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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Anderson, J.R., Betts, S., Ferris, J.L., Fincham, J.M.: Neural imaging to track mental states while using an intelligent tutoring system. Proceedings of the National Academy of Sciences 107(15), 7018–7023 (2010)
Cavanagh, J.F., Frank, M.J., Klein, T.J., Allen, J.J.B.: Frontal theta links prediction errors to behavioral adaptation in reinforcement learning. NeuroImage 49(4), 3198–3209 (2010)
Collins, A.G.E., Frank, M.J.: How much of reinforcement learning is working memory, not reinforcement learning? a behavioral, computational, and neurogenetic analysis. European Journal of Neuroscience 35(7), 1024–1035 (2012)
Fu, W.-T., Anderson, J.R.: From recurrent choice to skill learning: A reinforcement-learning model. Journal of Experimental Psychology: General 135(2), 184–206 (2006)
Jensen, O., Tesche, C.D.: Frontal theta activity in humans increases with memory load in a working memory task. The European Journal of Neuroscience 15(8), 1395–1399 (2002)
Klimesch, W., Doppelmayr, M., Schwaiger, J., Auinger, P., Winkler, T.: ‘Paradoxical’ alpha synchronization in a memory task. Cognitive Brain Research 7(4), 493–501 (1999)
Klimesch, W.: Memory processes, brain oscillations and EEG synchronization. International Journal of Psychophysiology 24(1-2), 61–100 (1996)
Klimesch, W.: EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews 29(2-3), 169–195 (1999)
Klimesch, W., Sauseng, P., Hanslmayr, S.: EEG alpha oscillations: The inhibition–timing hypothesis. Brain Research Reviews 53(1), 63–88 (2007)
Osipova, D., Takashima, A., Oostenveld, R., Fernández, G., Maris, E., Jensen, O.: Theta and gamma oscillations predict encoding and retrieval of declarative memory. The Journal of Neuroscience 26(28), 7523–7531 (2006); PMID: 16837600
Sauseng, P., Klimesch, W., Doppelmayr, M., Pecherstorfer, T., Freunberger, R., Hanslmayr, S.: EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Human Brain Mapping 26(2), 148–155 (2005)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning, 1st edn. MIT Press, Cambridge (1998)
Thomson, D.J.: Spectrum estimation and harmonic analysis. Proceedings of the IEEE 70(9), 1055–1096 (1982)
Tuladhar, A.M., ter Huurne, N., Schoffelen, J.-M., Maris, E., Oostenveld, R., Jensen, O.: Parieto-occipital sources account for the increase in alpha activity with working memory load. Human Brain Mapping 28(8), 785–792 (2007)
Walsh, M.M., Anderson, J.R.: Learning from delayed feedback: neural responses in temporal credit assignment. Cognitive, Affective, & Behavioral Neuroscience 11(2), 131–143 (2011)
Weiss, S., Müller, H.M., Rappelsberger, P.: Theta synchronization predicts efficient memory encoding of concrete and abstract nouns. Neuroreport 11(11), 2357–2361 (2000)
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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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