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Being Rational About Irrationality

The human brain tends to jump to conclusions based on limited information.

Portions of the following were taken from an article I wrote for ScientificAmerican.com in April.


Reason appears to have fallen on hard times. Since the 1970s, psychologists have accumulated a long list of cognitive biases that illustrate all of the ways we screw up. More recently, behavioral economists started studying how we systematically deviate from standard economic models. Social scientists such as Jonathan Haidt are pointing out that humans are led by their passions and our reasons are puny post-hoc rationalizations. Thanks to Malcolm Gladwell and other popular science writers, going with your gut is in; thinking it through is out. And since we’re predictably irrational, as Dan Ariely points out, we may as well give up when it comes to curbing our built-in biases.

My fellow bloggers have given their two-cents. Steven Mazie brought to life several classic studies originally conducted by Daniel Kahneman and Amos Tversky to explore the relationship between rationality, logic and probability theory. David Berreby rightly reminded readers that, “research into human irrationality… has the potential to cure some of our most important institutions of the habitual harms they inflict on us.” These comments stem from a post by Tauriq Moosa who rifted on a recent Jonah Lehrer post about the relationship between intelligence and cognitive biases. All of the posts do a nice job of covering the major discussion points surrounding rationality. However, by bringing to life the predominant research of judgment and decision-making they inherently do more harm than good in terms of understanding and avoiding cognitive biases.

One of the reoccurring points of Daniel Kahneman’s book Thinking, Fast and Slow is that it only takes a small amount of information to confidently form new world views that are seemingly objective and accurate but almost entirely subjective and inaccurate. That is, the human brain tends to jump to conclusions based on limited information.

The problem with blog posts on rationality and intuition is that readers seem to glance them over uncritically and reduce human cognition into a monism (i.e., “go with your gut,” or “think it through”). As a result, they ironically fall prey to the very biases they should be on the lookout for: jumping to conclusions based on limited information.

This cognitive tendency is a good thing most of the time. As cognitive scientists such as Gerd Gigerenzer points out, human rationality evolved to help us understand and organize the world by making it appear as simple as possible. Knowledge of logic and probability wasn’t important for our hunter-gatherer ancestors. But when it comes to writing about rationality and intuition we must remember that readers are going to jump to conclusions about how people jump to conclusions.

The popular literature on cognitive biases is enlightening, but let’s not be irrational about irrationality; exposure to X is not knowledge and control of X. Reading about cognitive biases, after all, does not free anybody from their nasty epistemological pitfalls.

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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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