Abstract
Extensive research has examined how information is maintained in working memory (WM), but it remains unknown how WM is used to guide behaviour. We addressed this question using a combination of electroencephalography, pattern analyses, and cognitive modelling with a task that required maintenance of two WM items and flexible priority shifts between them. This enabled us to discern neural states coding for immediately and prospectively task-relevant items, and to examine how these states contribute to WM-based decisions. We identified two qualitatively different neural states: a functionally latent state encoded both items, was unrelated to performance on the current trial, but predictive of performance accuracy over longer time scales. In contrast, a functionally active state encoded only the immediately task-relevant item, and closely tracked the quality of evidence integration on the current trial. These results delineate a hierarchy of functional states whereby latent memories supporting general maintenance are transformed into active decision-circuits to guide WM-based behaviour.
Competing Interest Statement
The authors have declared no competing interest.