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      Derivation of human chromatic discrimination ability from an information-theoretical notion of distance in color space

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          Abstract

          The accuracy with which humans can detect small chromatic differences varies throughout color space. For example, we are far more precise when discriminating two similar orange stimuli than two similar green stimuli. In order for two colors to be perceived as different, the neurons representing chromatic information must respond differently, and the difference must be larger than the trial-to-trial variability of the response to each separate color. Photoreceptors constitute the first stage in the processing of color information; many more stages are required before humans can consciously report whether two stimuli are perceived as chromatically distinguishable or not. Therefore, although photoreceptor absorption curves are expected to influence the accuracy of conscious discriminability, there is no reason to believe that they should suffice to explain it. Here we develop information-theoretical tools based on the Fisher metric that demonstrate that photoreceptor absorption properties explain ~87% of the variance of human color discrimination ability, as tested by previous behavioral experiments. In the context of this theory, the bottleneck in chromatic information processing is determined by photoreceptor absorption characteristics. Subsequent encoding stages modify only marginally the chromatic discriminability at the photoreceptor level.

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          The effect of correlated variability on the accuracy of a population code.

          We study the impact of correlated neuronal firing rate variability on the accuracy with which an encoded quantity can be extracted from a population of neurons. Contrary to widespread belief, correlations in the variabilities of neuronal firing rates do not, in general, limit the increase in coding accuracy provided by using large populations of encoding neurons. Furthermore, in some cases, but not all, correlations improve the accuracy of a population code.
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            The arrangement of the three cone classes in the living human eye.

            Human colour vision depends on three classes of receptor, the short- (S), medium- (M), and long- (L) wavelength-sensitive cones. These cone classes are interleaved in a single mosaic so that, at each point in the retina, only a single class of cone samples the retinal image. As a consequence, observers with normal trichromatic colour vision are necessarily colour blind on a local spatial scale. The limits this places on vision depend on the relative numbers and arrangement of cones. Although the topography of human S cones is known, the human L- and M-cone submosaics have resisted analysis. Adaptive optics, a technique used to overcome blur in ground-based telescopes, can also overcome blur in the eye, allowing the sharpest images ever taken of the living retina. Here we combine adaptive optics and retinal densitometry to obtain what are, to our knowledge, the first images of the arrangement of S, M and L cones in the living human eye. The proportion of L to M cones is strikingly different in two male subjects, each of whom has normal colour vision. The mosaics of both subjects have large patches in which either M or L cones are missing. This arrangement reduces the eye's ability to recover colour variations of high spatial frequency in the environment but may improve the recovery of luminance variations of high spatial frequency.
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              A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts.

              Bayesian observer models provide a principled account of the fact that our perception of the world rarely matches physical reality. The standard explanation is that our percepts are biased toward our prior beliefs. However, reported psychophysical data suggest that this view may be simplistic. We propose a new model formulation based on efficient coding that is fully specified for any given natural stimulus distribution. The model makes two new and seemingly anti-Bayesian predictions. First, it predicts that perception is often biased away from an observer's prior beliefs. Second, it predicts that stimulus uncertainty differentially affects perceptual bias depending on whether the uncertainty is induced by internal or external noise. We found that both model predictions match reported perceptual biases in perceived visual orientation and spatial frequency, and were able to explain data that have not been explained before. The model is general and should prove applicable to other perceptual variables and tasks.
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                Author and article information

                Journal
                2016-11-22
                Article
                10.1162/NECO_a_00903
                1611.07272
                3ba693f1-dffb-49a2-abee-e33f193bdfc1

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Neural Computation doi:10.1162/NECO_a_00903 pp 1-18 (2016)
                23 pages, 10 figures
                q-bio.NC

                Neurosciences
                Neurosciences

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