Forming a memory often entails the association of recent experience with present events. This recent experience is usually an information-rich and dynamic representation of the world around us. We here show that associating a static cue with a previously shown dynamic stimulus yields a detectable, dynamic representation of this stimulus. We further implicate this representation in the decrease of low-frequency power (∼4–30 Hz) in the ongoing EEG, which is a well-known correlate of successful memory formation. The reappearance of content-specific patterns in desynchronizing brain oscillations was observed in two sensory domains, that is, in a visual condition and in an auditory condition. Together with previous results, these data suggest a mechanism that generalizes across domains and processes, in which the decrease in oscillatory power allows for the dynamic representation of information in ongoing brain oscillations.

Observers often miss a second target (T2) if it follows an identified first target item (T1) within half a second in rapid serial visual presentation (RSVP), a finding termed the attentional blink. If two targets are presented in immediate succession, however, accuracy is excellent (Lag 1 sparing). The resource sharing hypothesis proposes a dynamic distribution of resources over a time span of up to 600 msec during the attentional blink. In contrast, the ST 2 model argues that working memory encoding is serial during the attentional blink and that, due to joint consolidation, Lag 1 is the only case where resources are shared. Experiment 1 investigates the P3 ERP component evoked by targets in RSVP. The results suggest that, in this context, P3 amplitude is an indication of bottom–up strength rather than a measure of cognitive resource allocation. Experiment 2, employing a two-target paradigm, suggests that T1 consolidation is not affected by the presentation of T2 during the attentional blink. However, if targets are presented in immediate succession (Lag 1 sparing), they are jointly encoded into working memory. We use the ST 2 model's neural network implementation, which replicates a range of behavioral results related to the attentional blink, to generate “virtual ERPs” by summing across activation traces. We compare virtual to human ERPs and show how the results suggest a serial nature of working memory encoding as implied by the ST 2 model.