In The Fake News Era, Are We Too Gullible? No, Says Cognitive Scientist
One of the oddest political hoaxes of recent times was Pizzagate, in which conspiracy theorists claimed that Hillary Clinton and her 2016 campaign chief ran a child sex ring from the basement of a Washington, DC, pizzeria.
To fight disinformation more effectively, he suggests, humans need to stop believing in one thing above all: our own gullibility.
Millions of believers spread the rumor on social media, abetted by Russian bots; one outraged netizen stormed the restaurant with an assault rifle and shot open what he took to be the dungeon door. (It actually led to a computer closet.) Pundits cited the imbroglio as evidence that Americans had lost the ability to tell fake news from the real thing, putting our democracy in peril.
Such fears, however, are nothing new. "For most of history, the concept of widespread credulity has been fundamental to our understanding of society," observes Hugo Mercier in Not Born Yesterday: The Science of Who We Trust and What We Believe (Princeton University Press, 2020). In the fourth century BCE, he points out, the historian Thucydides blamed Athens' defeat by Sparta on a demagogue who hoodwinked the public into supporting idiotic military strategies; Plato extended that argument to condemn democracy itself. Today, atheists and fundamentalists decry one another's gullibility, as do climate-change accepters and deniers. Leftists bemoan the masses' blind acceptance of the "dominant ideology," while conservatives accuse those who do revolt of being duped by cunning agitators.
What's changed, all sides agree, is the speed at which bamboozlement can propagate. In the digital age, it seems, a sucker is born every nanosecond.
The Case Against Credulity
Yet Mercier, a cognitive scientist at the Jean Nicod Institute in Paris, thinks we've got the problem backward. To fight disinformation more effectively, he suggests, humans need to stop believing in one thing above all: our own gullibility. "We don't credulously accept whatever we're told—even when those views are supported by the majority of the population, or by prestigious, charismatic individuals," he writes. "On the contrary, we are skilled at figuring out who to trust and what to believe, and, if anything, we're too hard rather than too easy to influence."
He bases those contentions on a growing body of research in neuropsychiatry, evolutionary psychology, and other fields. Humans, Mercier argues, are hardwired to balance openness with vigilance when assessing communicated information. To gauge a statement's accuracy, we instinctively test it from many angles, including: Does it jibe with what I already believe? Does the speaker share my interests? Has she demonstrated competence in this area? What's her reputation for trustworthiness? And, with more complex assertions: Does the argument make sense?
This process, Mercier says, enables us to learn much more from one another than do other animals, and to communicate in a far more complex way—key to our unparalleled adaptability. But it doesn't always save us from trusting liars or embracing demonstrably false beliefs. To better understand why, leapsmag spoke with the author.
How did you come to write Not Born Yesterday?
In 2010, I collaborated with the cognitive scientist Dan Sperber and some other colleagues on a paper called "Epistemic Vigilance," which laid out the argument that evolutionarily, it would make no sense for humans to be gullible. If you can be easily manipulated and influenced, you're going to be in major trouble. But as I talked to people, I kept encountering resistance. They'd tell me, "No, no, people are influenced by advertising, by political campaigns, by religious leaders." I started doing more research to see if I was wrong, and eventually I had enough to write a book.
With all the talk about "fake news" these days, the topic has gotten a lot more timely.
Yes. But on the whole, I'm skeptical that fake news matters very much. And all the energy we spend fighting it is energy not spent on other pursuits that may be better ways of improving our informational environment. The real challenge, I think, is not how to shut up people who say stupid things on the internet, but how to make it easier for people who say correct things to convince people.
"History shows that the audience's state of mind and material conditions matter more than the leader's powers of persuasion."
You start the book with an anecdote about your encounter with a con artist several years ago, who scammed you out of 20 euros. Why did you choose that anecdote?
Although I'm arguing that people aren't generally gullible, I'm not saying we're completely impervious to attempts at tricking us. It's just that we're much better than we think at resisting manipulation. And while there's a risk of trusting someone who doesn't deserve to be trusted, there's also a risk of not trusting someone who could have been trusted. You miss out on someone who could help you, or from whom you might have learned something—including figuring out who to trust.
You argue that in humans, vigilance and open-mindedness evolved hand-in-hand, leading to a set of cognitive mechanisms you call "open vigilance."
There's a common view that people start from a state of being gullible and easy to influence, and get better at rejecting information as they become smarter and more sophisticated. But that's not what really happens. It's much harder to get apes than humans to do anything they don't want to do, for example. And research suggests that over evolutionary time, the better our species became at telling what we should and shouldn't listen to, the more open to influence we became. Even small children have ways to evaluate what people tell them.
The most basic is what I call "plausibility checking": if you tell them you're 200 years old, they're going to find that highly suspicious. Kids pay attention to competence; if someone is an expert in the relevant field, they'll trust her more. They're likelier to trust someone who's nice to them. My colleagues and I have found that by age 2 ½, children can distinguish between very strong and very weak arguments. Obviously, these skills keep developing throughout your life.
But you've found that even the most forceful leaders—and their propaganda machines—have a hard time changing people's minds.
Throughout history, there's been this fear of demagogues leading whole countries into terrible decisions. In reality, these leaders are mostly good at feeling the crowd and figuring out what people want to hear. They're not really influencing [the masses]; they're surfing on pre-existing public opinion. We know from a recent study, for instance, that if you match cities in which Hitler gave campaign speeches in the late '20s through early '30s with similar cities in which he didn't give campaign speeches, there was no difference in vote share for the Nazis. Nazi propaganda managed to make Germans who were already anti-Semitic more likely to express their anti-Semitism or act on it. But Germans who were not already anti-Semitic were completely inured to the propaganda.
So why, in totalitarian regimes, do people seem so devoted to the ruler?
It's not a very complex psychology. In these regimes, the slightest show of discontent can be punished by death, or by you and your whole family being sent to a labor camp. That doesn't mean propaganda has no effect, but you can explain people's obedience without it.
What about cult leaders and religious extremists? Their followers seem willing to believe anything.
Prophets and preachers can inspire the kind of fervor that leads people to suicidal acts or doomed crusades. But history shows that the audience's state of mind and material conditions matter more than the leader's powers of persuasion. Only when people are ready for extreme actions can a charismatic figure provide the spark that lights the fire.
Once a religion becomes ubiquitous, the limits of its persuasive powers become clear. Every anthropologist knows that in societies that are nominally dominated by orthodox belief systems—whether Christian or Muslim or anything else—most people share a view of God, or the spirit, that's closer to what you find in societies that lack such religions. In the Middle Ages, for instance, you have records of priests complaining of how unruly the people are—how they spend the whole Mass chatting or gossiping, or go on pilgrimages mostly because of all the prostitutes and wine-drinking. They continue pagan practices. They resist attempts to make them pay tithes. It's very far from our image of how much people really bought the dominant religion.
"The mainstream media is extremely reliable. The scientific consensus is extremely reliable."
And what about all those wild rumors and conspiracy theories on social media? Don't those demonstrate widespread gullibility?
I think not, for two reasons. One is that most of these false beliefs tend to be held in a way that's not very deep. People may say Pizzagate is true, yet that belief doesn't really interact with the rest of their cognition or their behavior. If you really believe that children are being abused, then trying to free them is the moral and rational thing to do. But the only person who did that was the guy who took his assault weapon to the pizzeria. Most people just left one-star reviews of the restaurant.
The other reason is that most of these beliefs actually play some useful role for people. Before any ethnic massacre, for example, rumors circulate about atrocities having been committed by the targeted minority. But those beliefs aren't what's really driving the phenomenon. In the horrendous pogrom of Kishinev, Moldova, 100 years ago, you had these stories of blood libel—a child disappeared, typical stuff. And then what did the Christian inhabitants do? They raped the [Jewish] women, they pillaged the wine stores, they stole everything they could. They clearly wanted to get that stuff, and they made up something to justify it.
Where do skeptics like climate-change deniers and anti-vaxxers fit into the picture?
Most people in most countries accept that vaccination is good and that climate change is real and man-made. These ideas are deeply counter-intuitive, so the fact that scientists were able to get them across is quite fascinating. But the environment in which we live is vastly different from the one in which we evolved. There's a lot more information, which makes it harder to figure out who we can trust. The main effect is that we don't trust enough; we don't accept enough information. We also rely on shortcuts and heuristics—coarse cues of trustworthiness. There are people who abuse these cues. They may have a PhD or an MD, and they use those credentials to help them spread messages that are not true and not good. Mostly, they're affirming what people want to believe, but they may also be changing minds at the margins.
How can we improve people's ability to resist that kind of exploitation?
I wish I could tell you! That's literally my next project. Generally speaking, though, my advice is very vanilla. The mainstream media is extremely reliable. The scientific consensus is extremely reliable. If you trust those sources, you'll go wrong in a very few cases, but on the whole, they'll probably give you good results. Yet a lot of the problems that we attribute to people being stupid and irrational are not entirely their fault. If governments were less corrupt, if the pharmaceutical companies were irreproachable, these problems might not go away—but they would certainly be minimized.
Autonomous, indoor farming gives a boost to crops
The glass-encased cabinet looks like a display meant to hold reasonably priced watches, or drugstore beauty creams shipped from France. But instead of this stagnant merchandise, each of its five shelves is overgrown with leaves — moss-soft pea sprouts, spikes of Lolla rosa lettuces, pale bok choy, dark kale, purple basil or red-veined sorrel or green wisps of dill. The glass structure isn’t a cabinet, but rather a “micro farm.”
The gadget is on display at the Richmond, Virginia headquarters of Babylon Micro-Farms, a company that aims to make indoor farming in the U.S. more accessible and sustainable. Babylon’s soilless hydroponic growing system, which feeds plants via nutrient-enriched water, allows chefs on cruise ships, cafeterias and elsewhere to provide home-grown produce to patrons, just seconds after it’s harvested. Currently, there are over 200 functioning systems, either sold or leased to customers, and more of them are on the way.
The chef-farmers choose from among 45 types of herb and leafy-greens seeds, plop them into grow trays, and a few weeks later they pick and serve. While success is predicated on at least a small amount of these humans’ care, the systems are autonomously surveilled round-the-clock from Babylon’s base of operations. And artificial intelligence is helping to run the show.
Babylon piloted the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off.
Imagine consistently perfect greens and tomatoes and strawberries, grown hyper-locally, using less water, without chemicals or environmental contaminants. This is the hefty promise of controlled environment agriculture (CEA) — basically, indoor farms that can be hydroponic, aeroponic (plant roots are suspended and fed through misting), or aquaponic (where fish play a role in fertilizing vegetables). But whether they grow 4,160 leafy-green servings per year, like one Babylon farm, or millions of servings, like some of the large, centralized facilities starting to supply supermarkets across the U.S., they seek to minimize failure as much as possible.
Babylon’s soilless hydroponic growing system
Courtesy Babylon Micro-Farms
Here, AI is starting to play a pivotal role. CEA growers use it to help “make sense of what’s happening” to the plants in their care, says Scott Lowman, vice president of applied research at the Institute for Advanced Learning and Research (IALR) in Virginia, a state that’s investing heavily in CEA companies. And although these companies say they’re not aiming for a future with zero human employees, AI is certainly poised to take a lot of human farming intervention out of the equation — for better and worse.
Most of these companies are compiling their own data sets to identify anything that might block the success of their systems. Babylon had already integrated sensor data into its farms to measure heat and humidity, the nutrient content of water, and the amount of light plants receive. Last year, they got a National Science Foundation grant that allowed them to pilot the use of specialized cameras that take pictures in different spectrums to gather some less-obvious visual data about plants’ wellbeing and alert people if something seems off. “Will this plant be healthy tomorrow? Are there things…that the human eye can't see that the plant starts expressing?” says Amandeep Ratte, the company’s head of data science. “If our system can say, Hey, this plant is unhealthy, we can reach out to [users] preemptively about what they’re doing wrong, or is there a disease at the farm?” Ratte says. The earlier the better, to avoid crop failures.
Natural light accounts for 70 percent of Greenswell Growers’ energy use on a sunny day.
Courtesy Greenswell Growers
IALR’s Lowman says that other CEA companies are developing their AI systems to account for the different crops they grow — lettuces come in all shapes and sizes, after all, and each has different growing needs than, for example, tomatoes. The ways they run their operations differs also. Babylon is unusual in its decentralized structure. But centralized growing systems with one main location have variabilities, too. AeroFarms, which recently declared bankruptcy but will continue to run its 140,000-square foot vertical operation in Danville, Virginia, is entirely enclosed and reliant on the intense violet glow of grow lights to produce microgreens.
Different companies have different data needs. What data is essential to AeroFarms isn’t quite the same as for Greenswell Growers located in Goochland County, Virginia. Raising four kinds of lettuce in a 77,000-square-foot automated hydroponic greenhouse, the vagaries of naturally available light, which accounts for 70 percent of Greenswell’s energy use on a sunny day, affect operations. Their tech needs to account for “outside weather impacts,” says president Carl Gupton. “What adjustments do we have to make inside of the greenhouse to offset what's going on outside environmentally, to give that plant optimal conditions? When it's 85 percent humidity outside, the system needs to do X, Y and Z to get the conditions that we want inside.”
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen.
Nevertheless, every CEA system has the same core needs — consistent yield of high quality crops to keep up year-round supply to customers. Additionally, “Everybody’s got the same set of problems,” Gupton says. Pests may come into a facility with seeds. A disease called pythium, one of the most common in CEA, can damage plant roots. “Then you have root disease pressures that can also come internally — a change in [growing] substrate can change the way the plant performs,” Gupton says.
AI will help identify diseases, as well as when a plant is thirsty or overly hydrated, when it needs more or less calcium, phosphorous, nitrogen. So, while companies amass their own hyper-specific data sets, Lowman foresees a time within the next decade “when there will be some type of [open-source] database that has the most common types of plant stress identified” that growers will be able to tap into. Such databases will “create a community and move the science forward,” says Lowman.
In fact, IALR is working on assembling images for just such a database now. On so-called “smart tables” inside an Institute lab, a team is growing greens and subjects them to various stressors. Then, they’re administering treatments while taking images of every plant every 15 minutes, says Lowman. Some experiments generate 80,000 images; the challenge lies in analyzing and annotating the vast trove of them, marking each one to reflect outcome—for example increasing the phosphate delivery and the plant’s response to it. Eventually, they’ll be fed into AI systems to help them learn.
For all the enthusiasm surrounding this technology, it’s not without downsides. Training just one AI system can emit over 250,000 pounds of carbon dioxide, according to MIT Technology Review. AI could also be used “to enhance environmental benefit for CEA and optimize [its] energy consumption,” says Rozita Dara, a computer science professor at the University of Guelph in Canada, specializing in AI and data governance, “but we first need to collect data to measure [it].”
The chef-farmers can choose from 45 types of herb and leafy-greens seeds.
Courtesy Babylon Micro-Farms
Any system connected to the Internet of Things is also vulnerable to hacking; if CEA grows to the point where “there are many of these similar farms, and you're depending on feeding a population based on those, it would be quite scary,” Dara says. And there are privacy concerns, too, in systems where imaging is happening constantly. It’s partly for this reason, says Babylon’s Ratte, that the company’s in-farm cameras all “face down into the trays, so the only thing [visible] is pictures of plants.”
Tweaks to improve AI for CEA are happening all the time. Greenswell made its first harvest in 2022 and now has annual data points they can use to start making more intelligent choices about how to feed, water, and supply light to plants, says Gupton. Ratte says he’s confident Babylon’s system can already “get our customers reliable harvests. But in terms of how far we have to go, it's a different problem,” he says. For example, if AI could detect whether the farm is mostly empty—meaning the farm’s user hasn’t planted a new crop of greens—it can alert Babylon to check “what's going on with engagement with this user?” Ratte says. “Do they need more training? Did the main person responsible for the farm quit?”
Lowman says more automation is coming, offering greater ability for systems to identify problems and mitigate them on the spot. “We still have to develop datasets that are specific, so you can have a very clear control plan, [because] artificial intelligence is only as smart as what we tell it, and in plant science, there's so much variation,” he says. He believes AI’s next level will be “looking at those first early days of plant growth: when the seed germinates, how fast it germinates, what it looks like when it germinates.” Imaging all that and pairing it with AI, “can be a really powerful tool, for sure.”
Scientists make progress with growing organs for transplants
Story by Big Think
For over a century, scientists have dreamed of growing human organs sans humans. This technology could put an end to the scarcity of organs for transplants. But that’s just the tip of the iceberg. The capability to grow fully functional organs would revolutionize research. For example, scientists could observe mysterious biological processes, such as how human cells and organs develop a disease and respond (or fail to respond) to medication without involving human subjects.
Recently, a team of researchers from the University of Cambridge has laid the foundations not just for growing functional organs but functional synthetic embryos capable of developing a beating heart, gut, and brain. Their report was published in Nature.
The organoid revolution
In 1981, scientists discovered how to keep stem cells alive. This was a significant breakthrough, as stem cells have notoriously rigorous demands. Nevertheless, stem cells remained a relatively niche research area, mainly because scientists didn’t know how to convince the cells to turn into other cells.
Then, in 1987, scientists embedded isolated stem cells in a gelatinous protein mixture called Matrigel, which simulated the three-dimensional environment of animal tissue. The cells thrived, but they also did something remarkable: they created breast tissue capable of producing milk proteins. This was the first organoid — a clump of cells that behave and function like a real organ. The organoid revolution had begun, and it all started with a boob in Jello.
For the next 20 years, it was rare to find a scientist who identified as an “organoid researcher,” but there were many “stem cell researchers” who wanted to figure out how to turn stem cells into other cells. Eventually, they discovered the signals (called growth factors) that stem cells require to differentiate into other types of cells.
For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells.
By the end of the 2000s, researchers began combining stem cells, Matrigel, and the newly characterized growth factors to create dozens of organoids, from liver organoids capable of producing the bile salts necessary for digesting fat to brain organoids with components that resemble eyes, the spinal cord, and arguably, the beginnings of sentience.
Synthetic embryos
Organoids possess an intrinsic flaw: they are organ-like. They share some characteristics with real organs, making them powerful tools for research. However, no one has found a way to create an organoid with all the characteristics and functions of a real organ. But Magdalena Żernicka-Goetz, a developmental biologist, might have set the foundation for that discovery.
Żernicka-Goetz hypothesized that organoids fail to develop into fully functional organs because organs develop as a collective. Organoid research often uses embryonic stem cells, which are the cells from which the developing organism is created. However, there are two other types of stem cells in an early embryo: stem cells that become the placenta and those that become the yolk sac (where the embryo grows and gets its nutrients in early development). For a human embryo (and its organs) to develop successfully, there needs to be a “dialogue” between these three types of stem cells. In other words, Żernicka-Goetz suspected the best way to grow a functional organoid was to produce a synthetic embryoid.
As described in the aforementioned Nature paper, Żernicka-Goetz and her team mimicked the embryonic environment by mixing these three types of stem cells from mice. Amazingly, the stem cells self-organized into structures and progressed through the successive developmental stages until they had beating hearts and the foundations of the brain.
“Our mouse embryo model not only develops a brain, but also a beating heart [and] all the components that go on to make up the body,” said Żernicka-Goetz. “It’s just unbelievable that we’ve got this far. This has been the dream of our community for years and major focus of our work for a decade and finally we’ve done it.”
If the methods developed by Żernicka-Goetz’s team are successful with human stem cells, scientists someday could use them to guide the development of synthetic organs for patients awaiting transplants. It also opens the door to studying how embryos develop during pregnancy.