Sensory Cue Integration

Hardcover | September 28, 2011

EditorJulia Trommershauser, Konrad Kording, Michael S. Landy

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This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics isemerging as a common language in which cue-combination problems can be expressed. This book focuses on the emerging probabilistic way of thinking about these problems. These approaches derive from the realization that all our sensors are noisy and moreover are often affected by ambiguity. Forexample, mechanoreceptor outputs are variable and they cannot distinguish if a perceived force is caused by the weight of an object or by force we are producing ourselves. The computational approaches described in this book aim at formalizing the uncertainty of cues. They describe cue combination as the nervous systems attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this bookare concerned with the application of such statistical ideas to real-world cue-combination problems, such as shape and depth perception. Other parts of the book ask how uncertainty may be represented in the nervous system and used for cue combination. The broadening scope of probabilistic approaches to cue combination is highlighted in the breadth of topics covered in this book: the chapters summarize and discuss computational approaches and behavioral evidence aimed at understanding the combination of visual, auditory, proprioceptive, and hapticcues. Some chapters address the combination of cues within a single sensory modality while others address the combination across sensory modalities. Neural implementation, behavior, and theory are considered. The unifying aspect of this book is the focus on the uncertainty intrinsic to sensory cuesand the underlying question of how the nervous system deals with this uncertainty.The book is intended as a reference text for graduate students and professionals in perceptual psychology, computational neuroscience, cognitive neuroscience and sensory neurophysiology.

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This book provides an introduction into both computational models and experimental paradigms that are concerned with sensory cue integration both within and between sensory modalities. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics isemerging as a common language in which cue-combin...

Julia Trommershauser 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 Yor...
Format:HardcoverDimensions:464 pages, 7.4 × 10.12 × 1.1 inPublished:September 28, 2011Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0195387244

ISBN - 13:9780195387247

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Table of Contents

Preface1Theory and Fundamentals1. Ideal-Observer Models of Cue Integration2. Causal Inference in Sensorimotor Learning and Control3. The Role of Generative Knowledge in Object Perception4. Generative Probabilistic Modeling: Understanding Causal Sensorimotor Integration5. Modeling Cue Integration in Clutter6. Recruitment of New Visual Cues for Perceptual Appearance7. Combining image signals before 3D reconstruction: The Intrinsic Constraint Model of Cue Integration8. Cue Combination: Beyond So-Called "Optimality"II Behavioral Studies9. Priors and Learning in Cue Integration10. Combining Vision With Audition and Touch, In Adults and In Children11. The Statistical Relationship Between Depth, Visual Cues, and Human Perception12. Multisensory Perception: From Integration to Remapping13. From Integration to Segregation: When and How the Human Nervous System Combines Crossmodal Sensory Signals14. Cues and Pseudocues in Texture and Shape Perception15. Optimality Principles Apply to a Broad Range of Information Integration Problems in Perception and ActionIIINeural Implementation16. Self-Motion Perception: Multisensory Integration in Extrastriate Visual Cortex17. Probing Neural Correlates of Cue Integration Buneo, Apker, and Shi18. Computational Models of Multisensory Integration in the Cat Superior Colliculus19. Decoding the Cortical Representation of Depth20. Dynamic Cue Combination in Distributional Population Code Networks21. A Neural Implementation of Optimal Cue Integration22. Contextual Modulation of Visual Receptive Fields: A Bayesian Perspective