Modeling time series of animal behavior by means of a latent-state model with feedback

We describe a family of models developed for time series of animal feeding behavior. The models incorporate both an unobserved state, which can be interpreted as the motivational state of the animal, and a mechanism for feedback to this state from the observed behavior. We discuss methods for evaluating and maximizing the likelihood of an observed series of behaviors, and thereby estimating parameters, and for inferring the most likely sequence of underlying states. We indicate several extensions of the models, including the incorporation of random effects. We apply these methods in an analysis of the feeding behavior of the caterpillar Helicoverpa armigera, and thereby demonstrate the potential of this family of models as a tool in the investigation of behavior.