2019 Conference on Cognitive Computational Neuroscience

13-16 September 2019    Berlin, Germany

Technical Program

Paper Detail

Paper: PS-2A.63
Session: Poster Session 2A
Location: H Lichthof
Session Time: Sunday, September 15, 17:15 - 20:15
Presentation Time:Sunday, September 15, 17:15 - 20:15
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Oscillatory dynamics of active learning in the human brain
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1364-0
Authors: Daniel Pacheco Estefan, Xerxes D. Arsiwalla, Riccardo Zucca, IBEC, Spain; Alessandro Principe, Rodrigo Rocamora, Hospital del Mar, Spain; Nikolai Axmacher, Ruhr University Bochum, Germany; Paul F.M.J. Verschure, IBEC, Germany
Abstract: While the benefits of self-directed learning on human memory are well-acknowledged, little is known on its underlying neurophysiological substrate. Here, we investigated the key signatures of volitional learning in the brain as assessed by representational similarity analysis applied to human intracranial EEG (iEEG) data. Epilepsy patients performed an episodic memory task during virtual navigation which tests differences in recognition memory for self-directed versus passive learning. Consistent with previous literature, higher recognition accuracy was observed for items studied in active as opposed to passive movement conditions at the behavioral level. In addition, we demonstrate a critical role of hippocampal low-frequency oscillations for active learning. This is observed in 1) increased hippocampal 2-6Hz power for active versus passive information sampling and 2) significantly greater encoding-retrieval similarity (ERS) for volitional as compared to passive conditions in the first second after cue onset at retrieval. Follow-up analyses will address the contribution of activity at different frequencies for item-specific ERS and volitional versus passive learning. Together, these results offer a first perspective on the key oscillatory mechanisms underlying volitional learning in the human brain.