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  1. Distributional Models of Category Concepts Based on Names of Category Members.Matthijs Westera, Abhijeet Gupta, Gemma Boleda & Sebastian Padó - 2021 - Cognitive Science 45 (9):e13029.
    Cognitive scientists have long used distributional semantic representations of categories. The predominant approach uses distributional representations of category‐denoting nouns, such as “city” for the category city. We propose a novel scheme that represents categories as prototypes over representations of names of its members, such as “Barcelona,” “Mumbai,” and “Wuhan” for the category city. This name‐based representation empirically outperforms the noun‐based representation on two experiments (modeling human judgments of category relatedness and predicting category membership) with particular improvements for ambiguous nouns. We (...)
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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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  • Exploring What Is Encoded in Distributional Word Vectors: A Neurobiologically Motivated Analysis.Akira Utsumi - 2020 - Cognitive Science 44 (6):e12844.
    The pervasive use of distributional semantic models or word embeddings for both cognitive modeling and practical application is because of their remarkable ability to represent the meanings of words. However, relatively little effort has been made to explore what types of information are encoded in distributional word vectors. Knowing the internal knowledge embedded in word vectors is important for cognitive modeling using distributional semantic models. Therefore, in this paper, we attempt to identify the knowledge encoded in word vectors by conducting (...)
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  • 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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  • Archaeology Through Computational Linguistics: Inscription Statistics Predict Excavation Sites of Indus Valley Artifacts.Gabriel L. Recchia & Max M. Louwerse - 2016 - Cognitive Science 40 (8):2065-2080.
    Computational techniques comparing co-occurrences of city names in texts allow the relative longitudes and latitudes of cities to be estimated algorithmically. However, these techniques have not been applied to estimate the provenance of artifacts with unknown origins. Here, we estimate the geographic origin of artifacts from the Indus Valley Civilization, applying methods commonly used in cognitive science to the Indus script. We show that these methods can accurately predict the relative locations of archeological sites on the basis of artifacts of (...)
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  • Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring.José Á Martínez-Huertas, Guillermo Jorge-Botana & Ricardo Olmos - 2021 - Cognitive Science 45 (7):e13026.
    We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9‐year‐old children. The neural network was trained and validated in the child semantic space. (...)
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  • Symbol Interdependency in Symbolic and Embodied Cognition.Max M. Louwerse - 2011 - Topics in Cognitive Science 3 (2):273-302.
    Whether computational algorithms such as latent semantic analysis (LSA) can both extract meaning from language and advance theories of human cognition has become a topic of debate in cognitive science, whereby accounts of symbolic cognition and embodied cognition are often contrasted. Albeit for different reasons, in both accounts the importance of statistical regularities in linguistic surface structure tends to be underestimated. The current article gives an overview of the symbolic and embodied cognition accounts and shows how meaning induction attributed to (...)
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  • Representing Spatial Structure Through Maps and Language: Lord of the Rings Encodes the Spatial Structure of Middle Earth.Max M. Louwerse & Nick Benesh - 2012 - Cognitive Science 36 (8):1556-1569.
    Spatial mental representations can be derived from linguistic and non‐linguistic sources of information. This study tested whether these representations could be formed from statistical linguistic frequencies of city names, and to what extent participants differed in their performance when they estimated spatial locations from language or maps. In a computational linguistic study, we demonstrated that co‐occurrences of cities in Tolkien’s Lord of the Rings trilogy and The Hobbit predicted the authentic longitude and latitude of those cities in Middle Earth. In (...)
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  • Why imaginary worlds? The psychological foundations and cultural evolution of fictions with imaginary worlds.Edgar Dubourg & Nicolas Baumard - 2022 - Behavioral and Brain Sciences 45:e276.
    Imaginary worlds are extremely successful. The most popular fictions produced in the last few decades contain such a fictional world. They can be found in all fictional media, from novels (e.g., Lord of The Rings and Harry Potter) to films (e.g., Star Wars and Avatar), video games (e.g., The Legend of Zelda and Final Fantasy), graphic novels (e.g., One Piece and Naruto), and TV series (e.g., Star Trek and Game of Thrones), and they date as far back as ancient literature (...)
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