The dynamic representational nature of working memory storage
Working memory excels in keeping ready the momentary contents of all of human higher cognition despite being strictly limited in storage capacity. How the brain accomplishes this feat, beyond the temporary maintenance of just experienced information, is hardly understood. An emerging view suggests that working memory storage is topographically distributed according to the information’s endogenous level of abstraction. However, very little is known about how such distributed storage is orchestrated spatiotemporally, that is, how representations at different levels of abstraction are structured dynamically in time to suit current demands. 
In DeepStore, we use tailored experimental designs and newly developed multivariate analysis techniques to explore for the first time directly the depth-dimension of working memory storage, in terms of dynamic levels of abstraction. Combining functional imaging, magneto-/electroencephalography, and invasive neural recordings will make it possible to track levels of abstraction spatiotemporally, with millisecond precision.
In three ambitious experimental series, DeepStore will shed new light on long-standing open questions about working memory storage, including how it is modulated by attention, inattention, and distraction, and how multiple contents are stored simultaneously. The work will shape a new theory of the neurocognitive capacity limit in working memory. We will further test novel hypotheses about how working memory interfaces with long-term memory, and how it develops over the lifespan. Finally, we will combine our innovative human neuroimaging approach with direct electrophysiological recordings in non-human primates during the same task, to disclose the fine-grained neural mechanisms of dynamic abstraction down to the single-cell level. DeepStore is anticipated to provide fundamentally new insights into the dynamic and multi-layered nature of working memory, beyond the number and precision of items it can hold.