MORE THAN TWO centuries later, this story about Queen Square is still popular in London guidebooks. And whether or not it’s true, the neighborhood has evolved over the years as if to conform to it. A metal statue of Charlotte stands over the northern end of the square; the corner pub is called the Queen’s Larder; and the square’s quiet rectangular garden is now all but surrounded by people who work on brains and people whose brains need work. The National Hospital for Neurology and Neurosurgery—where a modern-day royal might well seek treatment—dominates one corner of Queen Square, and the world-renowned neuroscience research facilities of University College London round out its perimeter. During a week of perfect weather last July, dozens of neurological patients and their families passed silent time on wooden benches at the outer edges of the grass.

 

On a typical Monday, Karl Friston arrives on Queen Square at 12:25 pm and smokes a cigarette in the garden by the statue of Queen Charlotte. A slightly bent, solitary figure with thick gray hair, Friston is the scientific director of University College London’s storied Functional Imaging Laboratory, known to everyone who works there as the FIL. After finishing his cigarette, Friston walks to the western side of the square, enters a brick and limestone building, and heads to a seminar room on the fourth floor, where anywhere from two to two dozen people might be facing a blank white wall waiting for him. Friston likes to arrive five minutes late, so everyone else is already there.

His greeting to the group is liable to be his first substantial utterance of the day, as Friston prefers not to speak with other human beings before noon. (At home, he will have conversed with his wife and three sons via an agreed-upon series of smiles and grunts.) He also rarely meets people one-on-one. Instead, he prefers to hold open meetings like this one, where students, postdocs, and members of the public who desire Friston’s expertise—a category of person that has become almost comically broad in recent years—can seek his knowledge. “He believes that if one person has an idea or a question or project going on, the best way to learn about it is for the whole group to come together, hear the person, and then everybody gets a chance to ask questions and discuss. And so one person’s learning becomes everybody’s learning,” says David Benrimoh, a psychiatry resident at McGill University who studied under Friston for a year. “It’s very unique. As many things are with Karl.”

t the start of each Monday meeting, everyone goes around and states their questions at the outset. Friston walks in slow, deliberate circles as he listens, his glasses perched at the end of his nose, so that he is always lowering his head to see the person who is speaking. He then spends the next few hours answering the questions in turn. “A Victorian gentleman, with Victorian manners and tastes,” as one friend describes Friston, he responds to even the most confused questions with courtesy and rapid reformulation. The Q&A sessions—which I started calling “Ask Karl” meetings—are remarkable feats of endurance, memory, breadth of knowledge, and creative thinking. They often end when it is time for Friston to retreat to the minuscule metal balcony hanging off his office for another smoke.

Friston first became a heroic figure in academia for devising many of the most important tools that have made human brains legible to science. In 1990 he invented statistical parametric mapping, a computational technique that helps—as one neuroscientist put it—“squash and squish” brain images into a consistent shape so that researchers can do apples-to-apples comparisons of activity within different crania. Out of statistical parametric mapping came a corollary called voxel-­based morphometry, an imaging technique that was used in one famous study to show that the rear side of the hippocampus of London taxi drivers grew as they learned “the knowledge.”¹

A study published in Science in 2011 used yet a third brain-imaging-analysis software invented by Friston—dynamic causal modeling—to determine if people with severe brain damage were minimally conscious or simply vegetative.

When Friston was inducted into the Royal Society of Fellows in 2006, the academy described his impact on studies of the brain as “revolutionary” and said that more than 90 percent of papers published in brain imaging used his methods. Two years ago, the Allen Institute for Artificial Intelligence, a research outfit led by AI pioneer Oren Etzioni, calculated that Friston is the world’s most frequently cited neuroscientist. He has an h-­index—a metric used to measure the impact of a researcher’s publications—nearly twice the size of Albert Einstein’s. Last year Clarivate Analytics, which over more than two decades has successfully predicted 46 Nobel Prize winners in the sciences, ranked Friston among the three most likely winners in the physiology or medicine category.

What’s remarkable, however, is that few of the researchers who make the pilgrimage to see Friston these days have come to talk about brain imaging at all. Over a 10-day period this summer, Friston advised an astrophysicist, several philosophers, a computer engineer working on a more personable competitor to the Amazon Echo, the head of artificial intelligence for one of the world’s largest insurance companies, a neuroscientist seeking to build better hearing aids, and a psychiatrist with a startup that applies machine learning to help treat depression. And most of them had come because they were desperate to understand something else entirely.

For the past decade or so, Friston has devoted much of his time and effort to developing an idea he calls the free energy principle. (Friston refers to his neuroimaging research as a day job, the way a jazz musician might refer to his shift at the local public library.) With this idea, Friston believes he has identified nothing less than the organizing principle of all life, and all intelligence as well. “If you are alive,” he sets out to answer, “what sorts of behaviors must you show?”

First the bad news: The free energy principle is maddeningly difficult to understand. So difficult, in fact, that entire rooms of very, very smart people have tried and failed to grasp it. A Twitter account² with 3,000 followers exists simply to mock its opacity, and nearly every person I spoke with about it, including researchers whose work depends on it, told me they didn’t fully comprehend it.

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