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.
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