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We measured the just-noticeable difference (JND) in orientation variance between two textures ( Figure 1) as we varied the baseline (pedestal) variance present in both textures. JND's first fell as pedestal variance increased and then rose, producing a ‘dipper’ function similar to those previously reported for contrast, blur, and orientation-contrast discriminations. A dipper function (both facilitation and masking) is predicted on purely statistical grounds by a noisy variance-discrimination mechanism. However, for two out of three observers, the dipper function was significantly better fit when the mechanism was made incapable of discriminating between small sample variances. We speculate that a threshold nonlinearity like this prevents the visual system from including its intrinsic noise in texture representations and suggest that similar thresholds prevent the visibility of other artifacts that sensory coding would otherwise introduce, such as blur.
|Uncontrolled Keywords:||visual acuity, computational modeling, learning, middle vision, texture|
|Subjects:||R Medicine > RE Ophthalmology|
|Divisions:||School of Health Sciences > Department of Optometry & Visual Science|
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