Topic: behavioral sequences

Traditional approaches to studying decision-making in neuroscience focus on simplified behavioral tasks where animals perform repetitive, stereotyped actions to receive explicit rewards. While informative, these methods constrain our understanding of decision-making to short timescale behaviors driven by explicit goals. In natural environments, animals exhibit more complex, long-term behaviors driven by intrinsic motivations that are often unobservable. Recent works in time-varying inverse reinforcement learning (IRL) aim to capture shifting motivations in long-term, freely moving behaviors. H

Behavior analysis typically relies on trial-based repetitions of constrained tasks. We aimed to uncover how age, pain, and itch influence the patterns of behavioral sequences and their transitions in freely behaving animals using MoSeq, a machine learning network analysis tool developed by the Datta lab. Mice of different ages were treated with chloroquine (itch) or formalin (pain) and placed in an open field arena and recorded for 30 minutes via a high-speed color camera and infrared depth camera. A trained autoregressive hidden Markov model classified the mouse movements into sub-second syll