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Front Syst Neurosci. 2015 Sep 3;9:123. doi: 10.3389/fnsys.2015.00123. eCollection 2015.

Revealing hidden states in visual working memory using electroencephalography.

Author information

1
Department of Experimental Psychology, University of Groningen Groningen, Netherlands ; Oxford Centre for Human Brain Activity, University of Oxford Oxford, UK.
2
Department of Experimental Psychology, University of Oxford Oxford, UK.
3
Oxford Centre for Human Brain Activity, University of Oxford Oxford, UK ; Department of Experimental Psychology, University of Oxford Oxford, UK.

Abstract

It is often assumed that information in visual working memory (vWM) is maintained via persistent activity. However, recent evidence indicates that information in vWM could be maintained in an effectively "activity-silent" neural state. Silent vWM is consistent with recent cognitive and neural models, but poses an important experimental problem: how can we study these silent states using conventional measures of brain activity? We propose a novel approach that is analogous to echolocation: using a high-contrast visual stimulus, it may be possible to drive brain activity during vWM maintenance and measure the vWM-dependent impulse response. We recorded electroencephalography (EEG) while participants performed a vWM task in which a randomly oriented grating was remembered. Crucially, a high-contrast, task-irrelevant stimulus was shown in the maintenance period in half of the trials. The electrophysiological response from posterior channels was used to decode the orientations of the gratings. While orientations could be decoded during and shortly after stimulus presentation, decoding accuracy dropped back close to baseline in the delay. However, the visual evoked response from the task-irrelevant stimulus resulted in a clear re-emergence in decodability. This result provides important proof-of-concept for a promising and relatively simple approach to decode "activity-silent" vWM content using non-invasive EEG.

KEYWORDS:

EEG; dynamic coding; hidden state; multivariate pattern analysis; visual working memory

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