Quantifying informativeness of names in visual space

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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.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.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.language.iso eng
• dc.publisher Cognitive Science Society
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/715154
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112602GB-I00
• dc.rights This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword lexicon
• dc.subject.keyword naming
• dc.subject.keyword visual object representation
• dc.subject.keyword informativeness
• dc.subject.keyword information theory
• dc.title Quantifying informativeness of names in visual space
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion