Adaptive visual selection in feature space

Visual perception relies on efficient selection of task-relevant information for prioritized processing. A prevalent mode of selection is feature-based selection, and a key question in the literature is the shape of the selection profile—that is, when a feature is selected, what is the landscape of priority for all features in that dimension? Past studies have reported conflicting findings with both monotonic and nonmonotonic profiles. We hypothesized that feature selection can be adaptively adjusted based on stimulus factors (feature competition) and task demands (selection precision). In three experiments, we manipulated these contextual factors in a central task while measuring selection profile in a peripheral task. We found a nonmonotonic, surround suppression, profile when feature competition and selection precision was high, but observed a monotonic profile when these factors were low. Furthermore, manipulation of selection precision alone can shape selection profile independent of feature competition. These findings reconcile previous conflicting results and importantly, demonstrate that feature selection is highly adaptive, allowing flexible allocation of processing resources to ensure efficient extraction of visual information.