- 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

# Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

# Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration

- Chapter:
- (p.63) CHAPTER 4 Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
- Source:
- Sensory Cue Integration
- Author(s):
### Sethu Vijayakumar

### Timothy Hospedales

### Adrian Haith

- Publisher:
- Oxford University Press

This chapter argues that many aspects of human perception are best explained by adopting a modeling approach in which experimental subjects are assumed to possess a full generative probabilistic model of the task they are faced with, and that they use this model to make inferences about their environment and act optimally given the information available to them. It applies this generative modeling framework in two diverse settings—concurrent sensory and motor adaptation, and multisensory oddity detection—and shows, in both cases, that the data are best described by a full generative modeling approach.

*Keywords:*
perception, generative modeling, concurrent sensory, motor adaptation, multisensory oddity detection

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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