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Cognition is a complex and dynamic process. It is an essential goal toestimate latent attentional states based on behavioral measures in manysequences of behavioral tasks. Here, we propose a probabilistic modelingand inference framework for estimating the attentional state using simultaneous binary and continuous behavioral measures. The proposed modelextends the standard hidden Markov model (HMM) by explicitly modeling the state duration distribution, which yields a special example ofthe hidden semi-Markov model (HSMM). We validate our methods usingcomputer simulations and experimental data. In computer simulations,we systematically investigate the impacts of model mismatch and the latency distribution. For the experimental data collected from a rodent visual detection task, we validate the results with predictive log-likelihood. Our work is useful for many behavioral neuroscience experiments, wherethe common goal is to infer the discrete (binary or multinomial) statesequences from multiple behavioral measures.

Estimating Latent Attentional States Based on Simultaneous Binary and Continuous Behavioral Measures