MaBouDi, H. orcid.org/0000-0002-7612-6465, Roper, M., Guiraud, M. et al. (2 more authors) (Submitted: 2021) Automated video tracking and flight analysis show how bumblebees solve a pattern discrimination task using active vision. bioRxiv. (Submitted)
Abstract
Active vision, the ability of the visual system to actively sample and select relevant information out of a visual scene through eye and head movements, has been explored in a variety of animal species. Small-brained animals such as insects might rely even more on sequential acquisition of pattern features since there might be less parallel processing capacity in their brains than in vertebrates. To investigate how active vision strategies enable bees to solve visual tasks, here, we employed a simple visual discrimination task in which individual bees were presented with a multiplication symbol and a 45° rotated version of the same pattern (“plus sign”). High-speed videography of unrewarded tests and analysis of the bees’ flight paths shows that only a small region of the pattern is inspected before successfully accepting a target or rejecting a distractor. The bees’ scanning behaviour of the stimuli differed for plus signs and multiplication signs, but for each of these, the flight behaviour was consistent irrespective of whether the pattern was rewarding or unrewarding. Bees typically oriented themselves at ~±30° to the patterns such that only one eye had an unobscured view of stimuli. There was a significant preference for initially scanning the left side of the stimuli. Our results suggest that the bees’ movement may be an integral part of a strategy to efficiently analyse and encode their environment.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). For reuse permissions, please contact the Author(s). |
Keywords: | active vision; Bombus terrestris; cognitive strategy; flight analysis; scanning behaviour; visual recognition |
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 Feb 2022 07:22 |
Last Modified: | 25 Feb 2022 07:22 |
Status: | Submitted |
Publisher: | Cold Spring Harbor Laboratory |
Identification Number: | 10.1101/2021.03.09.434580 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:184023 |