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Yazhu Ling, Anya Hurlbert; Color and size interactions in a real 3D object similarity task. Journal of Vision 2004;4(9):5. doi: 10.1167/4.9.5.
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© ARVO (1962-2015); The Authors (2016-present)
In the natural world, objects are characterized by a variety of attributes, including color and shape. The contributions of these two attributes to object recognition are typically studied independently of each other, yet they are likely to interact in natural tasks. Here we examine whether color and size (a component of shape) interact in a real three-dimensional (3D) object similarity task, using solid domelike objects whose distinct apparent surface colors are independently controlled via spatially restricted illumination from a data projector hidden to the observer. The novel experimental setup preserves natural cues to 3D shape from shading, binocular disparity, motion parallax, and surface texture cues, while also providing the flexibility and ease of computer control. Observers performed three distinct tasks: two unimodal discrimination tasks, and an object similarity task. Depending on the task, the observer was instructed to select the indicated alternative object which was “bigger than,” “the same color as,” or “most similar to” the designated reference object, all of which varied in both size and color between trials. For both unimodal discrimination tasks, discrimination thresholds for the tested attribute (e.g., color) were increased by differences in the secondary attribute (e.g., size), although this effect was more robust in the color task. For the unimodal size-discrimination task, the strongest effects of the secondary attribute (color) occurred as a perceptual bias, which we call the “saturation-size effect”: Objects with more saturated colors appear larger than objects with less saturated colors. In the object similarity task, discrimination thresholds for color or size differences were significantly larger than in the unimodal discrimination tasks. We conclude that color and size interact in determining object similarity, and are effectively analyzed on a coarser scale, due to noise in the similarity estimates of the individual attributes, inter-attribute attentional interactions, or coarser coding of attributes at a “higher” level of object representation.
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