Cherian Thomas, Arun S P
Centre for Neuroscience, Indian Institute of Science, Bengaluru, 560012, India.
Atten Percept Psychophys. 2024 Nov;86(8):2721-2739. doi: 10.3758/s13414-024-02948-w. Epub 2024 Oct 26.
When a spiky object is occluded, we expect its spiky features to continue behind the occluder. Although many real-world objects contain complex features, it is unclear how more complex features are amodally completed and whether this process is automatic. To investigate this issue, we created pairs of displays with identical contour edges up to the point of occlusion, but with occluded portions exchanged. We then asked participants to search for oddball targets among distractors and asked whether relations between searches involving occluded displays would match better with relations between searches involving completions that are either globally consistent or inconsistent with the visible portions of these displays. Across two experiments involving simple and complex shapes, search times involving occluded displays matched better with those involving globally consistent compared with inconsistent displays. Analogous analyses on deep networks pretrained for object categorization revealed a similar pattern of results for simple but not complex shapes. Thus, deep networks seem to extrapolate simple occluded contours but not more complex contours. Taken together, our results show that amodal completion in humans is sophisticated and can be based on extrapolating global statistical properties.
当一个带尖刺的物体被遮挡时，我们预期其尖刺特征会在遮挡物后方延续。尽管许多现实世界的物体包含复杂特征，但尚不清楚更复杂的特征是如何以非模态方式完成的，以及这个过程是否是自动的。为了研究这个问题，我们创建了成对的显示，其轮廓边缘在遮挡点之前是相同的，但遮挡部分是互换的。然后，我们要求参与者在干扰物中搜索异常目标，并询问涉及遮挡显示的搜索之间的关系是否与涉及与这些显示的可见部分全局一致或不一致的完成情况的搜索之间的关系更匹配。在涉及简单和复杂形状的两个实验中，与不一致的显示相比，涉及遮挡显示的搜索时间与涉及全局一致的搜索时间更匹配。对为物体分类预训练的深度网络进行的类似分析显示，对于简单形状而非复杂形状，结果模式相似。因此，深度网络似乎可以推断简单的遮挡轮廓，但不能推断更复杂的轮廓。综合来看，我们的结果表明，人类的非模态完成是复杂的，并且可以基于推断全局统计属性。
