The local enhancement conundrum: in search of the adaptive value of a social learning mechanism

Michal Arbilly*, Kevin N. Laland

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Social learning mechanisms are widely thought to vary in their degree of complexity as well as in their prevalence in the natural world. While learning the properties of a stimulus that generalize to similar stimuli at other locations (stimulus enhancement) prima fade appears more useful to an animal than learning about a specific stimulus at a specific location (local enhancement), empirical evidence suggests that the latter is much more widespread in nature. Simulating populations engaged in a producer scrounger game, we sought to deploy mathematical models to identify the adaptive benefits of reliance on local enhancement and/or stimulus enhancement, and the alternative conditions favoring their evolution. Surprisingly, we found that while stimulus enhancement readily evolves, local enhancement is advantageous only under highly restricted conditions: when generalization of information was made unreliable or when error in social learning was high. Our results generate a conundrum over how seemingly conflicting empirical and theoretical findings can be reconciled. Perhaps the prevalence of local enhancement in nature is due to stimulus enhancement costs independent of the learning task itself (e.g. predation risk), perhaps natural habitats are often characterized by unreliable yet highly rewarding payoffs, or perhaps local enhancement occurs less frequently, and stimulus enhancement more frequently, than widely believed. (C) 2013 Elsevier Inc. All rights reserved.

Original languageEnglish
Pages (from-to)50-57
Number of pages8
JournalTheoretical Population Biology
Publication statusPublished - Feb 2014


  • Stimulus enhancement
  • Local enhancement
  • Social foraging
  • Producer-scrounger game
  • Game theory
  • Evolution
  • Animals
  • Strategies
  • Insights
  • Flocks
  • Sites
  • Model
  • Risk


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