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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

Figure 5.

Figure 5

Trade-off between discrimination and constancy. (Left) Each set of connected solid circles shows the trade-off between Inline graphic and Inline graphic for various optimizations of the steepness parameter n (see the text). Each set is for a different noise level (σn = 0.01, 0.025, 0.05, 0.075, 0.10), with the set closest to the upper right of the plot corresponding to the lowest noise level. The reference illuminant had intensity eref = 100, and the test illuminant had intensity etest = 160. The surface ensemble was specified by μr = 0.5 and σr = 0.3 and was common to both the reference and test environments. Both Inline graphic and Inline graphic were evaluated with respect to draws from this surface ensemble. The gain parameter was held fixed at g = 0.02045. The steepness parameter for the reference environment was n = 4.5. Parameters g = 0.02045 and n = 4.5 optimize discrimination performance for the reference environment when σn = 0.05. The open circles connected by the dashed line show the performance points that could be obtained for each noise level if there were no trade-off between discrimination and constancy. (Right) Equivalent trade-off noise plotted against visual noise level σn. See the discussion in the text.