Research published in the journal eLife today has the academic community buzzing.
Literally! The paper presents a model of decision-making in bees and outlines the paths in their brains that enable fast decision-making which enhances our understanding of insect brains, how our brains work and could lead to improvements in robot design.
The research revealed how honey bees have to balance effort, risk and reward, making rapid and accurate assessments of which flowers are mostly likely to offer food.
The study was led by Professor Andrew Barron from Macquarie University in Sydney, and Dr HaDi MaBouDi, Neville Dearden and Professor James Marshall from the University of Sheffield.
“Decision-making is at the core of cognition,” Professor Barron said.
“It’s the result of an evaluation of possible outcomes, and animal lives are full of decisions. A honey bee has a brain smaller than a sesame seed. And yet she can make decisions faster and more accurately than we can. A robot programmed to do a bee’s job would need the back up of a supercomputer.
Bees need to work quickly and efficiently, finding nectar and returning it to the hive, while avoiding predators. They need to make decisions. Which flower will have nectar? While they’re flying, they’re only prone to aerial attack. When they land to feed, they’re vulnerable to spiders and other predators, some of which use camouflage to look like flowers.
“We trained 20 bees to recognise five different coloured ‘flower disks’. Blue flowers always had sugar syrup,” says Dr MaBouDi. “Green flowers always had quinine [tonic water] with a bitter taste for bees. Other colours sometimes had glucose.”
“Then we introduced each bee to a ‘garden’ where the ‘flowers’ just had distilled water. We filmed each bee then watched more than 40 hours of video, tracking the path of the bees and timing how long it took them to make a decision.
“If the bees were confident that a flower would have food, then they quickly decided to land on it taking an average of 0.6 seconds),” says Dr MaBouDi. “If they were confident that a flower would not have food, they made a decision just as quickly.”
If they were unsure, then they took much more time – on average 1.4 seconds – and the time reflected the probability that a flower had food.
The team then built a computer model from first principles aiming to replicate the bees’ decision-making process. They found the structure of their computer model looked very similar to the physical layout of a bee brain.
AI researchers can learn much from insects and other ‘simple’ animals.
Feature Image: Th_otime Colin via Macquarie University