Decision making process of honeybees could inform design of robots

Led by Dr HaDi MaBouDi from Sheffield University’s Department of Computer Science with Professor Andrew Barron from Macquarie University in Sydney, the study has uncovered the complex strategies that honeybees use to decide which flowers to explore for nectar or pollen.

The study found that honeybee decisions are highly accurate - more so than humans - despite their brains being the same size as a sesame seed. The team’s findings have been published in eLife.

The Sheffield scientists said a new generation of robots and autonomous machines that think like bees will be able to make fast, accurate and efficient decisions autonomously. 

In the study, the researchers trained 20 bees to recognise five different coloured artificial flowers. Blue flowers contained sugar syrup, green flowers contained tonic water with a bitter taste that bees dislike, and the remaining colours sometimes had glucose. 

The team then introduced the bees to a custom-designed garden where the flowers only had distilled water to test their performance in different scenarios. The researchers filmed each bee then tracked their path and timed how long it took them to decide on which flower to visit. 

Results showed that if the bees were confident that a flower would have food, they quickly decided to land on it - on average in 0.6 seconds. If they were confident that a flower would not have food, they decided just as quickly. 

The scientists then built a computer model aiming to replicate the bees’ decision-making process, the structure of which looked very similar to the physical layout of a honeybee brain. 

In a statement, Dr HaDi MaBouDi said: “Each time a bee sets out to collect nectar, for example, it must use tiny variations in colour or odour to decide which flower it should land on and explore. Each mistake is costly, wasting energy and exposing the insect to potential dangers. To learn how to refine their choices through trial-and-error, bees only have at their disposal a brain the size of a pinhead, which contains fewer than a million neurons. And yet, they excel at this task, being both quick and accurate. 

“What we’ve done in this study is reveal the underlying mechanisms which drive these remarkable decision-making capabilities. We can now use these to design better, more robust and risk-averse robots and autonomous machines that can think like bees - some of the most efficient navigators in the natural world.”