Shinhae Ahn¹ (), Richard A. Abrams¹; ¹Washington University in St. Louis

Suppressing irrelevant distractors is crucial for achieving goal-directed behavior in dynamic and unpredictable environments. Previous studies have shown that when salient distractors more frequently appear at a specific location, participants learn to suppress attention to that location. This learned suppression reduces distractor interference, even when distractors remain perceptually salient. Statistical learning-based distractor suppression differs from feature-based suppression, which relies on top-down attentional control to filter out distractors based on task-relevant target features. Although reaching movements have provided insight into many aspects of attentional control, little is known about how such movements might augment our understanding of the distinct types of suppression. This study examined the mechanisms of statistical learning-based and feature-based distractor suppression through reaching movement trajectories of a mouse cursor during target selection. Distractor suppression type was manipulated across two experiments: (1) a singleton-detection task, where the target was defined as a unique shape among homogeneous distractors, and a color singleton distractor appeared more frequently at one location (high-probability) than others (low-probability), enabling statistical-based suppression; and (2) a feature-search task, where the target was a specific shape among heterogeneous distractors, facilitating feature-based suppression. In the singleton-detection task, reaching movement trajectories exhibited no significant differences between high-probability distractor locations and distractor-absent trials, reflecting effective statistical learning, whereas attraction biases toward the distractors were observed at low-probability locations. In contrast, in the feature-search task, the movement trajectories demonstrated repulsion biases away from the suppressed distractors. Overall, the reaching movement trajectories revealed distinct patterns caused by different distractor suppression mechanisms. These findings highlight the utility of reaching movements in uncovering the attentional control processes underlying statistical learning-based and feature-based distractor suppression.