid mapping from speech to conceptual representations. One distributed feature-based conceptual account assumes that the statistical characteristics of concepts’ features—the number of concepts they occur in and likelihood of co-occurrence —determine conceptual activation. To test these claims, we investigated the role of distinctiveness/sharedness and correlational strength in speech-to-meaning mapping, using a lexical decision task and computational simulations. Responses were faster for concepts with higher sharedness, suggesting that shared features are facilitatory in tasks like lexical decision (...)
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  • Heeding the voice of experience: The role of talker variation in lexical access.Sarah C. Creel, Richard N. Aslin & Michael K. Tanenhaus - 2008 - Cognition 106 (2):633-664.
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  • Connectionist Natural Language Processing: The State of the Art.Morten H. Christiansen & Nick Chater - 1999 - Cognitive Science 23 (4):417-437.
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  • Two ways of learning associations.Luke Boucher & Zoltán Dienes - 2003 - Cognitive Science 27 (6):807-842.
    How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two (...)
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