- 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

# Cue Combination: Beyond Optimality

# Cue Combination: Beyond Optimality

- Chapter:
- (p.144) CHAPTER 8 Cue Combination: Beyond Optimality
- Source:
- Sensory Cue Integration
- Author(s):
### Pedro Rosas

### Felix A. Wichmann

- Publisher:
- Oxford University Press

This chapter briefly introduces the robust-weak-fusion model, which offers an exceptionally clear and elegant framework within which to understand empirical studies on cue combination. Research on cue combination is an area in the cognitive neurosciences where *quantitative* models and predictions are the norm rather than the exception—and this is certainly a development that this book welcomes wholeheartedly. What they view critically, however, is the strong emphasis on so-called optimal cue combination. Optimal in the context of human cue combination typically refers to the minimum-variance unbiased estimator for multiple sources of information, corresponding to maximum-likelihood estimation when the probability distribution of the estimates based on each cue are Gaussian, independent, and the prior of the observer is uniform (noninformative). The central aim of this chapter is to spell out worries regarding both the term *optimality* as well as against the use of the minimum-variance unbiased estimator as the statistical tool to go from the reliability of a cue to its weight in robust weak fusion.

*Keywords:*
robust-weak-fusion model, cue combination, optimality, minimum-variance unbiased estimator, reliability, robust weak fusion

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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