{"doi":"10.1101/2024.11.13.623515","title":"Modeling Complex Animal Behavior with Latent State Inverse Reinforcement Learning","abstract":"Understanding complex animal behavior is crucial for linking brain computation to observed actions. While recent research has shifted towards modeling behavior as a dynamic process, few approaches exist for modeling long-term, naturalistic behaviors such as navigation. We introduce discrete Dynamical Inverse Reinforcement Learning (dDIRL), a latent state-dependent paradigm for modeling complex animal behavior over extended periods. dDIRL models animal behavior as being driven by internal state-specific rewards, with Markovian transitions between the distinct internal states. Using expectation-maximization, we infer reward functions corresponding to each internal states and the transition probabilities between them, from observed behavior. We applied dDIRL to water-starved mice navigating a labyrinth, analyzing each animal individually. Our results reveal three distinct internal states sufficient to describe behavior, including a consistent water-seeking state occupied for less than half the time. We also identified two clusters of animals with different exploration patterns in the labyrinth. dDIRL offers a nuanced understanding of how internal states and their associated rewards shape observed behavior in complex environments, paving the way for deeper insights into the neural basis of naturalistic behavior.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":506275,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9498,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":990258,"name":"Victor Geadah","orcid":"0000-0003-4645-2144","position":1,"is_corresponding":false},{"id":326728,"name":"Jonathan W. Pillow","orcid":"0000-0002-3638-8831","position":2,"is_corresponding":false},{"id":1274913,"name":"Aditi Jha","orcid":"0000-0002-9892-5807","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-19T02:10:50.984927Z","pmid":"39605387","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}