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dc.contributor.author Gualdoni, Eleonora
dc.contributor.author Kemp, Charles
dc.contributor.author Xu, Yang
dc.contributor.author Boleda, Gemma
dc.date.accessioned 2023-05-17T05:58:07Z
dc.date.available 2023-05-17T05:58:07Z
dc.date.issued 2023
dc.identifier.citation Gualdoni E, Kemp C, Xu Y, Boleda G. Quantifying informativeness of names in visual space. Paper presented at: 45th Annual Conference of the Cognitive Science Society (CogSci 2023); 2023 Jul 26-29; Sydney, Australia.
dc.identifier.uri http://hdl.handle.net/10230/56850
dc.description Comunicació presentada a 45th Annual Conference of the Cognitive Science Society (CogSci 2023), celebrada a Sydney (Austràlia) del 26 al 29 de juliol de 2023.
dc.description.abstract The human lexicon expresses a wide array of concepts with a limited set of words. Previous work has suggested that semantic categories are structured compactly to enable informative communication. Informativeness is typically quantified with respect to an entire semantic domain and not at the level of individual names. We develop a measure of name informativeness using an information-theoretic framework grounded in visual object representations derived from natural images. Our approach uses computer vision models to characterize informativeness of individual names with respect to large-scale data in a naturalistic setting. We show that our informativeness measure predicts degrees of specificity in lexical categories more precisely than alternative measures based on entropy and frequency. We also show that name informativeness jointly captures within-category similarity and distinctiveness across categories. Our analyses suggest how the variability of names from a broad part of the lexicon may be understood through the lens of information theory.
dc.description.sponsorship This research is an output of grant PID2020-112602GBI00/MICIN/AEI/10.13039/501100011033, funded by the Ministerio de Ciencia e Innovacion and the Agencia Estatal ´ de Investigacion (Spain), of grant agreement No. 715154 ´ funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, and of NSERC Alliance International-Catalyst Grant ALLRP 576149-22. The second author was supported by grant FT190100200 from the Australian Research Council.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Cognitive Science Society
dc.rights This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY).
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Quantifying informativeness of names in visual space
dc.type info:eu-repo/semantics/conferenceObject
dc.subject.keyword lexicon
dc.subject.keyword naming
dc.subject.keyword visual object representation
dc.subject.keyword informativeness
dc.subject.keyword information theory
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715154
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112602GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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