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Tanja C. Roembke; Bob McMurray – Cognitive Science, 2025
Computational and animal models suggest that the unlearning or pruning of incorrect meanings matters for word learning. However, it is currently unclear how such pruning occurs during word learning and to what extent it depends on supervised and unsupervised learning. In two experiments (N[subscript 1] = 40; N[subscript 2] = 42), adult…
Descriptors: Vocabulary Development, Computation, Models, Accuracy
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
De Deyne, Simon; Navarro, Danielle J.; Collell, Guillem; Perfors, Andrew – Cognitive Science, 2021
One of the main limitations of natural language-based approaches to meaning is that they do not incorporate multimodal representations the way humans do. In this study, we evaluate how well different kinds of models account for people's representations of both concrete and abstract concepts. The models we compare include unimodal distributional…
Descriptors: Models, Definitions, Concept Formation, Linguistics
Kumar, Abhilasha A.; Steyvers, Mark; Balota, David A. – Cognitive Science, 2021
Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether…
Descriptors: Semantics, Recall (Psychology), Cooperative Learning, Game Based Learning
Castro, Nichol; Stella, Massimo; Siew, Cynthia S. Q. – Cognitive Science, 2020
Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental…
Descriptors: Semantics, Phonology, Aphasia, Brain Hemisphere Functions
Gruenenfelder, Thomas M.; Recchia, Gabriel; Rubin, Tim; Jones, Michael N. – Cognitive Science, 2016
We compared the ability of three different contextual models of lexical semantic memory (BEAGLE, Latent Semantic Analysis, and the Topic model) and of a simple associative model (POC) 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…
Descriptors: Memory, Semantics, Associative Learning, Networks
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
Ambridge, Ben – Cognitive Science, 2013
A paradox at the heart of language acquisition research is that, to achieve adult-like competence, children must acquire the ability to generalize verbs into non-attested structures, while avoiding utterances that are deemed ungrammatical by native speakers. For example, children must learn that, to denote the reversal of an action,…
Descriptors: Generalization, Comparative Analysis, Verbs, Grammar