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  1. Individual Differences in the Strength of Taxonomic Versus Thematic Relations.Daniel Mirman & Kristen M. Graziano - 2012 - Journal of Experimental Psychology: General 141 (4):601-609.
  • 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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  • Conceptual and Linguistic Representations of Kinds and Classes.Sandeep Prasada, Laura Hennefield & Daniel Otap - 2012 - Cognitive Science 36 (7):1224-1250.
    We investigate the hypothesis that our conceptual systems provide two formally distinct ways of representing categories by investigating the manner in which lexical nominals (e.g., tree, picnic table) and phrasal nominals (e.g., black bird, birds that like rice) are interpreted. Four experiments found that lexical nominals may be mapped onto kind representations, whereas phrasal nominals map onto class representations but not kind representations. Experiment 1 found that phrasal nominals, unlike lexical nominals, are mapped onto categories whose members need not be (...)
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  • Acquiring Contextualized Concepts: A Connectionist Approach.Saskia van Dantzig, Antonino Raffone & Bernhard Hommel - 2011 - Cognitive Science 35 (6):1162-1189.
    Conceptual knowledge is acquired through recurrent experiences, by extracting statistical regularities at different levels of granularity. At a fine level, patterns of feature co-occurrence are categorized into objects. At a coarser level, patterns of concept co-occurrence are categorized into contexts. We present and test CONCAT, a connectionist model that simultaneously learns to categorize objects and contexts. The model contains two hierarchically organized CALM modules (Murre, Phaf, & Wolters, 1992). The first module, the Object Module, forms object representations based on co-occurrences (...)
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  • Can the Humped Animal's Knee Conceal its Name? Commentary On: “The Roles of Shared Vs. Distinctive Conceptual Features in Lexical Access”.Maria Montefinese & David Vinson - 2015 - Frontiers in Psychology 6.
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  • Simulating the N400 ERP Component as Semantic Network Error: Insights From a Feature-Based Connectionist Attractor Model of Word Meaning.Milena Rabovsky & Ken McRae - 2014 - Cognition 132 (1):68-89.
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  • Lexical Organization and Competition in First and Second Languages: Computational and Neural Mechanisms.Ping Li - 2009 - Cognitive Science 33 (4):629-664.
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