Brain self-regulation learning in the neurocomputational framework of active inference

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dc.catalogadorgjm
dc.contributor.advisorRodríguez Fernández, María
dc.contributor.advisorEl-Deredy, Wael
dc.contributor.authorVargas González, Gabriela Adriana
dc.contributor.otherPontificia Universidad Católica de Chile. Instituto de Ingeniería Biológica y Médica
dc.date.accessioned2023-12-12T14:09:21Z
dc.date.available2023-12-12T14:09:21Z
dc.date.issued2023
dc.date.updated2023-12-11T20:24:15Z
dc.descriptionTesis (Doctor in Biological and Medical Engineering)--Pontificia Universidad Católica de Chile, 2023.
dc.description.abstractNeurofeedback (NF), a cutting-edge technique in the realm of brain-computer interfaces (BCI), has proven to be a powerful tool for both scientific exploration and clinical rehabilitation. NF provides individuals with real-time information about their neural processes, enabling them to modulate and regulate their brain activity—a phenomenon known as 'brain self-regulation learning'. While NF holds great promise, it faces an efficiency hurdle. Remarkably, only 50% of participants successfully achieve self-regulation, limiting its clinical adoption. Existing models have struggled to fully elucidate the intricate interplay between reward mechanisms and cognitive functions, without fully succeeding. Herein lies the significance of Active Inference—a theoretical framework that illuminates this complex relationship. To address this gap, we propose using the framework of Active inference to understand the neural processes underlying self-regulation learning. Active inference provides a statistical model of the brain and a combination of computational modeling and neuroimaging techniques. By analyzing real-time functional MRI data and implementing agent-based simulations, we identify that learners exhibit a hierarchical computational anatomy as the neural substrate that supports the internal dynamics of the brain. Our findings underscore the importance of cognitive processes in self-regulation learning and provide insights for optimizing NF protocols.
dc.fechaingreso.objetodigital2023-12-12
dc.format.extentvii, 90 páginas
dc.fuente.origenAutoarchivo
dc.identifier.doi10.7764/tesisUC/IBM/75487
dc.identifier.urihttp://doi.org/10.7764/tesisUC/IBM/75487
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75487
dc.information.autorucInstituto de Ingeniería Biológica y Médica; Rodríguez Fernández, María; 0000-0003-1966-2920; 1031920
dc.information.autorucS/I; El-Deredy, Wael; 0000-0002-9822-1092; 1076958
dc.information.autorucInstituto de Ingeniería Biológica y Médica; Vargas González, Gabriela Adriana; S/I; 1092798
dc.language.isoen
dc.nota.accesoContenido completo
dc.rightsacceso abierto
dc.subjectNeurofeedback
dc.subjectBrain self-regulation
dc.subjectActive inference
dc.subjectBrain-computer interface
dc.subjectrtfMRI
dc.subjectMetacognition
dc.subject.ddc610
dc.subject.deweyMedicina y saludes_ES
dc.titleBrain self-regulation learning in the neurocomputational framework of active inference
dc.typetesis doctoral
sipa.codpersvinculados1031920
sipa.codpersvinculados1076958
sipa.codpersvinculados1092798
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