Abstract
This paper combines a reinforcement learning (RL) model and EEG data analysis to identify learning situations in a associative learning task with delayed feedback. We investigated neural correlates in occipital alpha and prefrontal theta band power of learning opportunities, identified by the RL model. We show that those parameters can also be used to differentiate between learning opportunities which lead to correct learning and those which do not. Finally, we show that learning situations can also be identified on a single trial basis.
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Putze, F., Holt, D.V., Schultz, T., Funke, J. (2014). Model-Based Identification of EEG Markers for Learning Opportunities in an Associative Learning Task with Delayed Feedback. In: Wermter, S., et al. Artificial Neural Networks and Machine Learning – ICANN 2014. ICANN 2014. Lecture Notes in Computer Science, vol 8681. Springer, Cham. https://doi.org/10.1007/978-3-319-11179-7_49
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DOI: https://doi.org/10.1007/978-3-319-11179-7_49
Publisher Name: Springer, Cham
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