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TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20370308T030000 RDATE:20371101T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:America/New_York EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:d3d4b89b532e2ffd98726cc826950d28 CATEGORIES:Human and computer vision series:Spring 2010 CREATED:20151203T141542 SUMMARY:Prediction, extrapolation and scheduling time to gather information in object motion LOCATION:University of Minnesota\, Psychology and Computer Science DESCRIPTION:
Prediction and extrapolation form key problems in many perceptual ta
sks, which are particularly salient in processing object motion. In t
he first part of the talk I will address the problem of scheduling time for
perceptual information gathering. Is it best to act now with un
certainty, or postpone until more information can be gathered? F
or example, how long to observe a tennis ball's trajectory before executing
an interceptive action? Longer observation times insure less uncerta
inty about the ball's trajectory but leave less time to make the movement,
increasing motor error. Recent results from our lab show that people
understand this trade-off and are able to schedule time for perception to m
inimize task errors. In general, scheduling time for perceptual infor
mation gathering is an instance of the exploration/exploitation problem, an
d I will discuss human and optimal behavior on this problem. Extrapol
ation with occlusion is a key exemplar of the need for predicition: an obje
ct moves along a variable path before disappearing and a prediction of wher
e the object will reemerge at a specified distance beyond the point of occl
usion is made. In general, predicting the trajectory of an object dur
ing occlusion requires an internal model of the object's motion to extrapol
ate future positions given the observed trajectory. In recent work (F
ulvio, Maloney & Schrater, VSS2009), we showed that people naturally ad
opt one of two kinds of generic motion extrapolation models in the absence
of feedback (i.e. no learning) - a constant acceleration model (producing q
uadratic extrapolation) or a constant velocity model (producing linear extr
apolation). How such predictive models are learned is an open q
uestion. To address this question, we had subjects extrapolate the mo
tion of a swarm of sample points generated by random walks from different f
amilies of dynamics. Simulation results from the ideal learner predic
t that learning motion models will depend on several factors, including&nbs
p; differential predictions of the motion models, consistency of the motion
type across trials and limited noise. To test these prediction
s, subjects performed a motion extrapolation task that involved positioning
a "bucket" with a mouse to capture the object as it emerged from occlusion
, and feedback was given at the end of each trial. While subject performanc
e was less than ideal, we provide clear evidence that they adapt their inte
rnal motion models toward the generative process in a manner consistent wit
h statistical learning.
background reading:
http://www.jneurosci.org/cgi/content/full/27/26/6984">http://www.jneurosc
i.org/cgi/content/full/27/26/6984
Prediction and extrapolation form key problems in many perceptual ta
sks, which are particularly salient in processing object motion. In t
he first part of the talk I will address the problem of scheduling time for
perceptual information gathering. Is it best to act now with un
certainty, or postpone until more information can be gathered? F
or example, how long to observe a tennis ball's trajectory before executing
an interceptive action? Longer observation times insure less uncerta
inty about the ball's trajectory but leave less time to make the movement,
increasing motor error. Recent results from our lab show that people
understand this trade-off and are able to schedule time for perception to m
inimize task errors. In general, scheduling time for perceptual infor
mation gathering is an instance of the exploration/exploitation problem, an
d I will discuss human and optimal behavior on this problem. Extrapol
ation with occlusion is a key exemplar of the need for predicition: an obje
ct moves along a variable path before disappearing and a prediction of wher
e the object will reemerge at a specified distance beyond the point of occl
usion is made. In general, predicting the trajectory of an object dur
ing occlusion requires an internal model of the object's motion to extrapol
ate future positions given the observed trajectory. In recent work (F
ulvio, Maloney & Schrater, VSS2009), we showed that people naturally ad
opt one of two kinds of generic motion extrapolation models in the absence
of feedback (i.e. no learning) - a constant acceleration model (producing q
uadratic extrapolation) or a constant velocity model (producing linear extr
apolation). How such predictive models are learned is an open q
uestion. To address this question, we had subjects extrapolate the mo
tion of a swarm of sample points generated by random walks from different f
amilies of dynamics. Simulation results from the ideal learner predic
t that learning motion models will depend on several factors, including&nbs
p; differential predictions of the motion models, consistency of the motion
type across trials and limited noise. To test these prediction
s, subjects performed a motion extrapolation task that involved positioning
a "bucket" with a mouse to capture the object as it emerged from occlusion
, and feedback was given at the end of each trial. While subject performanc
e was less than ideal, we provide clear evidence that they adapt their inte
rnal motion models toward the generative process in a manner consistent wit
h statistical learning.
background reading:
http://www.jneurosci.org/cgi/content/fu
ll/27/26/6984">http://www.jneurosci.org/cgi/content/full/27/26/6984