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John Hollander; Andrew Olney – Cognitive Science, 2024
Recent investigations on how people derive meaning from language have focused on task-dependent shifts between two cognitive systems. The symbolic (amodal) system represents meaning as the statistical relationships between words. The embodied (modal) system represents meaning through neurocognitive simulation of perceptual or sensorimotor systems…
Descriptors: Verbs, Symbolic Language, Language Processing, Semantics
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Westera, Matthijs; Gupta, Abhijeet; Boleda, Gemma; Padó, Sebastian – Cognitive Science, 2021
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…
Descriptors: Classification, Models, Nouns, Cognitive Processes
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Löhr, Guido; Michel, Christian – Cognitive Science, 2022
We propose a cognitive-psychological model of linguistic intuitions about copredication statements. In copredication statements, like "The book is heavy and informative," the nominal denotes two ontologically distinct entities at the same time. This has been considered a problem for standard truth-conditional semantics. In this paper, we…
Descriptors: Cognitive Processes, Intuition, Decision Making, Ethics
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Masato Nakamura; Shota Momma; Hiromu Sakai; Colin Phillips – Cognitive Science, 2024
Comprehenders generate expectations about upcoming lexical items in language processing using various types of contextual information. However, a number of studies have shown that argument roles do not impact neural and behavioral prediction measures. Despite these robust findings, some prior studies have suggested that lexical prediction might be…
Descriptors: Diagnostic Tests, Nouns, Language Processing, Verbs
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Wang, Wentao; Vong, Wai Keen; Kim, Najoung; Lake, Brenden M. – Cognitive Science, 2023
Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this…
Descriptors: Brain Hemisphere Functions, Linguistic Input, Longitudinal Studies, Self Concept
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Colunga, Eliana; Sims, Clare E. – Cognitive Science, 2017
In typical development, word learning goes from slow and laborious to fast and seemingly effortless. Typically developing 2-year-olds seem to intuit the whole range of things in a category from hearing a single instance named--they have word-learning biases. This is not the case for children with relatively small vocabularies ("late…
Descriptors: Child Language, Bias, Prediction, Nouns
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Barthel, Mathias; Sauppe, Sebastian – Cognitive Science, 2019
Speech planning is a sophisticated process. In dialog, it regularly starts in overlap with an incoming turn by a conversation partner. We show that planning spoken responses in overlap with incoming turns is associated with higher processing load than planning in silence. In a dialogic experiment, participants took turns with a confederate…
Descriptors: Language Processing, Psycholinguistics, Speech Communication, Dialogs (Language)
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Virpioja, Sami; Lehtonen, Minna; Hultén, Annika; Kivikari, Henna; Salmelin, Riitta; Lagus, Krista – Cognitive Science, 2018
Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of…
Descriptors: Statistical Analysis, Models, Morphology (Languages), Vocabulary
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Engelthaler, Tomas; Hills, Thomas T. – Cognitive Science, 2017
Do properties of a word's features influence the order of its acquisition in early word learning? Combining the principles of mutual exclusivity and shape bias, the present work takes a network analysis approach to understanding how feature distinctiveness predicts the order of early word learning. Distance networks were built from nouns with edge…
Descriptors: Vocabulary Development, Network Analysis, Prediction, Language Acquisition
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Vecchi, Eva M.; Marelli, Marco; Zamparelli, Roberto; Baroni, Marco – Cognitive Science, 2017
"Sophisticated senator" and "legislative onion." Whether or not you have ever heard of these things, we all have some intuition that one of them makes much less sense than the other. In this paper, we introduce a large dataset of human judgments about novel adjective-noun phrases. We use these data to test an approach to…
Descriptors: Form Classes (Languages), Semantics, Nouns, Novelty (Stimulus Dimension)
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Reilly, Jamie; Hung, Jinyi; Westbury, Chris – Cognitive Science, 2017
Arbitrary symbolism is a linguistic doctrine that predicts an orthogonal relationship between word forms and their corresponding meanings. Recent corpora analyses have demonstrated violations of arbitrary symbolism with respect to concreteness, a variable characterizing the sensorimotor salience of a word. In addition to qualitative semantic…
Descriptors: Computational Linguistics, Semantics, Word Recognition, Auditory Perception
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Runner, Jeffrey T.; Sussman, Rachel S.; Tanenhaus, Michael K. – Cognitive Science, 2006
Binding theory (e.g., Chomsky, 1981) has played a central role in both syntactic theory and models of language processing. Its constraints are designed to predict that the referential domains of pronouns and reflexives are nonoverlapping, that is, are complementary; these constraints are also thought to play a role in online reference resolution.…
Descriptors: Language Processing, Nouns, Eye Movements, Form Classes (Languages)