Sensory Cue Integration

This book PDF is perfect for those who love Psychology genre, written by Julia Trommershauser and published by Oxford University Press which was released on 21 September 2011 with total hardcover pages 464. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Sensory Cue Integration books below.

Sensory Cue Integration
Author : Julia Trommershauser
File Size : 44,6 Mb
Publisher : Oxford University Press
Language : English
Release Date : 21 September 2011
ISBN : 9780199874767
Pages : 464 pages
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Sensory Cue Integration by Julia Trommershauser Book PDF 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 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.

Sensory Cue Integration

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,

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