Active Learning in Psychophysics

When learning the parameters of a regression model, one may have control over the set of inputs for which one wants to query the corresponding outputs. Depending on the goals, prior knowledge and model some inputs may be more informative than others. Active learning or optimal experimental design deals with the problem of choosing the optimal inputs.

In this talk we present a general adaptive Bayesian method for experimental design which is more flexible and accurate then existing psychophysical methods and which allows more control over the optimisation of free parameters. In particular, it uses weighted marginal conditional entropies and takes into account nuisance parameters. We investigated the efficency of various enhancements useful for psychophysics such as block designs, dynamic termination and application to common experimental designs.

We also briefly discuss the application of the approach as a normative model of human eye movements.