Statistical learning of distractor shape modulates attentional capture

Physically salient but task-irrelevant stimuli have high attentional priority, although observers are able to capitalize on statistical regularities in the environment to more efficiently ignore such stimuli. Physically salient distractors that more frequently appear in a particular location are less distracting when they appear in this high probability location. Likewise, colors and orientations that are frequently associated with distractors become preferentially ignored with learning. Such statistically learned distractor suppression has been examined with respect to the frequency of elementary features across trials, and less is known about how statistics concerning the composition of distractor features within a trial influence attention, particularly with respect to how orientations combine to form shapes. Color, orientation, and location are also represented very early in vision, whereas more complex features such as shape are represented further downstream in the visual system; it remains unclear whether statistically leaned distractor suppression can operate over such downstream visual representations. In the present study, we demonstrate attentional capture by physically salient, shape-defined distractors that is reduced in magnitude for a high probability shape. Our findings demonstrate that statistical learning can modulate attentional priority at least at the level of basic shapes and is not restricted to modulations of priority at the earliest stages of visual information processing tied to elementary features.

Automated summary

Physically salient but task-irrelevant stimuli have high attentional priority, but observers can use statistical regularities to better ignore them. Additionally, statistical learning can modulate attentional priority for basic shapes, not just elementary features.