Testing models of peripheral encoding using metamerism in an oddity paradigm.

Most of the visual field is peripheral, and the periphery encodes visual input with less fidelity compared to the fovea. What information is encoded, and what is lost in the visual periphery? A systematic way to answer this question is to determine how sensitive the visual system is to different kinds of lossy image changes compared to the unmodified natural scene. If modified images are indiscriminable from the original scene, then the information discarded by the modification is not important for perception under the experimental conditions used. We measured the detectability of modifications of natural image structure using a temporal three-alternative oddity task, in which observers compared modified images to original natural scenes. We consider two lossy image transformations, Gaussian blur and Portilla and Simoncelli texture synthesis. Although our paradigm demonstrates metamerism (physically different images that appear the same) under some conditions, in general we find that humans can be capable of impressive sensitivity to deviations from natural appearance. The representations we examine here do not preserve all the information necessary to match the appearance of natural scenes in the periphery.

[1]  H. Blackwell,et al.  Studies of psychophysical methods for measuring visual thresholds. , 1952, Journal of the Optical Society of America.

[2]  J. Robson,et al.  Application of fourier analysis to the visibility of gratings , 1968, The Journal of physiology.

[3]  C Blakemore,et al.  On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images , 1969, The Journal of physiology.

[4]  H. BOUMA,et al.  Interaction Effects in Parafoveal Letter Recognition , 1970, Nature.

[5]  N. Graham,et al.  Detection of grating patterns containing two spatial frequencies: a comparison of single-channel and multiple-channels models. , 1971, Vision research.

[6]  H. Bouma,et al.  Eccentric vision: Adverse interactions between line segments , 1976, Vision Research.

[7]  J. Rovamo,et al.  Cortical magnification factor predicts the photopic contrast sensitivity of peripheral vision , 1978, Nature.

[8]  J. Robson,et al.  Probability summation and regional variation in contrast sensitivity across the visual field , 1981, Vision Research.

[9]  D. H. Kelly,et al.  Retinal inhomogeneity. I. Spatiotemporal contrast sensitivity. , 1984, Journal of the Optical Society of America. A, Optics and image science.

[10]  Andrew B. Watson,et al.  Window of visibility: a psychophysical theory of fidelity in time-sampled visual motion displays , 1986 .

[11]  M. Banks,et al.  Model for hyperopia, myopia, and presbyopia, with suggested therapeutic remedies (A) , 1986 .

[12]  M. Banks,et al.  Sensitivity loss in odd-symmetric mechanisms and phase anomalies in peripheral vision , 1987, Nature.

[13]  C. Gross,et al.  Visuotopic organization and extent of V3 and V4 of the macaque , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  A. Hendrickson,et al.  Human photoreceptor topography , 1990, The Journal of comparative neurology.

[15]  C. Curcio,et al.  Topography of ganglion cells in human retina , 1990, The Journal of comparative neurology.

[16]  R. L. Valois,et al.  Vernier acuity with stationary moving Gabors , 1991, Vision Research.

[17]  S J Anderson,et al.  Peripheral spatial vision: limits imposed by optics, photoreceptors, and receptor pooling. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[18]  Edward H. Adelson,et al.  The Design and Use of Steerable Filters , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  M. Banks,et al.  The effects of contrast, spatial scale, and orientation on foveal and peripheral phase discrimination , 1991, Vision Research.

[20]  E. Peli,et al.  Image invariance with changes in size: the role of peripheral contrast thresholds. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[21]  D. Levi,et al.  The two-dimensional shape of spatial interaction zones in the parafovea , 1992, Vision Research.

[22]  D. G. Albrecht,et al.  Cortical neurons: Isolation of contrast gain control , 1992, Vision Research.

[23]  B J Craven A table ofd′ forM-alternative odd-man-out forced-choice procedures , 1992, Perception & psychophysics.

[24]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[25]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[26]  D. Tolhurst,et al.  Discrimination of changes in the second-order statistics of natural and synthetic images , 1994, Vision Research.

[27]  J. Rovamo,et al.  Modelling contrast sensitivity as a function of retinal illuminance and grating area , 1994, Vision Research.

[28]  J. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, SIGGRAPH.

[29]  Wilson S. Geisler,et al.  Visual detection following retinal damage: predictions of an inhomogeneous retino-cortical model , 1996, Photonics West.

[30]  D. Tolhurst,et al.  Band-limited contrast in natural images explains the detectability of changes in the amplitude spectra , 1997, Vision Research.

[31]  Wilson S. Geisler,et al.  Real-time foveated multiresolution system for low-bandwidth video communication , 1998, Electronic Imaging.

[32]  D H Foster,et al.  Human Sensitivity to Phase Perturbations in Natural Images: A Statistical Framework , 2000, Perception.

[33]  B Séré,et al.  Nonhomogeneous Resolution of Images of Natural Scenes , 2000, Perception.

[34]  E. Peli,et al.  Discrimination of wide-field images as a test of a peripheral-vision model. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[35]  Wilson S. Geisler,et al.  Gaze-contingent real-time simulation of arbitrary visual fields , 2002, IS&T/SPIE Electronic Imaging.

[36]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[37]  A. Cowey,et al.  Human cortical magnification factor and its relation to visual acuity , 2004, Experimental Brain Research.

[38]  Lester C. Loschky,et al.  The limits of visual resolution in natural scene viewing , 2005 .

[39]  Frank Jäkel,et al.  Bayesian inference for psychometric functions. , 2005, Journal of vision.

[40]  F. Jäkel,et al.  Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers. , 2006, Journal of vision.

[41]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[42]  Benjamin J. Balas,et al.  Texture synthesis and perception: Using computational models to study texture representations in the human visual system , 2006, Vision Research.

[43]  Andrew Gelman,et al.  Multilevel (Hierarchical) Modeling: What It Can and Cannot Do , 2006, Technometrics.

[44]  D. Braun,et al.  Phase noise and the classification of natural images , 2006, Vision Research.

[45]  Yoav Tadmor,et al.  The perceived contrast of texture patches embedded in natural images , 2006, Vision Research.

[46]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[47]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[48]  Steven C Dakin,et al.  Contrast gain control in natural scenes. , 2007, Journal of vision.

[49]  D. Pelli,et al.  The uncrowded window of object recognition , 2008, Nature Neuroscience.

[50]  D. Levi Crowding—An essential bottleneck for object recognition: A mini-review , 2008, Vision Research.

[51]  P. Perona,et al.  Objects predict fixations better than early saliency. , 2008, Journal of vision.

[52]  Benjamin J. Balas,et al.  Attentive texture similarity as a categorization task: Comparing texture synthesis models , 2008, Pattern Recognit..

[53]  Bernhard Schölkopf,et al.  Center-surround patterns emerge as optimal predictors for human saccade targets. , 2009, Journal of vision.

[54]  B. C. Motter Central V4 Receptive Fields Are Scaled by the V1 Cortical Magnification and Correspond to a Constant-Sized Sampling of the V1 Surface , 2009, The Journal of Neuroscience.

[55]  Lester C. Loschky,et al.  The contributions of central versus peripheral vision to scene gist recognition. , 2009, Journal of vision.

[56]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[57]  S. Solomon,et al.  Contrast sensitivity in natural scenes depends on edge as well as spatial frequency structure. , 2009, Journal of vision.

[58]  Jan Drewes,et al.  Animal detection in natural scenes: critical features revisited. , 2010, Journal of vision.

[59]  D. Heeger,et al.  When size matters: attention affects performance by contrast or response gain , 2010, Nature Neuroscience.

[60]  P. Green,et al.  Measuring perceived differences in surface texture due to changes in higher order statistics. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[61]  Peter J Bex,et al.  (In) sensitivity to spatial distortion in natural scenes. , 2010, Journal of vision.

[62]  Thomas Serre,et al.  What are the Visual Features Underlying Rapid Object Recognition? , 2011, Front. Psychology.

[63]  Damon M. Chandler,et al.  On the perception of band-limited phase distortion in natural scenes , 2011, Electronic Imaging.

[64]  Eero P. Simoncelli,et al.  Metamers of the ventral stream , 2011, Nature Neuroscience.

[65]  Ingo Fründ,et al.  Inference for psychometric functions in the presence of nonstationary behavior. , 2011, Journal of vision.

[66]  D. Tolhurst,et al.  Discrimination of natural scenes in central and peripheral vision , 2011, Vision Research.

[67]  Peter Neri,et al.  Global Properties of Natural Scenes Shape Local Properties of Human Edge Detectors , 2011, Front. Psychology.

[68]  Thomas S A Wallis,et al.  Image correlates of crowding in natural scenes. , 2011, Journal of vision.

[69]  R. Rosenholtz,et al.  A summary statistic representation in peripheral vision explains visual search. , 2009, Journal of vision.

[70]  Krista A. Ehinger,et al.  Rethinking the Role of Top-Down Attention in Vision: Effects Attributable to a Lossy Representation in Peripheral Vision , 2011, Front. Psychology.

[71]  Erhardt Barth,et al.  A compressed sensing model of crowding in peripheral vision , 2012, Electronic Imaging.

[72]  T. Meese,et al.  The attenuation surface for contrast sensitivity has the form of a witch's hat within the central visual field. , 2012, Journal of vision.

[73]  Frédo Durand,et al.  A Benchmark of Computational Models of Saliency to Predict Human Fixations , 2012 .

[74]  B. Balas Contrast Negation and Texture Synthesis Differentially Disrupt Natural Texture Appearance , 2012, Front. Psychology.

[75]  Jeffrey N. Rouder,et al.  Default Bayes factors for ANOVA designs , 2012 .

[76]  Matthias Bethge,et al.  How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? , 2013, PLoS Comput. Biol..

[77]  Eero P. Simoncelli,et al.  A functional and perceptual signature of the second visual area in primates , 2013, Nature Neuroscience.

[78]  M. A. Goodale,et al.  What is the best fixation target? The effect of target shape on stability of fixational eye movements , 2013, Vision Research.

[79]  Cosma Rohilla Shalizi,et al.  Philosophy and the practice of Bayesian statistics. , 2010, The British journal of mathematical and statistical psychology.

[80]  E. Peli,et al.  Perceived contrast in complex images. , 2012, Journal of vision.

[81]  P. Bex,et al.  Peri-Saccadic Natural Vision , 2013, The Journal of Neuroscience.

[82]  P. Neri Semantic Control of Feature Extraction from Natural Scenes , 2014, The Journal of Neuroscience.

[83]  A. Watson A formula for human retinal ganglion cell receptive field density as a function of visual field location. , 2014, Journal of vision.

[84]  W. Geisler,et al.  Retina-V1 model of detectability across the visual field. , 2014, Journal of vision.

[85]  Pablo Artal,et al.  Optics of the eye and its impact in vision: a tutorial , 2014 .

[86]  H. Komatsu,et al.  Image statistics underlying natural texture selectivity of neurons in macaque V4 , 2014, Proceedings of the National Academy of Sciences.

[87]  Thomas S A Wallis,et al.  Characterization of field loss based on microperimetry is predictive of face recognition difficulties. , 2014, Investigative ophthalmology & visual science.

[88]  Kedarnath P Vilankar,et al.  Local masking in natural images: a database and analysis. , 2014, Journal of vision.

[89]  Eero P. Simoncelli,et al.  Representation of Naturalistic Image Structure in the Primate Visual Cortex. , 2014, Cold Spring Harbor symposia on quantitative biology.

[90]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[91]  Kedarnath P. Vilankar,et al.  Local edge statistics provide information regarding occlusion and nonocclusion edges in natural scenes. , 2014, Journal of vision.

[92]  Johannes Burge,et al.  Defocus blur discrimination in natural images with natural optics. , 2015, Journal of vision.

[93]  Thomas S A Wallis,et al.  Sensitivity to gaze-contingent contrast increments in naturalistic movies: An exploratory report and model comparison. , 2015, Journal of vision.

[94]  Wolfgang Einhäuser,et al.  A new approach to modeling the influence of image features on fixation selection in scenes , 2015, Annals of the New York Academy of Sciences.

[95]  Benjamin T. Vincent,et al.  A tutorial on Bayesian models of perception , 2015 .

[96]  Shravan Vasishth,et al.  Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists , 2015, 1506.06201.

[97]  Benjamin Balas,et al.  Invariant texture perception is harder with synthetic textures: Implications for models of texture processing , 2015, Vision Research.