Dichoptic difference thresholds for uniform color changes applied to natural scenes.

It has recently been shown that the visual system is more sensitive to uniform color and/or luminance changes applied to raw compared to phase-scrambled images of natural scenes (A. Yoonessi & F. A. A. Kingdom, 2008). Here we consider whether the mechanisms responsible for the differential sensitivity operate before or after the point at which the signals from the two eyes are combined. Knowing this should help determine the types of nonlinearities responsible. Thresholds for detecting uniform color transformations applied to raw and phase-scrambled natural scenes were measured under two conditions: monocular, in which the discriminand pairs were placed side by side, and dichoptic, in which they were dichoptically superimposed. Subjects were required to select the pair of images that were transformed from two pairs of images in which the other pair was untransformed. In the dichoptic condition, the transformed image pair was identifiable by its lustrous appearance. In line with our previous findings, thresholds in the monocular condition were higher for the phase-scrambled compared to raw scenes. However in the dichoptic condition there was no significant difference between raw and phase-scrambled thresholds, suggesting that the differential sensitivity was mediated by mechanisms lying beyond the point of binocular combination. It is suggested that cortical neurons sensitive to edges but suppressed by neighboring texture might be responsible for the higher sensitivity to transformations applied to raw compared to phase-scrambled images of natural scenes.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  J. Pokorny,et al.  Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm , 1975, Vision Research.

[3]  J. Bullier,et al.  Reaching beyond the classical receptive field of V1 neurons: horizontal or feedback axons? , 2003, Journal of Physiology-Paris.

[4]  Jonathan W Peirce,et al.  Cortical representation of color is binocular. , 2008, Journal of vision.

[5]  D. Field,et al.  Does spatial invariance result from insensitivity to change? , 2016, Journal of vision.

[6]  William Albert Hugh Rushton,et al.  The Ferrier Lecture, 1962 Visual adaptation , 1965, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[7]  David Williams,et al.  A visual nonlinearity fed by single cones , 1992, Vision Research.

[8]  R. Masland,et al.  Spatial scale and cellular substrate of contrast adaptation by retinal ganglion cells , 2001, Nature Neuroscience.

[9]  A. Derrington,et al.  Feedback from V 1 and inhibition from beyond the classical receptive field modulates the responses of neurons in the primate lateral geniculate nucleus , .

[10]  C. Enroth-Cugell,et al.  Adaptation and dynamics of cat retinal ganglion cells , 1973, The Journal of physiology.

[11]  R. Blake.  © 2001 Kluwer Academic Publishers. Printed in the Netherlands. 5 A Primer on Binocular Rivalry, Including Current Controversies , 2000 .

[12]  Luís Paulo Santos,et al.  A local model of eye adaptation for high dynamic range images , 2004, AFRIGRAPH '04.

[13]  C. Enroth-Cugell,et al.  Chapter 9 Visual adaptation and retinal gain controls , 1984 .

[14]  C. Zetzsche,et al.  Nonlinear and extra-classical receptive field properties and the statistics of natural scenes , 2001, Network.

[15]  R M Boynton.  Implications of the minimally distinct border. , 1973, Journal of the Optical Society of America.

[16]  Charlie Phoenix,et al.  Biologically Inspired? , 2003, Artificial Life.

[17]  Ione Fine,et al.  Visual segmentation based on the luminance and chromaticity statistics of natural scenes , 2010 .

[18]  Michael J. Berry,et al.  Adaptation of retinal processing to image contrast and spatial scale , 1997, Nature.

[19]  Ralph D Freeman,et al.  Contrast sensitivity is enhanced by expansive nonlinear processing in the lateral geniculate nucleus. , 2008, Journal of neurophysiology.

[20]  Frederick A A Kingdom,et al.  Comparison of sensitivity to color changes in natural and phase-scrambled scenes. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[21]  Andriana Olmos,et al.  A biologically inspired algorithm for the recovery of shading and reflectance images , 2004 .

[22]  C. Blakemore,et al.  Lateral inhibition between orientation detectors in the cat's visual cortex , 2004, Experimental Brain Research.

[23]  L. Maffei,et al.  The unresponsive regions of visual cortical receptive fields , 1976, Vision Research.

[25]  Nicolaas Prins,et al.  Different mechanisms encode the shapes of contours and contour-textures , 2010 .

[26]  Robert Shapley,et al.  Neural mechanisms for color perception in the primary visual cortex , 2002, Current Opinion in Neurobiology.

[27]  Nicolai Petkov,et al.  Contour detection based on nonclassical receptive field inhibition , 2003, IEEE Trans. Image Process..

[28]  Rajesh P. N. Rao,et al.  Predictive Coding , 2019, A Blueprint for the Hard Problem of Consciousness.

[29]  I. Ohzawa,et al.  Length and width tuning of neurons in the cat's primary visual cortex. , 1994, Journal of neurophysiology.

[30]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[31]  R. M. Boynton,et al.  Chromaticity diagram showing cone excitation by stimuli of equal luminance. , 1979, Journal of the Optical Society of America.

[32]  Amanda Parker,et al.  Feedback from V1 and inhibition from beyond the classical receptive field modulates the responses of neurons in the primate lateral geniculate nucleus , 2002, Visual Neuroscience.

[33]  B. Cleland,et al.  Visual adaptation is highly localized in the cat's retina. , 1988, The Journal of physiology.

[34]  S. H. Bartley,et al.  SUMMATION AND SUBTRACTION OF BRIGHTNESS IN BINOCULAR PERCEPTION , 1930 .

[35]  H. Wallach.  Brightness constancy and the nature of achromatic colors. , 1948, Journal of experimental psychology.

[36]  梁瀬 健,et al.  ミツバチのspectral sensitivity(生理) , 1960 .

[37]  J. Nelson,et al.  Intracortical facilitation among co-oriented, co-axially aligned simple cells in cat striate cortex , 2004, Experimental Brain Research.

[38]  David Williams,et al.  Interchangeable backgrounds for cone afterimages , 1979, Vision Research.

[39]  F A Wichmann,et al.  Ning for Helpful Comments and Suggestions. This Paper Benefited Con- Siderably from Conscientious Peer Review, and We Thank Our Reviewers the Psychometric Function: I. Fitting, Sampling, and Goodness of Fit , 2001 .

[40]  M. Webster,et al.  Adaptation and the color statistics of natural images , 1997, Vision Research.

[41]  A. Borst.  Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[42]  M. Georgeson,et al.  Binocular contrast vision at and above threshold. , 2006, Journal of vision.

[43]  M. Georgeson,et al.  Binocular interaction: contrast matching and contrast discrimination are predicted by the same model. , 2007, Spatial vision.

[44]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

[45]  C. Blakemore,et al.  Characteristics of surround inhibition in cat area 17 , 1997, Experimental Brain Research.

[46]  D. Tolhurst,et al.  Spatiochromatic Properties of Natural Images and Human Vision , 2002, Current Biology.

[47]  T E Cohn,et al.  Binocular vision: two possible central interactions between signals from two eyes. , 1976, Science.

[48]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[49]  C. Baker,et al.  First- and second-order information in natural images: a filter-based approach to image statistics. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.