Word Meaning Is Both Categorical and Continuous.
Most words have multiple meanings, but there are foundationally distinct accounts for this. Categorical theories posit that humans maintain discrete entries for distinct word meanings, as in a dictionary. Continuous ones eschew discrete sense representations, arguing that word meanings are best char...
| Publicado en: | Psychological Review Vol. 130; no. 5; pp. 1239 - 1262 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Psychological Association
Oct2023
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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=173050746&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 173050746 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0033295X PYV jtl: Psychological Review issn: 0033295X maglogo: N pubinfo: dt: Oct2023 vid: 130 iid: 5 pid: 34 pub: American Psychological Association artinfo: ui: 173050746 10.1037/rev0000420 ppf: 1239 ppct: 23 formats: tig: atl: Word Meaning Is Both Categorical and Continuous. aug: au: Trott, Sean Bergen, Benjamin affil: Department of Cognitive Science, University of California San Diego su: Semantics Language models Polysemy Mental representation Language ability testing sug: subj: Semantics Language models Polysemy Mental representation Language ability testing keyword: ambiguity continuous state space mental lexicon neural language models ambiguity continuous state space mental lexicon neural language models ab: Most words have multiple meanings, but there are foundationally distinct accounts for this. Categorical theories posit that humans maintain discrete entries for distinct word meanings, as in a dictionary. Continuous ones eschew discrete sense representations, arguing that word meanings are best characterized as trajectories through a continuous state space. Both kinds of approach face empirical challenges. In response, we introduce two novel "hybrid" theories, which reconcile discrete sense representations with a continuous view of word meaning. We then report on two behavioral experiments, pairing them with an analytical approach relying on neural language models to test these competing accounts. The experimental results are best explained by one of the novel hybrid accounts, which posits both distinct sense representations and a continuous meaning space. This hybrid account accommodates both the dynamic, context-dependent nature of word meaning, as well as the behavioral evidence for category-like structure in human lexical knowledge. We further develop and quantify the predictive power of several computational implementations of this hybrid account. These results raise questions for future research on lexical ambiguity, such as why and when discrete sense representations might emerge in the first place. They also connect to more general questions about the role of discrete versus gradient representations in cognitive processes and suggest that at least in this case, the best explanation is one that integrates both factors: Word meaning is both categorical and continuous. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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