ORA (Round 5): Hidden brain states underlying efficient representations in working memory

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Human working memory not only serves to keep track of changes around us, but is also vital to our ability to learn. When it comes to the nature of working memory, two facts are beyond doubt: First, it is notoriously limited and can hold only a handful of items at any given time. Second, this precious limited resource needs to be optimised, by organising memoranda into structured representations of objects and events.Beyond these basic facts, however, the nature of storage space in working memory is not well defined and is currently heavily debated. Even advanced measures of brain activity have so far not been able to provide decisive evidence. Fundamental challenges in measuring the neural correlates of working memory impede a better understanding of how memory limits arise and how they might be alleviated.Past research has assumed working memory is represented in persistent neural activity; however, we argue that neural coding for working memory actually goes far deeper than such static activity states. Until recently, there was no obvious method to explore working memory representations beyond persistent delay activity, but a new technique ('neural sonar') we developed allows a fresh perspective on the underlying, hidden states of the neural network. We will apply our new approach to determine the fundamental nature of capacity limits in working memory, and how encoding, maintenance and working memory-guided decision making can be optimised for superior memory performance.