Integration of contour and surface information in shape detection

In studies of shape perception, the detection of contours and the segregation of regions enclosed by these contours have mostly been treated in isolation. However, contours and surfaces somehow need to be combined to create a stable perception of shape. In this study, we used a 2AFC task with arrays of oriented Gabor elements to determine whether and to what extent human observers integrate information from the contour and from the interior surface of a shape embedded in this array. The saliency of the shapes depended on the alignment of Gabors along the shape outline and on the isolinearity of Gabors inside the shape. In two experiments we measured detectability of shapes defined by the contour cue, by the surface cue, and by the combination of both cues. As a first step, we matched performance in the two single-cue conditions. We then compared shape detectability in the double-cue condition with the two equally detectable single-cue conditions. Our results show a clear double-cue benefit: Participants used both cues to detect the shapes. Next, we compared performance in the double-cue condition with the performance predicted by two models of sensory cue combination: a minimum rule (probability summation) and an integration rule (information summation). Results from Experiment 2 indicate that participants applied a combination rule that was better than mere probability summation. We found no evidence against the integration rule.

Research highlights
► To detect a shape in a Gabor array, both contour and surface information are used.
► Combination rule outperforms probability summation.
► No evidence against optimal integration of surface and contour information.