What the Flip? What the P-N Flip Can Tell Us about Proactive Suppression

Joyce Tam, Chloe Callahan-Flintoft, Brad Wyble

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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 exem-plified 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. PD 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 PD 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 success-fully 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 pre-vious interpretations. Our account supports the occurrence of proactive suppression and demonstrates the benefits of incorpo-rating computational modeling into theory building.

Original languageEnglish (US)
Pages (from-to)2100-2112
Number of pages13
JournalJournal of cognitive neuroscience
Volume34
Issue number11
DOIs
StatePublished - Nov 2022

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience

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