Department of Psychology, Emory University, Atlanta, USA.
Sci Rep. 2019 Jun 27;9(1):9359. doi: 10.1038/s41598-019-45268-y.
With seemingly little effort, humans can both identify an object across large changes in orientation and extend category membership to novel exemplars. Although researchers argue that object shape is crucial in these cases, there are open questions as to how shape is represented for object recognition. Here we tested whether the human visual system incorporates a three-dimensional skeletal descriptor of shape to determine an object's identity. Skeletal models not only provide a compact description of an object's global shape structure, but also provide a quantitative metric by which to compare the visual similarity between shapes. Our results showed that a model of skeletal similarity explained the greatest amount of variance in participants' object dissimilarity judgments when compared with other computational models of visual similarity (Experiment 1). Moreover, parametric changes to an object's skeleton led to proportional changes in perceived similarity, even when controlling for another model of structure (Experiment 2). Importantly, participants preferentially categorized objects by their skeletons across changes to local shape contours and non-accidental properties (Experiment 3). Our findings highlight the importance of skeletal structure in vision, not only as a shape descriptor, but also as a diagnostic cue of object identity.
人类似乎毫不费力地既能在大角度的方向变化中识别物体，又能将类别成员扩展到新的范例。尽管研究人员认为物体的形状在这些情况下是至关重要的，但对于如何表示形状以进行物体识别仍存在一些悬而未决的问题。在这里，我们测试了人类视觉系统是否将三维骨骼描述符纳入其中，以确定物体的身份。骨骼模型不仅提供了物体全局形状结构的紧凑描述，而且还提供了一种定量指标，用于比较形状之间的视觉相似性。我们的结果表明，与其他视觉相似性的计算模型相比，骨骼相似性模型能够更好地解释参与者对物体相似性判断的差异（实验 1）。此外，即使在控制另一种结构模型的情况下，对物体骨骼进行参数化改变也会导致感知相似性的比例变化（实验 2）。重要的是，即使在局部形状轮廓和非偶然属性发生变化的情况下，参与者也会优先根据物体的骨骼进行分类（实验 3）。我们的研究结果强调了骨骼结构在视觉中的重要性，它不仅是一种形状描述符，也是物体身份的诊断线索。
