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David E. Anderson, John T. Serences, Edward K. Vogel, Edward Awh; Induced alpha rhythms reveal the content and quality of visual working memory representations with high temporal precision. Journal of Vision 2014;14(10):894. https://doi.org/10.1167/14.10.894.
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© ARVO (1962-2015); The Authors (2016-present)
In line with the hypothesis that neuronal oscillations coordinate the cellular assemblies that represent items in working memory (WM), we show that activity in the alpha frequency band (8-12 Hz) can be used to decode the content and quality of representations stored in visual WM. We acquired EEG data during an orientation WM task, and used a forward encoding model of orientation selectivity to reconstruct orientation-specific response profiles or channel tuning functions (CTFs). Because of reliable covariations between the spatial distribution of alpha power and the stored orientation value, this approach enabled the moment-by-moment tracking of the stored orientation during both the encoding and delay epochs of the trial. We measured the pattern of evoked and induced oscillatory power, which correspond to stimulus-driven and endogenously generated responses, respectively. Critically, these EEG-based CTFs were robust predictors of both between- and within-subject differences in mnemonic precision when decoding the spatial distribution of induced but not evoked alpha power. Experiments 2 and 3 established that these EEG-based CTFs are contingent on the voluntary storage goals of the observer. When observers were given a post-sample cue to store or drop the memorandum, the resulting CTF was sustained in the "store" condition and rapidly eliminated following the "drop" cue. When observers were instructed to store one of two simultaneously presented stimuli, only the stored item was represented in a sustained fashion throughout the delay period. These findings suggest that the synchronization of neural activity in the alpha frequency band plays a central role in the active storage of information in visual WM, and demonstrate a powerful approach for tracking the precision of online memories with high temporal resolution.
Meeting abstract presented at VSS 2014
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