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dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_AR
dc.contributor.authorRomano, Sergioes_AR
dc.contributor.authorSalles, Alejoes_AR
dc.contributor.authorAmalric, Mariees_AR
dc.contributor.authorDeahene, Stanislases_AR
dc.contributor.authorSigman, Marianoes_AR
dc.contributor.authorFigueira, Santiagoes_AR
dc.date.accessioned2018-11-30T01:51:23Z
dc.date.available2018-11-30T01:51:23Z
dc.date.issued2018-07-10
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0200420es_AR
dc.identifier.uri
dc.identifier.uries_AR
dc.identifier.uries_AR
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/11219
dc.description.abstractProbabilistic proposals of Language of Thoughts (LoTs) can explain learning across differ- ent domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we propose an extra validation step for the set of atomic productions defined by the experimenter. It starts by expanding the defined LoT grammar for the cognitive domain with a broader set of arbitrary productions and then uses Bayesian inference to prune the productions from the experimental data. The result allows the researcher to validate that the resulting grammar still matches the intuitive grammar cho- sen for the domain. We then test this method in the language of geometry, a specific LoT model for geometrical sequence learning. Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequence’s probability and its complexity consistent with the theoretical relationship for uni- versal languages described by Levin’s Coding Theorem.es_AR
dc.format.extent20 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.relation.ispartofPLoS ONE 13(7)), (2018). ISSN: 1932-6203es_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectGramáticaes_AR
dc.subjectLenguajees_AR
dc.subjectTeoría de la informaciónes_AR
dc.subjectSimetríaes_AR
dc.subjectAprendizajees_AR
dc.subjectLenguajes de programaciónes_AR
dc.subjectSintaxises_AR
dc.titleBayesian validation of grammar productions for the language of thoughtes_AR
dc.typeinfo:eu-repo/semantics/workingPaperes_AR
dc.subject.keywordGrammares_AR
dc.subject.keywordLanguagees_AR
dc.subject.keywordInformation theoryes_AR
dc.subject.keywordSymmetryes_AR
dc.subject.keywordLearninges_AR
dc.subject.keywordProgramming languageses_AR
dc.subject.keywordSyntaxes_AR
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_AR
dc.description.filiationFil: Romano, Sergio. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Buenos Aires, Argentina. CONICET-Universidad de Buenos Aires. Instituto de Investigación en Ciencias de la Computación (ICC). Buenos Aires, Argentinaes_AR
dc.description.filiationFil: Salles, Alejo. CONICET-Universidad de Buenos Aires. Instituto de Cálculo (IC). Buenos Aires, Argentinaes_AR
dc.description.filiationFil: Amalric, Marie. Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, Francees_AR
dc.description.filiationFil: Deahene, Stanislas. Cognitive Neuroimaging Unit, CEA DSV/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, Francees_AR
dc.description.filiationFil: Sigman, Mariano CONICET-Universidad Torcuato Di Tella. Escuela de Negocios. Laboratorio de Neurociencia, Buenos Aires, Argentinaes_AR
dc.description.filiationFil: Figueira, Santiago. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Computación. Buenos Aires, Argentina. CONICET-Universidad de Buenos Aires. Instituto de Investigación en Ciencias de la Computacón (ICC). Buenos Aires, Argentinaes_AR


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