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Abstract:
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How do humans acquire skills on complex visualmotor tasks? The aim of
explaining the evolution of learning is to bootstrap learning on such
tasks: we analyse a large sequential corpus of low-level visualmotor data and
construct models of the learning exhibited during the generation of this data
that can expose the subject's inadequacies and thus help correct them. The
data-driven construction of such models is challenging because visualmotor
performance data is
- 1. non-stationary---characterized by periods of slow evolution of a policy
punctuated by conceptual shifts in which policies change radically
- 2. sparse---available data occupies a millionth part of the
high-dimensional space it is drawn from and
- 3. has non-Gaussian noise, which is difficult to model and eliminate.
We develop data-driven algorithms that build and track models of the policies
across the conceptual shifts that are behaviorally equivalent to the subject
being modeled. We present a novel method for locating changepoints in the
data, i.e. points of radical policy shifts. We construct ``local'' models for
stationary policies between two changepoints, which are fitted by a novel
extension to locally weighted regression. These local models are rich enough
to capture individual differences in the task, and are simple enough to learn
in real-time. We experimentally demonstrate the effectiveness of our methods by
showing the closeness of fit of model performance to human learning curves. In
contrast to previous modeling work, we make no a priori assumptions about
the underlying cognitive architecture required to duplicate the behavior of
strategies. We also compare the performance of our methods to decision trees
and demonstrate the superiority of local models for the task of learning
compact representations of high-dimensional, noisy, non-stationary sequential
data.
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