Coding of low-level position and orientation information in human naturalistic vision

Orientation and position of small image segments are considered to be two fundamental low-level attributes in early visual processing, yet their encoding in complex natural stimuli is underexplored. By measuring the just-noticeable differences in noise perturbation, we investigated how orientation and position information of a large number of local elements (Gabors) were encoded separately or jointly. Importantly, the Gabors composed various classes of naturalistic stimuli that were equated by all low-level attributes and differed only in their higher-order configural complexity and familiarity. Although unable to consciously tell apart the type of perturbation, observers detected orientation and position noise significantly differently. Furthermore, when the Gabors were perturbed by both types of noise simultaneously, performance adhered to a reliability-based optimal probabilistic combination of individual attribute noises. Our results suggest that orientation and position are independently coded and probabilistically combined for naturalistic stimuli at the earliest stage of visual processing.

[1]  I Kovács,et al.  A closed curve is much more than an incomplete one: effect of closure in figure-ground segmentation. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Moshe Gur,et al.  Cerebral Cortex doi:10.1093/cercor/bhi003 Orientation and Direction Selectivity of Neurons in V1 of Alert Monkeys: Functional Relationships and Laminar Distributions , 2022 .

[3]  R. P. Millane,et al.  Scaling and Power Spectra of Natural Images , 2003 .

[4]  D. Fitzpatrick,et al.  Spatial coding of position and orientation in primary visual cortex , 2002, Nature Neuroscience.

[5]  D. Regan,et al.  Postadaptation orientation discrimination. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[6]  Robert F. Hess,et al.  Neural recruitment explains “weber's law” of spatial position , 1993, Vision Research.

[7]  H. Sompolinsky,et al.  Theory of orientation tuning in visual cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[9]  M A Georgeson,et al.  From filters to features: location, orientation, contrast and blur. , 1994, Ciba Foundation symposium.

[10]  József Fiser,et al.  Prior implicit knowledge shapes human threshold for orientation noise. , 2015, Journal of vision.

[11]  J Nachmias,et al.  Letter: Grating contrast: discrimination may be better than detection. , 1974, Vision research.

[12]  Behzad Mansouri,et al.  Orientation variance discrimination in amblyopia. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  D. Field,et al.  Sensitivity to contrast histogram differences in synthetic wavelet-textures , 2001, Vision Research.

[14]  S. Dakin,et al.  Context influences contour integration. , 2009, Journal of vision.

[15]  D. Tolhurst,et al.  Amplitude spectra of natural images. , 1992, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[16]  Katsumi Aoki,et al.  Recent development of flow visualization , 2004, J. Vis..

[17]  C. Koch,et al.  Recurrent excitation in neocortical circuits , 1995, Science.

[18]  O. Braddick.  Visual hyperacuity. , 1984, Nature.

[19]  G W Humphreys,et al.  Top-down processes in object identification: evidence from experimental psychology, neuropsychology and functional anatomy. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[20]  G. Orban,et al.  Human orientation discrimination tested with long stimuli , 1984, Vision Research.

[21]  S. Dakin,et al.  Snakes and ladders: the role of temporal modulation in visual contour integration , 2001, Vision Research.

[22]  R. Shapley,et al.  New perspectives on the mechanisms for orientation selectivity , 1997, Current Opinion in Neurobiology.

[23]  Joshua A Solomon,et al.  Efficiencies for the statistics of size discrimination. , 2011, Journal of vision.

[24]  Paul Schrater,et al.  Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Joshua A Solomon,et al.  Noise reveals visual mechanisms of detection and discrimination. , 2002, Journal of vision.

[26]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

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

[28]  Roger W Li,et al.  The receptive field and internal noise for position acuity change with feature separation. , 2006, Journal of vision.

[29]  J. Lund,et al.  Compulsory averaging of crowded orientation signals in human vision , 2001, Nature Neuroscience.

[30]  Roger W Li,et al.  Perceptual learning improves efficiency by re-tuning the decision 'template' for position discrimination , 2004, Nature Neuroscience.

[31]  R. Watt,et al.  The recognition and representation of edge blur: Evidence for spatial primitives in human vision , 1983, Vision Research.

[32]  D M Levi,et al.  Feature integration in pattern perception. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[33]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[34]  J. Saunders,et al.  Do humans optimally integrate stereo and texture information for judgments of surface slant? , 2003, Vision Research.

[35]  R. V. Novikova,et al.  Dynamics of orientation tuning in the cat striate cortex neurons , 1993, Neuroscience.

[36]  Dennis M Levi,et al.  Detecting disorder in spatial vision , 2000, Vision Research.

[37]  C W Tyler,et al.  Colour bit-stealing to enhance the luminance resolution of digital displays on a single pixel basis. , 1997, Spatial vision.

[38]  K. Fujii,et al.  Visualization for the analysis of fluid motion , 2005, J. Vis..

[39]  S. Nelson,et al.  An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[40]  E. Adelson.  Perceptual organization and the judgment of brightness. , 1993, Science.

[41]  J A Solomon,et al.  Perceived pattern regularity computed as a summary statistic: implications for camouflage , 2012, Proceedings of the Royal Society B: Biological Sciences.

[42]  P. O. Bishop,et al.  Spatial vision. , 1971, Annual review of psychology.

[43]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[44]  Dennis M Levi,et al.  Position jitter and undersampling in pattern perception , 1999, Vision Research.

[45]  C. Chubb,et al.  A 'dipper' function for texture discrimination based on orientation variance. , 2008, Journal of vision.

[46]  R. Hess,et al.  Discriminating local continuity in curved figures , 1999, Vision Research.

[47]  R. Shapley,et al.  Dynamics of orientation tuning in macaque V1: the role of global and tuned suppression. , 2003, Journal of neurophysiology.

[48]  George A. Alvarez,et al.  Visual Awareness Is Limited by the Representational Architecture of the Visual System , 2015, Journal of Cognitive Neuroscience.

[49]  J. Solomon.  The history of dipper functions , 2009, Attention, perception & psychophysics.

[50]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[51]  Christopher W. Tyler,et al.  Nonlinearities of near-threshold contrast transduction , 1997, Vision Research.

[52]  Kunihiko Fukushima,et al.  Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .

[53]  Philip H. Ramsey.  Nonparametric Statistical Methods , 1974, Technometrics.

[54]  John G. Flanagan,et al.  Development of Continuous and Discrete Neural Maps , 2007, Neuron.

[55]  M. Ernst,et al.  Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.

[56]  D. Chklovskii,et al.  Maps in the brain: what can we learn from them? , 2004, Annual review of neuroscience.

[57]  Robert F. Hess,et al.  Contributions of local orientation and position features to shape integration , 2005, Vision Research.

[58]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[59]  David Whitaker,et al.  Isolation of stimulus characteristics contributing to Weber's law for position , 2002, Vision Research.