The Influence of Learning on Evolution: A Mathematical Framework
The Influence of Learning on Evolution: A Mathematical Framework
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DOI：
10.1162/artl.2009.15.2.15204
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发表时间：
2009-04
期刊：
影响因子：
2.6
通讯作者：
Ingo Paenke;T. Kawecki;B. Sendhoff
中科院分区：
文献类型：
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作者：
Ingo Paenke;T. Kawecki;B. Sendhoff
如果表型学习影响个体的进化适应性，这反过来又会加速或减缓进化变化，那么就可以观察到鲍德温效应。学习引起的加速和减速的证据可以在文献中找到。虽然这两种结果都得到了特定数学或模拟模型的支持，但迄今为止还没有实现普遍的预测。在这里，我们提出了一个通用的框架来预测进化是否受益于学习。它是用增益函数来表示的，增益函数量化了由于学习而导致的适应度的比例变化，这取决于基因型值。与归纳证明，我们表明，一个积极的增益函数的衍生物意味着学习加速进化，和一个负意味着减速的条件下，人口分布的单调部分的健身景观。我们表明，增益函数框架解释了几个特定的仿真模型的结果。我们还使用增益函数框架来阐明最近果蝇生物实验的结果。
The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.