How honey bees make fast and accurate decisions

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
60 Downloads (Pure)

Abstract

Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.

Original languageEnglish
Article numbere86176
Pages (from-to)1-26
Number of pages26
JournaleLife
Volume12
DOIs
Publication statusPublished - 27 Jun 2023

Bibliographical note

Copyright the Author(s) 2023. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • action selection
  • apis mellifera
  • computational biology
  • decision-making
  • foraging
  • mushroom bodies
  • protocerebrum
  • sequential sampling model
  • systems biology

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