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Agreeing to take part in a brain experiment seemed fun. Until I saw the five monstrous syringes filled with blue goo.
It will only hurt a little, I am told.
The experiment I’m about to do has revealed something fundamental about our brain function – how we react to surprise.
Handling the unexpected has been a critical part of survival for our entire history: Cave lion! Avalanche! E-bike!
But how do our brains process the ever-going onslaught of sensory input while efficiently – and at times, unthinkingly – responding to the risks and rewards of daily life? The question has inspired decades of opposing thought.
The scientists fixing an electrode cap over my head, and injecting blobs of conductive gel from those horrifying syringes onto my scalp, have helped settle the debate.
Their results take us into the minds of World Cup strikers and goalkeepers, and one day could even help slash the energy demands of the monolithic data centres overtaking our suburbs. Here’s how it went down.
Do our brains prioritise surprise?
Scientists like Dr Reuben Rideaux, lead author of the new research in The Journal of Neuroscience, are fascinated with how our brains operate as the most hyper-efficient machines on Earth.
Our noodles can execute a billion-billion mathematical operations per second, using the equivalent power of a small fish tank pump.
“The more that we learn about the brain, the more we understand just how amazing what it does is,” Rideaux, from the University of Sydney, says. “What are the design principles of the brain that allow it to work so well and so efficiently?”
On the subject of surprise, scientists studying the brain are divided into two opposing camps.
One camp believes the brain fires more strongly when responding to predictable events, to ensure we behave appropriately to our most common day-to-day triggers.
The other camp theorises brain power is actually suppressed during predictable events to save energy, and that neurons fire up more when something unexpected happens.
Funnelling more energy into predictable events makes sense because they’re more frequent. But there’s more opportunity to gather new information during a surprise.
Does the brain prioritise energy for the expected or unexpected?
“There’s actually a lot of empirical evidence for both found over the last decade or so,” Rideaux says.
The test begins
Skull dotted with 64 flashing electrodes, chin lodged in a head frame, my eyes are trained on a black donut dashing across a grey computer screen as a camera calibrates the movement of my pupils.
Then the test begins.
Within a white circle, grey dots – fabulously called Gaussian blobs – flash in seemingly random spots. My job is to indicate on a keypad whether the dot has appeared to the left or right of the circle’s centre.
The flashes are quick and the game is fast-paced; I’m racing the clock to be as fast and accurate as possible.
Every so often, I’m tested if I can remember the exact location of the last grey blob after it disappears.
Across 30 minutes of testing, these seemingly random blobs are following a secret pattern. They often cluster on one side of the circle before leaping to the other.
The experimenters are stealthily teaching my brain to expect the dots on one side versus the other, and then testing whether I can better recall their locations if they appear where I expect them to, or in a surprise position.
My results are crunched by research co-author, syringe-wielder and cognitive neuroscientist Dr Dominic Tran.
My brain waves, pupil dilation and game results – along with the data from 40 other study participants – show we react faster to predictable events.
So fast, in fact, that our brains actually begin to respond to something we expect before it’s even happened: an observation the team was able to make using cutting-edge neuroimaging techniques.
That explains why a tennis player starts to react to a 170km/h serve before the ball’s even fired off her opponent’s racquet, saving precious milliseconds by predicting where the serve will land based on game experience and body language.
But the data also shows my brain waves spiked higher when I saw a blob in a surprise location. I, alongside the other participants, recalled the locations of these unexpected flashes more accurately.
The data suggests the brain slows during unexpected moments to capture more information, like it’s making a “software update”, said Rideaux, so it’s better prepared for the future. As a result, we remember these surprises much better.
So, do our brains prioritise the predictable or unexpected? The answer is both.
The brain flips between a fast auto-mode and a slow data-capturing mode in less than a blink, between 50 and 100 milliseconds.
Both pathways are efficient for the brain: it just chooses whether to react quickly with less detail, or react slower but with vivid detail and better memory of the event.
“The brain has its cake and eats it too,” said Rideaux.
World Cup brain science
Imagine it this way: sometimes you can drive home from work or school and barely remember doing it. Your brain is reacting quickly, with little resources, to the known turns in the road and stop signs.
But if you get rear-ended on the way home by a Mr Whippy van, no doubt that memory will lodge itself in your brain. You’ll be able to recall the moment in bright technicolour for years to come.
You could also picture a World Cup goalkeeper during a penalty shootout. Based on his knowledge of the striker and his opponent’s movement, the keeper starts to react to a kick even before boot meets ball. If he dives to the left corner, where he expected the ball, and saves the goal, the event passes as a blur.
But if the striker fakes-out then belts the ball into the middle of the net, while our goalkeeper dives to the side, the keeper will probably remember every detail of that ill-fated attempt in cringeworthy detail, right down to his crash onto the grass and exactly where the ball buried itself in the back of the goal.
Less voracious AI
Rideaux said findings that reveal how the brain operates so well could help address the AI energy crisis.
“Artificial intelligence has done incredible things. But it requires huge amounts of energy,” he said.
“By understanding principles of biological computation that allow it to process information really efficiently, we can hopefully apply those to artificial computation, to neural networks, in order to improve their performance.
“This is really important in terms of improving artificial intelligence, but also reducing its carbon footprint, which is on the rapid incline with data centres that are being built in order to serve the increasing energy demands of AI tools.”
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