  We introduce a new set of models and adaptive psychometric testing methods
for multidimensional psychophysics. In contrast to traditional adaptive
staircase methods like PEST and QUEST, the method is multi-dimensional and does
not require a grid over contextual dimensions, retaining sub-exponential
scaling in the number of stimulus dimensions. In contrast to more recent
multi-dimensional adaptive methods, our underlying model does not require a
parametric assumption about the interaction between intensity and the
additional dimensions. In addition, we introduce a new active sampling policy
that explicitly targets psychometric detection threshold estimation and does so
substantially faster than policies that attempt to estimate the full
psychometric function (though it still provides estimates of the function,
albeit with lower accuracy). Finally, we introduce AEPsych, a user-friendly
open-source package for nonparametric psychophysics that makes these
technically-challenging methods accessible to the broader community.
