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Assessing the utility of frequency tagging for tracking memory-based reactivation of word representations

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Lewis,  Ashley Glen
Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society;
Haskins Laboratories;
Donders Institute for Brain, Cognition and Behaviour, External Organizations;

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Citation

Lewis, A. G., Schriefers, H., Bastiaansen, M., & Schoffelen, J.-M. (2018). Assessing the utility of frequency tagging for tracking memory-based reactivation of word representations. Scientific Reports, 8: 7897. doi:10.1038/s41598-018-26091-3.


Cite as: https://hdl.handle.net/21.11116/0000-0001-69BC-6
Abstract
Reinstatement of memory-related neural activity measured with high temporal precision potentially provides a useful index for real-time monitoring of the timing of activation of memory content during cognitive processing. The utility of such an index extends to any situation where one is interested in the (relative) timing of activation of different sources of information in memory, a paradigm case of which is tracking lexical activation during language processing. Essential for this approach is that memory reinstatement effects are robust, so that their absence (in the average) definitively indicates that no lexical activation is present. We used electroencephalography to test the robustness of a reported subsequent memory finding involving reinstatement of frequency-specific entrained oscillatory brain activity during subsequent recognition. Participants learned lists of words presented on a background flickering at either 6 or 15 Hz to entrain a steady-state brain response. Target words subsequently presented on a non-flickering background that were correctly identified as previously seen exhibited reinstatement effects at both entrainment frequencies. Reliability of these statistical inferences was however critically dependent on the approach used for multiple comparisons correction. We conclude that effects are not robust enough to be used as a reliable index of lexical activation during language processing.