This is one of those pieces of research which leaves you wondering, if we had been intelligently designed, especially by a designer for whom we were a special creation with everything else created just for us, why it didn’t give us the best available from all the systems it had designed for other species.
For example, why don't we have the immune system of bats, the respiratory system of birds, the eyesight of the peregrine falcon or the cancer resistance of sharks and elephants? And now we learn that the little honeybee, Apis mellifera, with its brain the side of a sesame seed, can make decisions much more quickly that we can. Surly, with our large brains, there was room to incorporate a honeybee's decision-making process.
But these are problem for creationists and those who like to imagine they were the special design of an omniscient god who only wanted the best for them. For evolutionary biologists of course, the answer is that there is no design and no plan and humans are the result of their own evolution with its environmental selectors and compromises that enabled our ancestors to survive over time, and honeybees, bats, birds and sharks are the result of theirs.
Evolution isn't a process that can remember what it did in a different branch of the tree of life and copy that into another branch, as an intelligent designer could, which is why species can be arranged in nested hierarchies.
The research in question, which was focused on machine learning and robot autonomy, rather than exposing the infantile stupidity of creationism, was conducted by a team led by Professor Andrew Barron from Macquarie University, Sydney, NSW, Australia and Dr HaDi MaBouDi, Neville Dearden and Professor James Marshall from the University of Sheffield, South Yorkshire, England, UK.
It is explained in The Lighthouse, the online news magazine of Macquarie University:
New research reveals how we could design robots to think like bees.
Honey bees have to balance effort, risk and reward, making rapid and accurate assessments of which flowers are mostly likely to offer food for their hive. Research published in the journal eLife today reveals how millions of years of evolution has engineered honey bees to make fast decisions and reduce risk.
The study enhances our understanding of insect brains, how our own brains evolved, and how to design better robots.
The paper presents a model of decision-making in bees and outlines the paths in their brains that enable fast decision-making.
The study was led by Professor Andrew Barron from Macquarie University, and Dr HaDi MaBouDi, Neville Dearden and Professor James Marshall from the University of Sheffield.
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.Decision-making is at the core of cognition. 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.
Today’s autonomous robots largely work with the support of remote computing,. Drones are relatively brainless, they have to be in wireless communication with a data centre. This technology path will never allow a drone to truly explore Mars solo – NASA’s amazing rovers on Mars have travelled about 75 kilometres in years of exploration.
Professor Andrew Barron, co-author
Department of Computer Science
University of Sheffield, Sheffield, United Kingdom
And School of Natural Sciences,
Macquarie University, North Ryde, Australia
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.We trained 20 bees to recognise five different coloured ‘flower disks’. Blue flowers always had sugar syrup. 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. If they were confident that a flower would not have food, they made a decision just as quickly.
Dr HaDi MaBouDi, lead author
Department of Computer Science
And Sheffield Neuroscience Institute, University of Sheffield, Sheffield, United Kingdom.
Our study has demonstrated complex autonomous decision-making with minimal neural circuitry. Now we know how bees make such smart decisions, we are studying how they are so fast at gathering and sampling information. We think bees are using their flight movements to enhance their visual system to make them better at detecting the best flowers.
Professor James AR Marshall, co-author
Department of Computer Science
And Sheffield Neuroscience Institute
University of Sheffield, Sheffield, United KingdomAI researchers can learn much from insects and other ‘simple’ animals. Millions of years of evolution has led to incredibly efficient brains with very low power requirements. The future of AI in industry will be inspired by biology, says Professor Marshall, who co-founded Opteran, a company that reverse-engineers insect brain algorithms to enable machines to move autonomously, like nature.
Sample video of honeybee in the test.
The video was captured from an overhead perspective, providing a clear view of the bees' movements within the flight arena, showcasing their reactions to various stimuli. The black lines depict the orientation of the bee’s body at each frame of the video, offering further observations of their positioning and behaviour during the experiment.
The video was captured from an overhead perspective, providing a clear view of the bees' movements within the flight arena, showcasing their reactions to various stimuli. The black lines depict the orientation of the bee’s body at each frame of the video, offering further observations of their positioning and behaviour during the experiment.
Bees’ behaviour in a colour discrimination task.
(A & B) Each bee was given 18 training trials in which she could choose between two different colours: one rewarded and the other punished. The bee was free to select each colour and return to the hive when satiated marking the end of a trial. Stimuli positions in the arena were changed in each trial in a pseudo-random manner. Stimuli were 2 cm diameter-coloured disks on a small platform (5 cm tall). On the top of each colour was placed either 10 μl reward (50% sucrose) or punishment (quinine) in training, or distilled water in tests. Two different colours, four disks of each colour, were presented in each training trial and test. Five different colours were used in the training. The colours differed in the proportion of training bouts in which they offered reward and punishment (rewarded at 100, 66, 50, 33, and 0% of training trials). Two groups of bees were trained with different likelihoods of reward and punishment from each colour (see Materials and methods section and Figure 1—source data 1). (C) Following training, the bee was given three unreinforced tests where the positive or negative reinforcements were replaced with distilled water. Bees’ responses were analysed from video recordings of the first 120 s in the flight arena. In the easy colour discrimination test, bees were presented with three pairs of the 100% and 0% rewarded colours (blue and green). In the reduced reward likelihood test, bees were examined with 66% and 33% rewarded colours (yellow and orange). In the reduced evidence test. bees were given two colours intermediate between green and blue (D & E) Examples of flight paths showing the inspection activity of a bee during the easy discrimination test in accepting blue (D) and rejecting green (E). Each black line on the flight path corresponds to the bee’s body orientation in a single video frame with 4ms intervals between frames. Line colour: flight speed 0.0–0.5 m/s (See Video 1).
(A & B) Each bee was given 18 training trials in which she could choose between two different colours: one rewarded and the other punished. The bee was free to select each colour and return to the hive when satiated marking the end of a trial. Stimuli positions in the arena were changed in each trial in a pseudo-random manner. Stimuli were 2 cm diameter-coloured disks on a small platform (5 cm tall). On the top of each colour was placed either 10 μl reward (50% sucrose) or punishment (quinine) in training, or distilled water in tests. Two different colours, four disks of each colour, were presented in each training trial and test. Five different colours were used in the training. The colours differed in the proportion of training bouts in which they offered reward and punishment (rewarded at 100, 66, 50, 33, and 0% of training trials). Two groups of bees were trained with different likelihoods of reward and punishment from each colour (see Materials and methods section and Figure 1—source data 1). (C) Following training, the bee was given three unreinforced tests where the positive or negative reinforcements were replaced with distilled water. Bees’ responses were analysed from video recordings of the first 120 s in the flight arena. In the easy colour discrimination test, bees were presented with three pairs of the 100% and 0% rewarded colours (blue and green). In the reduced reward likelihood test, bees were examined with 66% and 33% rewarded colours (yellow and orange). In the reduced evidence test. bees were given two colours intermediate between green and blue (D & E) Examples of flight paths showing the inspection activity of a bee during the easy discrimination test in accepting blue (D) and rejecting green (E). Each black line on the flight path corresponds to the bee’s body orientation in a single video frame with 4ms intervals between frames. Line colour: flight speed 0.0–0.5 m/s (See Video 1).
The teams abstract in elife together with the editor's evaluation and the eLife Digest make interesting reading:
AbstractWhich just leaves creationists to explain why the scientists are wrong to ascribe this ability of honeybees to evolution and why their allegedly omnibenevolent deity failed to give humans this quick-thinking ability.
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.
HaDi MaBouDi, James AR Marshall, Neville Dearden, Andrew B Barron (2023)
How honey bees make fast and accurate decisions
eLife 12:e86176 https://doi.org/10.7554/eLife.86176
Copyright: © 2023 The authors.
Published by eLife. Open access.
Reprinted under a Creative Commons Attribution 4.0 International license (CC BY 4.0)
Editor's evaluation
This valuable study elucidates the honeybee's behavioral strategy to associate sensory cues with rewards of different values. Based on solid experimental evidence the study demonstrates how sensory evidence and reward likelihood quantitatively affect the decision-making process and the bees' response time. The behavioral paradigm and the proposed model could provide interesting insights for scientists studying decision-making in higher animal species.
eLife digest
In the natural world, decision-making processes are often intricate and challenging. Animals frequently encounter situations where they have limited information on which to rely to guide them, yet even simple choices can have far-reaching impact on survival.
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 sesame seed, which contains fewer than a million neurons. And yet, they excel at this task, being both quick and accurate. The underlying mechanisms which drive these remarkable decision-making capabilities remain unclear.
In response, MaBouDi et al. aimed to explore which strategies honeybees adopt to forage so effectively, and the neural systems that may underlie them. To do so, they released the insects in a ‘field’ containing artificial flowers in five different colours. The bees were trained to link each colour with a certain likelihood of receiving either a sugary liquid (reward) or bitter quinine (punishment); they were then tested on this knowledge.
Next, MaBouDi et al. recorded how the bees would navigate a ‘reduced evidence’ test, where the colour of the flowers were ambiguous and consisted in various blends of the originally rewarded or punished colours; and a ‘reduced reward likelihood’ test, where the sweet recompense was offered less often than before.
Response times and accuracy rates revealed a complex pattern of decision-making processes. How quickly the insects made a choice, and the types of mistakes they made (such as deciding to explore a non-rewarded flower, or to ignore a rewarded one) were dependent on both the quality of the evidence and the certainty of the reward. Such sophistication and subtlety in decision-making is comparable to that of primates.
Next, MaBouDi et al. developed a computational model which could faithfully replicate the pattern of decisions exhibited by the bees, while also being plausible biologically. This approach offered insights into how a small brain could execute such complex choices ‘on the fly’, and the type of neural circuits that would be required. Going forward, this knowledge could be harnessed to design more efficient decision-making algorithms for artificial systems, and in particular for autonomous robotics.
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