{"doi":"10.1016/j.anbehav.2025.123191","title":"Bayesian updating for self-assessment explains social dominance and winner–loser effects","abstract":"In animal contests, winners of previous contests often keep winning and losers keep losing. This coupling of previous experiences to future success, referred to as the winner-loser effect, plays a key role in stabilizing the resulting dominance hierarchies. Despite their importance, the cognitive mechanisms through which these effects occur are unknown. Identifying the mechanisms behind winner-loser effects requires identifying plausible models and generating predictions that can be used to test these alternative hypotheses. Winner-loser effects are often accompanied by a change in the aggressiveness of experienced individuals, which suggests individuals may be adjusting their self-assessment of their abilities after each contest. This updating of a prior estimate can be effectively described by Bayesian updating, and here we implement an agent-based model with continuous Bayesian updating to explore whether this is a plausible explanation of winner-loser effects. We first show that Bayesian updating reproduces known empirical results of typical dominance interactions. We then provide a series of testable predictions that can be used in future empirical work to distinguish Bayesian updating from simpler mechanisms. Our work demonstrates the utility of Bayesian updating as a mechanism to explain and ultimately predict changes in behaviour after salient social experiences.","journal":"Animal Behaviour","year":2025,"id":531374,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9462,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1411613,"name":"Kate L. Laskowski","orcid":"0000-0003-1523-9340","position":1,"is_corresponding":false},{"id":1411612,"name":"Ammon Perkes","orcid":"0000-0001-8932-8309","position":0,"is_corresponding":true}],"reference_count":92,"raw_metadata":null,"created_at":"2026-07-19T02:51:14.579161Z","pmid":"42027910","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":[]}