Edelman S
Department of Applied Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel.
Neural Comput. 1995 Mar;7(2):408-23. doi: 10.1162/neco.1995.7.2.408.
How does the brain represent visual objects? In simple perceptual generalization tasks, the human visual system performs as if it represents the stimuli in a low-dimensional metric psychological space (Shepard 1987). In theories of three-dimensional (3D) shape recognition, the role of feature-space representations [as opposed to structural (Biederman 1987) or pictorial (Ullman 1989) descriptions] has long been a major point of contention. If shapes are indeed represented as points in a feature space, patterns of perceived similarity among different objects must reflect the structure of this space. The feature space hypothesis can then be tested by presenting subjects with complex parameterized 3D shapes, and by relating the similarities among subjective representations, as revealed in the response data by multidimensional scaling (Shepard 1980), to the objective parameterization of the stimuli. The results of four such tests, accompanied by computational simulations, support the notion that discrimination among 3D objects may rely on a low-dimensional feature space representation, and suggest that this space may be spanned by explicitly encoded class prototypes.
大脑如何表征视觉对象？在简单的知觉泛化任务中，人类视觉系统的表现就好像它是在一个低维度量心理空间中表征刺激（谢泼德，1987年）。在三维（3D）形状识别理论中，特征空间表征的作用（与结构表征（比德曼，1987年）或图像表征（乌尔曼，1989年）相对）长期以来一直是一个主要的争论点。如果形状确实在特征空间中被表征为点，那么不同对象之间感知到的相似性模式必须反映这个空间的结构。然后，可以通过向受试者呈现复杂的参数化3D形状，并将多维缩放（谢泼德，1980年）在反应数据中揭示的主观表征之间的相似性与刺激的客观参数化联系起来，来检验特征空间假说。四个这样的测试结果，再加上计算模拟，支持了这样一种观点，即3D对象之间的辨别可能依赖于低维特征空间表征，并表明这个空间可能由明确编码的类别原型所跨越。
