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Can We Think Critically Anymore?
In A Field Guide to Lies, neuroscientist Daniel Levitin explains how to wade through an endless sea of data and statistics to hone our critical thinking skills.
In a May 2015 New Yorker article, satirist Andy Borowitz warned of a “powerful new strain of fact-resistant humans who are threatening the ability of Earth to sustain life.” Although humans are endowed with an ability to “receive and process information,” he writes, these faculties have been rendered “totally inactive.”
Readers enjoy Borowitz because his writing is uncomfortably close to reality. While most articles are close enough to the ballpark you can hear the game, this particular piece hardly seems satirical. The medium of the Internet, where most people get their information and news on a daily basis, is not designed for nuanced, critical thinking; it incites our brain’s reptilian response system: scan it, believe it, rage against it (or proudly repost it without having read the content).
Cognitive psychologist and neuroscientist Daniel Levitin would agree. In fact, he’s written an entire book on the subject. The author of insightful previous works, This Is Your Brain on Music and The Organized Mind, in A Field Guide to Lies: Critical Thinking in the Information Age he takes to task our seemingly growing inability to weigh multiple ideas in making informed decisions, relying instead on emotional reactivity clouded by invented statistics and murky evidence.
Misinformation has been a fixture of human life for thousands of years, and was documented in biblical times and classical Greece. The unique problem we face today is that misinformation has proliferated; it is devilishly entwined on the Internet with real information, making the two difficult to separate.
Instead of merely pointing out problems, Levitin offers solutions, stepping into a professorial role with three evaluations: numbers, words, and the world. Through these sections he explores the ways that researchers and companies manipulate statistics, teaching us how to properly read studies without the intended bias.
For example, consider this headline: In the U.S., 150,000 girls and young women die of anorexia each year. This headline would quickly garner tens of thousands of shares, with few of those trigger-happy social media experts thinking through such a stat. So Levitin does it for us. Each year roughly 85,000 women between fifteen and twenty-four die; increase the age to forty-four and you still only have 55,000. The above statistic is impossible, regardless of how sharable.
Throughout this section Levitin returned me to Intro to Logic at Rutgers in the early nineties. He discusses how corporations manipulate graphs to suit their needs, such as one used by Apple CEO Tim Cook. Instead of reporting on Apple’s sluggish iPhone sales in 2013, he instead showed a cumulative graph beginning with 2008. The line, which if reflecting for a poor quarter would include a lethargic ascent, instead focuses the eye on the Himalayan climb of the previous two years. You barely notice the leveling off since your eye returns to his figure standing below it.
Another example is C-Span, which advertises that its network is available in 100 million homes. Of course, there might only be ten people watching, but that wouldn’t sit well. Likewise polling results, some of the most widely skewed numbers currently in the media. He writes,
A sample is representative if every person or thing in the group you’re studying has an equally likely chance of being chosen. If not, your sample is biased.
Since most circulated polls are conducted on landlines, and the demographic that still uses these phones is older, no such poll would represent new voters, who probably have no clue what that curly cord at the end of the receiver is for.
Then there’s simple bias, a neurological habit fully on display this week regarding presidential health. Forget numbers, we’re a visual species. Hillary Clinton’s slip has been defined as everything from a minor tumble to an avalanche of skin, depending on the viewer’s political inclinations. Levitin explains the bigger picture:
We also have a tendency to apply critical thinking only to things we disagree with.
The Internet might very well have been designed for confirmation bias. If you have a theory, you’ll find some site purporting it to be true. (I’m constantly amazed at how many people post Natural News stories on my feed, as if anything on the site is valid.) Levitin notes that MartinLutherKing.org is run by a white supremacist group. Even experts get fooled: Reporter Jonathan Capehart published a Washington Post article “based on a tweet by a nonexistent congressman in a nonexistent district.”
In The Organized Mind, Levitin writes that the human brain can only process 120 bits of information per second—not exactly Intel. Besides, our brain does not just process data, but is constantly scanning our environment for potential threats. Since we don’t have tigers to run from, and since we generally don’t commune in person (compared to time spent online), our emotional reactivity is directed at apparitions.
Add to this the fact that our attention is pulled in thousands of directions every day from advertisers purposefully falsifying information, eschewing traditional marketing under cover of ‘brand ambassadors’ and invented data. Taking the time to contemplate and comprehend what Nicholas Carr calls ‘deep knowledge’ is a forgotten art. Two thousand years ago people memorized the 100,00 shloka (couplets) of the Mahabharata. Today we forget what we tweeted five minutes ago.
Just as memorization and critical thinking occur when we train our brain like a muscle, it is exceptionally easy to forgo effort when emotionally-charged information is presented right before our eyes. As Levitin writes,
The brain is a giant pattern detector, and it seeks to extract order and structure from what often appear to be random configurations. We see Orion the Hunter in the night sky not because the stars were organized that way but because our brains can project patterns onto randomness.
Sadly, we’re victims of our patterns. Carr wrote The Shallows because, ironically, he could no longer finish reading an entire book. He wanted to know what technology was doing to his brain. Levitin made his own case for this in The Organized Mind. A Field Guide to Lies is an exceptional follow-up, not only describing the mechanisms for how we read and understand, but giving practical and essential advice on what to do about it.
Derek Beres is working on his new book, Whole Motion: Training Your Brain and Body For Optimal Health (Carrel/Skyhorse, Spring 2017). He is based in Los Angeles. Stay in touch on Facebook and Twitter.
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
"You dream about these kinds of moments when you're a kid," said lead paleontologist David Schmidt.
- The triceratops skull was first discovered in 2019, but was excavated over the summer of 2020.
- It was discovered in the South Dakota Badlands, an area where the Triceratops roamed some 66 million years ago.
- Studying dinosaurs helps scientists better understand the evolution of all life on Earth.
Credit: David Schmidt / Westminster College<p style="margin-left: 20px;">"We had to be really careful," Schmidt told St. Louis Public Radio. "We couldn't disturb anything at all, because at that point, it was under law enforcement investigation. They were telling us, 'Don't even make footprints,' and I was thinking, 'How are we supposed to do that?'"</p><p>Another difficulty was the mammoth size of the skull: about 7 feet long and more than 3,000 pounds. (For context, the largest triceratops skull ever unearthed was about <a href="https://www.tandfonline.com/doi/abs/10.1080/02724634.2010.483632" target="_blank">8.2 feet long</a>.) The skull of Schmidt's dinosaur was likely a <em>Triceratops prorsus, </em>one of two species of triceratops that roamed what's now North America about 66 million years ago.</p>
Credit: David Schmidt / Westminster College<p>The triceratops was an herbivore, but it was also a favorite meal of the T<em>yrannosaurus rex</em>. That probably explains why the Dakotas contain many scattered triceratops bone fragments, and, less commonly, complete bones and skulls. In summer 2019, for example, a separate team on a dig in North Dakota made <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">headlines</a> after unearthing a complete triceratops skull that measured five feet in length.</p><p>Michael Kjelland, a biology professor who participated in that excavation, said digging up the dinosaur was like completing a "multi-piece, 3-D jigsaw puzzle" that required "engineering that rivaled SpaceX," he jokingly told the <a href="https://www.nytimes.com/2019/07/26/science/triceratops-skull-65-million-years-old.html" target="_blank">New York Times</a>.</p>
Morrison Formation in Colorado
James St. John via Flickr
|Credit: Nobu Tamura/Wikimedia Commons|
The scent of sickness: 5 questions answered about using dogs – and mice and ferrets – to detect disease
Could medical detection animals smell coronavirus?
Study confirms the existence of a special kind of groupthink in large groups.
- Large groups of people everywhere tend to come to the same conclusions.
- In small groups, there's a much wider diversity of ideas.
- The mechanics of a large group make some ideas practically inevitable.
The grouping game<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDE2Ni9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMjI2MzA4OX0.RLrswIWbuEzHNqsw0F7EUrp9jPn7OulLPqCxcZT11ik/img.jpg?width=980" id="159b8" class="rm-shortcode" data-rm-shortcode-id="0feb15d2d7dde144c710c2f4f1e5350c" data-rm-shortcode-name="rebelmouse-image" data-width="2767" data-height="382" />
Some of the shapes used in the experiment
Credit: Guilbeault, et al./University of Pennsylvania<p>The researchers tested their theory with 1,480 people playing an online "Grouping Game" via Amazon's Mechanical Turk platform. The individuals were paired with another participant or made a member of a group of 6, 8, 24, or 50 people. Each pair and group were tasked with categorizing the symbols shown above, and they could see each other's answers.</p><p>The small groups came up with wildly divergent categories—the entire experiment produced nearly 5,000 category suggestions—while the larger groups came up with categorization systems that were virtually identical to each other.</p><p><a href="https://www.asc.upenn.edu/news-events/news/why-independent-cultures-think-alike-its-not-in-the-brain" target="_blank">Says Centol</a>a, "Even though we predicted it, I was nevertheless stunned to see it really happen. This result challenges many long-held ideas about culture and how it forms."</p><p>Nor was this unanimity a matter of having teamed-up like-minded individuals. "If I assign an individual to a small group," says lead author Douglas Guilbeault, "they are much more likely to arrive at a category system that is very idiosyncratic and specific to them. But if I assign that same individual to a large group, I can predict the category system that they will end up creating, regardless of whatever unique viewpoint that person happens to bring to the table."</p>
Why this happens<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDE4NC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMjkzMDg0Nn0.u2hdEIgNw4drFZ2frzx0AJ_MAxIizuM98rdovQrIblk/img.jpg?width=980" id="d3444" class="rm-shortcode" data-rm-shortcode-id="5da57d66e388fad0f1c17afb09af90a7" data-rm-shortcode-name="rebelmouse-image" data-width="1440" data-height="822" />
The many categories suggested by small groups on the left, the few from large groups on the right
Credit: Guilbeault, et al./Nature Communications<p>The striking results of the experiment correspond to a <a href="https://www.nature.com/articles/s41562-019-0607-5" target="_blank">previous study</a> done by NDG that investigated tipping points for people's behavior in networks.</p><p>That study concluded that after an idea enters a discussion among a large network of people, it can gain irresistible traction by popping up again and again in enough individuals' conversations. In networks of 50 people or more, such ideas eventually reach critical mass and become a prevailing opinion.</p><p>The same phenomenon does not happen often enough within a smaller network, where fewer interactions offer an idea less of an opportunity to take hold.</p>