5 Modular Complexity of Area V2 in the Macaque Monkey

Total Page:16

File Type:pdf, Size:1020Kb

5 Modular Complexity of Area V2 in the Macaque Monkey 1243_book.fm Page 109 Thursday, May 22, 2003 10:45 AM Modular Complexity of 5 Area V2 in the Macaque Monkey Anna W. Roe CONTENTS 5.1 Introduction ..................................................................................................109 5.1.1 Optical Imaging of V2 .....................................................................110 5.2 Redefining Relationships with V1 ...............................................................112 5.2.1 Revision of Anatomical Connectivity..............................................112 5.2.2 Functional V1–V2 Interactions Revealed by Cross-Correlation.....116 5.2.3 Blobs-to-Thin Stripes: Three Classes of Color Interactions...........117 5.2.4 Interblobs-to-Thick/Pale Stripes: Two Classes of Oriented Interactions .......................................................................................117 5.3 Modular Complexity within V2 Stripes ......................................................118 5.3.1 Thin Stripes: Modular Representation of Surface Color and Brightness.........................................................................................119 5.3.2 Thick/Pale Stripes: Modular Representation of Complex Contours ...........................................................................................121 5.3.2.1 Diversity of Higher-Order Contour Cells in V2 ..............121 5.3.2.2 Localization: Evidence for Complex Orientation Domains in V2..................................................................123 5.3.2.3 Feedback: Changing Balance between Two Orientation Networks ...........................................................................123 5.3.3 Thick Stripes: Modular Representation of Relative Disparity........126 5.4 Border and Surface Capture: Foundation for Figure Integration................127 5.5 Summary and Proposal ................................................................................130 Acknowledgments..................................................................................................132 References..............................................................................................................132 5.1 INTRODUCTION Area V2 has traditionally been thought of as the second stage of visual cortical processing in the primate. In addition to receiving ascending feedforward inputs 0-8493-1243-4/02/$0.00+$1.50 © 2003 by CRC Press LLC 109 1243_book.fm Page 110 Thursday, May 22, 2003 10:45 AM 110 The Primate Visual System from primary visual cortex (V1), V2 also receives thalamic input from the pulvinar as well as significant feedback from visual areas in both the ventral and dorsal streams. Thus, as a distribution center for ascending magnocellular, parvocellular, and koniocellular derived inputs from the lateral geniculate nucleus (LGN), V2 is strategically positioned as both an integrator and feedback control point of what and where information. In both Old World and New World monkeys, when stained for cytochrome oxidase,1 V2 is characterized by a pattern of alternating dark and light cytochrome oxidase stripes.2,3 In general, these stripes run perpendicular to the V1/V2 border and alternate in a thin/pale/thick/pale manner from the central (lateral cortex) to peripheral (medial cortex) visual field representations in V2. In the macaque monkey there are approximately 14 sets of thin/pale/thick/pale stripes in dorsal V2, with each set spanning on average 4 mm across. This pattern has also been confirmed by other anatomical staining methods such as Cat-301 staining, which reveals the thick stripes,4,5 and staining for NOS/NADPH.6 These stripe organizations are established early in development and, at least from studies thus far, remain in the face of manipulations in visual experience.7,8 Thus, V2 stripes can be viewed as develop- mental,9 evolutionary10 (cf. Reference 11), and/or functional12,13 entities. The long-standing view that the thin, pale, and thick stripes subserve a tripartite division of color, form, and depth information processing in the early visual pathway has been supported by electrophysiological,12–16 2-deoxyglucose,17,18 and optical imaging studies.13,19–21 This functional segregation in V2 has found compelling parallels in psychophysics of orientation, color, and depth perception.22,23 However, these broad descriptions by no means suggest uniformity of any single stripe. In fact, it is clear that each stripe contains a mixture of response types,16,21,24,25 that neuronal responses are multimodal in nature (e.g., Reference 26), and that there is a significant and important degree of form, color, and disparity integration in V2. Unraveling how functional segregation and multimodal integration are incorporated is the key to understanding V2 function. It is also important to note that, in addition to their uneven and blobby appear- ance, V2 cytochrome stripes exhibit variations in patterning. At times, dark cyto- chrome stripes are seen merging with one another or failing to follow the classic thin/thick alternation. Furthermore, given the variability in the widths of dark cyto- chrome stripes, the relationship of “thick” cytochrome stripes with “disparity” and “thin” cytochrome stripes with “color” is a fickle one (for review, see Reference 27). Despite this, the terms “thin” and “thick” have become strongly associated with the functional terms “color” and “disparity.” Following this usage, in this chapter, the terms “thin” and “thick” are based on the conceptual functional view of what thin and thick stripes are (i.e., “color” or “disparity,” respectively) and not the thickness of stripe as seen in cytochrome oxidase staining. 5.1.1 OPTICAL IMAGING OF V2 Perhaps one of the most compelling views of functional division in V2 is visual- ization of stripe compartments by optical imaging methods. Intrinsic signal optical imaging is a functional mapping method that measures changes in cortical 1243_book.fm Page 111 Thursday, May 22, 2003 10:45 AM Modular Complexity of Area V2 in the Macaque Monkey 111 reflectance derived from changes in hemodynamic signals (for review, see Refer- ences 28 and 29). The primary advantage of this method is its high spatial resolution (tens of microns), which permits the visualization of 50 to 200 mm size functional domains. Because of the limitations of optical penetration and light scatter, detec- tion is thus far limited to the superficial cortical layers (although cf. Reference 30). By definition, optical imaging reveals a representation of the local population response; that is, the predominant response summed over many cells. Therefore, (1) imaged response is still consistent with a diversity of single unit responses at a single locale and does not indicate unitary function at any single location, (2) it is useful for revealing local biases in representation that may not be evident from single unit studies, and (3) in combination with single unit studies, it can provide a joint single unit/local population view of the local neuronal response. Despite its limitations (superficial layers only, slow temporal resolution, limited to exposed cortex), intrinsic signal imaging has proved useful for mapping well-established functional organizations, for revealing novel organizations (e.g., References 31 and 32), and promises to be a useful tool for studying cortical activation in the awake- behaving monkey.33–38 In the macaque monkey, most of dorsal V2 is buried within the lunate sulcus, leaving a 0.5- to 2-mm-wide strip just posterior to the lunate available on the surface for imaging (Figure 5.1A). Because cells in V2 are predominantly binocular, the V2 border with V1 is clearly demarcated by the lack of ocular dominance columns (Figure 5.1B, upper panel). Functional maps are obtained by imaging the cortex during visual stimulation with stimuli such as achromatic sinusoidal gratings and isoluminant color gratings presented at different orientations. Stimulus-induced acti- vation (usually seen as darkening or decrease in tissue reflectance) of cortical activity reveals functional organizations such as ocular dominance columns, orientation domains, blobs, and interblobs in V1, and stripe structures in V2. As shown in Figure 5.1, the thick/pale stripe location is clearly revealed by mapping for orientation (Figure 5.1B, lower panel). Dark cytochrome oxidase stripes can also be determined by mapping for general activation (Figure 5.1C, middle panel). Together with cyto- chrome oxidase histology (Figure 5.1C, top panel), which alone often leaves stripe identity uncertain, thick, pale, and thin stripes can be determined with reasonable confidence (Figure 5.1C). Other methods for mapping stripes, such as color or color vs. luminance response (see Figure 5.4 below), monocularity vs. binocularity (see Figure 5.6 below), and disparity response, will be discussed later in this chapter. Recent studies have furthered our understanding of both the internal organization of V2 and its relationship to V1. These new developments, deriving from studies using anatomical tract tracing, optical imaging, and electrophysiological methods, have led to further modifications and refinement of the long-standing views of V1–V2 connectivity. These new views have developed hand-in-hand with new understand- ings of V2 architecture and hypotheses regarding its relationship to visual perception. The goal of this chapter is to review and integrate these recent developments in our understanding of V2, and propose a conceptual framework for the role of V2 in visual processing. The studies described center on
Recommended publications
  • Optical Illusion - Wikipedia, the Free Encyclopedia
    Optical illusion - Wikipedia, the free encyclopedia Try Beta Log in / create account article discussion edit this page history [Hide] Wikipedia is there when you need it — now it needs you. $0.6M USD $7.5M USD Donate Now navigation Optical illusion Main page From Wikipedia, the free encyclopedia Contents Featured content This article is about visual perception. See Optical Illusion (album) for Current events information about the Time Requiem album. Random article An optical illusion (also called a visual illusion) is characterized by search visually perceived images that differ from objective reality. The information gathered by the eye is processed in the brain to give a percept that does not tally with a physical measurement of the stimulus source. There are three main types: literal optical illusions that create images that are interaction different from the objects that make them, physiological ones that are the An optical illusion. The square A About Wikipedia effects on the eyes and brain of excessive stimulation of a specific type is exactly the same shade of grey Community portal (brightness, tilt, color, movement), and cognitive illusions where the eye as square B. See Same color Recent changes and brain make unconscious inferences. illusion Contact Wikipedia Donate to Wikipedia Contents [hide] Help 1 Physiological illusions toolbox 2 Cognitive illusions 3 Explanation of cognitive illusions What links here 3.1 Perceptual organization Related changes 3.2 Depth and motion perception Upload file Special pages 3.3 Color and brightness
    [Show full text]
  • OPTICAL ILLUSIONS Matyas Molnar More Info, Examples, Sources
    OPTICAL ILLUSIONS Matyas Molnar More info, examples, sources • Mohit Gupta: Understanding optical illusions • https://www.eyebuydirect.com/understanding-perception-optical-illusions • https://www.rd.com/culture/optical-illusions/ • https://www.thisisinsider.com/classic-optical-illusions-2018-1#this-is- troxlers-fading-circle-if-you-stare-the-dot-for-at-least-20-seconds-the-circle- will-completely-fade-away-20 • https://interestingengineering.com/11-puzzling-optical-illusions-and-how- they-work • https://www.collective-evolution.com/2017/07/26/ex-nasa-scientists-share- concealed-information-about-the-face-pyramid-found-on-mars/ • https://www.buzzfeed.com/arielknutson/people-who-found-jesus-in-their- food Preface • Microscopy is a visualization technique – the danger: we tend to believe what we see but humans are fooled by their vision in many different ways • We see / don’t see what we – want / don’t want to see – learnt / didn’t learn to se – others expect / don’t expect us to see • Humans are not rational beings. Our perception and decisions are governed by our emotions and earlier experiences. We make decisions emotionally, only later we justify them with logical explanations. • Biggest effects and limitations are in the following levels: – Eyes – Brain – Environment – Culture, religion, belief systems • We cannot perceive the world objectively outside of our box • Is there any objective world outside us? Preface • Many times there are no consensus on how an actual optical illusion works. • Hard to explain optical illusions with one unified theory, numerous factors are involved • There are various theories and counter theories, and sometimes it's also not clear if the illusion or type of illusion acts on the physical visual system or on the brain level.
    [Show full text]
  • Understanding Optical Illusions
    Understanding Optical Illusions Mohit Gupta What are optical illusions? Perception: I see Light (Sensing) Truth: But this is an ! Oracle Optical Illusion in Nature Image Courtesy: http://apollo.lsc.vsc.edu/classes/met130/notes/chapter19/graphics/infer_mirage_road.jpg A Brightness Illusion Different kinds of illusions • Brightness and Contrast Illusions • Twisted Cord Illusions • Color Illusions • Perspective Illusions • Relative Motion Illusions • Illusions of Expressions Lightness Constancy Our Vision System tries to compensate for differences in illumination Why study optical illusions? • Studying how brain is fooled teaches us how it works “Illusions of the senses tell us the truth about perception” [Purkinje] • It makes us happy J : Al Seckel Simultaneous Contrast Illusions Low-level Vision Explanation Negative Positive Photo-receptors Photo-receptors Receptive Fields in the Retina - Inhibitory Light Excitatory - + - Light - Low-level Vision Explanation - - - + - - + - Positive - - Negative Gradient Gradient High-level Vision Explanation: Context Less Incident More Incident Illumination Illumination Higher Perceived Lower Perceived Reflectance Reflectance Brightness = Reflectance * Incident Illumination The Hermann grid illusion The Hermann grid: Low level Explanation - - + - - Lateral Inhibition The Hermann grid illusion Focus on one intersection Why does the illusion disappear? Receptive fields are smaller near the fovea (center) of the eye The Waved Grid: No illusion! Scintillating Grids: Straight and Curved Adelson’s checkerboard
    [Show full text]
  • Mariann Füzesiné Hudák UNDERSTANDING BASIC VISUAL
    Budapest University of Technology and Economics PhD School in Psychology – Cognitive Science Mariann Füzesiné Hudák UNDERSTANDING BASIC VISUAL MECHANISMS THROUGH VISUAL ILLUSIONS PhD Thesis Supervisor: Dr. Ilona Kovács Budapest, 2013 ‘There is no harm in doubt and skepticism, for it is through these that new discoveries are made.’ (Richard Feynman, Letter to Armando Garcia J, December 11, 1985) 1 Acknowledgements .......................................................................................................................... 3 2 Abstract ............................................................................................................................................ 4 3 Kivonat ............................................................................................................................................. 4 4 Summary .......................................................................................................................................... 4 5 Összefoglaló ..................................................................................................................................... 5 6 Introduction ...................................................................................................................................... 7 6.1 Looking into the ‘black box’ through illusions - An alternative to physiological studies ...... 7 6.2 Theories of lightness-brightness perception .......................................................................... 12 6.2.1 The roots ...........................................................................................................................
    [Show full text]
  • Visual Illusions 1077 Square on Top of 4 Disks Is Assumed
    Visual Illusions 1077 square on top of 4 disks is assumed. Note that just inside its edges should also be present in its the completion of the boundaries of the Kanizsa middle. This is a symbolic (logical) operation square is accompanied by a surface filling-in pro- that might be carried out in the high-level visual cess that leads to the square being perceived as cortex. Some studies have failed to find evidence brighter than the surrounding white background. for the spread of information thought to underlie In a related process, the black disks are surface perception in early retinotopic visual completed “behind” the reconstructed surface. areas, and this has been taken by some as sup- Filling-in and completion processes related to port for symbolic theories of surface perception. visible figures (the Kanizsa square) are referred Hence, further empirical work is required to test to as “modal,” while these processes are referred the traditional view as well as symbolic theories, to as amodal when related to invisible parts of and to resolve some inconsistencies in the find- figures (the parts of the disks occluded by the ings among some studies. Because the traditional Kanizsa square assumed in front by the visual view and symbolic theories are not mutually system). exclusive, elements from both theories might find Figures 1(c) and (d) illustrate the existence of support in the future. surface filling-in processes. Figure 1(c) shows that the physical gray levels in between the black bars P. De Weerd are the same. When the black bars are removed, See also Consciousness; Gestalt Approach; Object some regions in the stimulus are seen as much Perception; Object Perception: Physiology; Perceptual brighter and others as darker, an illusion referred Organization: Vision to as the Craik-O’Brien-Cornsweet illusion.
    [Show full text]
  • Scientific and Large Data Visualization 25 October 2018 Visual Perception Massimiliano Corsini Visual Computing Lab, ISTI
    Scientific and Large Data Visualization 25 October 2018 Visual Perception Massimiliano Corsini Visual Computing Lab, ISTI - CNR - Italy Overview • Intro • Our Eye • Receptive Field Model, CSF, Mach Bending, Cornsweet effect.. • What we really see • Preattentive Process • Gestalt Laws • Perception of lines and areas Human Visual System (HVS) • The Human Visual System (HVS) is subdivided into two parts: – Optical part our eye. – Visual Perception our brain (visual cortex). Our Eye The Retina • The retina is composed by a large number of photoreceptors (rods and cones). • 100 millions of rods, 6 millions of cones. • Cones are concentrated in the fovea (1.5-2 degrees). • Retinal ganglion cells send information, through the optic nerve, to the brain. Rods and Cones Distribution Visual Acuity • Points – 1 minute of arc. • Gratings – 1-2 minutes of arc. • Letter – 5 minutes of arc. • Vernier acuity (the ability to see if two segments are colinear) – 10 seconds of arc. Visual Acuity Snellen Chart Visual Acuity Figure by Vanessa Ezekowitz under CC-SA-BY 3.0. Contrast Sensitivity Function (CSF) • Our perception is sensitive to pattern contrast, frequency and orientation. • Also color influences the CSF. Figure by Martin Reddy. Contrast Sensitivity Function (CSF) Visual Cortex • LGN (Lateral Geniculate Nucleus) forwards pulses to V1. It is also connected with V2 and V3. • V1 is the primary visual cortex. It performs edge detection and global organization (inputs from V2, V3). Visual Cortex • V2 handles depth, foreground, illusory contours. • V3 supports global motion understanding. • V4 recognizes simple geometric shape. • V5/MT: motion perception integration and eye movements guidance. Receptive Field (in the retina) • The receptive field of a cell is the visual area over which a cell responds to light.
    [Show full text]
  • CROWN 85: Visual Perception: a Window to Brain and Behavior Lecture 6
    CROWN 85: Visual Perception: A Window to Brain and Behavior Lecture 6 lecture 6 outline Crown 85: Visual Perception: A Window to Brain and Behavior Crown 85 Winter 2016 Visual Perception: A Window to Brain and Behavior Lecture 6: The Central Visual: System (structure and processing) Reading: Joy of Perception Eye Brain and Vision Web Vision Looking: Information Processing in the Retina (Sinauer) Visual Pathways (Sinauer) Phototransduction (Sunauer) Several Werblin Videos on Visual Cortex OVERVIEW: Visual information leaves the retina via the optic nerve and is transmitted to structures in the brain. The aim of this lecture will be to see various cortical sites further of the original “photograph” into new codes which emphasize certain aspects of the image while discarding others. We will discuss how this code is refined as information is transmitted Lecture 6: Central Visual System (Structure and Processing along pathways to the brain. 1 2 two important questions about cortical processing from outline 2. Understand the following functional concepts: What types of patterns selectively activate a. receptive field f. orientationally tuned cells in the visual system? b. concentric on-center neuron [receptive fields] receptive field g. simple cell c. concentric off-center h. complex cell receptive field i. "grandmother" cell d. retinotopic map j. spatial frequency detector Are differing aspects of an image processed e. feature detector k. what vs where pathways by different parts of the brain? [concurrent pathways or streams] 4 receptive rield (RF) Receptive Field (Kalat figure 6.18) Map of how light presented to various positions in the visual field excites or inhibits the firing of a neuron (this map or pattern is the cell’s receptive field).
    [Show full text]
  • Visual Illusions
    VISUAL ILLUSIONS: PERCEPTION OF LUMINANCE, COLOR, AND MOTION IN HUMANS Inaugural-Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) an der Justus-Liebig-Universität Giessen Fachbereich 06: Psychologie und Sportwissenschaften Otto-Behaghel-Strasse 10F 35394 Giessen vorgelegt am 21. Dezember 2006 von Dipl. Psych. Kai Hamburger geboren am 5. Juni 1977 in Gedern 1. Berichterstatter und Betreuer Prof. Karl R. Gegenfurtner, Ph.D. (Psychologie, Giessen) 2. Berichterstatter Prof. Dr. Hans Irtel (Psychologie, Mannheim) To my grandfather Heinrich, my parents Elke and Rainer, my brother Sven, and to my fiancée Sandra. Acknowledgement First of all, I would like to express my gratitude to my godfather in the graduate program, Professor Karl R. Gegenfurtner (Giessen, Germany), and my two other supervisors, Professor Lothar Spillmann (Freiburg, Germany) and Professor Arthur G. Shapiro (Lewisburg, PA, U.S.A.). Karl, on very short notice you gave me the opportunity to join the graduate program ‘Neural Representation and Action Control – Neuroact’ and by doing so one of the best departments in the field of Vision Sciences. I became a member of an extraordinary lab, which still excites me. You allowed me to finish projects which were already in progress when I started in Giessen and you gave me plenty of rope to pursue my own interests. Thus, I was able to publish efficiently and furthermore gained deep insights into the field of Vision Sciences and even beyond. This was the best mentoring a natural scientist could think of. Thank you. Professor Spillmann, you paved my way into the Vision Sciences. At the beginning of my scientific career you gave me the opportunity to join your famous ‘Freiburg Psychophysics Laboratory’.
    [Show full text]
  • Ganglion Cells Are the Output Neurons of the Retina: They Produce a Train of Spikes
    NATURAL AND ARTIFICIAL VISION MODULE 387AA – ROBOTICS [WIF-LM] Marcello Calisti [email protected] The BioRobotics Institute Scuola Superiore Sant’Anna Content of the lectures • Natural part – theorectical (3h) • Artificial part – theorectical (3h) • Hands-on in Matlab (6h) The material on natural vision can be found on: Kandel et al., “Principle of Neuroscience”, McGraw Hill, 2013 Most of the material presented in these lessons can be found on the brilliant, seminal books on robotics and image analysis reported hereafter: 1. P.I. Corke, “Robotics, Vision & Control”, Springer 2011, ISBN 978-3-642-20143-1 2. R. Szeliski, “Computer Vision: Algorithms and Applications”, Springer-Verlag New York, 2010 3. R.C. Gonzalez & R.E. Woods, “Digital Image Processing (3rd edition)”, Prentice-Hall, 2006 Most of the images of these lessons are downloaded from RVC website http://www.petercorke.com/RVC/index.php and, despite they are free to use, they belong to the author of the book. Human vision is a creative process, far beyond simple trasduction Psychol Sci. 2006 Apr;17(4):287-91. The impact of emotion on perception: bias or enhanced processing? Zeelenberg R1, Wagenmakers EJ, Rotteveel M. Psychol Sci. 2003 Jan;14(1):7-13. Emotional facilitation of sensory processing in the visual cortex. Schupp HT1, Junghöfer M, Weike AI, Hamm AO. what we can What we learnt What we want Light is «different» depending on the animal we insect Ludimar Hermann Shiny Herman grid We saw contrast, not absolute value of illumination The Craik-Cornsweet illusion
    [Show full text]
  • Free-Energy and Illusions: the Cornsweet Effect 058 002 059 003 060 004 Harriet Brown* and Karl J
    ORIGINAL RESEARCH ARTICLE published: xx February 2012 doi: 10.3389/fpsyg.2012.00043 001 Free-energy and illusions: the Cornsweet effect 058 002 059 003 060 004 Harriet Brown* and Karl J. Friston 061 005 The Wellcome Trust Centre for Neuroimaging, University College London Queen Square, London, UK 062 006 063 007 064 Edited by: 008 In this paper, we review the nature of illusions using the free-energy formulation of Bayesian 065 Lars Muckli, University of Glasgow, perception. We reiterate the notion that illusory percepts are, in fact, Bayes-optimal and 009 UK 066 010 represent the most likely explanation for ambiguous sensory input. This point is illustrated 067 Reviewed by: 011 Gerrit W. Maus, University of using perhaps the simplest of visual illusions; namely, the Cornsweet effect. By using 068 012 California at Berkeley, USA plausible prior beliefs about the spatial gradients of illuminance and reflectance in visual 069 013 Peter De Weerd, Maastricht scenes, we show that the Cornsweet effect emerges as a natural consequence of Bayes- 070 University, Netherlands 014 optimal perception. Furthermore, we were able to simulate the appearance of secondary 071 015 *Correspondence: illusory percepts (Mach bands) as a function of stimulus contrast. The contrast-dependent 072 Harriet Brown, Wellcome Trust Centre 016 073 for Neuroimaging, Institute of emergence of the Cornsweet effect and subsequent appearance of Mach bands were 017 Neurology Queen Square, London simulated using a simple but plausible generative model. Because our generative model 074 018 WC1N 3BG, UK. was inverted using a neurobiologically plausible scheme, we could use the inversion as a 075 019 e-mail: [email protected] simulation of neuronal processing and implicit inference.
    [Show full text]
  • Sensorimotor Processing of Vertical Disparity
    SENSORIMOTOR PROCESSING OF VERTICAL DISPARITY ROBERT SCOTT ALLISON A thesis submitted to the Faculty of Graduate Studies in partial fulfilment of the requirements for the degree of Doctor of Philosophy Graduate Programme in Biology York University Toronto, Ontario March 1998 Natioral Library Bibliothèque nationale 191 of Canada du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue Wellington OttawaON K1AON4 Ottawa ON K1A ON4 Canada Canada The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or seIl reproduire, prêter, distribuer ou copies of ths thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/^, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fiom it Ni la thèse ni des extraits substantiels may be printed or otheIUrise de celle-ci ne doivent être imprimés reproduced without the author' s ou autrement reproduits sans son permission. autorisation. SENSORIMOTOR PROCESSING OF VERTICAL DZSPARITY by Robert S. Allison a dissertation subrnitted to the Faculty of Graduate Studies of York University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Permission has been granted to the LIBRARY OF YORK UNIVERSITY to lend or sel1 copies of this dissertation. to the NATIONAL LIBRARY OF CANADA to microfilm this dissertation and to tend or seil copies of the film, and to UNIVERSITY MICROFILMS to publish an abstract of this dissertation.
    [Show full text]
  • Neural Stimulation, Learning and Masking
    Seeing 3D surfaces: neural stimulation, learning and masking Vassilis Pelekanos A thesis submitted to the University of Birmingham for the degree of Doctor of Philosophy School of Psychology College of Life and Environmental Sciences University of Birmingham June 2015 University of Birmingham Research Archive e-theses repository This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder. Abstract Our everyday visual perception experience is of a richly detailed world full of bounded objects, slanted and extensive surfaces. Estimating surfaces’ depth is critical for visually guided interactions, yet, challenged by the limited two-dimensional input projected on each eye’s retina. Binocular disparity, the positional differences that objects project on the two retinae, is a powerful depth cue inducing stereopsis. In the present dissertation, I assessed the neural stages of the visual hierarchy that support the visual perception of disparity- defined three-dimensional (3D) surfaces. In the first experimental chapter (chapter 3), I used fMRI-guided rTMS to probe the cortical areas involved in the perception of slanted surfaces. Results hint at a functional contribution of the dorso-parietal visual stream (posterior parietal cortex; PPC) to slant estimation, however, further work is needed to fully understand the nature of its involvement.
    [Show full text]