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CATEGORIES:Machine Learning Journal Club
SUMMARY:Bayesian integration in sensorimotor learning - Ke
ith Vertanen (University of Cambridge)
DTSTART;TZID=Europe/London:20091030T110000
DTEND;TZID=Europe/London:20091030T120000
UID:TALK19972AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/19972
DESCRIPTION:This paper is by Konrad Kö\;rding and Daniel W
olpert\, Nature (2004). Available here:\nhttp://h
omepages.inf.ed.ac.uk/svijayak/teaching/MLSC/HW2pa
pers/Wolpert.pdf\n\nABSTRACT\n\nWhen we learn a ne
w motor skill\, such as playing an approaching ten
nis ball\, both our sensors and the task possess v
ariability. Our sensors provide imperfect informat
ion about the ball’s velocity\, so we can only est
imate it. Combining information from multiple moda
lities can reduce the error in this estimate1–4. O
n a longer time scale\, not all velocities are a p
riori equally probable\, and over the course of a
match there will be a probability distribution of
velocities. According to bayesian theory an optima
l estimate results from combining information abou
t the distribution of velocities—the prior—with ev
idence from sensory feedback. As uncertainty incre
ases\, when playing in fog or at dusk\, the system
should increasingly rely on prior knowledge. To u
se a bayesian strategy\, the brain would need to r
epresent the prior distribution and the level of u
ncertainty in the sensory feedback. Here we contro
l the statistical variations of a new sensorimotor
task and manipulate the uncertainty of the sensor
y feedback. We show that subjects internally repre
sent both the statistical distribution of the task
and their sensory uncertainty\, combining them in
a manner consistent with a performance-optimizing
bayesian process. The central nervous system ther
efore employs probabilistic models during sensorim
otor learning.\n
LOCATION:TCM Seminar Room\, Cavendish Laboratory\, Departme
nt of Physics
CONTACT:Emli-Mari Nel
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