MaBouDi, H. orcid.org/0000-0002-7612-6465, Marshall, J.A.R. orcid.org/0000-0002-1506-167X and Barron, A.B. orcid.org/0000-0002-8135-6628 (Submitted: 2020) Honey bees solve a multi-comparison ranking task by probability matching [preprint]. bioRxiv. (Submitted)
Abstract
Honey bees forage on a range of flowers, all of which can vary unpredictably in the amount and type of rewards they offer. In this environment bees are challenged with maximising 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 challenged bees with 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 matching their preferences to their history of reinforcement for each colour. We used a computational model to explore the feasibility of this probability matching strategy for the honey bee brain. The model suggested a structure like the honey bee mushroom body with reinforcement-related plasticity at both input and output was 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 utility and limitations of this simple cognitive strategy for foraging animals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Authors. |
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) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/P006094/1; EP/S030964/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Mar 2022 14:30 |
Last Modified: | 25 Mar 2022 14:30 |
Published Version: | https://doi.org/10.1098/rspb.2020.1525 |
Status: | Submitted |
Publisher: | Cold Spring Harbor Laboratory |
Identification Number: | 10.1101/2020.03.02.967661 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185038 |