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Quantum projections on conceptual subspaces: A deeper dive into methodological challenges and opportunities

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dc.contributor.authorMartínez Mingo, Alejandro
dc.contributor.authorOlmos, Ricardo
dc.contributor.authorJorge Botana, Guillermo
dc.contributor.authorMartínez Huertas, José Ángel
dc.date.accessioned2024-08-21T12:13:40Z
dc.date.available2024-08-21T12:13:40Z
dc.date.issued2024
dc.description.abstractIn alignment with the distributional hypothesis of language, the work “Quantum Projections on Conceptual Subspaces” (Martínez-Mingo A, Jorge-Botana G, Martinez-Huertas JÁ, et al. Quantum projections on conceptual subspaces. Cogn Syst Res 2023; 82: 101154) proposed a methodology for generating conceptual subspaces from textual information based on previous work (Martinez-Mingo A, Jorge-Botana G and Olmos R. Quantum approach for similarity evaluation in LSA vector space models. 2020). These subspaces enable the utilization of the quantum model of similarity put forth by Pothos and Busemeyer (Pothos E, Busemeyer J. A quantum probability explanation for violations of symmetry in similarity judgments. In Proceedings of the annual meeting of the cognitive science society, 2011, Vol. 33, No. 33), allowing for the empirical examination of the violations of assumptions concerning symmetry and triangular inequality (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Barque-Duran A, Scerrati E, et al. The triangle inequality constraint in similarity judgments. Prog Biophys Mol Biol 2017; 130: 26–32), as well as the diagnosticity effect (Tversky A. Features of similarity. Psychol Rev 1977; 84: 327–352; Yearsley JM, Pothos EM, Barque-Duran A, et al. Context effects in similarity judgments. J Exp Psychol Gen 2022; 151: 711–717), within a data-driven environment. These psychological biases, deeply studied by authors such as Tversky and Kahneman, inform us about the limitations of modeling psychological similarity measures using tools from classical geometry. This commentary aims to offer methodological clarifications, discuss theoretical and practical implications, and speculate on future directions in this field of research. Concretely, it aims to propose the use of different contours (conceptual or contextual) to generate the subspaces, which lead to subspaces of terms or contexts. Once these contours are defined, a differentiation is proposed between Aggregated Terms Subspaces (ATSs), Aggregated Contexts Subspaces (ACSs), and Aggregated Features Subspaces (AFSs) depending on whether we define the subspaces by grouping the terms or contexts within the contour, or from the latent dimensions of the semantic space obtained in the contour window. Finally, new data is provided on the violation of the triangular inequality assumption through the application of the quantum similarity model to ATSs.en
dc.description.versionversión publicada
dc.identifier.doihttp://doi.org/10.1177/00368504241238773
dc.identifier.issn0036-8504; eISSN: 2047-7163
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23309
dc.journal.issue2
dc.journal.titleScience Progress
dc.journal.volume107
dc.language.isoen
dc.publisherSAGE
dc.relation.centerFacultades y escuelas::Facultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/deed.es
dc.subject.keywordsNatural language processing
dc.subject.keywordscomputational linguistics
dc.subject.keywordscognitive processes
dc.subject.keywordsquantum cognition
dc.subject.keywordsartificial intelligence
dc.titleQuantum projections on conceptual subspaces: A deeper dive into methodological challenges and opportunitieses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationca510876-0be8-438a-a565-ac5f8953fb78
relation.isAuthorOfPublication.latestForDiscoveryca510876-0be8-438a-a565-ac5f8953fb78
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