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dc.contributor.authorFard, MHen_NZ
dc.contributor.authorPetrova, Ken_NZ
dc.contributor.authorGholami, Men_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2021-06-02T21:44:40Z
dc.date.available2021-06-02T21:44:40Z
dc.date.copyright2020en_NZ
dc.identifier.citationhe 2020 International Conference on Computational Science and Computational Intelligence, (CSCI'20: December 16-18, 2020, Las Vegas, USA), https://www.american-cse.org/csci2020/ (pp. 1-8). IEEE Computer Society.
dc.identifier.urihttp://hdl.handle.net/10292/14233
dc.description.abstractDeeper and long-lasting learning occurs through a critical review of prior knowledge in the light of the new context, and a transfer of the acquired knowledge to new settings. Attention to task is one of factors that enable transfer of learning (TL). This study adopts a cognitive neuroscience approach to the study of TL; more specifically, to the investigation of the relationship between attention to task and prior knowledge. The study uses a Brain Like Artificial Intelligence (BLAI) architecture (NeuCube) which is based on Spiking Neural Networks (SNN) to represent brain data during a series of cognitive tasks, and interpret them in the context of the research question. The experimental results indicate that modelling and analysing spatio-temporal brain data (STBD) using the SNN environment of NeuCube suggested a better understanding of the process of TL, and the associated brain activity patterns and relationships. The outcomes of this study are used to inform the design of a follow up study where SNN models will be built from STBD gathered from participants engaged in learning and in TL.
dc.publisherIEEE Computer Society
dc.relation.urihttps://www.american-cse.org/static/ci20-Book-of-abstracts-presentations-web.pdfen_NZ
dc.rightsCopyright © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectTransfer of learning; Attention; Machine learning; Spiking neural networks; NeuCube
dc.titleUsing EEG Data and NeuCube for the Study of Transfer of Learningen_NZ
dc.typeConference Contribution
dc.rights.accessrightsOpenAccessen_NZ
pubs.elements-id396293
aut.relation.conferenceThe 2020 International Conference on Computational Science and Computational Intelligenceen_NZ


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