Phenotypic plasticity, the baldwin effect, and the speeding up of evolution: The computational roots of an illusion

Mauro Santos, Eörs Szathmáry, José F. Fontanari

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

© 2015 Elsevier Ltd. An increasing number of dissident voices claim that the standard neo-Darwinian view of genes as 'leaders' and phenotypes as 'followers' during the process of adaptive evolution should be turned on its head. This idea is older than the rediscovery of Mendel's laws of inheritance, with the turn-of-the-twentieth-century notion eventually labeled as the 'Baldwin effect' as one of the many ways in which the standard neo-Darwinian view can be turned around. A condition for this effect is that environmentally induced variation such as phenotypic plasticity or learning is crucial for the initial establishment of a trait. This gives the additional time for natural selection to act on genetic variation and the adaptive trait can be eventually encoded in the genotype. An influential paper published in the late 1980s claimed the Baldwin effect to happen in computer simulations, and avowed that it was crucial to solve a difficult adaptive task. This generated much excitement among scholars in various disciplines that regard neo-Darwinian accounts to explain the evolutionary emergence of high-order phenotypic traits such as consciousness or language almost hopeless. Here, we use analytical and computational approaches to show that a standard population genetics treatment can easily crack what the scientific community has granted as an unsolvable adaptive problem without learning. Evolutionary psychologists and linguists have invoked the (claimed) Baldwin effect to make wild assertions that should not be taken seriously. What the Baldwin effect needs are plausible case-histories.
Original languageEnglish
Pages (from-to)127-136
JournalJournal of Theoretical Biology
Volume371
DOIs
Publication statusPublished - 1 Apr 2015

Keywords

  • Evolutionary search
  • Genetic algorithm
  • Learning
  • Speed of evolution
  • The baldwin effect

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