Coding of low-level position and orientation information in human naturalistic vision

Integration of contours defined by second-order contrast-modulation of texture

Boundaries in the visual world can be defined by changes in luminance and texture in the input image. A "contour integration" process joins together local changes into percepts of lines or edges. A previous study tested the integration of contours defined by second-order contrast-modulation. Their contours were placed in a background of random wavelets. Participants performed near chance. We re-visited second-order contour integration with a different task. Participants distinguished contours with "good continuation" from distractors. We measured thresholds in different amounts of external orientation or position noise. This gave two noise-masking functions. We also measured thresholds for contours with a baseline curvature to assess performance with more curvy targets. Our participants were able to discriminate the good continuation of second-order contours. Thresholds were higher than for first-order contours. In our modelling, we found this was due to multiple factors. There was a doubling of equivalent internal noise between first- and second-order contour integration. There was also a reduction in efficiency. The efficiency difference was only significant in our orientation noise condition. For both first- and second-order stimuli, participants were also able to perform our task with more curved contours. We conclude that humans can integrate second-order contours, even when they are curved. There is however reduced performance compared to first-order contours. We find both an impaired input to the integrating mechanism, and reduced efficiency seem responsible. Second-order contour integration may be more affected by the noise background used in the previous study. Difficulty segregating that background may explain their result.