The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion
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Authors
Author (Corporation)
Publication date
21.08.2019
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Type
01A - Journal article
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Parent work
Cognitive Processing
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DOI of the original publication
Series
Series number
Volume
20
Issue / Number
Pages / Duration
447-457
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Publisher / Publishing institution
Springer
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Abstract
Bayesian cognitive modeling has become a prominent tool for the cognitive sciences aiming at a deeper understanding of the human mind and applications in cognitive systems, e.g., humanoid or wearable robotics. Such approaches can capture human behavior adequately with a focus on the crossmodal processing of sensory information. We investigate whether the Bayesian causal inference model can estimate the proprioceptive drift observed in empirical studies.
Keywords
Rubber Leg Illusion, Bayesian Cognitive Modeling, Wearable Robotics
Subject (DDC)
100 - Philosophie und Psychologie
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ISBN
ISSN
1612-4790
1612-4782
1612-4782
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
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Peer review of the complete publication
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Citation
SCHÜRMANN, Tim, Joachim VOGT, Oliver CHRIST und Philipp BECKERLE, 2019. The Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusion. Cognitive Processing. 21 August 2019. Bd. 20, S. 447–457. DOI 10.1007/s10339-019-00928-9. Verfügbar unter: https://doi.org/10.26041/fhnw-1901