Jenner / Lappe Neuronal Processing of Optic Flow

1. Auflage 1999
ISBN: 978-0-08-085778-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band Volume 44, 321 Seiten

Reihe: International Review of Neurobiology

When we walk, drive a car, or fly an airplane, visual motion is used to control and guide our movement. Optic flow describes the characteristic pattern of visual motion that arises in these situations. This book is the first to take an in-depth look at the neuronal processing strategies that underlie the brain's ability to analyze and use optic flow for the control of self-motion. It does so in a variety of species which use optic flow in different behavioral contexts. The spectrum ranges from flying insects to birds, higher mammals and man. The contributions cover physiological and behavioral studies as well as computational models. Neuronal Processing of Optic Flow provides an authoritative and comprehensive overview of the current state of research on this topic written by a group of authors who have made essential contributions to shaping this field of research over the last ten years. - Provides the first detailed overview of the analysis of complex visual motion patterns in the brain - Includes physiological, behavioral, and computational aspects of optic flow processing - Highlights similarities and differences between different animal species and behavioral tasks - Covers human patients with visual motion deficits - Enhances the reader's understanding with many illustrations

Autoren/Hrsg.

Jenner, Peter

Lappe, Markus

Inhaltsverzeichnis

1;Front Cover;1
2;Neuronal Processing of Optic Flow;4
3;Copyright Page;5
4;CONTENTS;6
5;Contributors;10
6;Foreword;12
7;Preface;16
8;PART I: PERCEPTION;20
8.1;Chapter 1. Human Ego-Motion Perception;22
8.1.1;I. Introduction;22
8.1.2;II. Retinal Flow and Optic Flow;23
8.1.3;III. Basic Properties of Heading Perception;25
8.1.4;IV. The Rotation Problem;26
8.1.5;V. Special Visual Strategies to Solve the Rotation Problem;30
8.1.6;VI. Circular Heading and Curved Motion Path Percept;32
8.1.7;VII. Heading Perception and the Pattern of Flow;35
8.1.8;VIII. Temporal Properties of Heading Perception;37
8.1.9;IX. Heading Perception and Moving Objects;39
8.1.10;X. The Reciprocal Relation between Optic Flow and Ego-Motion;40
8.1.11;References;41
9;PART II: EYE MOVEMENTS;46
9.1;Chapter 2. Optic Flow and Eye Movements;48
9.1.1;I. Introduction;48
9.1.2;II. Gaze during Self-Motion;49
9.1.3;III. Ocular Reflexes during Self-Motion;51
9.1.4;IV. Optic Flow Induced Eye Movements;54
9.1.5;V. Implications of Eye Movements for Optic Flow Processing;61
9.1.6;VI. Conclusion;64
9.1.7;References;65
9.2;Chapter 3. The Role of MST Neurons during Ocular Tracking in 3D Space;68
9.2.1;I. Neuronal Activity in MST during Short-Latency Ocular Following;70
9.2.2;II. Neuronal Activity in MST during Short-Latency Vergence;76
9.2.3;III. Role of MST Neurons during Ocular Tracking in 3D Space;80
9.2.4;IV. Tracking Objects Moving in 3D Space;80
9.2.5;References;81
10;PART III: ANIMAL BEHAVIOR AND PHYSIOLOGY;84
10.1;Chapter 4. Visual Navigation in Flying Insects;86
10.1.1;I. Introduction;86
10.1.2;II. Peering Insects;87
10.1.3;III. Flying Insects;88
10.1.4;IV. Concluding Remarks;107
10.1.5;References;108
10.2;Chapter 5. Neuronal Matched Filters for Optic Flow Processing in Flying Insects;112
10.2.1;I. Introduction;112
10.2.2;II. Visually Guided Behavior and Optic Flow Processing in Flying Insects;113
10.2.3;III. How to Gain Self-Motion Information from Optic Flow;116
10.2.4;IV. The Fly Visual System;118
10.2.5;V. Mapping the Local Response Properties of Tangential Neurons;121
10.2.6;VI. Response Fields and Matched Filters for Optic Flow Processing;127
10.2.7;VII. Conclusion;130
10.2.8;References;134
10.3;Chapter 6. A Common Frame of Reference for the Analysis of Optic Flow and Vestibular Information;140
10.3.1;I. Object Motion versus Self-Motion;140
10.3.2;II. The Accessory Optic System;141
10.3.3;III. Conclusion;155
10.3.4;References;156
10.4;Chapter 7. Optic Flow and the Visual Guidance of Locomotion in the Cat;160
10.4.1;I. Introduction;160
10.4.2;II. Uses of Vision during Locomotion;161
10.4.3;III. Gaze during Visually Guided Locomotion;166
10.4.4;IV. Neural Mechanisms for Analyzing Optic Flow Information;169
10.4.5;V. Conclusion;185
10.4.6;References;186
11;PART IV: CORTICAL MECHANISMS;190
11.1;Chapter 8. Stages of Self-Motion Processing in Primate Posterior Parietal Cortex;192
11.1.1;I. Motion-Sensitive Areas in the Macaque Visual Cortical System;193
11.1.2;II. Cortical Vestibular Areas;210
11.1.3;III. Human Brain Areas Involved in the Processing of Self-Motion Information;211
11.1.4;IV. Conclusion;212
11.1.5;References;212
11.2;Chapter 9. Optic Flow Analysis for Self-Movement Perception;218
11.2.1;I. Introduction;218
11.2.2;II. MST Sensitivity to Heading Direction;219
11.2.3;III. MST Sensitivity to the Structure of the Environment;223
11.2.4;IV. MST Responses to Real Translational Self-Movement;226
11.2.5;V. Interactions between Optic Flow and Translational Self-Movement;229
11.2.6;VI. MST's Role in Self-Movement Perception;232
11.2.7;VII. A Distributed Network for Self-Movement Perception;233
11.2.8;References;235
11.3;Chapter 10. Neural Mechanisms for Self-Motion Perception in Area MST;238
11.3.1;I. Area MST–Optic Flow Selectivity;239
11.3.2;II. Area MST–Shifting Receptive Fields;243
11.3.3;III. Conclusion;250
11.3.4;References;250
11.4;Chapter 11. Computational Mechanisms for Optic Flow Analysis in Primate Cortex;254
11.4.1;I. Introduction;254
11.4.2;II. Foundations and Goals of Modeling;255
11.4.3;III. Models of Optic Flow Processing in Primates;257
11.4.4;IV. Comparisons with Physiology: Optic Flow Representation in Area MT;261
11.4.5;V. Comparisons with Physiology: Optic Flow Selectivity in Area MST;264
11.4.6;VI. Receptive Fields of Optic Flow Processing Neurons;273
11.4.7;VII. The Population Heading Map;275
11.4.8;VIII. Conclusion;283
11.4.9;References;284
11.5;Chapter 12. Human Cortical Areas Underlying the Perception of Optic Flow: Brain Imaging Studies;288
11.5.1;I. Introduction;288
11.5.2;II. New Techniques in Brain Imaging;293
11.5.3;III. Summary;308
11.5.4;References;309
11.6;Chapter 13. What Neurological Patients Tell Us about the Use of Optic Flow;314
11.6.1;I. Introduction;314
11.6.2;II. Functional Architecture of Motion for Navigation;314
11.6.3;III. Why Study Motion-Impaired Neurological Patients?;316
11.6.4;IV. The Radial Flow Field;318
11.6.5;V. Impairment of Locomotion and Recovery of Locomotor Function;321
11.6.6;VI. Heading Perception in the Presence of Objects;322
11.6.7;VII. Conclusion;329
11.6.8;References;330
12;Index;336

Leseproben

Human Ego-Motion Perception
A.V. van den Berg    Helmholtz School for Autonomous Systems Research, Department of Physiology, Faculty of Medicine, Erasmus University, Rotterdam, the Netherlands I Introduction
A seemingly simple task like walking an empty corridor without hitting the walls becomes very difficult when asked to do so blindfolded. Toddlers who have just learned to walk tip over when the walls of a movable room are set into motion (Stoffregen et al., 1987). Walking on a treadmill that is towed around at speeds different than the treadmill’s speed result in changes of the felt walking speed (Rieser et al., 1995). These examples illustrate that the interplay between vision, kinaesthetic, and vestibular information is of major importance to the control of locomotion. In order to serve locomotion, the visual system needs to represent ego-motion in a format that is useful to act in the environment. Thus, one needs to specify what sort of visual information is relevant to locomotion and if—and how—this visual information is acquired. Because locomotion is a broad description of many different tasks that require different elements of visual information (e.g., walking toward a target, making a turn, and avoiding obstacles), the required visual information is to some extent task-specific. For example, to prevent bumping into an obstacle, it is useful to perceive whether it is on one’s future path and how much time is left for corrective action. The distance to the object is not relevant except in proportion to the speed of forward motion. Consequently, much attention has been given in the psychophysical literature to the visual perception of heading and judgments of the time to contact. In this review, I will concentrate on the first of these tasks: the perception of heading. Gibson (1966, 1986) recognized that the visual motion field contains useful information for such tasks. He observed that the pattern of direction lines that connects a vantage point with objects in the environment expands when the vantage point moves forward. Only that direction line that coincides with the direction of forward motion remains stationary. Thus, the moving vantage point receives an expanding motion pattern that radiates outward from the direction of heading. This pattern of motion is called the optic flow, and its center is called the focus of outflow. In Gibson’s view, the focus of outflow labels the object or the location in the environment to which one is heading. There is no need for a specification of a reference frame for the measured flow. The array of visual objects serves as the frame with respect to which the heading direction is visually specified. These ideas of Gibson have served as a useful starting point for the analysis of visual perception of heading. One can find an excellent review of older literature in Warren (1995). II Retinal Flow and Optic Flow
Even when the observer is moving on a linear track, the flow on the retina will rarely be a purely expanding motion pattern. This holds because the retina is placed on top of a series of mobile supports (e.g., the hips, the torso, the head and the eye), which can all rotate relative to one another. It is useful therefore, to make a clear distinction between retinal and optical flow, the former depending on the translational and the rotational movements of the eye, whereas the latter only involves the translatory component of the eye. Both types of flow fields are typically represented by a collection of angular motion vectors, each attributed to a particular visual direction line (Fig. 1). This representation of the flow field is appropriate for heading analysis (Warren et al., 1991a), but derivatives of the flow field may be more appropriate for other tasks like shape from flow (Koenderink, 1986). Fig. 1 The retinal motion pattern depends on the pattern of motion on the screen and the eye’s rotation. If the motion pattern on the screen simulates the eye’s approach of a wall (upper panel), the effect of the eye rotation will be to shift the center of expansion on the retina relative to the center on the screen. One of the moving dots on the screen will be stable on the retina, whereas the dots that correspond to the focus on the screen will be moving relative to the retina. The shift on the retina will be in the same direction as the eye’s rotation (left and right panels). Its magnitude depends on the simulated speed of approach, the eye’s rotation and the simulated distance to the wall. If the simulated scene is not a wall, there may be no clear focus on the retina, yet there may be an apparent focus that is consistent with a “best fit” of an expanding flow field to the actual retinal flow. The eye’s translation causes angular motion away from the direction of heading with a magnitude that is inversely proportional to the distance. The eye’s rotation generates flow that consists of parallel motion across the retina with a magnitude that is independent of the distance. Its direction and magnitude merely depend on the orientation of the axis of rotation and the rotational velocity. More importantly, it does not even depend on the location of the rotational axis relative to the eye. This gives rise to an ambiguity in the relation between the instantaneous flow field and the eye’s motion through the environment. Moreover, the rotations usually change over time in direction and magnitude as does forward motion, leading to nonstationary flow. Yet, current research has mostly dealt with stationary flow patterns (but see Cutting et al., 1992). For the moment, we ignore these difficulties and discuss various studies that have dealt with heading perception from pure expanding retinal motion. III Basic Properties of Heading Perception
Studies of ego-motion perception have greatly profited from the advent of affordable fast graphics workstations that can simulate 3D scenes in real time. Typically, one simulates the retinal flow for an eye that moves through scenes without recognizable features (randomly located dots). Such patterns may evoke vivid perception of self-movement, called linear vection. Vection latency and strength depend on the display size, type of flow, direction of simulated motion (Telford and Frost, 1993) and the richness of motion in depth cues (Palmisano, 1996). Linear vection takes several seconds to build up, but the percept of ego-motion direction or heading occurs well within a second (Crowell et al., 1990; Warren and Kurtz, 1992; Crowell and Banks, 1993; Stone and Perrone, 1997), even when the sense of self movement is still relatively weak. Simple simulations in heading studies involve motion of an eye on a linear track. This turns out to be a relatively simple task if the eye fixates some stationary target on the screen, resulting in pure retinal expansion. Heading can then be discriminated from a reference target in the scene with a just noticeable difference (jnd) angle of 1–2° (Warren et al., 1988), which is thought to be sufficient for avoidance of obstacles during normal locomotion (Cutting et al., 1992; Cutting, 1986). This performance level is little affected by changes in the layout of the simulated scene (Warren et al., 1998; te Pas, 1996), the presentation time (down to 300 ms: Crowell et al., 1990; down to 228 ms: te Pas et al., 1998) or density of the simulated environment (down to 3 visible dots: Warren et al., 1988). Also, the retinal locus of the simulated heading does not affect discrimination performance very much although there is an accuracy gain of the central region over the periphery (Warren and Kurtz, 1992; Crowell and Banks, 1993; te Pas et al., 1998). Azimuthal and elevational components of heading may have different retinal loci of optimal discriminability. Azimuthal precision is slightly larger in the lower hemi-retina than in the upper half (D’Avossa and Kersten, 1996). In contrast to these rather mild effects of retinal location, there is a clear penalty paid when the focus is off-screen. If the flow within a small aperture is nearly parallel (because the focus is very eccentric), finding the focus of the flow vectors is strongly affected by noise of the visual processing (Koenderink and van Doorn, 1987). Indeed, the jnd between two heading directions increases by nearly two orders of magnitude (up to about 30°) when the focus is moved out from the center of a 10° diameter display to 60° eccentricity (Crowell and Banks, 1993). Thus, consistent with Gibson’s hypothesis, the pattern of expanding flow vectors provides the information for heading direction, and the well-known retinal inhomogeneity has a relatively minor effect on the performance. IV The Rotation Problem
Of course, the eye often rotates relative to the environment as we habitually turn our eyes and/or head to pursue targets in our environment or because we are moving on a curved trajectory. This adds a rotational component to the expansion flow, which destroys the focus at the direction of heading. For special layouts of the environment, like an extended wall, a new singular point appears in the direction of eye rotation (Fig. 1). Responding to this...