Abstract
Although ignoring visual distractions is paramount to efficient attentional selection, recent behavioral studies demonstrate that suppression is not under volitional control. Instead, suppression appears to only emerge when distractor information can be derived directly from experience, through statistical learning. In line with influential predictive processing theories, this may suggest that the brain may stop regarding a distractor as a distractor when it is fully expected, and raises the question how ‘learning to ignore’ is neurally implemented. In two behavioral and one EEG experiment we addressed this outstanding question and specifically examined how learned spatial and feature expectations influence pre-distractor anticipatory activity and distractor processing. Across searches the distractor, which accompanied the target on the majority of trials, appeared in one location more frequently. At this high probability distractor location distractor interference and target selection efficiency reduced, demonstrating learned spatial suppression. Compared to baseline, where targets and distractors only differed subtly (same spatial frequency), this spatial suppression benefit reduced when the task allowed for a unique attentional distractor set and was smallest when in addition a unique attentional target could be formed. Supporting the notion of a pre-stimulus distractor template, not only the target but also the distractor could be decoded prior to search display onset. However, spatial expectations did not modulate pre-stimulus alpha-band activity, a marker of top-down inhibition, nor could we decode the distractor location in anticipation of visual search. Instead, spatial expectations were only evident during stimulus processing. Distractors at predicted locations no longer elicited the Pd ERP component, indicating that attentional suppression or reorienting is no longer necessary when distractors can be expected. Together these findings demonstrate how spatial and feature expectations interactively shape attentional priorities and shed novel light on how learning to ignore is neurally implemented.