Adaptive Control of Working Memory

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Human working memory (WM) is a highly flexible system optimized for guiding goal-directed (cognitive) actions. However, the flexibility of WM comes at the cost of a strict capacity limitation, requiring that the content of WM is continuously adapted to ongoing retrieval demands. The present project investigates how this is achieved by mechanisms of adaptive cognitive control. We focus on three well-known phenomena from the cognitive control literature: frequency congruency effects, congruency sequence effects, and post-error adjustments. These three effects have previously been used to describe how adaptive cognitive control balances attention to relevant and irrelevant stimuli in the environment. Here, we consider equivalent effects in declarative WM tasks to investigate how WM content is continuously adapted to future retrieval demands. Using behavioral and EEG studies, we seek to examine (1) on which representational levels, declarative WM content is adaptively adjusted during phases of encoding and maintenance, (2) how proactive and retroactive control processes achieve this adaptiveness, and (3) whether the monitoring of errors and conflicts contribute to this ability in the same way as in adaptive control of attention. Finally, the project aims to advance existing computational models of WM by including and validating conflict-based adaptive control mechanisms.