In this issue, Pessoa emphasizes the importance of viewing neural activity from a perspective that functional networks form dynamically in a way that dramatically changes the functional contribution of individual brain areas. In this response, I argue that we should strive toward pluralism in understanding neural activity at both the emergent network and modular levels, on the bases that a purely emergent understanding would be incomplete, and that there are computational advantages to anatomically stable modularity.

It has been debated whether salient distractors in visual search can be proactively suppressed to completely prevent attentional capture, as the occurrence of proactive suppression implies that the initial shift of attention is not entirely driven by physical salience. While the presence of a Pd component in the EEG (associated with suppression) without a preceding N2pc component (associated with selection) has been used as evidence for proactive suppression, the link between these ERPs and the underlying mechanisms is not always clear. This is exemplified in two recent articles that observed the same waveform pattern, where an early Pd-like component flipped to a N2pc-like component, but provided vastly different interpretations (Drisdelle, B. L., & Eimer, E. P D components and distractor inhibition in visual search: New evidence for the signal suppression hypothesis. Psychophysiology , 58 , e13898, 2021; Kerzel, D., & Burra, N. Capture by context elements, not attentional suppression of distractors, explains the P D with small search displays. Journal of Cognitive Neuroscience , 32 , 1170–1183, 2020). Using RAGNAROC (Wyble et al., Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition. Psychological Review , 127 , 1163–1198, 2020), a computational model of reflexive attention, we successfully simulated this ERP pattern with minimal changes to its existing architecture, providing a parsimonious and mechanistic explanation for this flip in the EEG that is unique from both of the previous interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorporating computational modeling into theory building.

This article explores the time course of the functional interplay between detection and encoding stages of information processing in the brain and the role they play in conscious visual perception. We employed a multitarget rapid serial visual presentation (RSVP) approach and examined the electrophysiological P3 component elicited by a target terminating an RSVP sequence. Target-locked P3 activity was detected both at frontal and parietal recording sites and an independent component analysis confirmed the presence of two distinct P3 components. The posterior P3b varied with intertarget lag, with diminished amplitude and postponed latency at short relative to long lags—an electroencephalographic signature of the attentional blink (AB). Under analogous conditions, the anterior P3a was also reduced in amplitude but did not vary in latency. Collectively, the results provide an electrophysiological record of the interaction between frontal and posterior components linked to detection (P3a) and encoding (P3b) of visual information. Our findings suggest that, although the AB delays target encoding into working memory, it does not slow down detection of a target but instead reduces the efficacy of this process. A functional characterization of P3a in attentive tasks is discussed with reference to current models of the AB phenomenon.

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.