Sensory Cue Integration
Sensory Cue Integration
Assistant Professor
Assistant Professor
Professor of Psychology and Neural Science
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Abstract
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: 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.
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Front Matter
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SECTION I Introduction to Section I: Theory and Fundamentals
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CHAPTER 1
Ideal-Observer Models of Cue Integration
Michael S. Landy and others
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CHAPTER 2
Causal Inference in Sensorimotor Learning and Control
Kunlin Wei andKonrad P. Körding
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CHAPTER 3
The Role of Generative Knowledge in Object Perception
Peter W. Battaglia and others
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CHAPTER 4
Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
Sethu Vijayakumar and others
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CHAPTER 5
Modeling Cue Integration in Cluttered Environments
Maneesh Sahani andLouise Whiteley
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CHAPTER 6
Recruitment of New Visual Cues for Perceptual Appearance
Benjamin T. Backus
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CHAPTER 7
Combining Image Signals before Three-Dimensional Reconstruction: The Intrinsic Constraint Model of Cue Integration
Fulvio Domini andCorrado Caudek
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CHAPTER 8
Cue Combination: Beyond Optimality
Pedro Rosas andFelix A. Wichmann
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CHAPTER 1
Ideal-Observer Models of Cue Integration
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SECTION II Introduction to Section II: Behavioral Studies
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CHAPTER 9
Priors and Learning in Cue Integration
Anna Seydell and others
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CHAPTER 10
Multisensory Integration and Calibration in Adults and in Children
David Burr and others
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CHAPTER 11
The Statistical Relationship between Depth, Visual Cues, and Human Perception
Martin S. Banks and others
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CHAPTER 12
Multisensory Perception: From Integration to Remapping
Marc O. Ernst andMassimiliano Di Luca
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CHAPTER 13
Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference
Ladan Shams andUlrik Beierholm
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CHAPTER 14
Cues and Pseudocues in Texture and Shape Perception
Michael S. Landy and others
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CHAPTER 15
Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
Melchi M. Michel and others
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CHAPTER 9
Priors and Learning in Cue Integration
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Section III Introduction to Section III: Neural Implementation
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CHAPTER 16
Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
Christopher R. Fetsch and others
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CHAPTER 17
Probing Neural Correlates of Cue Integration
Christopher A. Buneo and others
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CHAPTER 18
Computational Models of Multisensory Integration in the Cat Superior Colliculus
Benjamin A. Rowland and others
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CHAPTER 19
Decoding the Cortical Representation of Depth
Andrew E. Welchman
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CHAPTER 20
Dynamic Cue Combination in Distributional Population Code Networks
Rama Natarajan andRichard S. Zemel
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CHAPTER 21
A Neural Implementation of Optimal Cue Integration
Wei Ji Ma and others
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CHAPTER 22
Contextual Modulations of Visual Receptive Fields: A Bayesian Perspective
Sophie Denève andTimm Lochmann
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CHAPTER 16
Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
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End Matter
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