Sensory Cue Integration

by Trommershauser, Julia; Kording, Konrad; Landy, Michael S.

• ISBN13: 9780195387247
• ISBN10: 0195387244
• Edition: 1st
• Format: Hardcover
• Copyright: 2011-09-14
• Publisher: Oxford University Press

Summary

This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed. This book focuses on the emerging probabilistic way of thinking about these problems. These approaches derive from the realization that all our sensors are noisy and moreover are often affected by ambiguity. For example, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The computational approaches described in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous system's attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are concerned with the application of such statistical ideas to real-world cue-combination problems, such as shape and depth perception. Other parts of the book ask how uncertainty may be represented in the nervous system and used for cue combination. The broadening scope of probabilistic approaches to cue combination is highlighted in the breadth of topics covered in this book: the chapters summarize and discuss computational approaches and behavioral evidence aimed at understanding the combination of visual, auditory, proprioceptive, and haptic cues. Some chapters address the combination of cues within a single sensory modality while others address the combination across sensory modalities. Neural implementation, behavior, and theory are considered. The unifying aspect of this book is the focus on the uncertainty intrinsic to sensory cues and the underlying question of how the nervous system deals with this uncertainty. The book is intended as a reference text for graduate students and professionals in perceptual psychology, computational neuroscience, cognitive neuroscience and sensory neurophysiology.

Table of Contents

Preface

I. Theory and Fundamentals

1. Ideal-Observer Models of Cue Integration
Landy, Banks, & Knill
2. Causal Inference in Sensorimotor Learning and Control
Wei & Körding
3. The Role of Generative Knowledge in Object Perception
Battaglia, Kersten, & Schrater
4. Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
Vijayakumar, Hospedales, & Haith
5. Modeling Cue Integration in Clutter
Sahani & Whiteley
6. Recruitment of New Visual Cues for Perceptual Appearance
Backus
7. Combining image signals before 3D reconstruction: The Intrinsic Constraint Model of Cue Integration
Domini & Caudek
8. Cue Combination: Beyond So-Called "Optimality"
Rosas & Wichmann

II. Behavioral Studies

9. Priors and Learning in Cue Integration
Seydell, Knill, & Trommershäuser
10. Combining Vision With Audition and Touch, In Adults and In Children
Burr, Binda, & Gori
11. The Statistical Relationship Between Depth, Visual Cues, and Human Perception
Banks, Burge, & Held
12. Multisensory Perception: From Integration to Remapping
Ernst & Di Luca
13. From Integration to Segregation: When and How the Human Nervous System Combines Crossmodal Sensory Signals
Shams & Beierholm
14. Cues and Pseudocues in Texture and Shape Perception
Landy, Ho, Serwe, Trommershäuser, & Maloney
15. Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
Michel, Brouwer, Jacobs, & Knill

III. Neural Implementation

16. Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
Fetsch, Gu, DeAngelis, & Angelaki
17. Probing Neural Correlates of Cue Integration
Buneo, Apker, & Shi
18. Computational Models of Multisensory Integration in the Cat Superior Colliculus
Rowland, Stein, & Stanford
19. Decoding the Cortical Representation of Depth
Welchman
20. Dynamic Cue Combination in Distributional Population Code Networks
Natarajan & Zemel
21. A Neural Implementation of Optimal Cue Integration
Ma, Beck, & Pouget
22. Contextual Modulation of Visual Receptive Fields: A Bayesian Perspective
Deneve & Lochmann