The Performance Cortex: How Neuroscience Is Redefining Athletic Genius

by Zach Schonbrun

While basketball centers can hover near the basket, and tennis players can overwhelm with a serve, baseball hitters are playing one against nine in every appearance. There is a perceived threshold for how good they can be (.400) as well as a floor for how bad they can be before patience runs out (.200). The difference between .400 and .200 can seem like the difference between a skateboard and the USS Nimitz.

When the experts decided not to swing, that inhibitory urge also correlated with activation in the supplementary motor area (SMA), a dense blob of cortex behind where the logo might be on a batting helmet. Among other things, the SMA is implicated in movement initiation. Studies on Parkinson’s patients often reveal abnormal connectivity between the SMA and other brain regions, like the thalamus.

The story lays out the case for “the real reason for brains.” They exist, he argues, to produce movement. “You may think we have one to perceive the world or think,” he says. “And that’s completely wrong.” In Wolpert’s view, there is no other explanation. If you don’t believe him, you could consider the humble sea squirt, which is born looking almost like a tadpole, with a brain, a nervous system, and even an eye. Throughout its juvenile life, which is only a few days, it swims around the ocean, siphoning in planktonic food particles from the seawater it ingests. At some point, it decides to attach itself to a hunk of coral, at which time the sea squirt summarily digests its own brain and never moves again.

A better way to think about this is with tennis. When playing tennis, you have to decide where you think the ball is going to bounce in order to return it. Bayes’ rule gives you two sources of information in order to make this prediction. One is sensory evidence: You see the ball coming toward you. But your senses are far from perfect. Furthermore, there could be spin on the ball, or wind affecting the ball flight, or it could be moving so rapidly that you don’t get a clear idea of where it’s headed. Instead of taking the sensory input as a certainty, you instead consider it a likelihood—the probability of seeing the ball bounce in a certain spot. Your brain uses a second source of information: prior knowledge. Tennis serves are not uniformly distributed around the court. They are likely to be confined to certain areas, areas that I know from experience are where my opponent likes to hit the ball. There’s my prior belief. When I plug these calculations into Bayes’ rule, I arrive at a different, predicted location of the ball.

When you are just beginning, you carefully follow instructions. You concentrate hard on every movement, focusing on how to maintain balance while coordinating the churning of your legs. Eventually, as you get better at it, you can phase out those explicit strategies for a more automatized one. The instructions are called “declarative knowledge,” while automatization is said to be “procedural.” When your bike-riding gets to be procedural, you don’t have to think about it; you just do it. The skill sinks in. It becomes, well, like riding a bike. Motor researchers have a phrase for this stepwise pattern of learning, which they call “scaffolding.” The early instructions support the construction of the skill until it is strong enough to support itself on its own.

Actually, a good illustration of the interaction between procedural and declarative was H.M. Remember that with almost no short-term memory, he managed to improve his skill at mirror drawing. But did he do so without utilizing any declarative knowledge?

A few years ago, a Brazilian neuroscientist, Suzana Herculano-Houzel, tried to figure out who originally estimated that the human brain contained 100 billion neurons. Turned out, nobody knew. The figure—a round, easy-to-remember number, astounding without seeming implausible—simply ossified into fact over time. She figured out a way to verify it. She ground down the exhumed brains of animals using a “brain blender,” a Tarantino-esque device that sounds just as macabre as it is. Herculano-Houzel devised a method to count the neurons in her homogenized brain soup.

DeCervo was trying to use its insights about rapid perceptual decision-making to help baseball teams win baseball games, but, to their frustration, those teams were increasingly concerned about its footprint on the players’ already heavily loaded schedules. And players were concerned about being guinea pigs for testing. Some feel the information could only be used to hurt them in the future. And why spend 40 minutes with an EEG cap on your head if there is no immediate performance benefit associated?

Anyway, there is also a darker concern. Baseball injuries and ailments have long been treated as the team’s issue, and players are routinely given medical evaluations before they are accepted as part of any trade deal. But, in the case of medical information that is not so readily available, like EEG recordings, the situation gets murkier. Baseball has already found itself in hot water in recent years for conducting DNA tests on prospects from Latin American countries to properly determine their ages. But in a few years, this could begin to seem trite. At Sloan, in fact, one of the sponsors was a company called Orig3n, promoting DNA tests at the swipe of the cheek. They had a booth in the main hallway with cotton swabs by the bucketful. One National League coach outlined the potential conflicts that could arise from the proliferation of more of this kind of testing. “If I do a whole genome isolation and I find this guy has [the mutation in one of the genes associated with] early-onset Alzheimer’s, which we know can present as early as 25, what do you do?

But at this point, neural markers are not considered to be medical information. Instead, in July 2017, the MLB Players Association released a three-page appendix to the CBA stipulating that information gathered from wearable technology—including off-field devices for measuring performance—can remain confidential to the team and available to the player upon request. Teams have told me they consider brain data of the players to be proprietary, even though, technically, they are not the ones collecting it. DeCervo is, and they are permitted to keep the data, too.

It requires only seven small lightbulbs attached to the major joints for us to distinguish the form of another human in a darkened room from that of an ape, a bear, a bookshelf, a robot. This only works, however, if there is movement.

The engram may be just the goal of a trained behavior, and what gets the first finger moving is really the key.

A robot Ping-Pong player (look it up) needs only to be turned on in order to immediately start functioning. Is our infatuation with warming up just for calisthenics, so the muscles get loose? Actually, experiments have tried warming up the muscles in other ways, without taking the court or field, and found that the athlete usually ends up looking rusty and performing poorly. It is not just for calisthenics. A warmup is said to be more legitimately required for a cognitive recalibration, like tuning a guitar before a set.

This is not simply a technical problem easily mended with conditioning or flexibility. It is wired into the brain. But why? Together with his graduate student Nicola Popp, Diedrichsen looked at his sequences and noticed some patterns. There were some transitions between chunks that were easier to produce than others. A transition from 2 to 3, for instance, involved taps of the index and middle fingers. That’s easy. That was something the participants could do relatively quickly. But when 5 was involved, especially if the pattern went 5-5, repeated pinkie presses are hard. It is difficult to reproduce the requisite amount of force, and therefore participants usually have to pause to do it. So Diedrichsen rewrote the pattern to break down into chunks that he thought would naturally require pauses anyway. For one group, the pattern went something like this: 1-2 | 3-5-1 | 3-3-2 And for another group he gave a pattern of chunking that was not so ideal: 1-2-3 | 5-1-3 | 3-2-1 | 3-4 The bad pattern actually cost participants 200 to 300 milliseconds compared to the good pattern after two days.

Herein, then, may be that lesson (or a warning) again for coaches about the right proceduralization for the right tasks. “Some ways of chunking are biomechanically better, can lead to better performance,” Diedrichsen says. “Once the system picks up one way of conceptualizing it, it’s very hard to get out of it. They don’t spontaneously get out of it.” Bad habits are very hard to break.

The commotion was concentrated in a few select regions, such as the superior parietal cortex, broad swaths of the cerebellum and, indeed, the SMA. This is the primary acreage of a compelling and widely misunderstood causeway of neural activity. It is called the “action observation network” (AON), which sounds like a YouTube channel or another subsidiary of ESPN. But I like to think of the AON as a constellation, twinkling meekly against the backdrop of a dark and frenetic galaxy. It is the pattern that might signify the seat of perceptual expertise.

Saccades, those fast movements, transpire so quickly that we typically do not notice them. We can’t notice them.

A well-known illusion is called the “flash-lag effect,” popularized by David Eagleman, a cognitive scientist at Stanford. There are various versions, but one I like is from Michael Bach, a vision researcher at the University of Freiburg. A blue line is rotating clockwise around an axis, like the second hand on a clock. As it circles, another blue line flashes at several points along the rotation. The lines actually line up evenly, and if you slow the rotation speed down to 1 rotation per minute you can see that. But at 10 rotations per minute, what you see is the flashing line lagging momentarily behind the rotating line.

The film could not just roll continuously; in order for the picture to appear clearly and coherently, a shutter was necessary to open and shut multiple times upon each small strip, producing a subtle flickering effect. Because the shutter speed was so fast, we barely noticed it, and our brains would splice the images together without interruption. But in the early days of cinema, when the shutter speed was slower, the flickering was more apparent. An early slang term for movies was “flicks.”