One of the ultimate goals of vision research is to understand how some elements are grouped together and differentiated from others to form object representations in a complex visual scene. There exists an extensive literature on this grouping/segmentation problem, but most of the studies have used un-recognizable stimuli that have little to do with object recognition per se. We used Gabor-rendered outlines of real-world objects to study some relationships between bottom-up and top-down processes in both spatial- and motion form perception. We manipulated low-level properties, such as element orientation and local motion, while incorporating higher-level properties, such as object complexity and identity, and found that adding local motion improved overall performance in both object detection and object identification tasks. Adding orientation jitter effectively decreased object detection performance in both static and motion conditions, and increased reaction time for identification in the static condition. Orientation jitter had much less effect on reaction times for identification in the local motion condition than in the static condition. Both contour properties ("good continuation") and object properties (identifiability) had a positive effect on detection and reaction time for identification.

One of the most important tasks of the visual system is the extraction of edges and object contours, and the integration of discrete elements to form a coherent global percept. A great deal is known about the spatial properties of contour extraction, but less is known about the dynamics and spatio-temporal aspects. We used Gabor-rendered outlines of real-world objects, where we could manipulate low-level properties, such as element orientation and phase, while incorporating higher-level properties, such as object complexity and identity, to study dynamic relationships in object detection. First we manipulated the time available for integration by changing back and forth between coherent and non-coherent orientations of the contour elements. We then manipulated contrast flicker by reversing the spatial phase of the Gabor elements at various frequencies. We found similar results to earlier studies on contour detection: detection was better for contrast flicker than for orientation flicker, and detection performance was curvature-dependent for orientation flicker but not for contrast flicker. Our results support the existence of at least two temporal frequency channels in the visual system, one low-pass and one band-pass peaking around 10-12 Hz. In addition, we found that object properties, such as identity and complexity, affected detection performance.

Using kinetic contours derived from everyday objects, we investigated how motion affects object identification. In order not to be distinguishable when static, kinetic contours were made from random dot displays consisting of two regions, inside and outside the object contour. In Experiment 1, the dots were moving in only one of two regions. The objects were identified nearly equally well as soon as the dots either in the figure or in the background started to move. RTs decreased with increasing motion coherence levels and were shorter for complex, less compact objects than for simple, more compact objects. In Experiment 2, objects could be identified when the dots were moving both in the figure and in the background with speed and direction differences between the two. A linear increase in either the speed difference or the direction difference caused a linear decrease in RT for correct identification. In addition, the combination of speed and motion differences appeared to be super-additive.