Neural Evidence for Feature-based Distractor Inhibition.

Abstract

Interference from a salient distractor is typically reduced when the appearance of the distractor follows either spatial or feature-based regularities. Although there is a growing body of literature on distractor location learning, the understanding of distractor feature learning remains limited. In the current study, we investigated distractor feature learning by using EEG measures. We assumed that learning benefits distractor handling, and we investigated the role of intertrial priming in distractor feature learning. Furthermore, we examined whether distractor feature learning influences later visual working memory (VWM) performance. Participants performed an adapted variant of the additional singleton task with a distractor that appeared more often in a specific color. The behavioral results provided additional evidence that observers can use distractor feature regularities to reduce distractor interference. At the neural level, we found a reduced PD with high-probability compared with low-probability distractors, suggesting that less suppression is required when the distractor appears in the more likely color. This reduced need for suppression was partly driven by intertrial priming. The PD elicited by repeated high-probability trials decreased over time, indicating that experience with the distractor reduced the need for suppression. In addition, the results showed that distractor feature learning did not affect VWM performance. Overall, our findings demonstrate that distractor feature learning decreases the interference of a salient distractor while also benefitting from intertrial priming processes, thereby improving attentional selection. In addition, it seems that learned distractor feature inhibition is not maintained in VWM when the task context is changed.