DOCSLIB.ORG

Conjunctive Coding of Complex Object Features

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Conjunctive Coding of Complex Object Features Conjunctive Coding of Complex Object Features by Jonathan Erez A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Psychology University of Toronto © Copyright by Jonathan Erez 2015 Conjunctive Coding of Complex Object Features Jonathan Erez A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Graduate Department of Psychology University of Toronto 2015 Abstract Critical to perceiving an object is the ability to bind its constituent features into a cohesive representation, yet the manner by which the visual system integrates object features to yield a unified percept remains unknown. Another important aspect of object perception is the ability to represent complex objects across different viewing angles, despite the drastic variability in appearance caused by shifting viewpoints. This dissertation describes a novel application of multivoxel pattern analysis (MVPA) of neuroimaging data that allows a direct investigation of whether neural representations integrate object features into a whole that is different from the sum of its parts and whether these representations are view invariant. Results showed that patterns of activity throughout the ventral visual stream (VVS), extending anteriorly into the perirhinal cortex (PRC), discriminated between the same sets of features when combined into different objects. Despite this sensitivity to the unique conjunctions of features, this activity was invariant to the viewpoints from which the conjunctions were presented. These results suggest that the manner in which our visual system processes complex objects depends on the explicit coding of the feature conjunctions comprising them, and that these representations are invariant to large changes in visual appearance caused by shifting viewpoints. To test whether experience with the objects changed the pattern of conjunctive representations across the VVS, participants ii were scanned in a separate fMRI study following an extensive behavioural training period. Results showed that both view-dependent and view-invariant conjunctive representations shifted to more posterior regions of the VVS, suggesting that training affected how unique feature conjunctions are represented across the VVS. These results support the representational- hierarchical view of cortical organization and contribute to our understanding of object perception and memory. iii Acknowledgments First and foremost, I would like to thank my advisor, Dr. Morgan Barense, for her support and guidance throughout my PhD. Thank you for your patience, generosity and for encouraging me to become a better researcher and writer. I would also like to thank the members of my thesis committee, Dr. Susanne Ferber and Dr. Brad Buchsbaum for their valued input and encouragement during this process. In addition, I wish to thank Dr. Rhodri Cusack for his theoretical and technical expertise. Many thanks to my labmates, Rachel Newsome, Lok-Kin Yeung, Danielle Douglas, and Celia Fidalgo and the rest the members of the Barense Lab for their friendship and for fruitful discussions over the years. Special thanks to Felicia Zhang and Will Kendall for their assistance with the experiments in this thesis, and thanks to all of the participants that agreed to be scanned, and rescanned. I also would like to thank NSERC, OGS, and the University of Toronto for their generous financial support of this research, as well as the hot dog van outside Sid Smith for staying open late most evenings to benefit from the aforementioned funding. I am grateful for my wife, Noam, for her unwavering support during this process. Your love and patience made this possible. Lastly, I’d like to thank my parents who inspired me to pursue my passions and instilled in me a thirst for knowledge. iv Contents Acknowledgments.......................................................................................................................... iv List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................. x List of Appendices ........................................................................................................................ xii General Introduction ....................................................................................................................... 1 1.1 The binding problem ............................................................................................................. 1 1.2 The view-invariance problem................................................................................................ 4 1.3 How might the brain solve these problems? ......................................................................... 7 1.4 fMRI MVPA ....................................................................................................................... 12 1.5 Thesis overview................................................................................................................... 15 Chapter 2: Neural representations of feature conjunctions composing complex objects ............. 17 2.1 Introduction ......................................................................................................................... 17 2.2 Materials and Methods ........................................................................................................ 23 Participants ............................................................................................................................ 23 Experimental Stimuli and Design ......................................................................................... 23 Tasks ..................................................................................................................................... 24 fMRI Data Acquisition ......................................................................................................... 27 Multi-Voxel Pattern Analysis (MVPA) ................................................................................ 27 Control analysis to test for sub-additivity of the BOLD signal ............................................ 33 v 2.3 Results and Discussion ........................................................................................................ 35 Chapter 3: Neural representations of feature conjunctions composing complex objects: View Invariance ...................................................................................................................................... 42 3.1 Introduction ......................................................................................................................... 42 3.2 Materials and Methods ........................................................................................................ 44 Participants ............................................................................................................................ 44 Experimental Stimuli and Design ......................................................................................... 44 Image similarity analysis. ..................................................................................................... 44 Experimental task.................................................................................................................. 48 3.3 Results and Discussion ........................................................................................................ 48 Chapter 4: Experience modifies the conjunctive neural representations necessary for complex object perception: behavioural training ........................................................................................ 53 4.1 Introduction ......................................................................................................................... 53 4.2 Materials and Methods ........................................................................................................ 57 Participants ............................................................................................................................ 57 Materials ............................................................................................................................... 57 Design and Behavioural Procedure ....................................................................................... 59 Data analysis ......................................................................................................................... 61 4.3 Results and discussion ......................................................................................................... 62 Visual search performance across training sessions ............................................................. 62 vi Effect of viewpoint congruency between sample target and choice target ........................... 65 4.4 General Discussion .............................................................................................................. 70 Chapter 5: Experience modifies the conjunctive neural representations necessary for complex object perception ........................................................................................................................... 74 5.1. Introduction .......................................................................................................................
Load more

Recommended publications
  • Lecture 4 Objects
    4 Perceiving and Recognizing Objects ClickChapter to edit 4 Perceiving Master title and style Recognizing Objects • What and Where Pathways • The Problems of Perceiving and Recognizing Objects • Middle Vision • Object Recognition ClickIntroduction to edit Master title style How do we recognize objects? • Retinal ganglion cells and LGN = Spots • Primary visual cortex = Bars How do spots and bars get turned into objects and surfaces? • Clearly our brains are doing something pretty sophisticated beyond V1. ClickWhat toand edit Where Master Pathways title style Objects in the brain • Extrastriate cortex: Brain regions bordering primary visual cortex that contains other areas involved in visual processing. V2, V3, V4, etc. ClickWhat toand edit Where Master Pathways title style Objects in the brain (continued) • After extrastriate cortex, processing of object information is split into a “what” pathway and a “where” pathway. “Where” pathway is concerned with the locations and shapes of objects but not their names or functions. “What” pathway is concerned with the names and functions of objects regardless of location. Figure 4.2 The main visual areas of the macaque monkey cortex Figure 4.3 A partial “wiring” diagram for the main visual areas of the brain Figure 4.4 The main visual areas of the human cortex ClickWhat toand edit Where Master Pathways title style The receptive fields of extrastriate cells are more sophisticated than those in striate cortex. They respond to visual properties important for perceiving objects. • For instance, “boundary ownership.” For a given boundary, which side is part of the object and which side is part of the background? Figure 4.5 Notice that the receptive field sees the same dark edge in both images ClickWhat toand edit Where Master Pathways title style The same visual input occurs in both (a) and (b) and a V1 neuron would respond equally to both.
    [Show full text]
  • Illusion Free Download
    ILLUSION FREE DOWNLOAD Sherrilyn Kenyon | 464 pages | 05 Feb 2015 | Little, Brown Book Group | 9781907411564 | English | London, United Kingdom Illusion (Ability) Visually perceived images that differ from objective reality. This urge to see depth is probably so strong because our ability to use two-dimensional information to infer a three dimensional world is essential Illusion allowing us to operate in the world. The exact height of the bars is Illusion important here, but the relative heights should look something like this:. A hallucination is a perception Illusion a thing or quality Illusion has no physical counterpart: Under the influence of LSD, Terry had hallucinations that the living-room floor was rippling. MD GtI. Name that government! Water-color illusions consist of object-hole effects and coloration. Then, using some impressive mental geometry, our brain adjusts the experienced length of the top line to be consistent with the size it would have if it were that far away: Illusion two lines are the same length on my retina, but different distances from me, the more distant line must be in Illusion longer. Choose the Right Synonym for illusion delusionillusionhallucinationmirage mean something that is believed to be true or real Illusion that is actually false or unreal. ISSN Illusion Ponzo illusion is an example of an illusion which uses monocular cues of depth perception to fool the eye. The phi phenomenon is yet another example of how the brain perceives motion, which is most often created by blinking lights in close succession. The cubic texture induces a Necker-cube -like optical illusion.
    [Show full text]
  • Innateness and (Bayesian) Visual Perception Reconciling Nativism and Development
    3 BRIAN J. SCHOLL Innateness and (Bayesian) Visual Perception Reconciling Nativism and Development 1 A Research Strategy Because innateness is such a complex and controversial issue when applied to higher level cognition, it can be useful to explore how nature and nurture interact in simpler, less controversial contexts. One such context is the study of certain aspects of visual perception—where especially rigorous models are possible, and where it is less controversial to claim that certain aspects of the visual system are in part innately specified. The hope is that scrutiny of these simpler contexts might yield lessons that can then be applied to debates about the possible innateness of other aspects of the mind. This chapter will explore a particular way in which visual processing may involve innate constraints and will attempt to show how such processing overcomes one enduring challenge to nativism. In particular, many challenges to nativist theories in other areas of cognitive psychology (e.g., ‘‘theory of mind,’’ infant cognition) have focused on the later development of such abili- ties, and have argued that such development is in conflict with innate origins (since those origins would have to be somehow changed or overwritten). Innate- ness, in these contexts, is seen as antidevelopmental, associated instead with static processes and principles. In contrast, certain perceptual models demonstrate how the very same mental processes can both be innately specified and yet develop richly in response to experience with the environment. In fact, this process is entirely unmysterious, as is made clear in certain formal theories of visual per- ception, including those that appeal to spontaneous endogenous stimulation, and those based on Bayesian inference.
    [Show full text]
  • Contour Junctions Underlie Neural Representations of Scene Categories
    bioRxiv preprint doi: https://doi.org/10.1101/044156; this version posted March 16, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Contour junctions underlie neural representations of scene categories in high-level human visual cortex Contour junctions underlie neural code of scenes Heeyoung Choo1, & Dirk. B. Walther1 1Department of Psychology, University of Toronto 100 St. George Street, Toronto, ON, M5S 3G3 Canada Correspondence should be addressed to Heeyoung Choo, 100 St. George Street, Toronto, ON, M5S 3G3 Canada. Phone: +1-416-946-3200 Email: [email protected] Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 1 bioRxiv preprint doi: https://doi.org/10.1101/044156; this version posted March 16, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Abstract 2 3 Humans efficiently grasp complex visual environments, making highly consistent judgments of entry- 4 level category despite their high variability in visual appearance. How does the human brain arrive at the 5 invariant neural representations underlying categorization of real-world environments? We here show that 6 the neural representation of visual environments in scenes-selective human visual cortex relies on statistics 7 of contour junctions, which provide cues for the three-dimensional arrangement of surfaces in a scene.
    [Show full text]
  • Object Perception As Bayesian Inference 1
    Object Perception as Bayesian Inference 1 Object Perception as Bayesian Inference Daniel Kersten Department of Psychology, University of Minnesota Pascal Mamassian Department of Psychology, University of Glasgow Alan Yuille Departments of Statistics and Psychology, University of California, Los Angeles KEYWORDS: shape, material, depth, perception, vision, neural, psychophysics, fMRI, com- puter vision ABSTRACT: We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous due to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or \noisy" local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features. CONTENTS OBJECT PERCEPTION: GEOMETRY AND MATERIAL : : : : : : : : : : : : : 2 INTRODUCTION TO BAYES : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 How to resolve ambiguity in object perception? : : : : : : : : : : : : : : : : : : : : : : 3 How does vision deal with the complexity of images? : : : : :
    [Show full text]
  • A Dictionary of Neurological Signs.Pdf
    A DICTIONARY OF NEUROLOGICAL SIGNS THIRD EDITION A DICTIONARY OF NEUROLOGICAL SIGNS THIRD EDITION A.J. LARNER MA, MD, MRCP (UK), DHMSA Consultant Neurologist Walton Centre for Neurology and Neurosurgery, Liverpool Honorary Lecturer in Neuroscience, University of Liverpool Society of Apothecaries’ Honorary Lecturer in the History of Medicine, University of Liverpool Liverpool, U.K. 123 Andrew J. Larner MA MD MRCP (UK) DHMSA Walton Centre for Neurology & Neurosurgery Lower Lane L9 7LJ Liverpool, UK ISBN 978-1-4419-7094-7 e-ISBN 978-1-4419-7095-4 DOI 10.1007/978-1-4419-7095-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2010937226 © Springer Science+Business Media, LLC 2001, 2006, 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made.
    [Show full text]
  • Object Recognition, Which Is Solved in Visual Areas Beyond V1
    Object vision (Chapter 4) Lecture 9 Jonathan Pillow Sensation & Perception (PSY 345 / NEU 325) Princeton University, Fall 2017 1 Introduction What do you see? 2 Introduction What do you see? 3 Introduction What do you see? 4 Introduction How did you recognize that all 3 images were of houses? How did you know that the 1st and 3rd images showed the same house? This is the problem of object recognition, which is solved in visual areas beyond V1. 5 Unfortunately, we still have no idea how to solve this problem. Not easy to see how to make Receptive Fields for houses the way we combined LGN receptive fields to make V1 receptive fields! house-detector receptive field? 6 Viewpoint Dependence View-dependent model - a model that will only recognize particular views of an object • template-based model e.g. “house” template Problem: need a neuron (or “template”) for every possible view of the object - quickly run out of neurons! 7 Middle Vision Middle vision: – after basic features have been extracted and before object recognition and scene understanding • Involves perception of edges and surfaces • Determines which regions of an image should be grouped together into objects 8 Finding edges • How do you find the edges of objects? • Cells in primary visual cortex have small receptive fields • How do you know which edges go together and which ones don’t? 9 Middle Vision Computer-based edge detectors are not as good as humans • Sometimes computers find too many edges • “Edge detection” is another failed theory (along with Fourier analysis!) of what V1 does.
    [Show full text]
  • Amodal Completion in the Absence of Image Tangent Discontinuities
    Perception, 1998, volume 27, pages 455-464 Amodal completion in the absence of image tangent discontinuities Peter Ulric Tse% Marc K Albert# Vision Sciences Laboratory, Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA; e-mail: [email protected]; [email protected] Received 26 August 1997, in revised form 31 December 1997 Abstract. It has been widely believed since Helmholtz that tangent discontinuities in image contours, such as T-junctions or L-junctions, will occur when one object occludes another. Here we describe a class of occlusion relationships where changes in 'border ownership' and amodal completion take place in the absence of tangent discontinuities in the image. We propose that even subtle curvature discontinuities can be a signal to potential changes in border ownership, and are valid ecological cues for occlusion in certain scenes. 1 Introduction When image fragments are perceived to be the visible portions of a single partially occluded object, the object is said to 'amodally complete' behind the occluder (eg Michotte 1964; Kanizsa 1979). It is generally believed that amodal completion requires tangent or 'first-order' discontinuities (ie discontinuities in the orientation of the tangent line, or first derivative, along the contour) at all visible junctions between the contours 'owned' by the occluded surface and those owned by the occluder (eg Clowes 1971; Huffman 1971; Waltz 1975; Lowe 1985; Malik 1987; Nakayama et al 1989; Kellman and Shipley 1991). Assuming no accidental alignment of surface borders, each contour in the image can only belong to one of the two surfaces adjacent to it.
    [Show full text]
  • Ldmk-Nak 859..877
    Perception, 2009, volume 38, pages 859 ^ 877 doi:10.1068/ldmk-nak Nakayama, Shimojo, and Ramachandran's 1990 paper [Nakayama K, Shimojo S, Ramachandran V S, 1990 ``Transparency: relation to depth, subjective contours,luminance,andneoncolorspreading''Perception19497^513.Originalpaperreprinted in the appendix.] Authors' update Surfaces revisited The target paper reviewed in this article was titled ``Transparency: relation to depth, subjective contours, luminance, and neon colour spreading'' coauthored by K Nakayama, S Shimojo, and V S Ramachandran, published in 1990. This paper, one of the first in a series on surface perception, examined how in untextured stereograms, local dispar- ity and luminance contrast can drastically change surface quality, subjective contours, and the effect of neon colour spreading. When we began to conceive this and related work, the ascendant view on visual perception was derived from the pioneering studies of the response properties of visual neurons with microelectrodes, including those of Barlow (1953), Lettvin et al (1959), and Hubel and Wiesel (1959, 1962). All suggested that there are remarkable operations on the image by earliest stages of the visual pathway, which bestowed selectivity to colour, orientation, motion direction, spatial frequency, binocular disparity, etc. As such, it would seem that an understanding of vision would come through more systematic description of the properties of single neuron selectivities. This viewpoint was well summarised by Horace Barlow in his famous neuron doctrine paper (Barlow 1972), which emphasised the importance of analysing the image in successive stages of processing by neurons with specific classes of receptive fields. Later work altered this conception somewhat by seeing receptive fields as linear filters.
    [Show full text]
  • Neural Correlates of Dynamic Object Recognition
    Neural correlates of dynamic object recognition Katja Martina Mayer Institute of Neuroscience, Newcastle University Thesis submitted for the degree Doctor of Philosophy September 2010 Table of contents Acknowledgements .................................................................................................. vii List of Figures ......................................................................................................... viii List of Tables ............................................................................................................ ix Abbreviations ............................................................................................................ xi 1. Abstract ...................................................................................................................... 1 2. Summary of previous research on recognition of multi-featured objects .................. 2 2.1. Motivation for research in object recognition .................................................... 2 2.2. The role of shape, colour, and motion in object recognition .............................. 3 2.3. Theories of object recognition ............................................................................ 7 2.3.1. Structural description models ...................................................................... 7 2.3.2. Image-based models .................................................................................... 9 2.3.3. Extensions to image-based models ............................................................
    [Show full text]
  • An Investigation Into the Neural Basis of Convergence Eye Movements
    An Investigation into the Neural Basis of Convergence Eye Movements Dissertation Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Emmanuel Owusu Graduate Program in Vision Science The Ohio State University 2018 Dissertation Committee Marjean T. Kulp, Advisor Nicklaus F. Fogt, Co-Advisor Andrew J. Toole Bradley Dougherty Michael J. Earley Xiangrui Li 1 Copyrighted by Emmanuel Owusu 2018 2 Abstract Introduction: Different components of convergence are tonic convergence, disparity convergence, accommodative convergence and proximal. However, it is not clear whether these different components ultimately draw on similar innervational control. Better understanding of the neurology of convergence eye movements could lead to improvement in interventions for deficits in convergence eye movements. Therefore, the purpose of this dissertation is to investigate the neural basis for convergence eye movements in binocularly normal young adults. Methods: Two approaches were used. First, clinical measurements were used to determine the correlations among accommodative, disparity and proximal convergence eye movements, as well as between proximal convergence and vergence facility. These correlations were used as an index of the extent of overlaps in their neurological control. Second, functional magnetic resonance imaging (fMRI) was performed on a group of adults with normal binocular function as they converged their eyes in response to stimuli for accommodative, disparity, proximal and voluntary convergence eye movements. Results: In the clinical study, stimulus gradient accommodative convergence was negatively correlated with far-near proximal convergence (Spearman’s correlation = -0.6111, p < 0.0001). However, the correlation may be at least in part attributable to the inclusion of gradient AC/A as a component of the calculation of far-near proximal convergence.
    [Show full text]
  • The Psychology of Perception
    The Psychology of Perception Perception Study Booklet 1........................................................................................................................Introduction 2........................................................................................................................Audition 3.....................................................................................The Body and Chemical Senses 4......................................................................Lights, Eyes the Brain and Spatial Vision 5...........................................................................................................Depth Perception 6.........................................................................................................Motion Perception 7...................................................................................................................Color Vision 8..............................................................................................................Face Perception 9...........................................................................................................Object Perception 10.............................................................................................Multi-sensory Integration 11.................................................................................................................Assessments 12.............................................................................................................Online Quizzes 13...........................................................................................................................Exams
    [Show full text]