Literature DB >> 26302201

Bayesian approaches to sensory integration for motor control.

Max Berniker1, Konrad Kording1.   

Abstract

The processing of sensory information is fundamental to the basic operation of the nervous system. Our nervous system uses this sensory information to gain knowledge of our bodies and the world around us. This knowledge is of great importance as it provides the coherent and accurate information necessary for successful motor control. Yet, all this knowledge is of an uncertain nature because we obtain information only through our noisy sensors. We are thus faced with the problem of integrating many uncertain pieces of information into estimates of the properties of our bodies and the surrounding world. Bayesian approaches to estimation formalize the problem of how this uncertain information should be integrated. Utilizing this approach, many studies make predictions that faithfully predict human sensorimotor behavior. WIREs Cogni Sci 2011 2 419-428 DOI: 10.1002/wcs.125 For further resources related to this article, please visit the WIREs website.
Copyright © 2011 John Wiley & Sons, Ltd.

Entities:  

Year:  2011        PMID: 26302201     DOI: 10.1002/wcs.125

Source DB:  PubMed          Journal:  Wiley Interdiscip Rev Cogn Sci        ISSN: 1939-5078


  20 in total

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Authors:  Daniel M Wolpert; Michael S Landy
Journal:  Curr Opin Neurobiol       Date:  2012-05-29       Impact factor: 6.627

2.  How much to trust the senses: likelihood learning.

Authors:  Yoshiyuki Sato; Konrad P Kording
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3.  Bayesian optimal adaptation explains age-related human sensorimotor changes.

Authors:  Faisal Karmali; Gregory T Whitman; Richard F Lewis
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4.  Simple spike dynamics of Purkinje cells in the macaque vestibulo-cerebellum during passive whole-body self-motion.

Authors:  Jean Laurens; Dora E Angelaki
Journal:  Proc Natl Acad Sci U S A       Date:  2020-01-27       Impact factor: 11.205

5.  A unified internal model theory to resolve the paradox of active versus passive self-motion sensation.

Authors:  Jean Laurens; Dora E Angelaki
Journal:  Elife       Date:  2017-10-18       Impact factor: 8.140

6.  The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion.

Authors:  Tim Schürmann; Joachim Vogt; Oliver Christ; Philipp Beckerle
Journal:  Cogn Process       Date:  2019-08-21

7.  Vibrotactile cuing revisited to reveal a possible challenge to sensorimotor adaptation.

Authors:  Beom-Chan Lee; Timothy A Thrasher; Charles S Layne; Bernard J Martin
Journal:  Exp Brain Res       Date:  2016-08-08       Impact factor: 1.972

Review 8.  Toward higher-performance bionic limbs for wider clinical use.

Authors:  Dario Farina; Ivan Vujaklija; Rickard Brånemark; Anthony M J Bull; Hans Dietl; Bernhard Graimann; Levi J Hargrove; Klaus-Peter Hoffmann; He Helen Huang; Thorvaldur Ingvarsson; Hilmar Bragi Janusson; Kristleifur Kristjánsson; Todd Kuiken; Silvestro Micera; Thomas Stieglitz; Agnes Sturma; Dustin Tyler; Richard F Ff Weir; Oskar C Aszmann
Journal:  Nat Biomed Eng       Date:  2021-05-31       Impact factor: 25.671

9.  Side effect of acting on the world: acquisition of action-outcome statistic relation alters visual interpretation of action outcome.

Authors:  Takahiro Kawabe
Journal:  Front Hum Neurosci       Date:  2013-09-25       Impact factor: 3.169

10.  Optimal Prediction of Moving Sound Source Direction in the Owl.

Authors:  Weston Cox; Brian J Fischer
Journal:  PLoS Comput Biol       Date:  2015-07-30       Impact factor: 4.475

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