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Modelling early visual processes of illiterate populations with Deep Belief Networks

datacite.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopt_PT
dc.contributor.advisorFernandes, Tânia Patrícia Gregório
dc.contributor.advisorCorreia, Luís Miguel Parreira e
dc.contributor.authorFottner, Nicola Alessandro
dc.date.accessioned2024-04-15T17:04:15Z
dc.date.available2024-04-15T17:04:15Z
dc.date.issued2023
dc.date.submitted2022
dc.descriptionTese de Mestrado, Ciência Cognitiva, 2023, Universidade de Lisboa, Faculdade de Ciênciaspt_PT
dc.description.abstractThe Neuronal Recycling Hypothesis (Dehaene, 2005; Dehaene & Cohen, 2007) proposes that the efficient computation and representation of written words at the orthographic stage of processing is enabled through the adaptation of pre-existing visual functions, which in turn, lead to the emergence of a specialised reading system. The present thesis aimed to investigate the emergence of neural detectors tuned to letters through biologically plausible computational models. A Deep Belief Network (DBN) was implemented as a model of visual shape perception, inspired by Testolin et al. (2017), and used to answer two questions: 1) does the DBN model generalise shape information that was learned from images of geometrical shapes towards classification of letters and pseudoletters (i.e., nonletters sharing the same features as letters); for example, classifying A as a triangle?; 2) is visual shape processing by a DBN sensitive to the same integration processes as those reflected in crowding effects (i.e., integration of adjacent information) by human observers; namely, by the congruency effect (better performance for targets surrounding by congruent than incongruent shapes)? The results showed that classification of letters and pseudoletters by our DBN was nonuniform across the different tested letter fonts, thus suggesting that decisions were not led by global shape. Interestingly, our model exhibited a congruence effect, and hence, a perceptual strategy similar to that previously found in illiterate adults (Fernandes et al., 2014). These results and further analyses also showed that our model’s perceptual strategy was not driven by low-level pixel similarities. The present work sets the stage to further emulate the transition from the illiterate to the ex-illiterate state, as done in the work of Hannagan et al. (2021) but with biologically more plausible learning algorithms (Bengio et al., 2015; Hinton & Salakhutdinov, 2006).pt_PT
dc.identifier.tid203524330
dc.identifier.urihttp://hdl.handle.net/10451/64280
dc.language.isoengpt_PT
dc.relationVisual word recognition and Orthographic processing: Experiments and contributions from Cognitive Psychology, Neurosciences, and Computational Modeling
dc.relationResearch Center for Psychological Science
dc.relationLASIGE - Extreme Computing
dc.subjectReconhecimento visual de palavraspt_PT
dc.subjectDetector de letraspt_PT
dc.subjectRedes neuraispt_PT
dc.subjectAprendizagem não supervisionadapt_PT
dc.subjectRedes Deep Beliefpt_PT
dc.subjectTeses de mestrado - 2023pt_PT
dc.titleModelling early visual processes of illiterate populations with Deep Belief Networkspt_PT
dc.typemaster thesis
dspace.entity.typePublication
oaire.awardTitleVisual word recognition and Orthographic processing: Experiments and contributions from Cognitive Psychology, Neurosciences, and Computational Modeling
oaire.awardTitleResearch Center for Psychological Science
oaire.awardTitleLASIGE - Extreme Computing
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/9471 - RIDTI/PTDC%2FPSI-GER%2F28184%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04527%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00408%2F2020/PT
oaire.fundingStream9471 - RIDTI
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
relation.isProjectOfPublication2d0158c8-99f5-4669-a450-54924f7b9f6d
relation.isProjectOfPublication36f36272-922b-4858-9f5e-3db12cc5f84b
relation.isProjectOfPublicationb429b8f0-500f-4a0b-8e91-33e0a200ad1c
relation.isProjectOfPublication.latestForDiscovery2d0158c8-99f5-4669-a450-54924f7b9f6d
thesis.degree.nameMestrado em Ciência Cognitivapt_PT

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