Using EEG Data and NeuCube for the Study of Transfer of Learning
aut.relation.conference | The 2020 International Conference on Computational Science and Computational Intelligence | en_NZ |
aut.researcher | Petrova, Krassimira | |
dc.contributor.author | Fard, MH | en_NZ |
dc.contributor.author | Petrova, K | en_NZ |
dc.contributor.author | Gholami, M | en_NZ |
dc.contributor.author | Kasabov, N | en_NZ |
dc.date.accessioned | 2021-06-02T21:44:40Z | |
dc.date.available | 2021-06-02T21:44:40Z | |
dc.date.copyright | 2020 | en_NZ |
dc.date.issued | 2020 | en_NZ |
dc.description.abstract | Deeper 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.identifier.citation | he 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.uri | https://hdl.handle.net/10292/14233 | |
dc.publisher | IEEE Computer Society | |
dc.relation.uri | https://www.american-cse.org/static/ci20-Book-of-abstracts-presentations-web.pdf | en_NZ |
dc.rights | Copyright © 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.rights.accessrights | OpenAccess | en_NZ |
dc.subject | Transfer of learning; Attention; Machine learning; Spiking neural networks; NeuCube | |
dc.title | Using EEG Data and NeuCube for the Study of Transfer of Learning | en_NZ |
dc.type | Conference Contribution | |
pubs.elements-id | 396293 | |
pubs.organisational-data | /AUT | |
pubs.organisational-data | /AUT/Faculty of Design & Creative Technologies | |
pubs.organisational-data | /AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences | |
pubs.organisational-data | /AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences/Network Security Research Group | |
pubs.organisational-data | /AUT/PBRF | |
pubs.organisational-data | /AUT/PBRF/PBRF Design and Creative Technologies | |
pubs.organisational-data | /AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS |
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