- 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

# Ideal-Observer Models of Cue Integration

# Ideal-Observer Models of Cue Integration

- Chapter:
- (p.5) CHAPTER 1 Ideal-Observer Models of Cue Integration
- Source:
- Sensory Cue Integration
- Author(s):
### Michael S. Landy

### Martin S. Banks

### David C. Knill

- Publisher:
- Oxford University Press

This chapter provides a general introduction to the field of cue combination from the perspective of optimal cue integration. It works through a number of qualitatively different problems and illustrate how building ideal observers helps formulate the scientific questions that need to be answered in order to understand how the brain solves these problems. It begins with a simple example of integration leading to a linear model of cue integration. This is followed by a summary of a general approach to optimality: Bayesian estimation and decision theory. It then reviews situations in which realistic generative models of sensory data lead to nonlinear ideal-observer models. Subsequent sections review empirical studies of cue combination and issues they raise, as well as open questions in the field.

*Keywords:*
cue combination, linear model, optimality, Bayesian estimation, decision theory, sensory data models, ideal-observer models

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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