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

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.

The outcomes will be disseminated to the scientific community in peer-reviewed journals, as well as in an expert meeting (workshop) that we will host at the end of the project. The experiments conducted in Oxford, Groningen and Bochum will provide for at least 3, 3 and 2 journal articles, respectively. We strive to make our work widely available, through open access whenever possible (including use of the pre-print server bioRxiv). In the interest of transparency, our research data will also be shared in open repositories (e.g., the Open Science Framework). Since 2015, all published data (and analysis scripts) generated from Stokes Lab have been made freely available via DataShareDrive, which has already stimulated exciting secondary projects from other researchers. To reach a broader readership, we will publish twice-yearly summary blog posts about our work in progress on our personal and institutional websites (e.g., MindWise at the University of Groningen).