I regress urban dictionary1/26/2024 One of the key developments in big data approaches to cognition is the emergence of distributional models of semantics, which learn the meaning of words from statistical patterns contained in very large sources of texts (see Jones et al., 2015 for a review). ![]() The results of this article provides insights into the cultural evolution of word meanings, and sheds light on alternative methodologies that can be used to understand lexical behavior.Īn emerging area within the psychological and cognitive sciences is the use of big data to develop and analyze theories of cognition ( Jones, 2017 Johns et al., in press). Additionally, it was demonstrated that users of a language have strong preferences for word meanings, such that definitions to words that do not conform to people’s conceptions are rejected by a community of language users. Overall, it was found that even for words that are not an active part of the language environment, there is a large amount of consistency in the word meanings that different people have. This was accomplished by mining a large amount of data from an online, crowdsourced dictionary and analyzing this data with a distributional model. The current article combines these two approaches, with the goal being to understand the consistency and preference that people have for word meanings. ![]() Two of the main developments of this line of research is the advent of distributional models of semantics (e.g., Landauer and Dumais, 1997), which learn the meaning of words from large text corpora, and the collection of mega datasets of human behavior (e.g., The English lexicon project Balota et al., 2007). Department of Communicative Disorders and Sciences, University at Buffalo, Buffalo, NY, United Statesīig data approaches to psychology have become increasing popular ( Jones, 2017).
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