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Sensory Cue Integration
Edited by Julia Trommershauser, Edited by Konrad Kording, and Edited by Michael S. Landy
456 pages
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178
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177x254mm
978-0-19-538724-7
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Hardback
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13 October 2011
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- A broad introduction to the theory of sensory cue integration
- Serves as an introduction to the field and as a reference text
- Addressed to researchers in multiple allied fields: perceptual psychology, computational neuroscience, cognitive neuroscience and sensory neurophysiology
- Covers theoretical issues and empirical results from both behavior and neurophysiology
- Addresses cue integration within a single sensory modality (e.g., multiple visual cues to depth) as well as intra-modal cue integration (e.g., auditory and visual cues to spatial location)
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 is emerging 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. For example, 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 system>'s attempt to minimize uncertainty in its estimates and to choose successful actions. Some computational approaches described in the chapters of this book are 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 haptic cues. 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 cues and 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.Readership: Cognitive psychologists
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Edited by Julia Trommershauser, Assistant Professor, Center for Neural Science, New York University, Edited by Konrad Kording, Assistant Professor, Rehabilitation Institute of Chicago, Northwestern University, and Edited by Michael S. Landy, Professor of Psychology and Neural Science, New York University 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.
Konrad Körding is an Assistant Professor at Northwestern University. His lab focuses on the statistical processing of information in the sensorimotor system.
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.
Contributors:
Dora Angelaki, Washington University School of Medicine , Anatomy and Neurobiology.;
Gregory Apker, School of Biological and Health Systems Engineering, Arizona State University;
Benjamin T. Backus, Graduate Program in Vision Science, SUNY College of Optometry;
Martin S. Banks, School of Optometry, University of California, Berkeley;
Peter W. Battaglia, Brain & Cognitive Sciences, Computer Science & Artificial Intelligence Laboratory, MIT;
Jeff Beck , Gatsby Computational Neuroscience Unit, University College London;
Paola Binda, Istituto di Neuroscienze del CNR;
Ulrik Ravnsborg Beierholm, Gatsby Computational Neuroscience Unit, University College London;
Anne-Marie Brouwer, TNO Human Factors;
Christopher A. Buneo, School of Biological and Health Systems Engineering, Arizona State University;
Johannes Burge, Center for Perceptual Systems, Dept. of Psychology, The University of Texas;
David Burr, Istituto di Neuroscienze del CNR;
Carrado Caudek, Dipartimento di Psicologia, Università degli Studi di Firenze;
Gregory C. DeAngelis, Dept. of Brain & Cognitive Sciences, University of Rochester ;
Sophie Denève, Group for Neural Theory, DEC, Ecole Normale Supérieure;
Massimiliano Di Luca, MPI for Biological Cybernetics;
Fulvio Domini, Cognitive and Linguistic Sciences, Brown University, Box 1978;
Christopher R. Fetsch, Washington University School of Medicine, Anatomy and Neurobiology;
Monica Gori, Italian Institute of Technology, University of Genoa;
Yong Gu, Washington University School of Medicine, Anatomy and Neurobiology;
Adrian Haith, Dept. of Biomedical Engineering, Johns Hopkins University;
Robert T. Held, Joint Graduate Group in Bioengineering, University of California, San Francisco and University of California, Berkeley;
Yun-Xian Ho, Dept. of Biomedical Informatics, Vanderbilt University Medical Center;
Timothy Hospedales, Dept. of Computer Science, Queen Mary University of London;
Robert A. Jacobs, Center for Visual Science, Department of Brain & Cognitive Sciences, University of Rochester;
Dan Kersten, Dept. of Psychology, University of Minnesota;
David C. Knill, Center for Visual Science, University of Rochester;
Konrad Körding, Dept. of Physiology, Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University;
Michael S. Landy, Dept. of Psychology and Center for Neural Science, New York University;
Timm Lochmann, Group for Neural Theory, DEC, Ecole Normale Supérieure;
Wei Ji Ma, Dept. of Neuroscience, Baylor College of Medicine;
Laurence T. Maloney, Dept. of Psychology and Center for Neural Science, New York University;
Melchi Michel, Center for Perceptual Systems, Dept. of Psychology, The University of Texas;
Rama Natarajan, Center for Neural Science, New York University;
Alexandre Pouget, Center for Visual Sciences, Brain and Cognitive Science Dept;
Pedro Rosas, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile;
Benjamin Rowland, Department of Neurobiology & Anatomy, Wake Forest University School of Medicine;
Maneesh Sahani, Gatsby Computational Neuroscience Unit, University College London;
Paul R. Schrater, Dept. of Psychology & Computer Science, University of Minnesota;
Sascha Serwe, FernUniversität in Hagen, Institut für Psychologie;
Anna Seydell, Laboratory of Integrative Neuroscience and Cognition, Dept. of Physiology & Biophysics, Georgetown University Medical Center;
Ladan Shams, Department. of Psychology, University of California, Los Angeles;
Ying Shi, School of Biological and Health Systems Engineering, Arizona State University;
Terrence R. Stanford, Department of Neurobiology & Anatomy, Wake Forest University School of Medicine;
Barry E. Stein, Department of Neurobiology & Anatomy, Wake Forest University School of Medicine;
Julia Trommershäuser, Center for Neural Science, New York University;
Sethu Vijayakumar, 1.28 Informatics Forum, School of Informatics, University of Edinburgh;
Kunlin Wei, Dept. of Physiology, Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University;
Andrew Welchman, School of Psychology, University of Birmingham;
Louise Whiteley, Gatsby Computational Neuroscience Unit, University College London;
Felix Wichmann, Modellierung kognitiver Prozesse, Technische Universität Berlin, and Bernstein Center for Computational Neuroscience;
Richard Zemel, Department of Computer Science, University of Toronto
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Preface
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
Backus
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
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The specification in this catalogue, including without limitation price, format, extent, number of illustrations, and month of publication, was as accurate as possible at the time the catalogue was compiled. Occasionally, due to the nature of some contractual restrictions, we are unable to ship a specific product to a particular territory. Jacket images are provisional and liable to change before publication.
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