Using EEG Data and NeuCube for the Study of Transfer of Learning
Fard, MH; Petrova, K; Gholami, M; Kasabov, N
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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.