Unfolding Suppression: statistical learning drives suppression through dynamic attentional states modulated by threat

Abstract

How does statistical learning drive distractor suppression? A central debate in current research concerns whether the underlying mechanism operates proactively or reactively. Here, we propose a Temporal Dynamics of Attentional Suppression (TDAS) hypothesis, successfully reconciling these seemingly contradictory views. Across four visual search experiments, participants faced a probabilistic electric shock when a neutral distractor (Experiments 1a, 1b) or a threat-associated distractor (Experiments 2a, 2b) appeared at a threat-related high-probability location. Trial-averaged results showed faster responses and reduced fixations on distractors, but slower responses and fewer fixations on targets at these locations, consistent with learned distractor suppression. SMART analyses for eye-tracking data further revealed that learned suppression emerged from temporal state transitions: from distractor dominance via attentional limbo states to proactive suppression. Critically, spatial probability gates suppression pathways. At low-probability locations, suppression transitions to limbo states, while at high-probability locations, suppression either unfolds through the full three-state sequence or shifts directly from attentional limbo states to proactive suppression. Furthermore, threat signals accelerated these transitions compared to neutral contexts. Overall, these findings contribute to resolving the ongoing debate about the mechanisms of learned distractor suppression. They also offer a new theoretical perspective by establishing spatiotemporally distributed suppression as a core principle of attentional selection, thereby providing a unifying framework for understanding the dynamic nature of attentional control.