Sign in
NeurosciencebioRxiv
Preprint

Controlling the flow of interruption in working memory

Nicole HakimiD, Tobias Feldmann‐WüstefeldiD, Edward AwhiD, Edward K. VogeliD

Abstract

Visual working memory (WM) must maintain relevant information, despite the constant influx of both relevant and irrelevant information. Attentional control mechanisms help determine which of this new information gets access to our capacity-limited WM system. Previous work has treated attentional control as a monolithic process-either distractors capture attention or they are suppressed. Here, we provide evidence that attentional capture may instead be broken down into at least two distinct sub-component processes: 1) spatial capture, which refers to when spatial attention shifts towards the location of irrelevant stimuli, and 2) item-based capture, which refers to when item-based WM representations of irrelevant stimuli are formed. To dissociate these two sub-component processes of attentional capture, we utilized a series of EEG components that track WM maintenance (contralateral delay activity), suppression (distractor positivity), item individuation (N2pc), and spatial attention (lateralized alpha power). We show that relevant interrupters trigger both spatial and item-based capture, which means that they undermine WM maintenance more. Irrelevant interrupters, however, only trigger spatial capture from which ongoing WM representations can recover more easily. This fractionation of attentional capture into distinct sub-component processes provides a framework by which the fate of ongoing WM processes after interruption can be explained.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISVu-Cheung '21Hakim '19Hakim '19Günseli '19Özdemir '25Raya '23
77% semantic matchNeuroscience