Statistical learning drives anticipatory micro-saccades toward suppressed distractor locations

Statistical learning enables individuals to suppress distracting but salient information, yet the mechanisms underlying this suppression remain unclear. While some suggest that suppression is proactive, occurring without first attending to distractor locations, others argue that it is reactive, requiring covert attention toward the distractor location before suppression can occur. To address this fierce 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 appeared more frequently at one specific location than at others. Learning high-probability distractor locations reduced attentional capture by salient distractors, coinciding with increased micro-saccade rates and decodable representations of these locations in alpha power (8-14 Hz) before stimulus onset. Strikingly, these anticipatory micro-saccades were more frequently directed toward high-probability distractor locations than away from them, supporting a reactive suppression mechanism. Overall, these findings highlight a crucial role for the oculomotor system in encoding and responding to learned spatial regularities.