Is visual search a high-level phenomenon? Evidence from structure perception in 3-D scatterplots
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Increasing use of 3-D scatterplots for trend detection in visual analytics, raises the theoretical question: what constitutes an object in such a task? Previously (Shovman et al, 2008 Perception 37 79 ^ 80) we have shown that detection of a 3-D position of a single outlier point exhibits characteristics of serial visual search. According to feature integration theory (FIT) (Treisman and Gelade, 1980 Cognitive Psychology 12 97 ^ 136) that implies that every point is a complex perceptual object, and therefore detection of trends or patterns in a scatterplot will take longer with increasing number of constituent points. Conversely, according to reverse hierarchy theory (RHT) (Hochstein and Ahissar, 2002 Neuron 36 791 ^ 804) the object is the highest-level task- relevant arrangement of points. Therefore, RHT predicts that trend detection will take longer with more point groups. Participants identified (2 ^ 4AFC task) a group of points whose position- ing exhibited a 3-D structure when actively rotated. Number of points per group (64, 100, 144 196, 256) and the number of groups (2, 3, or 4) were manipulated independently. The dependent variable was the scene rotation duration, ie the time when the 3-D structure was potentially visible. Rotation times increased with number of point groups and decreased with number of points per group. This is consistent with RHT and contradicts FIT. In conjunction with previous experiments (Shovman et al, 2009 IEEE Proceedings of International Conference InformationVisual- isation pp 135 ^ 138), these data support connecting processes of visual search to task-relevant, high-level semantics of a scene rather than to its low-level visual features.