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- Title Pages
- Preface
- Contributors
- Workshop Attendees
- SECTION I Introduction to Section I: Theory and Fundamentals
- CHAPTER 1 Ideal-Observer Models of Cue Integration
- CHAPTER 2 Causal Inference in Sensorimotor Learning and Control
- CHAPTER 3 The Role of Generative Knowledge in Object Perception
- CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
- CHAPTER 5 Modeling Cue Integration in Cluttered Environments
- CHAPTER 6 Recruitment of New Visual Cues for Perceptual Appearance
- CHAPTER 7 Combining Image Signals before Three-Dimensional Reconstruction: The Intrinsic Constraint Model of Cue Integration
- CHAPTER 8 Cue Combination: Beyond Optimality
- SECTION II Introduction to Section II: Behavioral Studies
- CHAPTER 9 Priors and Learning in Cue Integration
- CHAPTER 10 Multisensory Integration and Calibration in Adults and in Children
- CHAPTER 11 The Statistical Relationship between Depth, Visual Cues, and Human Perception
- CHAPTER 12 Multisensory Perception: From Integration to Remapping
- CHAPTER 13 Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference
- CHAPTER 14 Cues and Pseudocues in Texture and Shape Perception
- CHAPTER 15 Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
- Section III Introduction to Section III: Neural Implementation
- CHAPTER 16 Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
- CHAPTER 17 Probing Neural Correlates of Cue Integration
- CHAPTER 18 Computational Models of Multisensory Integration in the Cat Superior Colliculus
- CHAPTER 19 Decoding the Cortical Representation of Depth
- CHAPTER 20 Dynamic Cue Combination in Distributional Population Code Networks
- CHAPTER 21 A Neural Implementation of Optimal Cue Integration
- CHAPTER 22 Contextual Modulations of Visual Receptive Fields: A Bayesian Perspective
- Index

# (p.292) (p.293) Section III Introduction to Section III: Neural Implementation

# (p.292) (p.293) Section III Introduction to Section III: Neural Implementation

- Source:
- Sensory Cue Integration
- Publisher:
- Oxford University Press

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- Title Pages
- Preface
- Contributors
- Workshop Attendees
- SECTION I Introduction to Section I: Theory and Fundamentals
- CHAPTER 1 Ideal-Observer Models of Cue Integration
- CHAPTER 2 Causal Inference in Sensorimotor Learning and Control
- CHAPTER 3 The Role of Generative Knowledge in Object Perception
- CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
- CHAPTER 5 Modeling Cue Integration in Cluttered Environments
- CHAPTER 6 Recruitment of New Visual Cues for Perceptual Appearance
- CHAPTER 7 Combining Image Signals before Three-Dimensional Reconstruction: The Intrinsic Constraint Model of Cue Integration
- CHAPTER 8 Cue Combination: Beyond Optimality
- SECTION II Introduction to Section II: Behavioral Studies
- CHAPTER 9 Priors and Learning in Cue Integration
- CHAPTER 10 Multisensory Integration and Calibration in Adults and in Children
- CHAPTER 11 The Statistical Relationship between Depth, Visual Cues, and Human Perception
- CHAPTER 12 Multisensory Perception: From Integration to Remapping
- CHAPTER 13 Humans' Multisensory Perception, from Integration to Segregation, Follows Bayesian Inference
- CHAPTER 14 Cues and Pseudocues in Texture and Shape Perception
- CHAPTER 15 Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
- Section III Introduction to Section III: Neural Implementation
- CHAPTER 16 Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
- CHAPTER 17 Probing Neural Correlates of Cue Integration
- CHAPTER 18 Computational Models of Multisensory Integration in the Cat Superior Colliculus
- CHAPTER 19 Decoding the Cortical Representation of Depth
- CHAPTER 20 Dynamic Cue Combination in Distributional Population Code Networks
- CHAPTER 21 A Neural Implementation of Optimal Cue Integration
- CHAPTER 22 Contextual Modulations of Visual Receptive Fields: A Bayesian Perspective
- Index