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dc.contributor.authorSchürmann, Tim
dc.contributor.authorVogt, Joachim
dc.contributor.authorChrist, Oliver
dc.contributor.authorBeckerle, Philipp
dc.date.accessioned2019-12-12T14:55:23Z
dc.date.available2019-12-12T14:55:23Z
dc.date.issued2019-08-21
dc.identifier.doihttps://doi.org/10.1007/s10339-019-00928-9
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/30158
dc.identifier.urihttp://dx.doi.org/10.26041/fhnw-1901
dc.description.abstractBayesian 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.en_US
dc.description.urihttps://link.springer.com/article/10.1007%2Fs10339-019-00928-9#citeasen_US
dc.language.isoenen_US
dc.relation.ispartofCognitive Processingen_US
dc.accessRightsAnonymous*
dc.subjectRubber Leg Illusion; Bayesian Cognitive Modeling; Wearable Roboticsen_US
dc.subject.ddc100 - Philosophie und Psychologieen_US
dc.titleThe Bayesian causal inference model benefits from an informed prior to predict proprioceptive drift in the rubber foot illusionen_US
dc.type01 - Zeitschriftenartikel, Journalartikel oder Magazin*
dc.volume20en_US
dc.audienceScienceen_US
fhnw.publicationStatePublisheden_US
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publicationen_US
fhnw.InventedHereYesen_US
fhnw.PublishedSwitzerlandNoen_US
fhnw.pagination447-457en_US
fhnw.IsStudentsWorknoen_US
fhnw.publicationOnlineJaen_US


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