Academic Journal
Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors.
| Title: | Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors. |
|---|---|
| Authors: | Urbaniak R; Basis Research Institute, New York, 10026, USA., Xie M; Basis Research Institute, New York, 10026, USA.; Arizona State University, School for the Future of Innovation in Society, Tempe, 85287, USA.; New York Academy of Sciences, New York, 10006, USA.; Columbia University, New York, 10027, USA., Mackevicius E; Basis Research Institute, New York, 10026, USA. emily@basis.ai.; Columbia University, New York, 10027, USA. emily@basis.ai. |
| Source: | Scientific reports [Sci Rep] 2024 Sep 18; Vol. 14 (1), pp. 21770. Date of Electronic Publication: 2024 Sep 18. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London : Nature Publishing Group, copyright 2011- |
| MeSH Terms: | Cognition*/physiology , Nerve Net*/physiology , Movement* , Feeding Behavior* , Mass Behavior* , Models, Statistical*, Birds/physiology ; Computer Simulation ; Neural Networks, Computer ; Animals |
| Abstract: | Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to the components of the value functions. We test this framework by simulating foraging agents and using Bayesian statistical modeling to correctly identify the factors that best predict the agents' behavior. As further validation, we use this framework to analyze an open-source locust foraging dataset. Finally, we collect new multi-agent real-world bird foraging data, and apply this method to analyze the preferences of different species. Together, this work provides an initial roadmap to integrate cognitive, neuroscience, and statistical approaches for reasoning about animal foraging in complex multi-agent environments. (© 2024. The Author(s).) |
| References: | Science. 2005 Jul 22;309(5734):619-23. (PMID: 16040709) Neuron. 2017 Feb 8;93(3):480-490. (PMID: 28182904) Nature. 2007 Oct 25;449(7165):1044-8. (PMID: 17960243) Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29311-29320. (PMID: 33229521) Nat Rev Neurosci. 2018 Jul;19(7):419-427. (PMID: 29752468) Nebr Symp Motiv. 2017;64:163-199. (PMID: 30351563) Cogn Affect Behav Neurosci. 2015 Dec;15(4):837-53. (PMID: 25917000) Curr Biol. 2022 May 23;32(10):R482-R493. (PMID: 35609550) Elife. 2019 Oct 01;8:. (PMID: 31570119) Nat Commun. 2021 Aug 16;12(1):4942. (PMID: 34400622) Dev Neurobiol. 2007 Mar;67(4):406-14. (PMID: 17443797) Behav Neurosci. 1996 Oct;110(5):946-64. (PMID: 8918998) Nature. 2014 Mar 13;507(7491):233-7. (PMID: 24598544) PLoS One. 2018 Mar 7;13(3):e0193049. (PMID: 29513700) Science. 2013 Apr 19;340(6130):367-72. (PMID: 23599496) Science. 2017 Jan 13;355(6321):176-180. (PMID: 28082589) iScience. 2023 Mar 13;26(4):106388. (PMID: 37034978) Curr Opin Behav Sci. 2020 Apr;32:155-166. (PMID: 35419465) Curr Biol. 2022 Sep 12;32(17):3676-3689.e5. (PMID: 35863351) Neuron. 2018 Jul 11;99(1):179-193.e7. (PMID: 30008297) Trends Ecol Evol. 2023 Apr;38(4):346-354. (PMID: 36509561) Cell. 2024 Apr 11;187(8):1922-1935.e20. (PMID: 38554707) Proc Natl Acad Sci U S A. 2017 May 30;114(22):5589-5594. (PMID: 28507154) Proc Natl Acad Sci U S A. 2009 Jul 14;106(28):11478-83. (PMID: 19574462) Elife. 2024 Feb 29;12:. (PMID: 38420996) Elife. 2023 Mar 16;12:. (PMID: 36928104) Philos Trans R Soc Lond B Biol Sci. 2007 Oct 29;362(1486):1857-71. (PMID: 17827099) Nat Neurosci. 2017 Nov;20(11):1643-1653. (PMID: 28967910) J Neurosci. 2021 Feb 3;41(5):911-919. (PMID: 33443081) Brain Res. 1971 Nov;34(1):171-5. (PMID: 5124915) J Neurol Neurosurg Psychiatry. 1957 Feb;20(1):11-21. (PMID: 13406589) Proc Natl Acad Sci U S A. 2012 Mar 27;109(13):4786-91. (PMID: 22427355) Behav Neurosci. 2021 Oct;135(5):601-609. (PMID: 34096743) Phys Rev Res. 2022 Aug-Oct;4(3):. (PMID: 36090768) J Neurosci. 1987 Jul;7(7):1951-68. (PMID: 3612226) Curr Opin Neurobiol. 2015 Jun;32:148-55. (PMID: 25932978) Nat Methods. 2022 Apr;19(4):486-495. (PMID: 35379947) Elife. 2023 Mar 16;12:. (PMID: 36927826) Science. 2021 Jul 16;373(6552):343-348. (PMID: 34437154) Ecol Evol. 2017 Feb 26;7(7):2025-2038. (PMID: 28405270) Cogn Affect Behav Neurosci. 2019 Apr;19(2):225-230. (PMID: 30607832) Neuron. 2019 Apr 17;102(2):407-419.e8. (PMID: 30824353) Science. 2018 Jan 12;359(6372):218-224. (PMID: 29326274) Elife. 2023 Mar 16;12:. (PMID: 36927625) Nat Methods. 2022 Apr;19(4):496-504. (PMID: 35414125) Trends Cogn Sci. 2019 Apr;23(4):278-292. (PMID: 30824227) |
| Grant Information: | K99 NS131256 United States NS NINDS NIH HHS; R00 NS131256 United States NS NINDS NIH HHS; Society of Fellows Simons Foundation; 1K99NS131256-01 U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) |
| Entry Date(s): | Date Created: 20240918 Date Completed: 20250128 Latest Revision: 20250128 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC11411083 |
| DOI: | 10.1038/s41598-024-71931-0 |
| PMID: | 39294261 |
| Database: | MEDLINE |
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| Header | DbId: cmedm DbLabel: MEDLINE An: 39294261 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22Urbaniak+R%22">Urbaniak R</searchLink>; Basis Research Institute, New York, 10026, USA.<br /><searchLink fieldCode="AU" term="%22Xie+M%22">Xie M</searchLink>; Basis Research Institute, New York, 10026, USA.; Arizona State University, School for the Future of Innovation in Society, Tempe, 85287, USA.; New York Academy of Sciences, New York, 10006, USA.; Columbia University, New York, 10027, USA.<br /><searchLink fieldCode="AU" term="%22Mackevicius+E%22">Mackevicius E</searchLink>; Basis Research Institute, New York, 10026, USA. emily@basis.ai.; Columbia University, New York, 10027, USA. emily@basis.ai. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101563288%22">Scientific reports</searchLink> [Sci Rep] 2024 Sep 18; Vol. 14 (1), pp. 21770. <i>Date of Electronic Publication: </i>2024 Sep 18. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article – Name: Language Label: Language Group: Lang Data: English – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Nature+Publishing+Group%22">Nature Publishing Group </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101563288 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2045-2322 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2220452322%22">20452322 </searchLink><i>NLM ISO Abbreviation: </i>Sci Rep <i>Subsets: </i>MEDLINE – Name: PublisherInfo Label: Imprint Name(s) Group: PubInfo Data: <i>Original Publication</i>: London : Nature Publishing Group, copyright 2011- – Name: SubjectMESH Label: MeSH Terms Group: Su Data: <searchLink fieldCode="MM" term="%22Cognition%22">Cognition*</searchLink>/<searchLink fieldCode="MM" term="%22Cognition+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Nerve+Net%22">Nerve Net*</searchLink>/<searchLink fieldCode="MM" term="%22Nerve+Net+physiology%22">physiology</searchLink> <br /><searchLink fieldCode="MM" term="%22Movement%22">Movement*</searchLink> <br /><searchLink fieldCode="MM" term="%22Feeding+Behavior%22">Feeding Behavior*</searchLink> <br /><searchLink fieldCode="MM" term="%22Mass+Behavior%22">Mass Behavior*</searchLink> <br /><searchLink fieldCode="MM" term="%22Models%2C+Statistical%22">Models, Statistical*</searchLink><br /><searchLink fieldCode="MH" term="%22Birds%22">Birds</searchLink>/<searchLink fieldCode="MH" term="%22Birds+physiology%22">physiology</searchLink> ; <searchLink fieldCode="MH" term="%22Computer+Simulation%22">Computer Simulation</searchLink> ; <searchLink fieldCode="MH" term="%22Neural+Networks%2C+Computer%22">Neural Networks, Computer</searchLink> ; <searchLink fieldCode="MH" term="%22Animals%22">Animals</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Foraging for food is a rich and ubiquitous animal behavior that involves complex cognitive decisions, and interactions between different individuals and species. There has been exciting recent progress in understanding multi-agent foraging behavior from cognitive, neuroscience, and statistical perspectives, but integrating these perspectives can be elusive. This paper seeks to unify these perspectives, allowing statistical analysis of observational animal movement data to shed light on the viability of cognitive models of foraging strategies. We start with cognitive agents with internal preferences expressed as value functions, and implement this in a biologically plausible neural network, and an equivalent statistical model, where statistical predictors of agents' movements correspond to the components of the value functions. We test this framework by simulating foraging agents and using Bayesian statistical modeling to correctly identify the factors that best predict the agents' behavior. As further validation, we use this framework to analyze an open-source locust foraging dataset. Finally, we collect new multi-agent real-world bird foraging data, and apply this method to analyze the preferences of different species. Together, this work provides an initial roadmap to integrate cognitive, neuroscience, and statistical approaches for reasoning about animal foraging in complex multi-agent environments.<br /> (© 2024. The Author(s).) – Name: Ref Label: References Group: RefInfo Data: Science. 2005 Jul 22;309(5734):619-23. (PMID: <searchLink fieldCode="PM" term="%2216040709%22">16040709)</searchLink><br />Neuron. 2017 Feb 8;93(3):480-490. (PMID: <searchLink fieldCode="PM" term="%2228182904%22">28182904)</searchLink><br />Nature. 2007 Oct 25;449(7165):1044-8. (PMID: <searchLink fieldCode="PM" term="%2217960243%22">17960243)</searchLink><br />Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29311-29320. (PMID: <searchLink fieldCode="PM" term="%2233229521%22">33229521)</searchLink><br />Nat Rev Neurosci. 2018 Jul;19(7):419-427. (PMID: <searchLink fieldCode="PM" term="%2229752468%22">29752468)</searchLink><br />Nebr Symp Motiv. 2017;64:163-199. (PMID: <searchLink fieldCode="PM" term="%2230351563%22">30351563)</searchLink><br />Cogn Affect Behav Neurosci. 2015 Dec;15(4):837-53. (PMID: <searchLink fieldCode="PM" term="%2225917000%22">25917000)</searchLink><br />Curr Biol. 2022 May 23;32(10):R482-R493. (PMID: <searchLink fieldCode="PM" term="%2235609550%22">35609550)</searchLink><br />Elife. 2019 Oct 01;8:. (PMID: <searchLink fieldCode="PM" term="%2231570119%22">31570119)</searchLink><br />Nat Commun. 2021 Aug 16;12(1):4942. (PMID: <searchLink fieldCode="PM" term="%2234400622%22">34400622)</searchLink><br />Dev Neurobiol. 2007 Mar;67(4):406-14. (PMID: <searchLink fieldCode="PM" term="%2217443797%22">17443797)</searchLink><br />Behav Neurosci. 1996 Oct;110(5):946-64. (PMID: <searchLink fieldCode="PM" term="%228918998%22">8918998)</searchLink><br />Nature. 2014 Mar 13;507(7491):233-7. (PMID: <searchLink fieldCode="PM" term="%2224598544%22">24598544)</searchLink><br />PLoS One. 2018 Mar 7;13(3):e0193049. (PMID: <searchLink fieldCode="PM" term="%2229513700%22">29513700)</searchLink><br />Science. 2013 Apr 19;340(6130):367-72. (PMID: <searchLink fieldCode="PM" term="%2223599496%22">23599496)</searchLink><br />Science. 2017 Jan 13;355(6321):176-180. (PMID: <searchLink fieldCode="PM" term="%2228082589%22">28082589)</searchLink><br />iScience. 2023 Mar 13;26(4):106388. (PMID: <searchLink fieldCode="PM" term="%2237034978%22">37034978)</searchLink><br />Curr Opin Behav Sci. 2020 Apr;32:155-166. (PMID: <searchLink fieldCode="PM" term="%2235419465%22">35419465)</searchLink><br />Curr Biol. 2022 Sep 12;32(17):3676-3689.e5. (PMID: <searchLink fieldCode="PM" term="%2235863351%22">35863351)</searchLink><br />Neuron. 2018 Jul 11;99(1):179-193.e7. (PMID: <searchLink fieldCode="PM" term="%2230008297%22">30008297)</searchLink><br />Trends Ecol Evol. 2023 Apr;38(4):346-354. (PMID: <searchLink fieldCode="PM" term="%2236509561%22">36509561)</searchLink><br />Cell. 2024 Apr 11;187(8):1922-1935.e20. (PMID: <searchLink fieldCode="PM" term="%2238554707%22">38554707)</searchLink><br />Proc Natl Acad Sci U S A. 2017 May 30;114(22):5589-5594. (PMID: <searchLink fieldCode="PM" term="%2228507154%22">28507154)</searchLink><br />Proc Natl Acad Sci U S A. 2009 Jul 14;106(28):11478-83. (PMID: <searchLink fieldCode="PM" term="%2219574462%22">19574462)</searchLink><br />Elife. 2024 Feb 29;12:. (PMID: <searchLink fieldCode="PM" term="%2238420996%22">38420996)</searchLink><br />Elife. 2023 Mar 16;12:. (PMID: <searchLink fieldCode="PM" term="%2236928104%22">36928104)</searchLink><br />Philos Trans R Soc Lond B Biol Sci. 2007 Oct 29;362(1486):1857-71. (PMID: <searchLink fieldCode="PM" term="%2217827099%22">17827099)</searchLink><br />Nat Neurosci. 2017 Nov;20(11):1643-1653. (PMID: <searchLink fieldCode="PM" term="%2228967910%22">28967910)</searchLink><br />J Neurosci. 2021 Feb 3;41(5):911-919. (PMID: <searchLink fieldCode="PM" term="%2233443081%22">33443081)</searchLink><br />Brain Res. 1971 Nov;34(1):171-5. (PMID: <searchLink fieldCode="PM" term="%225124915%22">5124915)</searchLink><br />J Neurol Neurosurg Psychiatry. 1957 Feb;20(1):11-21. (PMID: <searchLink fieldCode="PM" term="%2213406589%22">13406589)</searchLink><br />Proc Natl Acad Sci U S A. 2012 Mar 27;109(13):4786-91. (PMID: <searchLink fieldCode="PM" term="%2222427355%22">22427355)</searchLink><br />Behav Neurosci. 2021 Oct;135(5):601-609. (PMID: <searchLink fieldCode="PM" term="%2234096743%22">34096743)</searchLink><br />Phys Rev Res. 2022 Aug-Oct;4(3):. (PMID: <searchLink fieldCode="PM" term="%2236090768%22">36090768)</searchLink><br />J Neurosci. 1987 Jul;7(7):1951-68. (PMID: <searchLink fieldCode="PM" term="%223612226%22">3612226)</searchLink><br />Curr Opin Neurobiol. 2015 Jun;32:148-55. (PMID: <searchLink fieldCode="PM" term="%2225932978%22">25932978)</searchLink><br />Nat Methods. 2022 Apr;19(4):486-495. (PMID: <searchLink fieldCode="PM" term="%2235379947%22">35379947)</searchLink><br />Elife. 2023 Mar 16;12:. (PMID: <searchLink fieldCode="PM" term="%2236927826%22">36927826)</searchLink><br />Science. 2021 Jul 16;373(6552):343-348. (PMID: <searchLink fieldCode="PM" term="%2234437154%22">34437154)</searchLink><br />Ecol Evol. 2017 Feb 26;7(7):2025-2038. (PMID: <searchLink fieldCode="PM" term="%2228405270%22">28405270)</searchLink><br />Cogn Affect Behav Neurosci. 2019 Apr;19(2):225-230. (PMID: <searchLink fieldCode="PM" term="%2230607832%22">30607832)</searchLink><br />Neuron. 2019 Apr 17;102(2):407-419.e8. (PMID: <searchLink fieldCode="PM" term="%2230824353%22">30824353)</searchLink><br />Science. 2018 Jan 12;359(6372):218-224. (PMID: <searchLink fieldCode="PM" term="%2229326274%22">29326274)</searchLink><br />Elife. 2023 Mar 16;12:. (PMID: <searchLink fieldCode="PM" term="%2236927625%22">36927625)</searchLink><br />Nat Methods. 2022 Apr;19(4):496-504. (PMID: <searchLink fieldCode="PM" term="%2235414125%22">35414125)</searchLink><br />Trends Cogn Sci. 2019 Apr;23(4):278-292. 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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1038/s41598-024-71931-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: 21770 Subjects: – SubjectFull: Birds physiology Type: general – SubjectFull: Computer Simulation Type: general – SubjectFull: Neural Networks, Computer Type: general – SubjectFull: Animals Type: general – SubjectFull: Cognition physiology Type: general – SubjectFull: Nerve Net physiology Type: general – SubjectFull: Movement Type: general – SubjectFull: Feeding Behavior Type: general – SubjectFull: Mass Behavior Type: general – SubjectFull: Models, Statistical Type: general Titles: – TitleFull: Linking cognitive strategy, neural mechanism, and movement statistics in group foraging behaviors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Urbaniak R – PersonEntity: Name: NameFull: Xie M – PersonEntity: Name: NameFull: Mackevicius E IsPartOfRelationships: – BibEntity: Dates: – D: 18 M: 09 Text: 2024 Sep 18 Type: published Y: 2024 Identifiers: – Type: issn-electronic Value: 2045-2322 Numbering: – Type: volume Value: 14 – Type: issue Value: 1 Titles: – TitleFull: Scientific reports Type: main |
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