Modulations of saliency signals at two hierarchical levels of priority computation revealed by spatial statistical distractor learning.

Many attention theories assume that selection is guided by a preattentive, spatial representation of the scene that combines bottom-up stimulus information with top-down influences (task goals and prior experience) to code for potentially relevant locations (priority map). At which level(s) of priority computation top-down influences modulate bottom-up stimulus signals is an open question. In a visual-search task, here we induced experience-driven spatial suppression (statistical learning) by presenting 1 of 2 salient distractors more frequently in one display region than the other. When a distractor standing out in the same dimension as the target was spatially biased in Experiment 1, processing of both the target and another, spatially unbiased distractor standing out in a different dimension was likewise hampered in the suppressed region. This indicates that constraining spatial suppression to a specific distractor feature is not possible, and participants instead resort to purely space-based (distractor-feature-independent) suppression at a supradimensional, overall-priority map. In line with a common locus of suppression, a novel computational model of distraction in visual search captures all 3 location effects with a single spatial-weighting parameter. In contrast, when the different-dimension distractor was spatially biased in Experiment 2, processing of other objects in the suppressed region was unaffected, indicating suppression constrained to a subordinate, dimension-specific level of priority computation. In sum, we demonstrate experience-driven top-down modulations of saliency signals at the overall-priority and dimension-specific levels that do not reach down to the specific distractor features. (PsycInfo Database Record (c) 2021 APA, all rights reserved).