Sensory Cue Integration


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Table of Contents


I. Theory and Fundamentals

1. Ideal-Observer Models of Cue Integration
Landy, Banks, & Knill
2. Causal Inference in Sensorimotor Learning and Control
Wei & Körding
3. The Role of Generative Knowledge in Object Perception
Battaglia, Kersten, & Schrater
4. Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration
Vijayakumar, Hospedales, & Haith
5. Modeling Cue Integration in Clutter
Sahani & Whiteley
6. Recruitment of New Visual Cues for Perceptual Appearance
7. Combining image signals before 3D reconstruction: The Intrinsic Constraint Model of Cue Integration
Domini & Caudek
8. Cue Combination: Beyond So-Called "Optimality"
Rosas & Wichmann

II. Behavioral Studies

9. Priors and Learning in Cue Integration
Seydell, Knill, & Trommershäuser
10. Combining Vision With Audition and Touch, In Adults and In Children
Burr, Binda, & Gori
11. The Statistical Relationship Between Depth, Visual Cues, and Human Perception
Banks, Burge, & Held
12. Multisensory Perception: From Integration to Remapping
Ernst & Di Luca
13. From Integration to Segregation: When and How the Human Nervous System Combines Crossmodal Sensory Signals
Shams & Beierholm
14. Cues and Pseudocues in Texture and Shape Perception
Landy, Ho, Serwe, Trommershäuser, & Maloney
15. Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and Action
Michel, Brouwer, Jacobs, & Knill

III. Neural Implementation

16. Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex
Fetsch, Gu, DeAngelis, & Angelaki
17. Probing Neural Correlates of Cue Integration
Buneo, Apker, & Shi
18. Computational Models of Multisensory Integration in the Cat Superior Colliculus
Rowland, Stein, & Stanford
19. Decoding the Cortical Representation of Depth
20. Dynamic Cue Combination in Distributional Population Code Networks
Natarajan & Zemel
21. A Neural Implementation of Optimal Cue Integration
Ma, Beck, & Pouget
22. Contextual Modulation of Visual Receptive Fields: A Bayesian Perspective
Deneve & Lochmann

About the Author

Julia Trommershäuser spent three years as a postdoctoral researcher at New York University. From 2004-2009, she was a researcher in the Department of Psychology at Giessen University, Germany, funded by an Emmy-Noether Research Award by the German Science Foundation (DFG). She currently works at the Center for Neural Science at New York University on questions in the field of human decision-making under risk and uncertainty, trying to understand how the brain
makes use of sensory processing, feedback and prior knowledge.

Konrad Körding is an Assistant Professor at Northwestern University. His lab focuses on the statistical processing of information in the sensorimotor system. He conducts behavioral experiments in humans to ask how people deal with uncertainty and he collaborates with physiologists to ask how the brain does it.

Michael Landy is a Full Professor of Psychology and Neural Science at New York University. His research concerns visual and visuomotor behavior, often comparing human performance to that of a statistically ideal participant. He works in a number of different research areas including spatial visual coding (e.g., texture segmentation), depth perception (including depth cue integration) and movement planning under risk.

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