When we interact with objects in the world, the forces we exert are
finely tuned to the dynamics of the situation. As our sensors do
not provide perfect knowledge about the environment, a key problem
is how to estimate the appropriate forces. Two sources of information
can be used to generate such an estimate: sensory inputs about the
object and knowledge about previously experienced objects, termed
prior information. Bayesian integration defines the way in which
these two sources of information should be combined to produce an
optimal estimate. To investigate whether subjects use such a strategy
in force estimation, we designed a novel sensorimotor estimation
task. We controlled the distribution of forces experienced over the
course of an experiment thereby defining the prior. We show that
subjects integrate sensory information with their prior experience
to generate an estimate. Moreover, subjects could learn different
prior distributions. These results suggest that the CNS uses Bayesian
models when estimating force requirements.
Eine VP muss auf eine Pertubation reagieren die sich leicht von einer
vorhergehenden Pertubation unterscheidet.
Dabei fällt die Reaktion abhängig von der bekannten Häufigkeitsverteilung
der Stärke der Pertubationen ab.
Fazit: Nicht nur bei visueller verarbeitung sondern auch wenn es um
Kräfte geht, werden Priorwahrscheinlochkeiten berücksichtigt
%0 Journal Article
%1 Koerding:2004
%A Körding, Konrad P
%A pi Ku, Shih
%A Wolpert, Daniel M.
%D 2004
%J Journal of Neurophysiology
%K bayesian control; estimation; force integration; motor reality; sensory; virtual
%P 3161-3165
%R 10.1152/jn.00275.2004
%T Bayesian Integration in Force Estimation
%V 92
%X When we interact with objects in the world, the forces we exert are
finely tuned to the dynamics of the situation. As our sensors do
not provide perfect knowledge about the environment, a key problem
is how to estimate the appropriate forces. Two sources of information
can be used to generate such an estimate: sensory inputs about the
object and knowledge about previously experienced objects, termed
prior information. Bayesian integration defines the way in which
these two sources of information should be combined to produce an
optimal estimate. To investigate whether subjects use such a strategy
in force estimation, we designed a novel sensorimotor estimation
task. We controlled the distribution of forces experienced over the
course of an experiment thereby defining the prior. We show that
subjects integrate sensory information with their prior experience
to generate an estimate. Moreover, subjects could learn different
prior distributions. These results suggest that the CNS uses Bayesian
models when estimating force requirements.
@article{Koerding:2004,
abstract = {When we interact with objects in the world, the forces we exert are
finely tuned to the dynamics of the situation. As our sensors do
not provide perfect knowledge about the environment, a key problem
is how to estimate the appropriate forces. Two sources of information
can be used to generate such an estimate: sensory inputs about the
object and knowledge about previously experienced objects, termed
prior information. Bayesian integration defines the way in which
these two sources of information should be combined to produce an
optimal estimate. To investigate whether subjects use such a strategy
in force estimation, we designed a novel sensorimotor estimation
task. We controlled the distribution of forces experienced over the
course of an experiment thereby defining the prior. We show that
subjects integrate sensory information with their prior experience
to generate an estimate. Moreover, subjects could learn different
prior distributions. These results suggest that the CNS uses Bayesian
models when estimating force requirements.},
added-at = {2009-06-26T15:25:19.000+0200},
author = {K{\"o}rding, Konrad P and pi Ku, Shih and Wolpert, Daniel M.},
biburl = {https://www.bibsonomy.org/bibtex/25a58912eea0f7db8343a238aba352f71/butz},
comment = {Eine VP muss auf eine Pertubation reagieren die sich leicht von einer
vorhergehenden Pertubation unterscheidet.
Dabei fällt die Reaktion abhängig von der bekannten Häufigkeitsverteilung
der Stärke der Pertubationen ab.
Fazit: Nicht nur bei visueller verarbeitung sondern auch wenn es um
Kräfte geht, werden Priorwahrscheinlochkeiten berücksichtigt},
description = {diverse cognitive systems bib},
doi = {10.1152/jn.00275.2004},
interhash = {2118e75fa5c84b6d2acdc20c2f24a928},
intrahash = {5a58912eea0f7db8343a238aba352f71},
journal = {Journal of Neurophysiology},
keywords = {bayesian control; estimation; force integration; motor reality; sensory; virtual},
owner = {martin},
pages = {3161-3165},
timestamp = {2009-06-26T15:25:43.000+0200},
title = {Bayesian Integration in Force Estimation},
volume = 92,
year = 2004
}