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Quantum projections on conceptual subspaces

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dc.contributor.authorMartínez Mingo, Alejandro
dc.contributor.authorJorge Botana, Guillermo de
dc.contributor.authorOlmos Albacete, Ricardo
dc.contributor.authorMartínez Huertas, José Ángel
dc.date.accessioned2024-05-20T11:50:05Z
dc.date.available2024-05-20T11:50:05Z
dc.date.issued2023-12
dc.description.abstractOne of the main challenges of cognitive science is to explain the representation of conceptual knowledge and the mechanisms involved in evaluating the similarities between these representations. Theories that attempt to explain this phenomenon should account for the fact that conceptual knowledge is not static. In line with this thinking, many studies suggest that the representation of a concept changes depending on context. Traditionally, concepts have been studied as vectors within a geometric space, sometimes called Semantic-Vector Space Models (S-VSMs). However, S-VSMs have certain limitations in emulating human biases or context effects when the similarity of concepts is judged. Such limitations are related to the use of a classical geometric approach that represents a concept as a point in space. Recently, some theories have proposed the use of sequential projections of subspaces based on Quantum Probability Theory (Busemeyer and Bruza, 2012; Pothos et al., 2013). They argue that this theoretical approach may facilitate accounting for human similarity biases and context effects in a more natural way. More specifically, Pothos and Busemeyer (2011) proposed the Quantum Similarity Model (QSM) to determine expectation in conceptual spaces in a non-monotonic logic frame. To the best of our knowledge, previous data-driven studies have used the QSM subspaces in a unidimensional way. In this paper, we present a data-driven method to generate these conceptual subspaces in a multidimensional manner using a traditional S-VSM. We present an illustration of the method taking Tversky’s classical examples to explain the effects of Asymmetry, Triangular Inequality, and the Diagnosticity by means of sequential projections of those conceptual subspaces.en
dc.description.versionversión final
dc.identifier.doihttps://doi.org/10.1016/j.cogsys.2023.101154
dc.identifier.issn1389-0417
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12612
dc.journal.titleCognitive Systems Research
dc.journal.volume82
dc.language.isoen
dc.publisherElsevier
dc.relation.centerFacultades y escuelas::Facultad de Psicología
dc.relation.departmentMetodología de las Ciencias del Comportamiento
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsQuantum similarity model
dc.subject.keywordsSemantic-vector space models
dc.subject.keywordsComputational linguistics
dc.subject.keywordsSimilarity
dc.titleQuantum projections on conceptual subspaceses
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
person.familyNameMartínez Huertas
person.givenNameJosé Ángel
person.identifier.orcid0000-0002-6700-6832
relation.isAuthorOfPublicationca510876-0be8-438a-a565-ac5f8953fb78
relation.isAuthorOfPublication.latestForDiscoveryca510876-0be8-438a-a565-ac5f8953fb78
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