Irrelevant objects of expertise compete with faces during visual search

Prior work suggests that nonface objects of expertise can interfere with the perception of faces when the two categories are alternately presented, suggesting competition for shared perceptual resources. Here, we ask whether task-irrelevant distractors from a category of expertise compete when faces are presented in a standard visual search task. Participants searched for a target (face or sofa) in an array containing both relevant and irrelevant distractors. The number of distractors from the target category (face or sofa) remained constant, whereas the number of distractors from the irrelevant category (cars) varied. Search slopes, calculated as a function of the number of irrelevant cars, were correlated with car expertise. The effect was not due to car distractors grabbing attention, because they did not compete with sofa targets. Objects of expertise interfere with face perception even when they are task irrelevant, visually distinct, and separated in space from faces.     

Expertise increases the functional overlap between face and object perception

Recent studies indicate that expertise with objects can interfere with face processing. Although competition occurs between faces and objects of expertise, it remains unclear whether this reflects an expertise-specific bottleneck or the fact that objects of expertise grab attention and thereby consume more central resources. We investigated the perceptual costs of expertise by measuring visual thresholds for identifying targets embedded within RSVP sequences presented at varying temporal rates. Car experts and novices searched for face targets among face and car distractors, or watch targets among watch and car distractors. Remarkably, car experts were slower than novices at identifying faces among task-irrelevant cars, yet faster than novices at identifying watches among cars. This suggests that car expertise leads to greater functional overlap between cars and faces while reducing the functional overlap between cars and objects, a result incompatible with the notion of an encapsulated module for exclusive processing of faces.