Thanks to Brilliant for supporting this episode of SciShow.
If you’re looking to start the year by building new habits,
there’s no better place to channel all that productive energy than
with today’s sponsor, Brilliant; check them out at
Brilliant.org/SciShow.
[♪ INTRO]
Driving can be pretty dangerous. In the U.S., over two and a
half million people get injured in traffic accidents each year…that’s
about one person every twelve seconds. And hundreds of thousands of those accidents
happen because of a distraction. Like, you could get distracted by
stuff going on around you, talking to another passenger,
or just changing the car’s temperature, until something unexpected happens.
So, to keep our brains on track and our bodies safe,
scientists are turning to artificial intelligence to learn how our minds handle
distractions and help keep us in a mental sweet spot we’re while driving.
While you’re driving on a busy highway, you might be simultaneously watching out
for other cars, turning on the music, and talking to someone in the passenger seat,
all while you’re trying to maneuver multiple tons of steel going
at least 80 kilometers an hour. And juggling all those
tasks at the same time is what researchers call cognitive load or workload.
Generally, you’ll want to keep your workload from getting too high.
Otherwise, you’re less likely to be able to respond
to a sudden threat by doing things like changing lanes to get out of the way.
And scientists are trying to find ways to measure this workload to design
and engineer better cars and roads. But this isn’t as straightforward as asking
drivers directly how they’re feeling. Because getting an already preoccupied
driver to perfectly describe how much mental energy they’re using
might affect their workload by adding to it. After all,
asking someone every couple of seconds if they’re distracted can be… a bit distracting!
But a workaround to this problem might be looking out for physical signs
that someone’s mind is working hard, like increased heart rate,
and getting an idea of the size of their workload that way.
Like, in a study from 2014, researchers had twenty-two participants in
a driving simulator doing an “n-back” test. That’s when you recall things from your memory
based on a sequence of items, like numbers. So, for example, someone says the numbers “three,
seven, five” and participants are then asked to recall which number
was read out two steps before, in this case,
“three” when the number “five” is read. The idea was to use the n-back test to
increase the workload of each driver. While this test was happening,
researchers measured participants’ heart rates and how much electricity their skin conducted.
And scientists found that both the participants' heart rate and skin conductance
increased when the task was made harder. And their driving behavior,
like how well they stayed in a lane, changed as well, but not as drastically.
In another 2014 study, the same researchers flipped the script.
They recruited even more participants to drive on real highways
while performing the n-back test on and off at periodic intervals.
Then, they used the data they collected to train an algorithm to recognize
when the participants were doing a task and experiencing a higher workload.
The algorithm used heart, skin, and driving data for
when the task was being done and when it wasn’t. So it could tease out features in the data that
helped distinguish between high and low workloads.
And it worked! The algorithm was able to determine whether
a driver was or wasn’t experiencing a high workload with 90% accuracy.
But if strapping an electrocardiogram to measure your heart rate while you drive
sounds stress-inducing, scientists are exploring
other ways of measuring workload too. For instance, scientists could use functional
near-infrared spectroscopy, or fNIRS, which shines
infrared lights near a person’s skin and measures how much light bounces back.
The amount of light absorbed by the person’s blood
can tell scientists the amount of oxygen in the blood.
And the whole setup can be made small enough to fit into a headband,
which someone could wear so that scientists could
measure the oxygen content of the blood in their brain.
If there's a lot of oxygen around, their brain is working hard.
Using the data collected by the fNIRS headband, the same researchers developed another algorithm
that could also be used to measure the driver’s workload in a
simulator while they used the n-back test. And the algorithm worked pretty well
in this scenario too, with an accuracy of around 80%.
With effective ways of measuring a driver’s workload,
the hope is that carmakers can test out different interfaces in future cars
with real drivers, to find designs that are simple and safe to use.
In fact, new cars could even include indicators for how distracted a driver might be
in real-time so that they can change their behavior or slow down
to manage their cognitive workload while driving,
sort of like a fuel efficiency gauge, but for the mind.
All these studies covered when drivers get overwhelmed,
but being underwhelmed can be just as bad! Like when you’re on a familiar road
or when you’re simply driving for a really long time, it becomes easier to tune
out from what’s happening on the road. And as cars are being developed
to become smarter and do more of the driving for you,
the risks of boredom on the road get higher. Because when we do less and less,
it becomes harder for our minds to snap back and take control when we need to.
To tackle this, researchers are tinkering with ways to
artificially add some workload when drivers are understimulated.
For instance, a 2017 study had participants drive in a simulator with and without a
kind of smartphone “game” that encouraged them to stick to changing speed limits.
The phone was positioned in a legally allowed holder, and the game was designed
to avoid being too showy or distracting. When the game was introduced, the participants
drove with less risky behavior, like speeding, and stayed more
engaged on the otherwise quiet, boring roads in the simulator.
Which is not to say that playing games while driving
is something that you should do, but it goes to show boredom
can affect how much attention you’re paying to what you’re doing.
So, while some machines doing our jobs might make us tune out,
there’s a hope that soon, they’ll also be able to help us tune back in.
And if you want to learn more about the nitty-gritty of how machines tune us in,
you should check out today’s sponsor, Brilliant. They are an online learning platform
with courses about science, engineering, computer science, and math.
With their interactive course, Algorithm Fundamentals,
they can help you learn how to program without having to dig through the weeds
of coding syntax through these fun, interactive challenges.
You can shift around blocks of "pseudocode,” and then you can get immediate
feedback on your results! If you'd like to check them out,
head over to brilliant.org/scishow or click on the link in the description.
The first 200 people will get 20% off Brilliant's annual premium subscription,
and checking them out also helps us, so thanks!
[♪ OUTRO]