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Decision-making and action selection in honeybees: a theoretical and experimental study

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posted on 28.03.2022, 22:06 by Lianne F. S. Meah
Decision-making is an integral part of everyday life for animals of all species. Some decisions are rapid and based on sensory input alone, others rely on factors such as context and internal motivation. The possibilities for the experimental investigation of choice behaviour in mammals, especially in humans, are seemingly endless. However, neuroscience has struggled to define the neural circuitry behind decision-making processes due to the complex structure of the mammalian brain. For this work we turn to the honeybee for inspiration. With a brain composed of approximately 10 to the power of 6 neurons and sized at a tiny 1mm to the power of 3, it may be assumed that such an insect produces mere `programmed' behaviours, yet, the honeybee exhibits a rich, elaborate behavioural repertoire and a large capacity for learning in a variety of different paradigms. Indeed, the honeybee has been identified as a powerful model for decision-making. Sequential sampling models, originating in psychology, have been used to explain rapid decision-making behaviours. Such models assume that noisy sensory evidence is integrated over time until a threshold is reached, whereby a decision is made. These models have proven popular because they are able to fit biological data and are furthermore supported by neural evidence. Additionally, they explain the speed-accuracy trade-off, a behavioural phenomenon also demonstrated in bees. For this work we examine honeybee choice behaviour in different levels of satiation, and show that hungry bees are faster and less accurate than partially satiated bees in a simple choice task. We suggest that differences in choice behaviour may be attributed to a simple mechanism which alters the level of the decision threshold according to how satiated the bee is. We further speculate that the honeybee olfactory system may be a drift-diffusion channel, and develop a simple computational model, based on honeybee neurobiology ,with simulations that match behavioural results.


Table of Contents

1. Introduction -- 2. Literature review -- 3. The role of inhibition in decision making -- 4. Is honeybee decision-making described by a drift-diffusion process? -- 5. A computational model of decision-making -- 6. Future work and conclusions -- Appendices.


Bibliography: pages 177-214 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Biological Sciences

Department, Centre or School

Department of Biological Sciences

Year of Award


Principal Supervisor

James Marshall

Additional Supervisor 1

Andrew B. Barron

Additional Supervisor 2

Eleni Vasilaki


Copyright Lianne F. S. Meah 2018.. Copyright disclaimer: http://mq.edu.au/library/copyright




1 online resource (214 pages) diagrams, graphs, tables

Former Identifiers

mq:70562 http://hdl.handle.net/1959.14/1265496