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.

视觉世界的边界可以通过输入图像中亮度和纹理的变化来定义。“轮廓整合”过程将局部变化连接到线条或边缘的感知中。之前的一项研究测试了由二阶对比度调制定义的轮廓的整合。它们的轮廓被放置在随机小波的背景中。参与者的表现接近机会。我们用不同的任务重新访问了二阶轮廓集成。参与者区分具有“良好连续性”的轮廓与干扰项。我们测量了不同数量的外部方向或位置噪声的阈值。这给出了两个噪声屏蔽功能。我们还测量了具有基线曲率的轮廓的阈值，以评估更多弯曲目标的性能。我们的参与者能够区分二阶轮廓的良好延续。阈值高于一阶轮廓。在我们的建模中，我们发现这是由多种因素造成的。一阶和二阶轮廓积分之间的等效内部噪声翻了一番。效率也有所下降。效率差异仅在我们的定向噪声条件下才显着。对于一阶和二阶刺激，参与者还能够以更弯曲的轮廓执行我们的任务。我们得出结论，人类可以整合二阶轮廓，即使它们是弯曲的。然而，与一阶轮廓相比，性能有所降低。我们发现整合机制的输入受损和效率降低似乎是负责任的。二阶轮廓积分可能更受先前研究中使用的噪声背景的影响。难以分离该背景可能可以解释他们的结果。