Feature-invariant processing of spatial segregation based on temporal asynchrony.

Temporal asynchrony is a cue for the perceptual segregation of spatial regions. Past research found attribute invariance of this phenomenon such that asynchrony induces perceptual segmentation regardless of the changing attribute type, and it does so even when asynchrony occurs between different attributes. To test the generality of this finding and obtain insights into the underlying computational mechanism, we compared the segmentation performance for changes in luminance, color, motion direction, and their combinations. Our task was to detect the target quadrant in which a periodic alternation in attribute was phase-delayed compared to the remaining quadrants. When stimulus elements made a square-wave attribute change, target detection was not clearly attribute invariant, being more difficult for motion direction change than for luminance or color changes and nearly impossible for the combination of motion direction and luminance or color. We suspect that waveform mismatch might cause anomalous behavior of motion direction since a square-wave change in motion direction is a triangular-wave change in the spatial phase (i.e., a second-order change in the direction of the spatial phase change). In agreement with this idea, we found that the segregation performance was strongly affected by the waveform type (square wave, triangular wave, or their combination), and when this factor was controlled, the performance was nearly, though not perfectly, invariant against attribute type. The results were discussed with a model in which different visual attributes share a common asynchrony-based segmentation mechanism.

Psychophysical measurement of perceived motion flow of naturalistic scenes.

The neural and computational mechanisms underlying visual motion perception have been extensively investigated over several decades, but little attempt has been made to measure and analyze, how human observers perceive the map of motion vectors, or optical flow, in complex naturalistic scenes. Here, we developed a psychophysical method to assess human-perceived motion flows using local vector matching and a flash probe. The estimated perceived flow for naturalistic movies agreed with the physically correct flow (ground truth) at many points, but also showed consistent deviations from the ground truth (flow illusions) at other points. Comparisons with the predictions of various computational models, including cutting-edge computer vision algorithms and coordinate transformation models, indicated that some flow illusions are attributable to lower-level factors such as spatiotemporal pooling and signal loss, while others reflect higher-level computations, including vector decomposition. Our study demonstrates a promising data-driven psychophysical paradigm for an advanced understanding of visual motion perception.