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.
|Title of host publication||The 2020 International Conference on Computational Science and Computational Intelligence, (CSCI'20: December 16-18, 2020, Las Vegas, USA), https://www.american-cse.org/csci2020/|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|Publication status||Accepted/In press - 9 Nov 2020|
- transfer of learning
- machine learning
- spiking neurqal networks