MaBouDi, H. orcid.org/0000-0002-7612-6465, Marshall, J.A.R. orcid.org/0000-0002-1506-167X, Dearden, N. et al. (1 more author) (2023) How honey bees make fast and accurate decisions. eLife, 12. e86176. ISSN 2050-084X
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023, MaBouDi et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited: https://creativecommons.org/licenses/by/4.0/ |
Keywords: | action selection; apis mellifera; computational biology; decision-making; foraging; mushroom bodies; protocerebrum; sequential sampling model; systems biology; Bees; Animals; Reproducibility of Results; Flowers; Pollen; Reward; Color |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jul 2023 12:05 |
Last Modified: | 04 Jul 2023 12:05 |
Status: | Published |
Publisher: | eLife Sciences Publications, Ltd |
Refereed: | Yes |
Identification Number: | 10.7554/elife.86176 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201130 |