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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