  Animal behavior is not driven simply by its current observations, but is
strongly influenced by internal states. Estimating the structure of these
internal states is crucial for understanding the neural basis of behavior. In
principle, internal states can be estimated by inverting behavior models, as in
inverse model-based Reinforcement Learning. However, this requires careful
parameterization and risks model-mismatch to the animal. Here we take a
data-driven approach to infer latent states directly from observations of
behavior, using a partially observable switching semi-Markov process. This
process has two elements critical for capturing animal behavior: it captures
non-exponential distribution of times between observations, and transitions
between latent states depend on the animal's actions, features that require
more complex non-markovian models to represent. To demonstrate the utility of
our approach, we apply it to the observations of a simulated optimal agent
performing a foraging task, and find that latent dynamics extracted by the
model has correspondences with the belief dynamics of the agent. Finally, we
apply our model to identify latent states in the behaviors of monkey performing
a foraging task, and find clusters of latent states that identify periods of
time consistent with expectant waiting. This data-driven behavioral model will
be valuable for inferring latent cognitive states, and thereby for measuring
neural representations of those states.
