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  1. Division of Labor in Vocabulary Structure: Insights From Corpus Analyses.Morten H. Christiansen & Padraic Monaghan - 2016 - Topics in Cognitive Science 8 (3):610-624.
    Psychologists have used experimental methods to study language for more than a century. However, only with the recent availability of large-scale linguistic databases has a more complete picture begun to emerge of how language is actually used, and what information is available as input to language acquisition. Analyses of such “big data” have resulted in reappraisals of key assumptions about the nature of language. As an example, we focus on corpus-based research that has shed new light on the arbitrariness of (...)
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  • Visual and Affective Multimodal Models of Word Meaning in Language and Mind.Simon De Deyne, Danielle J. Navarro, Guillem Collell & Andrew Perfors - 2021 - Cognitive Science 45 (1):e12922.
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  • A Dynamic Approach to Recognition Memory.Gregory E. Cox & Richard M. Shiffrin - 2017 - Psychological Review 124 (6):795-860.
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  • When Meaning Is Not Enough: Distributional and Semantic Cues to Word Categorization in Child Directed Speech.Feijoo Sara, Muñoz Carmen, Amadó Anna & Serrat Elisabet - 2017 - Frontiers in Psychology 8.
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  • Computational Methods to Extract Meaning From Text and Advance Theories of Human Cognition.Danielle S. McNamara - 2011 - Topics in Cognitive Science 3 (1):3-17.
    Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research (...)
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  • The Emotions of Abstract Words: A Distributional Semantic Analysis.Alessandro Lenci, Gianluca E. Lebani & Lucia C. Passaro - 2018 - Topics in Cognitive Science 10 (3):550-572.
  • 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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  • Thinking in Words: Language as an Embodied Medium of Thought.Guy Dove - 2014 - Topics in Cognitive Science 6 (3):371-389.
    Recently, there has been a great deal of interest in the idea that natural language enhances and extends our cognitive capabilities. Supporters of embodied cognition have been particularly interested in the way in which language may provide a solution to the problem of abstract concepts. Toward this end, some have emphasized the way in which language may act as form of cognitive scaffolding and others have emphasized the potential importance of language-based distributional information. This essay defends a version of the (...)
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  • Reconciling Embodied and Distributional Accounts of Meaning in Language.Mark Andrews, Stefan Frank & Gabriella Vigliocco - 2014 - Topics in Cognitive Science 6 (3):359-370.
    Over the past 15 years, there have been two increasingly popular approaches to the study of meaning in cognitive science. One, based on theories of embodied cognition, treats meaning as a simulation of perceptual and motor states. An alternative approach treats meaning as a consequence of the statistical distribution of words across spoken and written language. On the surface, these appear to be opposing scientific paradigms. In this review, we aim to show how recent cross-disciplinary developments have done much to (...)
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  • Constructing Semantic Models From Words, Images, and Emojis.Armand S. Rotaru & Gabriella Vigliocco - 2020 - Cognitive Science 44 (4).
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  • Modeling the Structure and Dynamics of Semantic Processing.Armand S. Rotaru, Gabriella Vigliocco & Stefan L. Frank - 2018 - Cognitive Science 42 (8):2890-2917.
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  • Semantic Coherence Facilitates Distributional Learning.Ouyang Long, Boroditsky Lera & C. Frank Michael - 2017 - Cognitive Science 41 (S4):855-884.
    Computational models have shown that purely statistical knowledge about words’ linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that “postman” and “mailman” are semantically similar because they have quantitatively similar patterns of association with other words. In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, (...)
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  • 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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  • Incremental Bayesian Category Learning From Natural Language.Lea Frermann & Mirella Lapata - 2016 - Cognitive Science 40 (6):1333-1381.
    Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...)
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  • Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis.Akira Utsumi - 2020 - Cognitive Science 44 (6).
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  • Information and Processes Underlying Semantic and Episodic Memory Across Tasks, Items, and Individuals.Gregory E. Cox, Pernille Hemmer, William R. Aue & Amy H. Criss - 2018 - Journal of Experimental Psychology: General 147 (4):545-590.
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  • Three Symbol Ungrounding Problems: Abstract Concepts and the Future of Embodied Cognition.Guy Dove - 2016 - Psychonomic Bulletin and Review 4 (23):1109-1121.
    A great deal of research has focused on the question of whether or not concepts are embodied as a rule. Supporters of embodiment have pointed to studies that implicate affective and sensorimotor systems in cognitive tasks, while critics of embodiment have offered nonembodied explanations of these results and pointed to studies that implicate amodal systems. Abstract concepts have tended to be viewed as an important test case in this polemical debate. This essay argues that we need to move beyond a (...)
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  • Network Growth Modeling to Capture Individual Lexical Learning.Nicole M. Beckage & Eliana Colunga - 2019 - Complexity 2019:1-17.
    Network models of language provide a systematic way of linking cognitive processes to the structure and connectivity of language. Using network growth models to capture learning, we focus on the study of the emergence of complexity in early language learners. Specifically, we capture the emergent structure of young toddler’s vocabularies through network growth models assuming underlying knowledge representations of semantic and phonological networks. In construction and analyses of these network growth models, we explore whether phonological or semantic relationships between words (...)
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