{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:06:00Z","timestamp":1777521960058,"version":"3.51.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2009,10,20]],"date-time":"2009-10-20T00:00:00Z","timestamp":1255996800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2010,4]]},"abstract":"<jats:p>Using a new agent-based model that mimics the learning process in hand-reaching behavior of individual mice, we show that mouse hand preference is probabilistic, dependent on the environment and prior learning. We quantify the learning capabilities of three inbred strains and show that population distributions of hand preference emerge from the properties of individual mice. The model informs our understanding of gene\u2014environment interactions because it accommodates genotypic differences in learning and memory abilities, and environmental biases. We tuned each strain\u2019s model to match their experimental hand-preference distributions in unbiased worlds and, by comparing simulations and experiments, identified and quantified a constitutive left-bias in hand preference of one strain. The models, tuned for unbiased worlds, match experimental measures in left- and right-biased worlds and in biased worlds after previous training. New measures quantitatively assess this matching, revealing that two strains, previously considered non-learners of hand preference, actually have significant learning ability and we confirm this with new experiments. Model mice match the kinetics of hand-preference learning of one strain and predict the limits of learning. We conclude that genetically evolved hand-preference behavior in mice is inherently probabilistic to provide robustness and allow constant adaptability to ever-changing environments.<\/jats:p>","DOI":"10.1177\/1059712309339859","type":"journal-article","created":{"date-parts":[[2009,10,20]],"date-time":"2009-10-20T20:47:00Z","timestamp":1256071620000},"page":"116-131","source":"Crossref","is-referenced-by-count":9,"title":["Dynamic Agent-Based Model of Hand-Preference Behavior Patterns in the Mouse"],"prefix":"10.1177","volume":"18","author":[{"given":"Andre S.","family":"Ribeiro","sequence":"first","affiliation":[{"name":"Computational Systems Biology Research Group, Institute of Signal Processing, Tampere University of Technology, Finland, , Center for Computational Physics, University of Coimbra, Portugal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Lloyd-Price","sequence":"additional","affiliation":[{"name":"Computational Systems Biology Research Group, Institute of Signal Processing, Tampere University of Technology, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brenda A.","family":"Eales","sequence":"additional","affiliation":[{"name":"Department of Medical Genetics, Institute of Child and Maternal Health, Faculty of Medicine and Department of Biological Sciences, Faculty of Science, University of Calgary, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fred G.","family":"Biddle","sequence":"additional","affiliation":[{"name":"Department of Medical Genetics, Institute of Child and Maternal Health, Faculty of Medicine and Department of Biological Sciences, Faculty of Science, University of Calgary, 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