David H Brainard | Search PubMed

PubMed2002

Surface-illuminant ambiguity and color constancy: effects of scene complexity and depth cues.

Two experiments were conducted to study how scene complexity and cues to depth affect human color constancy. Specifically, two levels of scene complexity were compared. The low-complexity scene contained two walls with the same surface reflectance and a test patch which provided no information about the illuminant. In addition to the surfaces visible in the low-complexity scene, the high-complexity scene contained two rectangular solid objects and 24 paper samples with diverse surface reflectances. Observers viewed illuminated objects in an experimental chamber and adjusted the test patch until it appeared achromatic. Achromatic settings made tinder two different illuminants were used to compute an index that quantified the degree of constancy. Two experiments were conducted: one in which observers viewed the stimuli directly, and one in which they viewed the scenes through an optical system that reduced cues to depth. In each experiment, constancy was assessed for two conditions. In the valid-cue condition, many cues provided valid information about the illuminant change. In the invalid-cue condition, some image cues provided invalid information. Four broad conclusions are drawn from the data: (a) constancy is generally better in the valid-cue condition than in the invalid-cue condition: (b) for the stimulus configuration used, increasing image complexity has little effect in the valid-cue condition but leads to increased constancy in the invalid-cue condition; (c) for the stimulus configuration used, reducing cues to depth has little effect for either constancy condition: and (d) there is moderate individual variation in the degree of constancy exhibited, particularly in the degree to which the complexity manipulation affects performance.

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PubMed2002

What makes a categorization task difficult?

To understand why some categorization tasks are more difficult than others, we consider five factors that may affect human performance--namely, covariance complexity, optimal accuracy level with and without internal noise, orientation of the optimal categorization rule, and class separability. We argue that covariance complexity, an information-theoretic measure of complexity, is an excellent predictor of task difficulty. We present an experiment that consists of five conditions using a simulated medical decision-making task In the task human observers view hundreds of hypothetical patient profiles and classify each profile into Disease Category A or B. Each profile is a continuous-valued, three-dimensional stimulus consisting of three vertical bars, where each bar height represents the result of a medical test. Across the five conditions, covariance complexity was systematically manipulated. Results indicate that variation in performance is largely a function of covariance complexity and partly a function of internal noise. The remaining three factors do not explain performance results. We present a challenge to categorization theorists to design models that account for human performance as predicted by covariance complexity.

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