(2019) used a distributional model to assess differences in memory retrieval performance across tens of thousands of participants spanning the aging spectrum. (2018b) used a distributional model to analyze the changes that were occurring in patients who were developing a cognitive impairment, while Taler et al. Taking a different applied approach, Johns et al. For example, Johns and Jamieson (2018) analyzed a large sample of fiction books to understand individual variance in language usage. Furthermore, they have begun to be used not just as theories of human behavior, but as analysis tools to quantify linguistic data. The representations derived from distributional models have been used to examine behavior across a number of domains, including lexical organization ( Jones et al., 2012 Hoffman et al., 2013 Hsiao and Nation, 2018 for a review, see Jones et al., 2017), episodic memory ( Johns et al., 2012b, 2014 Mewhort et al., 2017), morphological processing ( Marelli and Baroni, 2015 Marelli et al., 2017 Westbury and Hollis, 2018), lexical-perceptual integration ( Andrews et al., 2009 Johns and Jones, 2012 Lazaridou et al., 2017), prediction ( Frank and Willems, 2017), decision ( Bhatia and Stewart, 2018), and sentence processing ( Johns and Jones, 2015). The insight that these models exploit is that lexical semantic behavior seems to be systematically related to the co-occurrence of words within the natural language environment. The original, and best known, model of this class is Latent Semantic Analysis (LSA Landauer and Dumais, 1997), which spurred the development of many new approaches (e.g., Lund and Burgess, 1996 Griffiths et al., 2007 Jones and Mewhort, 2007 Shaoul and Westbury, 2010 Mikolov et al., 2013 Jamieson et al., 2018). 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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