Sensory cue integration (Book, 2011) [WorldCat.org]
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Sensory cue integration
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Sensory cue integration

Author: Julia Trommershauser; Konrad Kording; Michael S Landy
Publisher: Oxford ; New York : Oxford University Press, 2011.
Series: Computational neuroscience.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that 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  Read more...
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Document Type: Book
All Authors / Contributors: Julia Trommershauser; Konrad Kording; Michael S Landy
ISBN: 9780195387247 0195387244
OCLC Number: 689548961
Description: xiii, 446 pages : color illustrations ; 27 cm.
Contents: Ideal-observer models of cue integration / Michael S. Landy, Martin S. Banks and David C. Knill --
Casual inference in sensorimotor learning and control / Kunlin Wei and Konrad P. Kording --
The role of generative knowledge in object perception / Peter W. Battaglia, Daniel Kersten and Paul Schrater --
Generative probabilistic modeling: understanding casual sensorimotor integration / Sethu Vijayakumar, Timothy Hospedales and Adrian Haith --
Modeling cue integration in cluttered environments / Maneesh Sahani and Louise Whiteley --
Recruitment of new visual cues for perceptual appearance / Benjamin T. Backus --
Combining image signals before three-dimensional reconstruction: the intrinsic constraint model of cue integration / Fulvio Domini and Carrado Caudek --
Cue combination: beyond optimality / Pedro Rosas and Felix A. Wichmann --
Priors and learning in cue integration / Anna Seydell, David C. Knill and Julia Trommershauser --
Multisensory integration and calibration in adults and children / David Burr, Paola Binda and Monica Gori --
The Statistical relationship between depth, visual cues, and human perception / Martin S. Banks, Johannes Burge and Robert T. Held --
Multisensory perception: from integration to remapping / Marc O. Ernst and Massimiliano Di Luca --
Humans multisensory perception, from integration to segregation, follows Bayesian inference / Ladan Shams and Ulrik Beierholm --
Cues and pseudocues in texture and shape perception / Michael S. Landy [and others] --
Optimality principles apply to a broad range of information integration problems in perception and action / Melchi M. Mitchel, Anne-Marie Brouwer, Robert A. Jacobs and David C. Knill --
Self-motion perception: multisensory integration in extrastriate visual cortex / Christopher R. Fetsch, Yong Gu, Gregory C. DeAngelis, and Dora E. Angelaki --
Probing neural correlates of cue integration / Christopher A. Buneo, Gregory Apker and Ying Shi --
Computational models of multisensory integration in the cat superior colliculus / Benjamin A. Rowland, Barry E. Stein and Terrence R. Stanford --
Decoding the cortical representation of depth / Andrew E. Welchman --
Dynamic cue combination in distributional population code networks / Rama Natarajan and Richard S. Zemel --
A neural implementation of optimal cue integration / Wei Ji Ma, Jeff Beck and Alexandre Pouget --
Contextual modulations of visual receptive fields: a Bayesian perspective / Sophie Deneve and Timm Lochmann.
Series Title: Computational neuroscience.
Responsibility: edited by Julia Trommershäuser, Konrad Körding, and Michael S. Landy.

Abstract:

This book is concerned with sensory cue integration both within and between sensory modalities, and focuses on the emerging way of thinking about cue combination in terms of uncertainty. These probabilistic approaches derive from the realization that 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 probabilistic approaches elaborated 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. Others ask how uncertainty may be represented in the nervous system and used for cue combination. Importantly, across behavioral, electrophysiological and theoretical approaches, Bayesian statistics is emerging as a common language in which cue-combination problems can be expressed

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