Department of Psychology and Neuroscience, University of Colorado.
Department of Psychological and Brain Sciences, Indiana University.
J Exp Psychol Hum Percept Perform. 2013 Feb;39(1):111-132. doi: 10.1037/a0029059. Epub 2012 Jul 16.
Diverse evidence shows that perceptually integral dimensions, such as those composing color, are represented holistically. However, the nature of these holistic representations is poorly understood. Extant theories, such as those founded on multidimensional scaling or general recognition theory, model integral stimulus spaces using a Cartesian coordinate system, just as with spaces defined by separable dimensions. This approach entails a rich geometrical structure that has never been questioned but may not be psychologically meaningful for integral dimensions. In particular, Cartesian models carry a notion of orthogonality of component dimensions, such that if 1 dimension is diagnostic for a classification or discrimination task, another can be selected as uniquely irrelevant. This article advances an alternative model in which integral dimensions are characterized as topological spaces. The Cartesian and topological models are tested in a series of experiments using the perceptual-learning phenomenon of dimension differentiation, whereby discrimination training with integral-dimension stimuli can induce an analytic representation of those stimuli. Under the present task design, the 2 models make contrasting predictions regarding the analytic representation that will be learned. Results consistently support the Cartesian model. These findings indicate that perceptual representations of integral dimensions are surprisingly structured, despite their holistic, unanalyzed nature.
多种证据表明，感知上完整的维度，如构成颜色的维度，是以整体的方式呈现的。然而，这些整体表示的性质还不太清楚。现有的理论，如基于多维标度或广义识别理论的理论，使用笛卡尔坐标系来构建积分刺激空间，就像使用可分离维度定义的空间一样。这种方法需要一个丰富的几何结构，这个结构从未受到质疑，但对于整体维度来说，可能在心理上没有意义。特别是，笛卡尔模型带有组件维度正交性的概念，即如果 1 个维度对分类或辨别任务具有诊断意义，则可以选择另一个维度作为唯一不相关的维度。本文提出了一种替代模型，其中积分维度被描述为拓扑空间。在一系列使用维度分化的感知学习现象的实验中，对这两种模型进行了测试，其中，使用积分维度的刺激进行辨别训练可以诱导这些刺激的分析表示。在当前的任务设计下，这两种模型对将被学习的分析表示做出了相反的预测。结果一致支持笛卡尔模型。这些发现表明，尽管整体上没有经过分析，但对整体维度的感知表示的结构却出人意料。
