  Despite increasing attention paid to the need for fast, scalable methods to
analyze next-generation neuroscience data, comparatively little attention has
been paid to the development of similar methods for behavioral analysis. Just
as the volume and complexity of brain data have grown, behavioral paradigms in
systems neuroscience have likewise become more naturalistic and less
constrained, necessitating an increase in the flexibility and scalability of
the models used to study them. In particular, key assumptions made in the
analysis of typical decision paradigms --- optimality; analytic tractability;
discrete, low-dimensional action spaces --- may be untenable in richer tasks.
Here, using the case of a two-player, real-time, continuous strategic game as
an example, we show how the use of modern machine learning methods allows us to
relax each of these assumptions. Following an inverse reinforcement learning
approach, we are able to succinctly characterize the joint distribution over
players' actions via a generative model that allows us to simulate realistic
game play. We compare simulated play from a number of generative time series
models and show that ours successfully resists mode collapse while generating
trajectories with the rich variability of real behavior. Together, these
methods offer a rich class of models for the analysis of continuous action
tasks at the single-trial level.
