 A model for brain life history evolutionMauricio González-Forero, Timm Faulwasser and Laurent Lehmann PLOS Computational Biology, 2017, vol. 13, issue 3, 1-28 Abstract: Complex 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.Author summary: Complex cognition and relatively large brains occur in a diversity of mammal, bird, and fish species among others, and there is a large number of mostly verbal hypotheses to explain what causes their evolution in certain species but not others. However, these hypotheses have scarcely exploited the power of formulating them in mathematical terms, which has been very useful to understand the evolution of other traits. To address this issue, we formulate a mathematical model that allows incorporating many of those hypotheses and that can be used to obtain predictions for how much and how fast the brain should grow depending on the hypothesis assumed. We apply the model to humans in a setting where each individual must extract energy from the environment alone (e.g., by hunting or cooking) but possibly with its mother’s help when young (“me vs nature”). We find that a me-vs-nature setting can be enough to produce a variety of human features, including large brain sizes and an adolescence life stage. Our model can be used to compare hypotheses promoting brain evolution, such as harsh environments or plentiful social interactions. Date: 2017 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005380 DOI: 10.1371/journal.pcbi.1005380 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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