| We construct models of the evolution of human learning on a visualmotor task by analysing a large sequential corpus of low-level performance data generated from it. The performance data is drawn sparsely from a large, high-dimensional space, is non-stationary---slowly evolving control policies are punctuated by radical conceptual shifts---and has non-Gaussian noise, which is difficult to model.; We develop novel, data-driven algorithms for identifying the conceptual shifts, and for constructing compact representations of the subjects' stationary control policies. The policy models are "local" and use a novel extension to locally weighted regression. The closeness of fit of model performance to human learning curves experimentally demonstrates the effectiveness of our methods. In contrast to previous modeling work, we make no a priori assumptions about the underlying cognitive architecture required to duplicate subject behavior. By comparing the performance of our methods to decision trees, we demonstrate the superiority of local models for learning compact representations of high-dimensional, noisy, non-stationary sequential data. |
