Honeybees solve a multi-comparison ranking task by probability matching

HaDi MaBouDi, James A. R. Marshall, Andrew B. Barron*

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Honeybees forage on diverse flowers which vary in the amount and type of rewards they offer, and bees are challenged with maximizing the resources they gather for their colony. That bees are effective foragers is clear, but how bees solve this type of complex multi-choice task is unknown. Here, we set bees a five-comparison choice task in which five colours differed in their probability of offering reward and punishment. The colours were ranked such that high ranked colours were more likely to offer reward, and the ranking was unambiguous. Bees' choices in unrewarded tests matched their individual experiences of reward and punishment of each colour, indicating bees solved this test not by comparing or ranking colours but by basing their colour choices on their history of reinforcement for each colour. Computational modelling suggests a structure like the honeybee mushroom body with reinforcement-related plasticity at both input and output can be sufficient for this cognitive strategy. We discuss how probability matching enables effective choices to be made without a need to compare any stimuli directly, and the use and limitations of this simple cognitive strategy for foraging animals.

Original languageEnglish
Article number20201525
Pages (from-to)1-9
Number of pages9
JournalProceedings. Biological sciences
Volume287
Issue number1934
DOIs
Publication statusPublished - 9 Sep 2020

Keywords

  • colour learning
  • ecological rationality
  • multi-armed bandit task
  • mushroom body
  • probability matching
  • reinforcement learning

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