Sensory Cue IntegrationJulia Trommershauser, Konrad Kording, Michael S. Landy Oxford University Press, 21.09.2011 - 464 Seiten 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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Sensory Cue Integration Julia Trommershauser,Konrad Kording,Michael S. Landy Eingeschränkte Leseprobe - 2011 |
Sensory Cue Integration Julia Trommershauser,Konrad Kording,Michael S. Landy Keine Leseprobe verfügbar - 2011 |
Häufige Begriffe und Wortgruppen
adaptation areas auditory Bayesian Bayesian inference behavior binocular blur brain causal chapter computational convexity correlation cortex cue combination cue integration cue recruitment decoding depth cues discrepancy discrimination disparity distance effect encoded Ernst error estimate example experiment experimental Figure fMRI Gaussian haptic human ideal observer inference input Journal Knill Körding Landy learning likelihood function linear maps measured minimum-variance unbiased estimator mixture model modalities motion motor multisensory integration neural neurons Neuroscience noise object ofthe optic flow optimal cue parameters perceived perception population codes position posterior distribution Pouget predictions prior knowledge probabilistic probability distribution processing proprioceptive pseudocue psychometric functions psychophysical receptive fields reliability represent saccades scene sensory cues sensory signals shape single-cue slant spatial spikes statistics stimulus structure subjects surface target task temporal texture thresholds trials unimodal values variable variance vestibular Vision Research visual and haptic visual cues weights