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. Author manuscript; available in PMC: 2009 Apr 21.

Published in final edited form as: Neural Comput. 2007 Oct;19(10):2610–2637. doi: 10.1162/neco.2007.19.10.2610

Chromatic example, surface ensemble change results. Trade-off between discrimination and constancy for surface ensemble changes. Same format as Figure 8. The reference and test environment illuminants were D65, the reference environment surface ensemble was the baseline esemble, and the test environment surface ensembles were (from top to bottom) the blue, yellow, and red ensembles. The individual sets of connected points in the left panels show performance for noise levels σ_n = 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50. In evaluating , the adaptation parameters for the reference environment were those that optimized discrimination performance in the reference environment. These parameters were optimized separately for each noise level.

Inline graphic — Chromatic example, surface ensemble change results. Trade-off between discrimination and constancy for surface ensemble changes. Same format as Figure 8. The reference and test environment illuminants were D65, the reference environment surface ensemble was the baseline esemble, and the test environment surface ensembles were (from top to bottom) the blue, yellow, and red ensembles. The individual sets of connected points in the left panels show performance for noise levels σ_n = 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50. In evaluating , the adaptation parameters for the reference environment were those that optimized discrimination performance in the reference environment. These parameters were optimized separately for each noise level.