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dc.contributor.authorGonzález Forero, Mauricio
dc.contributor.authorFaulwasser, Timm
dc.contributor.authorLehmann, Laurent
dc.date.accessioned2017-03-13T12:30:21Z
dc.date.available2017-03-13T12:30:21Z
dc.date.issued2017-03-09
dc.identifier.citationGonzález Forero , M , Faulwasser , T & Lehmann , L 2017 , ' A model for brain life history evolution ' , PLoS Computational Biology , vol. 13 , no. 3 , e1005380 . https://doi.org/10.1371/journal.pcbi.1005380en
dc.identifier.issn1553-734X
dc.identifier.otherPURE: 249347430
dc.identifier.otherPURE UUID: 6855ec8f-24b3-4aa8-9c80-e3164aa45bef
dc.identifier.otherScopus: 85016820022
dc.identifier.otherORCID: /0000-0003-1015-3089/work/60427699
dc.identifier.otherWOS: 000398031900036
dc.identifier.urihttp://hdl.handle.net/10023/10459
dc.descriptionThis work was funded by Swiss NSF grant PP00P3-146340 to LL http://www.snf.ch/en/Pages/default.aspx.en
dc.description.abstractComplex cognition and relatively large brains are distributed across various taxa, and many primarily verbal hypotheses exist to explain such diversity. Yet, mathematical approaches formalizing verbal hypotheses would help deepen the understanding of brain and cognition evolution. With this aim, we combine elements of life history and metabolic theories to formulate a metabolically explicit mathematical model for brain life history evolution. We assume that some of the brain’s energetic expense is due to production (learning) and maintenance (memory) of energy-extraction skills (or cognitive abilities, knowledge, information, etc.). We also assume that individuals use such skills to extract energy from the environment, and can allocate this energy to grow and maintain the body, including brain and reproductive tissues. The model can be used to ask what fraction of growth energy should be allocated at each age, given natural selection, to growing brain and other tissues under various biological settings. We apply the model to find uninvadable allocation strategies under a baseline setting (“me vs nature”), namely when energy-extraction challenges are environmentally determined and are overcome individually but possibly with maternal help, and use modern-human data to estimate model’s parameter values. The resulting uninvadable strategies yield predictions for brain and body mass throughout ontogeny and for the ages at maturity, adulthood, and brain growth arrest. We find that: (1) a me-vs-nature setting is enough to generate adult brain and body mass of ancient human scale and a sequence of childhood, adolescence, and adulthood stages; (2) large brains are favored by intermediately challenging environments, moderately effective skills, and metabolically expensive memory; and (3) adult skill is proportional to brain mass when metabolic costs of memory saturate the brain metabolic rate allocated to skills.
dc.format.extent28
dc.language.isoeng
dc.relation.ispartofPLoS Computational Biologyen
dc.rights© 2017 González-Forero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectQH301 Biologyen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectDASen
dc.subject.lccQH301en
dc.subject.lccQA75en
dc.titleA model for brain life history evolutionen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1005380
dc.description.statusPeer revieweden


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