Statistical learning drives anticipatory micro-saccades toward suppressed distractor locations

Statistical learning enables individuals to suppress locations associated with salient distractors, yet the mechanisms underlying this suppression remain unclear. Proactive accounts propose that suppression operates without prior attentional allocation to distractor locations, whereas reactive accounts suggest that suppression requires covert attention to the distractor location before it can be engaged. To address this ongoing debate, the current study recorded micro-saccades—an index of covert attention—during the pre-stimulus interval, alongside electroencephalogram (EEG) data collected during a visual search task. Participants were instructed to ignore a salient distractor that occurred more frequently at a specific location. Statistical learning reduced attentional capture for distractors presented at this high-probability location, and this effect was accompanied by oculomotor markers: micro-saccade rates decreased before stimulus onset relative to a control condition. Strikingly, these anticipatory micro-saccades were more often directed toward high-probability distractor locations than away from them, consistent with a reactive suppression mechanism. In parallel, alpha-band activity (8–14 Hz) carried decodable representations of high-probability distractor locations, indicating preparatory neural tuning. Overall, these findings provide evidence that the oculomotor system is closely involved in encoding and responding to learned spatial regularities.