Accounting for item-level variance in recognition memory: Comparing word frequency and contextual diversity.
Contextual diversity modifies word frequency by ignoring the repetition of words in context (Adelman, Brown, & Quesada, 2006, Psychological Science, 17(9), 814–823). Semantic diversity modifies contextual diversity by taking into account the uniqueness of the contexts that a word occurs in when calc...
| Publicado en: | Memory & Cognition Vol. 50; no. 5; pp. 1013 - 1033 |
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| Formato: | Artículo |
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Springer Nature
Jul2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=157542306&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 157542306 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0090502X MEG jtl: Memory & Cognition issn: 0090502X maglogo: N pubinfo: dt: Jul2022 vid: 50 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 157542306 10.3758/s13421-021-01249-z ppf: 1013 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.2MB tig: atl: Accounting for item-level variance in recognition memory: Comparing word frequency and contextual diversity. aug: au: Johns, Brendan T. affil: Department of Psychology, McGill University, 2001 McGill College Avenue, Montreal, H3A 1G1, Quebec, Canada su: Recognition (Psychology) Semantics Task performance Behavior Phonological awareness Mathematical models Theory sug: subj: Recognition (Psychology) Semantics Task performance Behavior Phonological awareness Mathematical models Theory keyword: Computational modeling Corpus-based models Distributional semantics Recognition memory Word frequency Computational modeling Corpus-based models Distributional semantics Recognition memory Word frequency ab: Contextual diversity modifies word frequency by ignoring the repetition of words in context (Adelman, Brown, & Quesada, 2006, Psychological Science, 17(9), 814–823). Semantic diversity modifies contextual diversity by taking into account the uniqueness of the contexts that a word occurs in when calculating lexical strength (Jones, Johns, & Recchia, 2012, Canadian Journal of Experimental Psychology, 66, 115–124). Recent research has demonstrated that measures based on contextual and semantic diversity provide a considerable improvement over word frequency when accounting for lexical organization data (Johns, 2021, Psychological Review, 128, 525–557; Johns, Dye, & Jones, 2020a, Quarterly Journal of Experimental Psychology, 73, 841–855). The article demonstrates that these same findings generalize to word-level episodic recognition rates, using the previously released data of Cortese, Khanna, and Hacker (Cortese et al., 2010, Memory, 18, 595–609) and Cortese, McCarty, and Schock (Cortese et al., 2015, Quarterly Journal of Experimental Psychology, 68, 1489–1501). It was found that including the best fitting contextual diversity model allowed for a very large increase in variance accounted for over previously used variables, such as word frequency, signalling commonality with results from the lexical organization literature. The findings of this article suggest that current trends in the collection of megadata sets of human behavior (e.g., Balota et al., 2007, Behavior Research Methods, 39(3), 445–459) provide a promising avenue to develop new theoretically oriented models of word-level episodic recognition data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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