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  • Friday, December 17, 2010
    Prediction, extrapolation and scheduling time to gather information in object motion

    Prediction and extrapolation form key problems in many perceptual tasks,

    which are particularly salient in processing object motion. In the first

    part of the talk I will address the problem of scheduling time for

    perceptual information gathering. Is it best to act now with

    uncertainty, or postpone until more information can be gathered? For

    example, how long to observe a tennis ball's trajectory before executing

    an interceptive action? Longer observation times insure less

    uncertainty 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 minimize task errors. In general, scheduling time for

    perceptual information gathering is an instance of the

    exploration/exploitation problem, and I will discuss human and optimal

    behavior on this problem. Extrapolation with occlusion is a key

    exemplar of the need for predicition: an object moves along a variable

    path before disappearing and a prediction of where the object will

    reemerge at a specified distance beyond the point of occlusion is made.

    In general, predicting the trajectory of an object during occlusion

    requires an internal model of the object's motion to extrapolate future

    positions given the observed trajectory. In recent work (Fulvio,

    Maloney & Schrater, VSS2009), we showed that people naturally adopt one

    of two kinds of generic motion extrapolation models in the absence of

    feedback (i.e. no learning) - a constant acceleration model (producing

    quadratic extrapolation) or a constant velocity model (producing linear

    extrapolation). How such predictive models are learned is an open

    question. To address this question, we had subjects extrapolate the

    motion of a swarm of sample points generated by random walks from

    different families of dynamics. Simulation results from the ideal

    learner predict that learning motion models will depend on several

    factors, including differential predictions of the motion models,

    consistency of the motion type across trials and limited noise. To

    test these predictions, 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 performance was less than ideal, we provide clear

    evidence that they adapt their internal motion models toward the

    generative process in a manner consistent with statistical


    Paul Schrater
    University of Minnesota