Dynamic hidden states underlying working-memory-guided behavior.

Abstract

Recent theoretical models propose that working memory is mediated by rapid transitions in ‘activity-silent’ neural states (e.g., short-term synaptic plasticity). According to the dynamic coding framework, such hidden state transitions flexibly configure memory networks for memory-guided behaviour, and dissolve them equally fast to allow forgetting. We developed a novel perturbation approach to measure mnemonic hidden states in electroencephalogram (EEG). By ‘pinging the brain’ during maintenance, we show that memory item-specific information is decodable from the impulse response, even in the absence of attention and lingering delay activity. Moreover, hidden memories are remarkably flexible: An instruction cue that directs people to forget one item is sufficient to wipe the corresponding trace from the hidden state. In contrast, temporarily unattended items remain robustly coded in the hidden state, decoupling attentional focus from cue-directed forgetting. Finally, the strength of hidden-state coding predicts the accuracy of working memory guided behaviour, including memory precision.