The project aims to understand how humans process information in Working Memory (WM). When information is temporarily maintained (or ‘stored’) in WM, the underlying brain processes do not resemble static storage, but are highly dynamic and constantly changing. The precise nature and functional role of these dynamics remains poorly understood. A critical factor in understanding the dynamics of WM processing is that the brain can represent any kind of memory contents (e.g. our car keys) at different levels of abstraction. In WM, our car keys might at any time take the form of a concrete mental image, an abstract conceptual idea, or a verbal label -- and recruit distinct brain areas and neural processes accordingly. Our project aims to understand how the brain juggles these multiple layers of endogenous representation in ways that enable powerful cognition and adaptive behaviour, despite WM's strictly limited storage capacity.
With its goal of delineating endogenous dynamics of WM information, the scope of the project is not limited to explicitly mnemonic functions but extends to cognitive information processing more generally (e.g. in perception, decision making, and transfer learning). Beyond its deliverables for the basic sciences, the project is expected to offer new insights into the role(s) of WM in learning and cognitive aging, and to provide new leverage points for the educational and clinical sectors in understanding and treating WM-associated deficiencies (e.g. in ADHD, dementia, depression, Parkinson’s, Alzheimer’s, and schizophrenic diseases). There is also public interest in understanding how memory works, including its frequent failures, and potential avenues for training and improvement. Another beneficiary can be AI research and its aspirations for better resource efficiency, where capacity-limited human WM can serve as a biological model system.
The main objectives of the project are (i) to uncover the spatiotemporal dynamics of abstraction and concretization in WM. The research program supports this goal at multiple levels of investigation, combining functional imaging, EEG/MEG, eye-tracking, behavioral models, and invasive recordings with novel representational geometry analysis approaches; (ii) to examine how WM-representational dynamics interact with long-term storage and learning. And (iii) to uncover the neural mechanisms underlying flexible representational transformations. Covering a wide range of paradigmatic WM settings, including single- and multi-item maintenance, (in-)attention and distraction, and varying task rules, the overarching goal is to deepen our understanding of how capacity-limited WM can supply the mind with just the right information, at just the right time, and in just the right format.
