How honey bees make fast and accurate decisions
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Curated by eLife
eLife assessment
This study presents important findings on the decision-making capacities of honey bees in controlled conditions. The evidence supporting the study is solid, however, the explanation of the methods, importance, and novelty of the study requires further clarification. With a deeper development of the relevance of this study, the reader will have a clear idea of how this study contributes to the field.
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- Evaluated articles (eLife)
- Neuroscience (eLife)
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.
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Author Response
Reviewer #1 (Public Review):
The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.
The study is well …
Author Response
Reviewer #1 (Public Review):
The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.
The study is well supported by the signal detection theory and the experiments are well designed which is a major strength. However, the methods are not completely clear, so would be better to make a clearer description. Another weakness is the lack of clear explanations of the importance and relevance of the model.
Given the experimental design was optimal, the authors could potentially achieve the aims of this study.
Thank you for expressing your interest and providing constructive inputs. Based on your suggestions, we have thoroughly revised our manuscript to offer a more comprehensive explanation of the rationale behind our approach, as well as its comparison to existing knowledge and methods in the field. We believe that these revisions will significantly enhance the comprehensibility of our study and facilitate a better understanding of our findings.
Reviewer #2 (Public Review):
By elegantly designing experiments, MaBouDi et al. elucidated honeybee's behavioral strategy to quantitatively associate sensory cues with valences. The description is simple and concise enough to understand the logic. Particularly, the authors clearly demonstrated how sensory evidence and reward likelihood quantitatively affect the decision-making process and animals' response time. Their behavioral characterization approach and proposed model could also be helpful for studies using higher animal species. I have a few doubts regarding the definition of rejection behavior and the structure of the model that is critical to lead their main conclusions.
Thank you for your interest and valuable feedback. We greatly appreciate your input, and as a result, we have thoroughly reviewed your comments and implemented significant revisions to our manuscript. We have taken care to provide more comprehensive explanations of our methods, results, and the proposed model in order to enhance the overall comprehensibility of our study. Our intention is to ensure that readers can better understand our findings through these revisions.
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eLife assessment
This study presents important findings on the decision-making capacities of honey bees in controlled conditions. The evidence supporting the study is solid, however, the explanation of the methods, importance, and novelty of the study requires further clarification. With a deeper development of the relevance of this study, the reader will have a clear idea of how this study contributes to the field.
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Reviewer #1 (Public Review):
The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.
The study is well supported by the …
Reviewer #1 (Public Review):
The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.
The study is well supported by the signal detection theory and the experiments are well designed which is a major strength. However, the methods are not completely clear, so would be better to make a clearer description. Another weakness is the lack of clear explanations of the importance and relevance of the model.
Given the experimental design was optimal, the authors could potentially achieve the aims of this study.
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Reviewer #2 (Public Review):
By elegantly designing experiments, MaBouDi et al. elucidated honeybee's behavioral strategy to quantitatively associate sensory cues with valences. The description is simple and concise enough to understand the logic. Particularly, the authors clearly demonstrated how sensory evidence and reward likelihood quantitatively affect the decision-making process and animals' response time. Their behavioral characterization approach and proposed model could also be helpful for studies using higher animal species. I have a few doubts regarding the definition of rejection behavior and the structure of the model that is critical to lead their main conclusions.
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