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Predicting the Future Primes Your Brain for Learning

Every time that you make a prediction you get a little bomb of dopamine in the reward pathways of your brain. That dopamine helps you pay closer attention, to process information more effectively, and to be more engaged with what’s going.

Jane McGonigal: If you need a little extra energy or you need to pay really close attention to something today, here’s what you should do. You should make a prediction about anything. It can be a prediction of how many emails you’ll get in the next hour. It could be a prediction about who’s going to win the baseball game tonight. It could be a prediction of how many jumping jacks can you do in 60 seconds. Make a prediction and then just wait to see if you’re right or not. Every time that you make a prediction, you get a little bomb of dopamine in the reward pathways of your brain. And the reason why you get a little dopamine bomb is that your brain is preparing itself to learn. So we often think of dopamine as the reward neurochemical — when there’s something we really want like a cookie or a pat on the back, we get this dopamine hit in anticipation of it. So we think about it as being really related to pleasure and fun and satisfaction. But the reason why dopamine feels good is that the brain is actually trying to trick us into learning or improving our strategies for being successful or getting what we want.

So dopamine is going to prime your brain to pay closer attention, to process information more effectively, to be more engaged with what’s going on and so just make a little prediction about anything. You’ll get the dopamine hit; your brain will go into high gear; and you could really use this in very strategic ways. For example, if you’re giving a presentation, ask your audience at the beginning of your presentation to make a prediction. It could be about your presentation. It could be about something in their lives. You’ve given them a dopamine hit; now they’re paying attention. They’re going to actually learn more from what you’re about to say. So make a prediction. It’s a really easy way to get the benefits and the learning benefits specifically of a dopamine bomb. You’ll also just feel happier, which is good too.

Every time that you make a prediction, says author and video game designer Jane McGonigal, you get a little bomb of dopamine in the reward pathways of your brain. That dopamine helps you pay closer attention, to process information more effectively, and to be more engaged with what’s going. So if you want to boost your ability to learn or get those you're teaching primed for learning, encourage prediction-making. It's a simple little mind hack to get your brain running on all cylinders.

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An algorithm may allow doctors to assess PTSD candidates for early intervention after traumatic ER visits.

Image source: camillo jimenez/Unsplash
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  • 10-15% of people visiting emergency rooms eventually develop symptoms of long-lasting PTSD.
  • Early treatment is available but there's been no way to tell who needs it.
  • Using clinical data already being collected, machine learning can identify who's at risk.

The psychological scars a traumatic experience can leave behind may have a more profound effect on a person than the original traumatic experience. Long after an acute emergency is resolved, victims of post-traumatic stress disorder (PTSD) continue to suffer its consequences.

In the U.S. some 30 million patients are annually treated in emergency departments (EDs) for a range of traumatic injuries. Add to that urgent admissions to the ED with the onset of COVID-19 symptoms. Health experts predict that some 10 percent to 15 percent of these people will develop long-lasting PTSD within a year of the initial incident. While there are interventions that can help individuals avoid PTSD, there's been no reliable way to identify those most likely to need it.

That may now have changed. A multi-disciplinary team of researchers has developed a method for predicting who is most likely to develop PTSD after a traumatic emergency-room experience. Their study is published in the journal Nature Medicine.

70 data points and machine learning

nurse wrapping patient's arm

Image source: Creators Collective/Unsplash

Study lead author Katharina Schultebraucks of Columbia University's Department Vagelos College of Physicians and Surgeons says:

"For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment. The earlier we can treat those at risk, the better the likely outcomes."

The new PTSD test uses machine learning and 70 clinical data points plus a clinical stress-level assessment to develop a PTSD score for an individual that identifies their risk of acquiring the condition.

Among the 70 data points are stress hormone levels, inflammatory signals, high blood pressure, and an anxiety-level assessment. Says Schultebraucks, "We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response. The idea was to create a tool that would be universally available and would add little burden to ED personnel."

Researchers used data from adult trauma survivors in Atlanta, Georgia (377 individuals) and New York City (221 individuals) to test their system.

Of this cohort, 90 percent of those predicted to be at high risk developed long-lasting PTSD symptoms within a year of the initial traumatic event — just 5 percent of people who never developed PTSD symptoms had been erroneously identified as being at risk.

On the other side of the coin, 29 percent of individuals were 'false negatives," tagged by the algorithm as not being at risk of PTSD, but then developing symptoms.

Going forward

person leaning their head on another's shoulder

Image source: Külli Kittus/Unsplash

Schultebraucks looks forward to more testing as the researchers continue to refine their algorithm and to instill confidence in the approach among ED clinicians: "Because previous models for predicting PTSD risk have not been validated in independent samples like our model, they haven't been adopted in clinical practice." She expects that, "Testing and validation of our model in larger samples will be necessary for the algorithm to be ready-to-use in the general population."

"Currently only 7% of level-1 trauma centers routinely screen for PTSD," notes Schultebraucks. "We hope that the algorithm will provide ED clinicians with a rapid, automatic readout that they could use for discharge planning and the prevention of PTSD." She envisions the algorithm being implemented in the future as a feature of electronic medical records.

The researchers also plan to test their algorithm at predicting PTSD in people whose traumatic experiences come in the form of health events such as heart attacks and strokes, as opposed to visits to the emergency department.

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