Using EEG Data and NeuCube for the Study of Transfer of Learning

Mojgan Fard, Maryam Doborjeh, Krassie Petrova, Nikola Kasabov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationThe 2020 International Conference on Computational Science and Computational Intelligence, (CSCI'20: December 16-18, 2020, Las Vegas, USA), https://www.american-cse.org/csci2020/
PublisherIEEE Computer Society
Pages1-8
Number of pages8
Publication statusAccepted/In press - 9 Nov 2020

Keywords

  • transfer of learning
  • machine learning
  • attention
  • NeuCube
  • spiking neurqal networks

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