Abstract
Introduction: Research shows the visual system efficiently encodes peripheral objects as statistical representations, known as ensembles. However, few studies have explored the role of ensembles in visual search. Some models suggest that attention is drawn to ensembles with average qualities similar to the target (Im et al. 2015). Other models propose that learned ensemble-target associations facilitate visual search by cueing the location of the target. (Alvarez, 2011). Our project examines the function of ensembles to understand how they are used to facilitate target search and localization. Methods: Participants (N=20 per experiment) located a target line in one of two groups of lines, which formed ensembles in opposite locations on the screen. The average orientation of one ensemble matched the target orientation. The non-matching ensemble was 30 to 60 degrees different. Participants reported whether the target was in the left- or right-side ensemble, or was absent. In Experiment 1, the target was equally likely to be in all locations. In Experiment 2, the target was in the matching ensemble on 75% of trials and in either the non-matching ensemble or absent in 25% of trials. Results: Results from both experiments indicated that participants made significantly more initial saccades toward the matching ensemble, suggesting that the ensembles captured attention. Only in Experiment 2 did participants have faster response times, suggesting that participants used ensembles as cues to the target location after learning the ensemble-target association. This is further supported by evidence that participants were less likely to check the opposite ensemble after finding the target. This pattern suggests ensembles primarily influence visual search by acting as learned cues to the targets location. Conclusion: These results suggest that target-matching ensembles capture attention, but the effect on visual search is small unless there is a meaningful association between the ensemble and the target.
Meeting abstract presented at VSS 2017