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Abstract:
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Experiments in cognitive science are evolving in complexity in order to answer more difficult and fundamental questions about the mind. As a result, rich data is being collected in real-time for the purpose of exploring the dynamic nature of the brain. For example, continuous response digital interfaces are increasingly used to measure perception-action systems in psychophysics. The statistical problem related to these systems reduces to linking time-varying covariates to a continuous response variate. Often, the data from experiments suggest that an underlying system is linear. We will introduce ideas from functional data analysis, differential equations and mixed-effects modeling, which as a collection of tools, are useful for detecting nonlinear dynamics in perception-action systems.
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