DMN Connectivity Predicts Location-Learning-Related Attentional Capture Vulnerability

Abstract

Learned spatial regularities can reduce attentional capture by salient distractors, but observers vary in how strongly such regularities shape distractor interference. We asked whether this variability is associated with default mode network (DMN) functional connectivity, a network measure linked to individual differences in attention, internally guided cognition, and contextual memory. Using an existing fMRI visual-search dataset (N = 33), we tested whether task-run DMN connectivity was related to capture by distractors assigned to a learned spatial-probability history. Participants belonged to two training groups that differed in which distractor dimension carried the spatial bias: same-dimension distractors in one group and different-dimension distractors in the other. This design allowed us to ask whether the DMN association followed the design-defined biased dimension rather than general distractibility or a fixed physical distractor feature, while considering that the biased dimension also involved greater exposure. Higher within-DMN connectivity was associated with larger capture scores for the biased distractor dimension, whereas capture by the unbiased dimension showed little corresponding relation. This association remained positive after controls for inter-trial priming, head motion, nuisance signals, task-regressor residualization, global-signal regression, motion scrubbing, and trial-level exposure history. Exploratory edge-level analyses provided descriptive convergence, showing that capture-associated positive edges were concentrated around the DMN. These findings suggest that DMN connectivity may relate to individual differences in vulnerability to distractors embedded in learned spatial-probability histories.