Figure Legend: From: Some observations on contrast detection in noise

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Figure Legend: From: Some observations on contrast detection in noise Journal of Vision. 2008;8(9):4. doi:10.1167/8.9.4 Figure Legend: Illustration of the stimuli (left) and model components (right) discussed in the text. Stimuli with a narrow-band spatial-frequency spectrum are often assumed to be processed in a single spatially localized, spatial-frequency and orientation-selective filter. Ideally, an observer uses a filter that is an exact template of the signal (iii). Human deviations from ideal observer performance are typically interpreted as stemming from internal noise sources (i and vi) on the one hand and limited efficiency or sampling (ii) on the other hand (this refers to using only a sample of the available image information, for instance only the image pixels corresponding to the white pixels in ii). Early noise has the same dimensionality as the signal (i), while late noise is introduced prior to the decision, where the signal representation is 1-D (vi). If the signal representation at the decisional stage is directly based on the (rectified) filter response, a linear threshold-vs.-noise-power function is to be expected. To explain the pedestal effect seen in contrast discrimination, models of pattern vision must include a nonlinear post-filter stage, for instance a Naka–Rushton transformation (v). If the output of the human psychophysical filters mediating detection of a Gabor stimulus in noise is subject to rectification (iv), nonlinear transduction (v) and dominant late noise (vi) prior to the decisional stage, a dipper-shaped threshold-vs.-noise-power function is to be expected. The rectifying and Naka–Rushton nonlinearities are presented as different stages in this figure to disentangle their effects on the internal signal representation. They could, however, be collapsed into a single positively accelerated nonlinearity. Further, as mentioned in the text, the rectification need not be full-wave. Date of download: 1/3/2018 The Association for Research in Vision and Ophthalmology Copyright © 2018. All rights reserved.