12 found
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  1.  14
    Representing Word Meaning and Order Information in a Composite Holographic Lexicon.Michael N. Jones & Douglas J. K. Mewhort - 2007 - Psychological Review 114 (1):1-37.
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  2.  10
    Optimal Foraging in Semantic Memory.Thomas T. Hills, Michael N. Jones & Peter M. Todd - 2012 - Psychological Review 119 (2):431-440.
  3. Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation.Brian Riordan & Michael N. Jones - 2011 - Topics in Cognitive Science 3 (2):303-345.
    Abstract Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations (...)
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  4. Perceptual Inference Through Global Lexical Similarity.Brendan T. Johns & Michael N. Jones - 2012 - Topics in Cognitive Science 4 (1):103-120.
    The literature contains a disconnect between accounts of how humans learn lexical semantic representations for words. Theories generally propose that lexical semantics are learned either through perceptual experience or through exposure to regularities in language. We propose here a model to integrate these two information sources. Specifically, the model uses the global structure of memory to exploit the redundancy between language and perception in order to generate inferred perceptual representations for words with which the model has no perceptual experience. We (...)
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  5.  5
    New Perspectives on the Aging Lexicon.Dirk U. Wulff, Simon De Deyne, Michael N. Jones & Rui Mata - 2019 - Trends in Cognitive Sciences 23 (8):686-698.
  6.  53
    Foraging in Semantic Fields: How We Search Through Memory.Thomas T. Hills, Peter M. Todd & Michael N. Jones - 2015 - Topics in Cognitive Science 7 (3):513-534.
    When searching for concepts in memory—as in the verbal fluency task of naming all the animals one can think of—people appear to explore internal mental representations in much the same way that animals forage in physical space: searching locally within patches of information before transitioning globally between patches. However, the definition of the patches being searched in mental space is not well specified. Do we search by activating explicit predefined categories and recall items from within that category, or do we (...)
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  7.  11
    The Role of Semantic Diversity in Word Recognition Across Aging and Bilingualism.Brendan T. Johns, Christine L. Sheppard, Michael N. Jones & Vanessa Taler - 2016 - Frontiers in Psychology 7.
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  8.  21
    Hidden Processes in Structural Representations: A Reply to Abbott, Austerweil, and Griffiths.Michael N. Jones, Thomas T. Hills & Peter M. Todd - 2015 - Psychological Review 122 (3):570-574.
  9.  31
    Quantity and Diversity: Simulating Early Word Learning Environments.Jessica L. Montag, Michael N. Jones & Linda B. Smith - 2018 - Cognitive Science 42 (S2):375-412.
    The words in children's language learning environments are strongly predictive of cognitive development and school achievement. But how do we measure language environments and do so at the scale of the many words that children hear day in, day out? The quantity and quality of words in a child's input are typically measured in terms of total amount of talk and the lexical diversity in that talk. There are disagreements in the literature whether amount or diversity is the more critical (...)
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  10.  14
    Graph‐Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory.Thomas M. Gruenenfelder, Gabriel Recchia, Tim Rubin & Michael N. Jones - 2016 - Cognitive Science 40 (6):1460-1495.
    We compared the ability of three different contextual models of lexical semantic memory and of a simple associative model to predict the properties of semantic networks derived from word association norms. None of the semantic models were able to accurately predict all of the network properties. All three contextual models over-predicted clustering in the norms, whereas the associative model under-predicted clustering. Only a hybrid model that assumed that some of the responses were based on a contextual model and others on (...)
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  11.  5
    Grammatical Number Processing and Anticipatory Eye Movements Are Not Tightly Coordinated in English Spoken Language Comprehension.Brian Riordan, Melody Dye & Michael N. Jones - 2015 - Frontiers in Psychology 6.
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  12.  7
    The Role of Negative Information in Distributional Semantic Learning.Brendan T. Johns, Douglas J. K. Mewhort & Michael N. Jones - 2019 - Cognitive Science 43 (5):e12730.
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