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In 2014, a team of neuroscientists, including Dr. Earl Miller, the Picower Professor of Neuroscience at MIT, gave macaque monkeys a carefully standardized task: categorize visual dot patterns into one of two groups. As the animals learned, the researchers recorded brain activity, hoping to understand how learning changes neural activity. Nearly a decade later, Miller — alongside researchers from Dartmouth, including Dr. Anand Pathak and Prof. Richard Granger — gave the same task to a very different subject. It wasn’t a primate at all, but a computational model that the team wired to work like the real brain circuits that control learning and decision-making. Dr. Miller and his colleagues hoped it would produce patterns of neural activity similar to what they observed in the macaques. What they didn’t expect was that the model’s output would point them to something they had missed the first time around. “We saw some peculiar brain activity in the model,” Miller says. “There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn’t a quirk of the model — the monkeys’ brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.” The new work, published in Nature Communications, puts a name to these overlooked signals: incongruent neurons, or ICNs, and explores theories as to why a primate brain might want to keep alternate options in mind, even if they’re not the right ones at the moment. Beyond identifying a previously unrecognized class of neurons involved in learning, the study shows that the model behaves like a brain and generates realistic brain activity, even without being trained on neural data. The findings could have major implications for testing potential neurological drugs and for using computational models to investigate how cognition emerges and functions. Built like a brain Computational models use mathematical equations to express the electrical and chemical activity of neurons. In that sense, the model is “wired” to behave like a brain. Most existing models fall into one of two camps: those that are biologically accurate and those designed to perform cognitive tasks like learning and decision-making. Biologically detailed models are built to mimic a brain, and they can reproduce physiological activity such as neurons spiking and oscillating. But they don’t typically include the more complex circuitry involved in cognitive tasks like learning or decision-making.  On the other hand, cognitive models, including the neural networks that run AI, can reliably perform cognitive tasks like learning and categorization, but the underlying architecture is much simpler than a real brain. That means that they can’t tell you how a real brain performs these tasks — they just perform the task using other machinery.  “It was eerily similar to what we saw happening in the macaques’ brains,” Miller tells Big Think. So, if researchers want to use these models to predict how the brain performs cognitive tasks, they need models that are both built like a brain and able to perform cognitive tasks. That was the gap Miller and his colleagues set out to fill. In this case, Pathak and Granger, from Dartmouth, built a model of the corticostriatal circuit, a loop connecting the brain’s cortex, involved in perception, planning, and memory, with the brain’s striatum, which helps select actions and learn from feedback. The circuit is central to decision-making and learning, and it’s exactly what the macaque monkeys rely on during the visual categorization task. The corticostriatal circuit is also implicated in disorders ranging from Parkinson’s disease to schizophrenia. If the team could build a model that was biologically realistic and capable of…

First thing: This has nothing to do with what direction UNC should or should not take with its basketball program or Hubert Davis. That conversation doesn’t interest me right now. Second thing: This has nothing to do with what Davis should or should not have done with respect to staffing, recruiting, defensive tactics, offensive scheme, or game management. Those conversations don’t interest me right now. My top five moments as a UNC basketball fan: The Shot: NCAA Finals 1982 May’s Day: NCAA Finals 2005 K’s Last Stand: NCAA Final Four 2022 K’s Final Home Game: ACC Regular Season, 2022 The Coronation: NCAA Finals 2009 In a program with a history as rich as ours, I’m sure many of you have a very different top five. I’m not going to explain why these five have such a hold on my memory. Please note that I heard my mother curse three times in her lifetime: twice during the Bloody Montross game and once during Tyler’s broken nose game. The joy in her voice on the phone after winning those last two games against K is something I’ll never forget. For those two wins alone, I could be a Davis fan for life. I love how Hubert became a Tar Heel. Dean Smith and Roy Williams advised Davis to attend a mid-major out of high school and work on his game there. They had doubts about his athleticism and how that might limit his ability to contribute on a perennial title contender. During an in-home visit, Davis responded to those concerns: “You might be right that I can’t play at that level, but you won’t know for sure unless you give me a chance.” After a couple of days, Dean offered him a scholarship but warned him that he might not play. Davis played a little and then a lot. His junior year, Davis, King Rice, George Lynch, and Rick Fox spearheaded UNC’s first trip back to a Final Four since the 1982 national championship. That eight-year drought, combined with the rise of Coach K’s career at Duke, had many UNC fans loudly chomping at the bit to move on from Dean Smith. Ending that drought helped lay the groundwork that would become UNC’s second national title under Smith. Davis, the guy who had to plead for a spot on UNC’s roster, left the program as a first-round NBA pick. Davis played a huge role in righting the ship with that Final Four run. The year prior, UNC had finished the season unranked while Duke made its third straight run to the final weekend. The ’90-‘91 Final Four was a finger in the eye of a lot of hot take headlines and talk radio (yes, those existed back then) proclaiming Smith obsolete, UNC basketball irrelevant, and Duke the new king of the hill. Lynch was the more complete player. Fox lead the team in points and charisma. Rice, a player infamously booed on his home floor, was the redemption story. Chilcutt was the hard hat grinder down low. Davis? Hubert Davis was the surprise, the missing piece and final ingredient whose .625 field goal percentage delivered timely buckets again and again. He wasn’t the best player on that team, but Davis was its second leading scorer and my favorite — the player who recruited Dean rather than being recruited to UNC and who made the most of it. For that alone, I could be a Davis fan for life. I loved Hubert’s work on ESPN. ESPN had not gone full Stephen A. Smith by the time Hubert got there, but things were definitely trending in that direction. Davis was entertaining and insightful, but he never played to the audience. He didn’t warp who he was to get attention; he never became an act. Being a media figure means a constant deluge of consultant advice to “be more this, be less that.” The name of the game had become “bright contrasts,” polarizing opinions that reduced everything to an either/or choice, everyone either a villain or hero. Davis refused to play that game, and he remained a compelling analyst by remaining true to himself. It was in my mind the embodiment of a UNC education. I love how much Hubert loves UNC. I’m a romantic about most things, I freely admit…

This article is an early look at our upcoming special issue on Mastery. Check back in on January 28 to catch the full issue. After years of studies and six months in New York, I was convinced I’d mastered English. I was cracking jokes with American friends, binge-watching shows without subtitles, and even thinking in English half the time. Then I moved to London for my first job at Google, and suddenly, I felt like I’d never truly master the language. Colleagues used phrases I’d never heard. Cultural references flew over my head. I found myself nodding along in meetings, pretending to understand jokes that left me completely lost. It felt terrible. I was encountering the growing pains inherent to mastery, but everything I’d been told about getting good at something had set me up to misinterpret this growth as failure. Our cultural narrative about mastery is not just incomplete. It’s actively misleading, and we’ve mythologized mastery in ways that make people quit right when they might be breaking into new territories. The five lies we tell ourselves about mastery The problem starts with how we think about mastery itself. We carry a set of deeply ingrained assumptions about how it works — assumptions that feel obvious and true, but that are actually counterproductive. Here are the most damaging ones: Misconception #1: Mastery is a destination. We imagine crossing a finish line where we’ll finally “arrive” as experts. Watch any master craftsperson, though, and you’ll see someone still questioning their approach, still discovering new techniques, still experimenting and pushing into uncharted territory. Misconception #2: Improvement is linear. We expect steady, measurable progress: practice more, get better, repeat. Reality looks more like a stock chart: long plateaus punctuated by sudden jumps, with occasional dips when you’re integrating something new. Misconception #3: Mastery requires extreme intensity. Research shows that sustainable, consistent practice beats sporadic bursts of intensity. Someone who practices 30 minutes daily for a year will typically outpace someone who practices 3 hours once a week. Misconception #4: Technique is everything. We obsess over the mechanics: the “right” way to hold the instrument, the perfect form, the exact method. But while technique matters enormously, so do mindset, feedback loops, rest, and environmental support. Misconception #5: Mastery feels easy once achieved. This is perhaps the most dangerous myth of all. Even masters experience frustration and have to revisit fundamentals, but they’ve learned to find satisfaction in the process itself, not just the outcomes. So, if all our assumptions about mastery are wrong, what actually works? The answer lies in how our brains learn and adapt. George Wylesol Achieving mastery through experimentation Our brains adapt most rapidly when faced with novel challenges, not repetitive drilling. Perhaps most importantly, studies on what researchers call “desirable difficulty” show that struggle isn’t a sign that you’re failing. It’s a sign that your brain is forming new neural pathways. That’s why experts don’t just repeat what they know — they constantly experiment at the edge of their abilities. Instead of grinding through repetition, they treat every practice session like a mini-laboratory. Here’s what that looks like: Run tiny experiments. Let go of the idea of mastery as a destination. Instead, experiment with different approaches. A programmer might experiment for a few days with coding without looking at Stack Overflow or ChatGPT. A musician might practice scales for 10 minutes before touching any songs for two weeks. These tiny experiments let you test the boundaries of your knowledge while embracing the in-betweens. Design feedback loops. Create systems that help you notice what’s working. You might track which new words you actually use in conversation, photograph your work at different stages to see patterns in your process, or ask for…

Having lost both games on their road trip out to California, the North Carolina Tar Heel men’s basketball team comes into this week fully licking their wounds. Both games, and the couple before that too, have highlighted some bad habits for the team, especially on the defensive end. While many — quite possibly a majority of — fans are in panic mode, there’s still season left for this team to prove that they’re different. If they’re going to do that, I suggest they start tomorrow night against Notre Dame. The Fighting Irish certainly haven’t looked like a team ready to go on the road and win of late, as they come into this game having lost their last four. Now, one of those losses was somewhat controversial, but before that they did beat a Stanford team that just beat UNC, holding them to just 40 points in the process. However, Notre Dame is just 10-8 overall and ranked in the 70s/80s according to metrics like KenPom and Torvik’s. Considering what’s happened in recent games, the first thing we probably have to think about when it comes to the Tar Heels’ opponents is what they’re like on offense. According to KenPom, Notre Dame’s offense is fine but nothing special, ranking 108th in the nation. However on raw percentages, they’re solid, knocking down 36.2% of their threes — albeit on 23.7 attempts per game — which is very middle of the pack in terms of D1 teams. A fairly large part of Notre Dame’s recent struggles stems from the Irish missing leading scorer Markus Burton. After averaging over 20 points per game last year, Burton was going for 18.5 so far this year when he suffered a hand injury back in December. According to the most recent updates, Burton is expected to be out until February, so it seems unlikely that he’ll magically reappear for this game. That being said, a lot of UNC’s recent defensive struggles have come from struggling to shut down opposing guards, either while shooting or driving, and ND’s next two highest scorers are also guards. Jalen Haralson and Braeden Shrewsberry — son of head coach Micah — are Notre Dame’s other double-digit scorers. Shrewsberry is the dangerous one to watch as far as three-point shooting goes, as he’s made 42.7% of them on over six attempts per game so far this year. On the defensive end, ND rates a little better by the metrics, as their D is the 55th — at least at time of writing — best in the nation according to KenPom’s Defensive Rating. Opponents are scoring 69.3 points per game against the Irish on average. If teams can get into the lane and/or into their bigs, they can have success, as opposing teams shoot over 50% on two-point attempts against the Irish. That being said, ND has done a decent job of making that difficult for teams to do, as teams average just 30.9 two-point attempts per game against them, which is one of the better figures in the nation. Size-wise, Garrett Sundra is the only Notre Dame player over 6’7” who plays more than 15 minutes per game. With Caleb Wilson and Henri Veesaar, UNC should have somewhat of a size advantage, but it’s not as if they’ll be able to just walk all over the Irish bigs. After the past couple game, it’s frankly hard to know what just to expect from Carolina at the moment. This should be a game they’re favored in, but that didn’t help them in the last two. Notre Dame aren’t world beaters, but they certainly could burn the Tar Heels if UNC hasn’t figured out their (expletive).

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