The Edge of Chaos

Deterministic Dynamics and the Mathematical Architecture of Consciousness

To understand the human brain, we must first strip away the colloquial definition of "chaos." In everyday language, chaos is synonymous with randomness, stochastic noise, or a complete lack of structure. But in physics and non-linear dynamics, chaos is highly structured, deterministic, and bounded. It describes a system governed by strict rules that nonetheless produces complex, non-repeating, and deeply unpredictable behavior.

The realization that the brain utilizes this specific physical phenomenon is one of the most profound paradigm shifts in modern neuroscience. The human mind does not function despite chaos; it functions because of it. By resting precisely on the mathematical boundary between rigid order and formless noise, the brain achieves the infinite flexibility required for consciousness, perception, and memory. Measuring this phenomenon requires translating the abstract philosophy of mind into the rigorous geometry of physics.

Self-Organized Criticality and the Sandpile of Cognition

In physics, systems tend to fall into one of two states. The first is ordered (subcritical), like a crystal lattice or a swinging pendulum. These systems are highly predictable, rigid, and resistant to change. The second is disordered (supercritical), like a heated gas. These are completely random, noisy, and incapable of maintaining structure or transmitting information coherently.

For decades, scientists believed a healthy brain was a highly ordered system. We now understand that a perfectly ordered brain is a dysfunctional one. Instead, the brain naturally tunes itself to a phase transition point known as Self-Organized Criticality (SOC), commonly referred to as the "edge of chaos."

To conceptualize SOC, physicists often use the analogy of a dripping sandpile (a model pioneered by physicist Per Bak). As you drop grains of sand one by one, a mound forms. Eventually, the pile reaches a critical slope. At this exact angle, dropping one more grain might do nothing, or it might trigger a localized slide, or it might cause a catastrophic avalanche that reshapes the entire pile. The system has organized itself into a state of maximum sensitivity.

The brain operates in this exact critical state. Sitting on the boundary between order and randomness maximizes the brain's computational capacity. A single electrical impulse from a neuron can either die out immediately or trigger a "neural avalanche"—a scale-free cascade of firing that sweeps across the cortex. This allows the brain to be incredibly sensitive to microscopic external inputs (the famous "butterfly effect" of chaos theory) without allowing that activity to spiral into an uncontrollable, seizure-like storm.

Strange Attractors and the Flow of Thought

If you map the continuous state of a chaotic system mathematically, you do not get a random scatterplot. You get a highly distinct geometric shape called a strange attractor. It represents a state where a system's trajectory circles around a specific pattern in phase space (a multidimensional map of all possible states), but never crosses its own path exactly twice.

Think of a strange attractor as a gravitational well for your thoughts. When you recognize a familiar face, recall a childhood memory, or execute a practiced physical movement, your neural firing patterns fall into one of these attractors. Because it operates as an attractor, the thought has immense coherence and stability—you recognize your mother’s face from any angle, in any lighting. However, because the attractor is "strange" (chaotic rather than perfectly periodic), the neural pattern never repeats with one hundred percent exactness.

This subtle, deterministic variability is crucial. It prevents the brain from getting locked into infinite, repeating loops. It allows you to hold a thought, explore its nuances, and then gracefully let the neural pattern collapse so you can transition to the next thought.

Metastability: The Freedom to Jump

Because the brain hovers at the edge of chaos, constantly traversing these strange attractors, it exists in a state of continuous metastability. It is a system characterized by "wobbling" stability, much like the aerodynamic profile of a modern fighter jet.

A commercial airliner is designed to be aerodynamically stable; if the pilot releases the controls, the plane naturally levels out. But this inherent stability makes the aircraft sluggish. A fighter jet, by contrast, is designed to be aerodynamically unstable. It is constantly attempting to flip out of control, kept in check only by thousands of rapid, computerized micro-adjustments per second. This controlled chaos allows the jet to execute violent, instantaneous maneuvers that a stable plane could never physically achieve.

The brain mirrors the fighter jet. By actively avoiding deep, entrenched stability, neural networks can rapidly assemble, process information, disassemble, and reconfigure themselves. This metastability allows human cognition to shift instantly from deeply analyzing a complex mathematical equation to reacting reflexively to a glass shattering in the next room.

Reconstructing the Geometry of the Mind

Recognizing that the brain relies on chaos is only half the battle; measuring it in a living human requires an extraordinary mathematical translation. Researchers cannot embed probes into every one of the brain's 86 billion neurons. Instead, they rely on Electroencephalograms (EEG) to record the electrical voltage fluctuating on the scalp over time.

The fundamental problem is dimensionality. The brain is a vastly multidimensional system, but an EEG channel provides only a one-dimensional time-series: a single undulating line of voltage. If you simply examine the raw waveform, you can extract basic frequencies like alpha or beta waves, but the chaotic dynamics remain hidden. To find the chaos, researchers must mathematically rebuild the multidimensional geometry of the system that cast that one-dimensional electrical shadow.

In 1981, mathematician Floris Takens provided the solution with a profound theorem. Takens' Theorem proved that you can reconstruct the topological properties of a complex, multidimensional phase space using just one single variable measured over time, provided the system is deterministic.

Researchers achieve this using a technique called Delay Coordinate Embedding. Instead of searching for new external data, they take the original EEG signal, denoted as x(t), and plot it against delayed versions of itself. For instance, the x-axis represents x(t) (the voltage right now), the y-axis represents x(t - d) (the voltage a specific number of milliseconds ago), and the z-axis represents x(t - 2d). By plotting these delayed signals against each other in three or more dimensions, the previously flat squiggly line loops back on itself, ballooning out to reveal the attractor—the actual, reconstructed geometric shape of the brain's hidden neural dynamics.

Quantifying the Butterfly Effect: The Lyapunov Exponent

Once researchers have successfully reconstructed the brain's geometric shape, they can apply physics equations to measure its level of chaos. Chaos is formally defined by sensitive dependence on initial conditions. If two brain states begin in almost identical configurations, do they remain similar over time, or do their paths wildly and unpredictably diverge?

To calculate this, researchers pick two points on the reconstructed attractor that are infinitesimally close to one another. Let us define the initial distance between these two points as \delta Z_0. They then let the system evolve forward in time and measure the distance between those same two trajectories at a later time, defined as \delta Z(t).

The continuous separation of these paths is governed by the equation:

\delta Z(t) ≈ e^{\lambda t} \delta Z_0

In this equation, \lambda represents the Lyapunov exponent. It is the exact mathematical measure of the rate at which the trajectories are pulling apart. Because a complex system like the brain possesses many dimensions, it actually produces a whole spectrum of Lyapunov exponents. Researchers rely on algorithms, such as Wolf's algorithm, to calculate the Largest Lyapunov Exponent (LLE), which serves as the dominant indicator of the system's state.

The precise value of \lambda reveals exactly where the brain sits on the spectrum of order and chaos. When \lambda is a negative number, the system is deeply ordered. The trajectories converge, meaning the system is locked into a rigid, predictable loop, insensitive to new inputs. In human neurology, the Lyapunov exponent drops into this negative, subcritical territory as a person falls into the deeper stages of unconscious sleep or a coma.

When \lambda hovers exactly around zero, the system is at the true edge of chaos. The trajectories neither perfectly converge nor exponentially diverge. The brain is critically balanced, allowing for maximum computational flexibility and information transfer without losing structural integrity.

When \lambda is a positive number, the system is formally chaotic. The trajectories exponentially diverge over time, meaning the brain is actively exploring complex, non-repeating states. Healthy, awake EEG data typically yields a positive Largest Lyapunov Exponent, proving that waking human consciousness operates fundamentally within a chaotic regime.

The Clinical Reality of Losing the Edge

This mathematical framework provides far more than theoretical insight; it serves as a crucial clinical diagnostic tool. We can clearly observe the necessity of this chaotic edge by analyzing what happens mathematically when the brain loses it.

When the brain suffers from too much order—a state of subcriticality—the chaotic dynamics fail and neurons synchronize perfectly. The system becomes dangerously rigid. In network physics, perfect synchronization in a complex system causes it to lock up. In clinical neurology, a massive wave of perfectly ordered, synchronous neural firing manifests as an epileptic seizure. Counterintuitively, the Lyapunov exponent actually decreases rapidly just before a seizure begins. The healthy, chaotic divergence of the waking brain collapses, the system becomes abnormally ordered, and the seizure takes over.

Conversely, when the brain slips into supercriticality, it suffers from too much disorder. The intricate strange attractors dissolve, and the system falls into pure stochastic noise. The brain can no longer maintain a coherent thought, bind sensory information together, or filter out irrelevant stimuli. This mathematical state closely mirrors the acute, fragmented phases of psychosis or advanced schizophrenia.

Ultimately, chaos is not a flaw in human cognition, nor is it merely biological noise. A purely ordered machine can only calculate what it has been explicitly programmed to calculate. A purely random system can calculate nothing at all. It is strictly in the mathematical tension between the two—in the beautiful, deterministic chaos—that adaptability, creativity, and consciousness are forged.

Summary

This presentation, delivered at Google by neuroscientist, entrepreneur, and former Googler Anne-Laure Le Cunff, explores the concept of the "experimental mindset" as a framework for navigating uncertainty, fostering innovation, and avoiding burnout. Introduced by Alison Parrin, head of the Google School for Leaders, Le Cunff shares insights from her research at King's College London and her personal experiences to advocate for shifting our definition of success from linear goals to curiosity-driven learning.

The Trap of Linear Goals and the Productivity Paradox

Le Cunff begins with a personal anecdote from her tenure at Google. Driven by imposter syndrome, she overcommitted, overplanned, and routinely ignored early signs of physical exhaustion. This culminated in a medical emergency where she was diagnosed with a severe blood clot in her arm that threatened to travel to her lungs. Strikingly, her immediate reaction to the urgent need for surgery was to check her calendar to ensure it did not conflict with product launches.

This extreme response highlights a broader paradox facing modern knowledge workers: while hired to navigate complexity and think creatively, individuals frequently respond to uncertainty by over-compensating, over-planning, and seeking absolute control. This pathology stems from societal reliance on "linear goals"—neat, structured paths such as multi-year career plans, long-term mortgages, or rigid project launch calendars. While linear goals offer comfort, they fail in a highly nonlinear world subject to changing market trends, disruptive technologies, and global crises. When plans inevitably fail, individuals resort to self-blame, hiding their setbacks, and suffering from chronic stress.

The Scientific Definition of Success

In contrast to linear approaches, scientists define success not as reaching a pre-determined destination, but as learning something new. Within a laboratory, an unexpected result is met with interest rather than self-judgment.

Le Cunff explains that this mindset aligns with the brain's natural perception-action cycle:

• Perception: Gathering data from the environment.

• Prediction: Formulating a hypothesis.

• Action: Testing the prediction.

• Correction: Adapting predictions based on the outcome of the action.

While the human brain evolved to minimize uncertainty for survival, thriving in the modern world requires replacing this survival-driven anxiety with systematic curiosity. Research indicates that approaching challenges experimentally leads to faster problem-solving and significantly reduces anxiety.

The Framework of Tiny Experiments

To bring the scientific method into daily life, Le Cunff introduces the concept of Tiny Experiments. The framework consists of three main phases:

• Self-Anthropology (Observation): Observing oneself, thoughts, and environments objectively without preconceptions. This is powered by metacognition (thinking about thinking) to recognize habits, energy drains, and emotional reactions.

• The Pact (Hypothesis & Testing): Creating a simple, clear protocol to test a change. A "pact" requires only two ingredients:

• The Action: What is being tested.

• The Duration: A set number of trials or a predefined timeframe (e.g., 10 days). Deciding the duration in advance prevents confirmation bias and prevents abandoning the experiment prematurely.

• The Growth Loop (Reflection): Analyzing the collected data, drawing conclusions, and sharing results. By pairing action with reflection, individuals enter "growth loops" rather than endlessly repeating the same cycles of behavior.

Dismantling Cognitive Scripts

Our choices are often dictated by deeply embedded cultural "cognitive scripts." Le Cunff highlights three major scripts that limit individual growth:

• The Sequel Script: Making decisions based purely on past actions (e.g., feeling obligated to stay in a career path simply because it matches one's university degree).

• The Crowd-Pleaser Script: Making choices primarily to secure external validation, praise, or admiration from colleagues, family, or friends.

• The Epic Script: The belief that every effort must be massive, world-altering, and globally impactful. This leads to profound unhappiness for those who have not found a singular "passion," and causes an identity collapse when a large-scale project (such as a startup) fails.

Implementing the Mindset in Leadership and Teams

Le Cunff emphasizes that adopting an experimental approach is highly effective for teams. Leaders can build psychological safety and "social flow" by shifting from the expectation of having all the answers to facilitating collaborative discovery.

Key strategies for teams include:

• Leading with "I don't know": Demonstrating vulnerability and curiosity when facing unknown variables.

• Creating a Sandbox: Encouraging every team member to run their own tiny experiments and report back.

• Monthly Curiosity Circles: Gathering teams to share what they experimented with, what worked, and what unexpected outcomes or failures occurred.

• Redefining Success: Valuing people for the quality of their questions rather than the sheer volume of guaranteed answers.

Practical Applications and Q&A Insights

• Neurodivergence: The experimental mindset is highly suited for neurodivergent individuals (such as those with ADHD), as its nonlinear, curiosity-driven nature provides a flexible alternative to rigid, curriculum-like environments.

• Tracking Tools ("Plus, Minus, Next"): To track experiments, Le Cunff recommends a three-column framework:

• Plus: What went well.

• Minus: What did not go well.

• Next: What to adjust or implement in the next cycle.

• Limits on Scope: To prevent burnout and ensure clean data, individuals should run only one experiment at a time, or at most two experiments in entirely separate areas of life (e.g., one at work and one related to personal health).

Transcript

ALISON PARRIN: Welcome to Talks at Google. I'm Alison Parrin. I have the privilege of heading up the Google School for Leaders, which is Google's internal center of excellence for all things manager and leadership development.

Today, I am delighted to welcome Anne-Laure Le Cunff, who is an award-winning neuroscientist and entrepreneur. She founded Ness Labs, a platform supporting healthier ways to work and learn, and her newsletter is currently read by more than 100,000 knowledge workers. Her research at King's College London focuses on the neuroscience of lifelong learning and curiosity, which I'm sure we're all very fascinated to learn more about.

Her book, Tiny Experiments, is a transformative guide for living a more experimental life, turning uncertainty into curiosity, and carving a path of self-discovery. Previously, she worked at Google as an executive on digital health projects such as Wear OS apps and Google Fit. Her work has been featured in Wired, Forbes, and the Financial Times. Please join me in welcoming Anne-Laure to Google.

ANNE-LAURE LE CUNFF: Thank you. I'm so happy to be here today. I actually started my career right here in this office. So being back and being able to share some of my work and research since I left Google feels particularly special. So thank you for having me.

I'm going to start by sharing a photo I have never, ever shared publicly before.

As I mentioned, I used to work at Google. I started in London, and then I moved to San Francisco. And it was my dream job. I also was constantly worried that someone would figure out that they had made a hiring mistake, that I didn't belong there with all of these smart people. And as a result, I responded to that uncertainty by saying yes to absolutely everything and by desperately seeking a sense of control.

My task list looked something like that. I was also trying to timebox every single gap in my calendar. I was overplanning, overcommitting, and I was the "yes girl" in the office. Anything you needed me to do, I would say yes to. I was dangerously dancing with burnout. But I loved my job, I loved my team, and I loved our mission. So I kept on pushing through.

Until one day, I was in front of the mirror, brushing my teeth, getting ready for work, and I noticed that my entire arm had turned purple.

So I went to the Google infirmary in Mountain View. And the nurse had one look at my arm and said, "You need to go to the hospital right now." I went to the hospital. And there, the doctor said, "We need to perform surgery as quickly as possible. You have a blood clot in your arm that's threatening to travel to your lungs."

And what did I do in that moment in front of the doctors? I said, "One second. I need to check my calendar." And right there, in the doctor's office, I opened my calendar, and I proceeded to thoroughly check that the moment we would schedule that surgery would not conflict with any of the product launches I was working on.

As you can see, my arm is fine. It healed. We actually took that photo right after the surgery. But this moment stayed with me. It made me reconsider our entire relationship to work. It made me ask, why is it that so many of us push ourselves to the edge in the name of productivity? And it made me question our relationship to uncertainty in our personal and professional lives.

This is actually a really interesting paradox. As knowledge workers, we are hired for our capacity to solve problems, to think creatively, to deal with complexity. But somehow, when we're faced with uncertainty, we have this tendency to want to feel in control, to seek certainty. And because of that, we sometimes push ourselves to extremes—such as checking your calendar when someone is asking when you can schedule your surgery.

To understand it, we need to go back to the very definition of success. Success is something we all want. We praise it. We admire it. We want it. We also spend a lot of time trying to measure it. We have KPIs at Google, OKRs, performance reviews. We also very often measure our own success based on the success of others. It's as if we were all looking at a giant leaderboard, constantly asking who's doing better, bigger, faster work.

So what is this success that we're all so ardently chasing? If you open a dictionary, the most common definition of success you will find is something like this: reaching a desired outcome.

Now, please indulge me while I break down this definition and we try to understand what is this definition we've all agreed on as a society.

Reaching—there's the idea of movement, progressing towards something. Towards what? Something we desire, something we feel like is positive. And that thing is an outcome, which implies that in order to be successful, you need to reach a specific destination.

This might seem like the only obvious definition of success, but it's not. This definition of success is actually based on something called linear goals. Linear goals are based on the idea that in order to be successful, you need to have a clear vision and a clear plan. And if you start looking around, you'll notice that those linear goals are everywhere in our life and in our work.

We have four-year university degrees, followed by a five-year career plan, followed by a 30-year mortgage. And even in the way we manage our work on a daily basis, we might say, "Here are the features we're going to launch in the fall. And here are our sales targets. And here are the marketing activities that we'll put into place in order to reach those linear goals."

And it feels really good to have this sense of certainty. But there's only one problem with linear goals: it's that we don't live in a linear world. We live in a nonlinear world.

You have market trends that keep on shifting, new technologies that can disrupt your industry, and, as we've seen recently, global events that can change everything overnight. And so things rarely go to plan. Instead of going from point A to point B in a very neat way, we find ourselves navigating this complex web of twists and turns with unpredictability at each crossroads.

And what do we do when we can't achieve our goals? We blame ourselves. And very often, we might even want to hide our failures from others. In today's world, in this nonlinear world, clinging to linear goals can only lead to frustration, to overwhelm, and very often to burnout.

So, you know who has a completely different definition of success? Scientists.

For a scientist, success is not reaching a specific destination. Success is learning something new. Whatever the outcome, whatever the results, they're able to look at it without self-blame or self-judgment.

And today, I want to convince you to start treating your work and maybe your entire life like a laboratory. I want to convince you that being curious is much more powerful than feeling certain. I want you to start imagining what it would look like if we approached any challenge as an opportunity for experimentation. And to do that, we're going to study how to develop an experimental mindset.

So the first thing is, how do scientists react when they get an unexpected result? When a scientist doesn't get what they expected, they don't say, "Shame, shame, shame, I'm such a bad scientist." No, they look at it and they ask themselves, "Huh, what's going on here? What can we learn from this?" And this is because they understand that we need failure to learn. Failure is an inherent part of learning.

What's interesting is that this kind of thinking, knowing that failure is a part of learning, is actually aligned with the way your brain works—or, should I say, the way your brain would like to work if you didn't force it to follow linear goals.

The way your brain works is based on something neuroscientists call the perception-action cycle. It's fairly simple. First, you're going to perceive some information in the environment, some data. Based on that data, your brain is going to formulate a hypothesis, a prediction. Sometimes that prediction is correct, and it's great. Sometimes the prediction is wrong, and that's okay, too. As long as you're still alive and you're not dead because of that wrong prediction, your brain is going to use that new data, that new information, and make a new prediction. And this is how you learn through experimentation.

Where it gets a little bit more complicated is that your brain is also optimized for survival. So it's trying to reduce uncertainty as quickly as possible. And that makes sense from an evolutionary perspective. If you think back on our ancestors and the environment in which they were evolving, the more information you had—whether it's, "Where are the resources?" or "What's that weird noise in the bushes over there?"—the more you knew, the more certainty you had, the more likely you were to survive.

But I think we all agree that whether it comes to our work, our relationships, or our health, us modern humans want more than just surviving. We want to thrive. And in order to do this, we need to replace this desperate need for certainty with curiosity instead.

You're still using your perception-action cycle. But instead of trying to resolve uncertainty as quickly as possible, you're using that uncertainty as an opportunity to learn and to grow. What's amazing is that there is research showing that when you approach challenges in this curious way, in a more experimental way, not only are you going to find solutions faster, but you're also going to experience less anxiety and stress in the process, which is pretty neat.

The reason why scientists are so good at this, at having this experimental mindset, is not because they're smarter than all of us. It's because they've been trained to do so.

For anyone who has studied science at school, you probably remember this experimental cycle. And again, it's quite simple:

• You start with observation, where you ask, "What is the current situation?"

• Then, based on that current situation, you formulate a hypothesis. You ask, "What could be different?"

• Then you start data collection. You test that hypothesis.

• And finally, you analyze the data. Based on those results, you update your observations so you can design your next experiment.

This is the experimental cycle. It can only work when you pair action with reflection. You need to do something, look at the result, and then change the way you behave based on that information that you just collected.

When you use them all together, they form an operating system for how to develop an experimental mindset:

• First, knowing that failure is an inherent part of learning;

• Believing that curiosity always, always beats certainty;

• And always pairing action with reflection, reflection with action.

When you use this operating system, not only are you going to be able to navigate uncertainty in a smoother way, but again, you're going to be able to keep your sanity in the process and not feel as stressed and anxious.

The nice thing about this experimental cycle that I just showed you is that you don't need a lab to use it. You can actually run your own tiny experiments for any challenge that you're facing in your life and in your work. What I'm about to show you is, in essence, a very simple way to take the scientific method out of the lab and to apply it to any area of life and work, so you can go from that free-floating anxiety to systematic curiosity.

I know that the kind of people who would be interested in this talk are the kind of people who are probably problem solvers, who like getting things done, and who are quite creative. I know it's exciting to execute on something, but not so fast. When you want to design an experiment, it always, always starts with observation.

I like to call this self-anthropology. Just like an anthropologist goes and studies a new culture with no preconceptions whatsoever, you can actually study the way you think, the way you live, the way you work, and pretend that you don't know anything about the way things are done. Really take notes and ask yourself, "Why are we doing things the way we are?"

This is really an exercise in paying attention—paying attention to how things are so you can start imagining how they could be. And this is what will help you plant the seed of a hypothesis. You start with observation, which then allows you to imagine something that you might want to try.

Then you're ready to create your mini protocol for experimentation. When you want to experiment, you don't have to have a full, complicated experiment like a scientist would in the lab. You only need two ingredients:

• You need to know what you're going to test.

• You need to know the number of trials (the duration).

You need to know the action and the duration. And that's it. This is your protocol for experimentation.

I call this a "pact" because it's a commitment to curiosity. It's a commitment to collecting the data and withholding judgment until you have the results. You say, "I am going to commit to trying this thing for this duration."

It's very important to commit to the duration before you get started. First, you need several trials to know if something is working or not. If not, it might be just a coincidence. Second, if you don't commit to your duration in advance, you might be tempted to stop the experiment in the middle if you're not seeing what you want to see. This is why scientists decide the number of trials in advance. This way, you avoid confirmation bias—finding the result that you actually want to find—and you withhold judgment until the end, when you can actually look at it and analyze all of the data together.

I want to show you how flexible this approach is. You can use tiny experiments for literally anything. You can experiment with acquiring new skills, trying new tools, doing new research, or connecting with new people. Here, I've focused on work-related examples, but you can actually use tiny experiments in literally anything. I've seen people run tiny experiments to experiment with their health, meditation, creative hobbies, and even with dating. So it works with literally anything.

The last step, once you're done collecting your data, is to reflect on the results. This is the moment where you take some notes, where you might want to discuss it with other people, and where you share with others what you learned. This is the last part of the experimental cycle, and this part is extremely important because this is what allows you to learn from the experiment and to close this loop. That way, you can implement whatever you learned into the next cycle of experimentation.

This is what allows you to not just keep going in circles, but to really grow through the cycles. This is what people call "growth loops"—where you grow through each loop that you close, implementing the data into the next experiment.

How do you embody this experimental mindset? How can you actually learn and lead like a scientist?

The great thing about tiny experiments is that you actually don't need to get any kind of buy-in from anyone. You just need to notice that maybe something might be worth trying, and that's enough to design a tiny experiment.

That being said, we can actually grow and learn better and much faster when we experiment together. To do this, we need to reimagine some of the ways that we envision leadership. Whether you're leading a project or a team, you might be putting a lot of pressure on yourself to look like you know where you're going, to look like you're the expert, and to look like you have all of the answers.

But one of the most powerful things that you can do as a leader when faced with uncertainty is saying, "I don't know, but let's figure it out together." This is how you open a space for experimentation and for learning in public, where everybody is learning together, including from our failures.

Even better, you can encourage people around you—actively encourage them—to design their own tiny experiments. This way, you can unlock "social flow," where that information is flowing between team members and everybody can grow together.

For instance, what might it look like if, with your team, you were hosting a monthly curiosity circle where everybody would share their experiments, what worked, and what didn't? This is a great way to learn together and to create that safe space for experimentation as a team.

For all of this to work, though, we need to redefine success, not as a fixed destination that is based on linear goals, but as something that we learn together. Success is learning something new.

Ultimately, this shift in mindset is all about defaulting to curiosity. It's about learning to fall in love again with problems. It's about letting go of the fear of failure, the imposter syndrome, and analysis paralysis. It's about internalizing the belief that if you approach it with curiosity, any challenge can be an opportunity for growth and discovery.

Tiny experiments can lead to big changes. Imagine a culture where it's completely normal to walk around and to ask people, "What have you been experimenting with? What did you learn? What was your latest failure?" Not only would this be the kind of culture where there's more space for innovation and imagination, but that would also be the kind of culture where we value people not based on the quantity of answers they provide, but based on the quality of the questions they ask.

And this—this is the power of an experimental mindset. Thank you.

ALISON PARRIN: Thank you for such a bold invitation to consider how we are operating and what we might need from ourselves in a world that is consumed with certainty, knowing, and specific goals. I see such possibility and opportunity in the ideas that you share. I'm really excited that we have this opportunity to be able to discuss them together.

So let's start with this notion of certainty. You talk about how we are wired for certainty, and I think that's one of the things that we see. What are ways in which we can become more comfortable with uncertainty? Experimentation is obviously one route, but how should we be thinking about that and just embracing that discomfort?

ANNE-LAURE LE CUNFF: I think the first step is to acknowledge the fact that it's completely normal to feel anxiety when we're faced with uncertainty. Again, that's what our brains are designed for: to reduce that uncertainty. Whenever we're faced with a situation where we're not quite sure what's going on, what the threats or risks are, or who the other players are, our brain wants to reduce that uncertainty as quickly as possible.

So I think there is sometimes a lot of self-blame around uncertainty, where you feel like, "How come everybody looks so comfortable and I'm so scared?" That's the first step: knowing that that's normal, and it's probably the case that other people around you are just a bit better at hiding it. We're all feeling scared when we're uncertain. That's step number one.

And number two—this is why the book is called Tiny Experiments. We don't have to necessarily go for something really big and scary straight away. We can start with something very small. This is how you start building that muscle of playing with uncertainty and having a relationship with it that is similar to a scientist's. When they see something they don't understand, they actually light up. They feel like, "Ooh, juicy. There's something interesting here."

You start tiny. You start by looking at little things you don't understand. The more you do this and the more you experiment, the more you're going to find yourself in situations where you're out of your depth, but somehow, you feel like that's exciting.

ALISON PARRIN: One of the things you talk about there is the importance of observation and seeing different things and how you're reacting to them. How can we improve our skill of observation and do it more frequently?

ANNE-LAURE LE CUNFF: There's a uniquely human capability called metacognition. Scientists love jargon, but it really just means "thinking about thinking." We know that most mammals are able to think. Anyone who has a pet knows that—you look at a cat or a dog, and you know they can think, right? But as humans, we're able to observe our own thoughts, our own emotions, and our own behaviors.

I think this is a great way to start with observation: by turning that eye, that attention, towards yourself. We're usually better at observing the outside world. We're happy to observe what's going on, take a few notes, and share them with the team. But it's a bit more uncomfortable to just observe how we're feeling—the tension, the uncertainty, and the anxiety that we can have when we're navigating challenging moments at work and in our personal lives.

So something very simple that anyone can do is just taking a little bit of time every day to write a few notes and just observe: How was today? How did you feel? Not just the external measures of how things went, but how did it feel internally? By practicing doing this, you'll become a lot better at naturally observing how things are around you before making any decision.

ALISON PARRIN: So journaling and reflection—is that best done individually, or can I do it with other people? Is there a better way?

ANNE-LAURE LE CUNFF: The better way is the way you actually do. Journaling is great; there's so much research showing how good it is for your mental health and your creativity. But the fact is, lots of people don't like it. So that's why I tell people, if that's not working for you, there are lots of other ways to engage with active observation.

If, for you, it's finding either a friend or a colleague you feel quite close to and saying, "Hey, once every couple of weeks, let's grab coffee together—and this is just to share how we're doing. That's it." You can talk about work, about mental health, or about creative projects you're exploring. When you verbalize what is going on in your mind and with your emotions, it really forces you to understand and articulate them. Whether you do this through writing in a journal or through talking with someone, it doesn't really matter, as long as you do it.

ALISON PARRIN: Got it. I can imagine people wondering, in a world that already feels very full and somewhat overwhelming, "I just don't have time for that. I can't find the time, and I'm not really sure why that's going to be beneficial." What would you say to them in that context?

ANNE-LAURE LE CUNFF: I would say, just experiment with it. That's why my book is actually not that prescriptive in terms of how you implement these things, because I think it looks different for everyone. The idea here is to experiment with different ways for you to pay attention to how you feel, your productivity, how you work, communicate, lead, and relate to other people, and then run tiny experiments so you can see what works and what doesn't. You can adjust your approach and adapt instead of sticking to the same rigid approach over and over again.

For some people, that might look like taking only two minutes a week to reflect on the important things that came up in the past week. So I don't believe that nobody has time for it. We also know that we spend a lot of time doing other things that are not so good for us. If you took five minutes out of scrolling on your phone and used that for self-reflection, you would probably benefit a lot from it. It's rarely a matter of not having enough time; it's more a matter of not having found the right way for you to do this. You can't find that way just by reading a book. You actually need to experiment and see what works for you.

ALISON PARRIN: The key is the word "tiny"—that it really can be small.

ANNE-LAURE LE CUNFF: Yes, at least when you start. It can grow bigger if something you like actually works really well for you.

An example of a tiny experiment that I actually run myself: I used to be terrified of public speaking. I'm talking terrified as in stomach cramps and nightmares for weeks before I had any kind of presentation. So I asked myself, "What is the tiniest experiment I could run around this?"

I decided that, for the next 10 days, I was going to record myself with my phone for one minute, and post it on Instagram unedited. One minute. It was absolutely terrifying. But after a few days, I could already feel like opening my phone and starting to record myself was less and less scary.

After I finished that experiment—the set duration for this pact—I said, "Actually, I think I kind of liked it towards the end." So I asked, "What is a slightly more ambitious version of this experiment?" For this next phase, I decided that every month, I was going to find an online workshop that I could present. I wasn't ready to go on stage in person yet, but from the comfort of my home in my pajamas, I could do this.

Once I completed that duration, I moved to the experiment I'm currently doing: once a quarter, I need to find something quite big and scary where I need to be on stage in person. I'm already starting to feel a little bit less anxious.

So you can keep them tiny, and you should certainly start tiny. But if, in the process of experimenting, you discover that something is quite interesting and you want to grow through this and experiment more, you can also make them a little bit bigger.

ALISON PARRIN: Got it. I appreciate the fact that you've experimented in that way, because by doing that, you've shared with us the gift of your knowledge. We would never have that had you not been able to do that. So thank you.

One of the things I find fascinating when I looked at how you were describing experiments was the fact that there isn't actually a hypothesis there in the visual. It says, "I'm going to do X by X or for X period of time," but there isn't a statement of what you expect to learn. Can you talk more about that lack of the predefined thing that you might learn, and what that opens up?

ANNE-LAURE LE CUNFF: Yes. I didn't want to put the entire book in the presentation, so I skipped over that part a little bit. But there is a hypothesis, just not exactly as a scientist would write one in a paper. When you run an experiment, the hypothesis is usually along the lines of, "This is going to work," or "This is not going to work."

For example, for me, I was very scared of public speaking, and I had the hypothesis that maybe starting by recording those little videos might help. But you can also run experiments with the hypothesis that something is not going to work.

A personal example: a great way to find experiments is when you hear yourself saying something that sounds like you have a fixed mindset. I was talking with someone about meditation, and I said, "I'm so bad at meditation. It doesn't work for me. I tried." We all know those apps with the 10-session onboarding for 10 days. I had never managed to get past day three. That's how bad I was.

When I heard myself say this, I thought, "Oh, wait a minute, that's interesting—fixed mindset here." So how could I be more experimental with this? I designed an experiment, and I started fully convinced that it would not work. But I wanted to experiment anyway, collect the data, and see the result.

I committed to meditating every morning for 15 days. I decided to actually run this experiment in public. I kept a public Google Doc where, every day after I meditated, I wrote some notes and shared it online. I had a lot of people leave comments and give me advice. When I wrote, "Why is it itchy everywhere? Why can't I stay still?" people replied, "That's completely normal. Here are some techniques."

Not only did I complete the experiment, but I actually enjoyed it. I ended up being wrong. Now, I don't meditate every day—it's not that kind of miraculous story—but it's part of my toolkit now. If I feel particularly anxious, I'll sit for 15 minutes and meditate, which was something I could not have imagined before. So you can absolutely have a hypothesis, even if it is simply, "I think this is not going to work, but I still want to try it."

ALISON PARRIN: In that example, the idea of public accountability sounds like it was powerful. What role does accountability play with experimentation?

ANNE-LAURE LE CUNFF: You can run your experiments on your own; you don't have to share them with anyone. But it can really help to add this layer of learning in public, especially if it's something where you have quite a bit of personal resistance.

Maybe you've tried it before. Maybe it was a habit you tried to build in the past, and you couldn't do it. With habits, I find it completely crazy that we pick a new habit and say, "I'm going to commit to this for the rest of my life," when we've never even tried it before. You should run a tiny experiment first to see if it works, and if it does, then turn it into a habit.

Learning in public and having that accountability can be helpful to actually stick to it and collect the data. I highly recommend that if you're running an experiment where you feel like you'll be tempted to quit in the middle, do it in public. That's going to be helpful.

Second, in the spirit of shared knowledge and generosity, if it's an experiment where your friends, family, or team might benefit, you can share it with them. What's really important is to remember that it's not just about sharing the experiments that worked, but also the ones where you got an unexpected result. There is a lot of value in saying, "Hey, I tried this thing. It doesn't work. Don't do it." You're saving people a lot of time and energy by doing this, and it can be an amazing contribution. So learning in public is completely optional, but can be really helpful.

ALISON PARRIN: As a leader of a team, how would you encourage me to try this with my team?

ANNE-LAURE LE CUNFF: I would ask each person on your team to design a tiny experiment. You could pick a theme, a product, or a challenge that you're facing as a team and say, "For the next month, let's all run an experiment. And let's all report back at the end of the month."

Everybody can share what worked and what didn't. When you have this kind of scaffolding for experimentation, you're creating a sandbox—a playground where it's okay, and even encouraged, to share unexpected results. Instead of saying, "This is success, this is where we must go," you start from a research question or a hypothesis: "We think this might work, but we don't know. Let's try it, collect the data, regroup, and learn from each other." This is a very simple and tactical way to do it.

At a more strategic level, it's about leading by example. This means being okay with saying, "I don't know," but matching it with, "Let's figure it out together. Is there an experiment we can design to find the answer?" That is how leaders can truly encourage their teams to develop an experimental mindset.

ALISON PARRIN: Yes, and I think that comes along with a lot of courage, in terms of being able to say, "You know what? I don't know." That's certainly one of the things we work with our leaders on here.

One of the topics I found fascinating in your book was the idea of cognitive scripts. I'm wondering if you might be able to share a few thoughts around those.

ANNE-LAURE LE CUNFF: This is a fascinating concept based on an elegant study from 1979. Researchers basically asked people, "If you are put in this specific situation, how do you act? What do you do?" What they found is that most people, when placed in the same scenario, end up acting in exactly the same way.

This is highly useful in many scenarios. For example, if you go to the doctor, you know you are supposed to wait in the waiting room until they call your name, then go into the office, and then they check what is wrong. If the doctor comes out of their office into the waiting room and asks you to undress in front of everybody, you would feel extremely uncomfortable. That's because they've gone off-script. There's a script we've all agreed on, and the doctor is not following it.

So we have all of these scripts that are very useful for functioning as a society—going to a doctor, going to a restaurant, and so on. The problem is that scientists discovered we also follow these cognitive scripts in many other areas of our lives: how we choose our jobs, our careers, how we dress, the way we talk, and the subjects we study.

In the book, I share three big cognitive scripts—or rather, buckets of scripts—that are useful to notice in your life or work:

• The Sequel Script: This is when you make decisions based entirely on the decisions you made in the past. You feel like whatever you decide to do today needs to make sense based on what you did yesterday. This is why a lot of people, when they finish university, only look at jobs that align with their studies instead of considering other options. It's also why we rewrite our CVs when applying for new jobs to make it look like we had a neat, intentional narrative all along.

• The Crowd-Pleaser Script: This is when you make decisions based on what you think will make the people around you happy—whether that is your team, friends, family, or spouse. We often limit the scope of our decisions because we only go for things that will be praised, admired, or recognized by others.

• The Epic Script: Inspired by Hollywood, this is the idea that whatever you do, it must be massive, highly impactful, and save the world—and anything less than that is a failure. This one is particularly insidious because, as a society, we've decided this is a script we should all follow: Follow your passion, follow your dream, change the world. Because of this, a lot of people feel miserable because they haven't found their passion yet, wondering why everyone else has figured it out. Another problem is that people put all of their eggs in one basket. When that one thing doesn't work out, their entire sense of self-worth and identity collapses. We see this a lot with startup founders when their company fails, leaving them depressed for months or years.

I highly encourage everyone to think about these scripts and identify different areas where you might be following them at a subconscious level. Use your observation skills and ask, "Is there a way I could do things a little bit differently? Is there a way I could experiment with an approach that is slightly off-script?"

ALISON PARRIN: Yes, I think we have a lot of homework to do when we get back, in terms of thinking about how some of these patterns show up. I can see a lot of those patterns in my own stories.

In a moment, we're going to move to Q&A. If anybody has any questions, please begin to line up behind the microphone. While you're doing that, I will ask you one final question. I am excited to leave here this afternoon and begin a tiny experiment. What advice do you have?

ANNE-LAURE LE CUNFF: I'll go back to starting with observation. I recommend doing a 24-hour exercise in self-anthropology.

Choose a day during your week that is a pretty typical day—so don't do this on a Saturday when you are going to a festival. Do it on a normal workday. Just like an anthropologist, start taking little notes throughout the day, in between meetings or tasks, and ask yourself: What is giving me energy? What is draining my energy? When do I feel particularly curious and excited?

You'll very quickly notice patterns. Maybe you just finished a meeting and were particularly excited, wanting to spend more time on that topic. Equally, maybe you just had a conversation with someone and all you wanted to do was crawl into bed and disappear. Make little notes of these moments—the good, the bad, and the challenging.

When you notice these patterns, they can become the seed of a hypothesis. How can I do things differently? Maybe you've been running your meetings exactly the same way forever without ever questioning why. Maybe you've been working on the exact same project for years without questioning if there might be another project you'd enjoy more. Maybe you can experiment with your time management, calendar, or productivity.

So that's my advice: pick one day, do 24 hours of self-anthropology and observation, and then choose one tiny experiment with one action and one duration. Keep it tiny, don't go for something massive at first, and see what you learn.

ALISON PARRIN: I love that. Thank you. We have our first question from the audience, please go ahead.

CHAUNCEY: Hi, I'm Chauncey. I heard you were an APMM (Associate Product Marketing Manager) when you were here previously. I was too, and graduated recently, so it's really cool to see how far you've come and the great work you're doing. I'm actually going through a big life stage at the moment where I think this book is literally the Holy Grail; my mind is blown. I had one question around neurodivergence. How do you see these experiments affecting those who might move through the world with their brains wired a little bit differently?

ANNE-LAURE LE CUNFF: This experimental mindset is actually perfect for neurodivergent people. My job at King's College London is actually based at the ADHD Research Lab. Although this book is not explicitly about neurodiversity, that was always on my mind while writing it.

What you find with neurodivergent people is that they often have a more nonlinear way of thinking. It can feel incredibly constraining and uncomfortable for them to follow rigid, step-by-step curriculum-like approaches. The experimental framework is perfect because it starts with curiosity—which is typically very high in neurodivergent individuals—and leverages it to explore, experiment, and organically discover what works and what doesn't.

CHAUNCEY: Amazing. Thank you so much.

ANNE-LAURE LE CUNFF: Thank you.

ALISON PARRIN: We have a couple of questions coming in on Dory, but we'll take one more in the room first.

SPEAKER 1: Hey, Anne-Laure, lovely to see you. I used to work with Anne-Laure many, many moons ago, so it's wonderful to see you again. I was actually thinking of a very similar question to the one just asked, so I'll ask a different one instead. I'd love to know, what have been some of your own personal favorite tiny experiments, and have there been any surprising learnings along the way?

ANNE-LAURE LE CUNFF: I love the meditation one because I love the experiments where I start out convinced that it's not going to work, and then I'm proven wrong. As a scientist, both in and out of the lab, being proven wrong is the best feeling.

Another one I did recently was very simple but extremely good for me. While I was on my tour for this book, I had to record a lot of podcasts. At some point, I realized that on some days, I was indoors from 8:00 AM to 8:00 PM with back-to-back meetings and zero breaks. So I thought, "Maybe taking some walks will help."

I made a commitment—which is key to the tiny experiment format—and said, "I'm going to take a 20-minute walk every day for the next 20 days, and see if it helps." I did that for the last 20 days of my book tour. This time, I was not proven wrong; it really, really helped my mental health.

This just goes to show you that experiments don't need to be complicated or groundbreaking. You don't need to reinvent the wheel; it can be something very simple. Thank you.

ALISON PARRIN: One of the popular questions on Dory: "Do you recommend specific practical tools, like journaling, specific apps, or mental models, for tracking, managing, and reflecting on experiments?"

ANNE-LAURE LE CUNFF: There is a very simple tool featured in the book that is helpful for tracking, reflecting, and deciding what to implement in your next cycle. If I had named it while I was still working at Google, the name might be snazzier, but I came up with it on my own: it's called Plus, Minus, Next.

It features three columns:

• Plus: You write down everything that went well.

• Minus: You write down everything that didn't go so well.

• Next: (with a little arrow) What you want to tweak, adjust, or implement in your next cycle based on what you just learned.

I usually use this as a weekly review for my experiments on Sunday evenings or Monday mornings. If you are running a short, daily, highly intense experiment, you can do it every day since it only takes a few bullet points per column.

The nice thing is that if you like to do some form of annual review, you can look back at all of these templates at the end of the year and see all the experiments you've run over the past 12 months. It's a wonderful tool for reflection.

ALISON PARRIN: Love it. Thank you. Next question in the room, please.

SPEAKER 2: Hi, thank you. Joffrey here. My question is around the current climate. I think everyone knows that the company and a lot of departments have gone through a lot of changes recently, and anxiety around job security is at a certain level. Do you have any tips on how to influence leadership or the culture in general to make it more "failure-friendly"?

I feel like the "scripting" to avoid failure is stronger than ever right now. Sometimes we are subtly encouraged not to report on certain numbers if they don't look good, and to shape the narrative to look a little better. It would be wonderful to influence the culture so we can actually learn from each other and treat failures as a good thing.

ANNE-LAURE LE CUNFF: Yes, absolutely. This is why it is incredibly helpful to frame any project that has a lot of uncertainty as an "experiment."

The issue arises when we have a highly uncertain project and we link it directly to a rigid, linear goal or a specific destination. When it doesn't work out and we don't reach that destination, the exact behavior you described happens: we try to construct a post-hoc narrative explaining why it failed, why nobody is to blame, and how we'll do things differently next time. But we aren't truly learning because we're just trying to hide the failure and make it look like a success. The incentives get skewed, and the process is no longer designed for learning.

If you start from the very beginning by explicitly stating, "This is just an experiment. Here are the parameters. We think this is going to work, but we aren't entirely sure. We will run it for this set duration, and then report back on exactly what worked and what didn't," you completely transform the definition of success. It shifts from a binary outcome—where you either succeeded or you failed and have to hide it—to a collaborative process where the sole goal is to learn something together.

SPEAKER 2: Thank you.

ALISON PARRIN: Next question, please.

SPEAKER 3: Hi, Anne-Laure. My question is about the complexity of internalizing this experimental mindset. We all have different baselines—some have a genetic propensity to be more anxious or crave certainty, childhood experiences can shape our associations with risk, and then we have the prefrontal cortex telling us we can reason through this and change.

How malleable is this trait? I know practice is essential, but do we eventually have to accept our personal limits with uncertainty, or can we keep practicing and eventually become fully, comfortably experimental?

ANNE-LAURE LE CUNFF: This is a great question, because a common misconception about the experimental mindset is that it's designed to completely eliminate uncertainty and anxiety. It isn't. Anxiety is a completely natural evolutionary reaction, and it is incredibly difficult to get rid of entirely.

What you want to do instead is notice it, accept it, recognize that it is perfectly normal, and then design an experiment around the challenge anyway. By converting your anxiety from paralysis into active experimentation, you naturally reduce the uncertainty. You are never going to eliminate the fear of uncertainty completely—that's simply not possible. But experimenting gives you a sense of agency. You can say, "This is scary, and I don't know what's going to happen, but I have agency. I can experiment and discover my own answers."

ALISON PARRIN: Love that. Thank you. I think we have time for one last question.

SPEAKER 4: Hi, Anne-Laure, thank you. I was reading your chapter, "A Deeper Sense of Time," right before the talk, and I completely agree with your views on how we take productivity hacks too far.

I wonder, could designing tiny experiments and trying to get good at things in this structured way be interpreted as just another productivity hack? Is there an upper limit to this? Is it possible to do too many experiments and inadvertently push yourself back into burnout? How do those two ideas coexist in your head—not obsessing over productivity, yet constantly designing experiments?

ANNE-LAURE LE CUNFF: Yes, that's a very fair question. First, I don't view tiny experiments as a productivity hack in the traditional sense, but I do think they can help you discover ways to be productive without sacrificing your mental health. It is about questioning the way you work to find a gentler approach that yields the same result.

To your second point: yes, there is absolutely such a thing as running too many experiments. I highly recommend running only one experiment at a time for two main reasons:

First, you want to actually complete the experiment. If you are trying to run five, six, or seven experiments simultaneously, it is highly unlikely you will have the bandwidth to stick to them and collect clean data. It's much better to focus on just one.

Second, if you are changing multiple variables in your life and work at the same time, it becomes impossible to isolate which experiment is actually having a positive impact and which one isn't working.

The only exception is if you really want to run two experiments at once, ensure they are in completely separate areas of your life—for example, one work-related experiment regarding calendar management, and one personal experiment regarding your diet or health. But if you can, stick to just one experiment at a time.

SPEAKER 4: Thank you.

ALISON PARRIN: Well, thank you. Thank you for joining us. I am really excited, as I said, to go away and try this. I will invoke the power of observation over the next 24 hours, and I look forward to seeing what we learn. Thank you for the invitation to all of us to think about how to experiment more and become more comfortable with uncertainty. Thank you.

ANNE-LAURE LE CUNFF: Thank you so much for having me.

Summary

Overview

This comprehensive discussion features computational neuroscientist Professor Konrad Kording of the University of Pennsylvania. Kording bridges the gap between biological neuroscience, machine learning, and physics. The conversation explores the massive computational complexity of individual biological neurons, the current limitations of neuroscience in simulating simple organisms like C. elegans, a proposed roadmap for "compilers" to decode the brain's physical wiring into functional models, and a grounded economic framework detailing the limits of artificial intelligence (AI) scaling in the physical world.

Key Themes and Insights

1. The Unexpected Computational Power of a Single Neuron

In artificial neural networks (ANNs), a neuron is represented as a simple mathematical node that sums weighted inputs and applies an activation function. In contrast, a biological neuron is a highly complex, non-linear processing unit:

• Structural Anatomy: Biological neurons consist of a cell body (soma), dendrites (input structures up to a millimeter long), and axons (output wires ranging from micrometers to meters, as seen in a giraffe's motor pathway).

• High-Dimensional Parameter Space: While a basic ANN node has a single weight parameter per input, a biological neuron has roughly 10,000 synapses, each requiring at least 10 parameters to account for temporal dynamics (e.g., facilitating or depressing synapses, delay times, and local biophysics). Combined with local dendritic non-linearities (such as NMDA or calcium spikes) and millions of regulatory ion channels, a single neuron may require millions of parameters to be fully modeled.

• The Multilayer Analogy: Kording’s research demonstrates that a single biological neuron behaves computationally like a three-to-four-layer artificial neural network. To prove this, his lab successfully trained a simulated single neuron to solve complex image classification tasks like MNIST (handwritten digit recognition) and CIFAR-10.

• Non-Synaptic Communication: In addition to chemical neurotransmission, neurons physically touching one another can influence each other's electrical state directly via ephaptic coupling (electrical fields), adding another layer of unmodeled physiological complexity.

2. The Backpropagation and Credit Assignment Problem

A core mystery in neuroscience is how the brain learns and self-corrects without a global coordinator:

• The Contrast with AI: ANNs use backpropagation—a global algorithm that calculates gradients backwards through a well-defined bounding box to adjust weights.

• Local Constraints: Biological synapses operate strictly on local information: upstream activity, downstream activity, and diffuse neuromodulators (like dopamine for reward prediction errors or serotonin for saliency/attention).

• Theories of Biological Gradient Descent:

• Twin Networks: The brain might feature student networks paired with parallel "trainer" networks (resembling a master-apprentice dynamic), though connectomic data does not show evidence of these redundant pathways.

• Temporal Multiplexing: The same neuron could operate in different phases over time—calculating a forward decision in one phase and backpropagating error signals in another. While mathematically viable, definitive biological proof remains elusive.

• Hebbian Learning as Gradients: Classic Hebbian mechanisms ("neurons that fire together, wire together") align closely with gradient descent, as local plasticity rules naturally approximate zero-gradient conditions when pre- or post-synaptic cells are inactive.

3. Why We Cannot Simulate C. elegans (The Inverse Problem)

Despite having mapped the complete connectome (all 300 neurons and ~10,000 synapses) of the roundworm C. elegans, scientists cannot accurately simulate its behavior. Kording attributes this failure to a fundamental mathematical barrier: the Inverse Problem.

• Low-Dimensional Manifolds: When recording neural activity, the system's states "slush" within a highly constrained, low-dimensional manifold. For example, 300 neurons might move along only a few principal dimensions.

• Mathematical Underdetermination: Trying to deduce the exact causal interactions (weights) of a 10,000-parameter network from highly correlated, low-dimensional output data is an ill-conditioned problem. There are an infinite number of different physical network designs that can yield the exact same output.

• The Limits of Predictive ML: While machine learning is exceptional at "forward modeling" (predicting future neural states within a known environment), it is highly unreliable at solving the inverse causal problem. It cannot predict what will happen when a neuron is physically perturbed or changed out of its normal training distribution.

4. The Solution: Synaptic and Cellular "Compilers"

To overcome the Inverse Problem, Kording advocates for a bottom-up physical roadmap in neuroscience:

• The Concept of a "Compiler": Instead of inferring synaptic weights from activity data (top-down), neuroscientists must build a "compiler" that takes structural and molecular images of synapses and translates them directly into physiological properties (e.g., synaptic strength in picoamps, temporal speeds).

• The Methodology: This requires scaling up robotics and automated high-throughput experiments. Researchers must patch-clamp synapses to record their physiological electrical currents, immediately freeze and image those exact synapses using automated electron microscopy (EM) with molecular labeling, and use machine learning to map the visual/molecular properties to the measured physiological currents.

• Downstream Value: Once we can compile anatomical structures directly into electrical simulation parameters, we can reliably simulate neural circuits, map diseases, and extract novel, highly efficient learning rules to advance machine learning.

5. Moravec's Paradox and the Lack of Causal AI

AI models excel at knowledge work (writing code, summarizing papers) but fail at basic robotics. Kording explains this discrepancy through the lens of causality:

• Correlational vs. Causal Models: Large language models (LLMs) are trained to predict the next token based on correlations. They lack an active, causal world model.

• The Human Motor System: Humans, starting from infancy, actively run causal experiments (e.g., moving a hand, touching a face, flipping a light switch) to map the physical environment. Our motor actions are planned along sparse, causal trajectories (e.g., using a cup to move coffee to our mouth).

• Biological Pre-Wiring: Simple organisms like insects rely on evolutionarily hard-coded motor policies (e.g., flies navigating toward light). Complex mammals rely on highly intricate, homeostatic, multi-dimensional reward systems (hundreds of parallel homeostatic variables controlled by the hypothalamus) to steer open-ended causal learning.

6. Macroeconomics of AI: The CES Model and Physical Constraints

Co-authored with his wife, Anna Maria Ionescu, Kording’s recent economic paper applies the Constant Elasticity of Substitution (CES) model to analyze the macroeconomic effects of unlimited artificial intelligence:

• The Fallacy of Infinite Intelligence Takeoff: Silicon Valley narratives often predict that zero-cost intelligence will lead to hyper-exponential economic growth. Kording argues this ignores physical reality.

• The Physical Bottleneck: Real-world production requires both physical inputs (heavy machinery, raw materials, human bodies) and intelligence inputs. If intelligence becomes virtually free, economic productivity becomes strictly bounded by physical limits (e.g., a combustion engine's maximum power, raw material supply chains, or the speed at which cement cures).

• The Human-Centric Labor Shield: Human labor consists of physical components (hands, human presence) and intelligence. While AI can substitute for pure cognitive tasks, physical human labor (nursing, teaching, construction outliers) remains highly non-substitutable. As cognitive tasks cheapen, the economic value of physical, high-touch human interaction will scale, mitigating the risk of sudden, widespread economic displacement.

Transcript

Juan: A real neuron is really complicated and might be doing much, much more work than one neuron in an artificial neural network would do.

Konrad Kording: We found that a single neuron can solve MNIST. It's a very concrete research program, and it will have trouble getting funded because it's so different from all the other things we currently do. The idea that simulations must always be a far-future dream should go away. We should start simulating brains.

Juan: With me today is Professor Konrad Kording, a computational neuroscientist known for bridging neuroscience, machine learning, and motor control. He is a professor at the University of Pennsylvania, spanning neuroscience, bioengineering, and physics. He's one of the top scientists working to understand how our brains work. He's done tons of pioneering work, including some of the earliest Bayesian brain hypotheses and figuring out how motor control works. He's a clear contrarian thinker on what neuroscience does and does not yet understand about the brain, and he consistently pushes for more rigorous, more falsifiable models in neuroscience and beyond. Konrad, thank you for being here, and let's get started.

Konrad Kording: Thanks so much for having me, Juan.

Juan: Great. So, we're going to start from the bottom up. What is a neuron, and how does it work?

Konrad Kording: Well, that's a great question. Neurons are small structures that do all the information processing in brains. They usually have a cell body that is maybe 10 micrometers big—a hundredth of a millimeter—and then they have a piece that receives information from other neurons. That is called a dendrite, and it might be a millimeter long, though there's a large variation there. Then it has an axon, which is the wire along which it sends the signals it produces. This may be very short, maybe just a few micrometers, or it might go all the way from the brain to the feet of a giraffe, which would be meters in scale.

In neuroscience, we think of them as input-output devices. We have inputs, which are called the synapses, from other neurons. The cell puts all these inputs together to produce an output, which is spikes. Ultimately, these spikes control your body and are the basis of all the interactions that happen in your brain.

Juan: And when a neuron interacts with another neuron, how does that communication happen through a synapse?

Konrad Kording: Yes. In most cases, neurons are what we call spiking. There's an electrical signal that comes out of the neuron, and that then reaches the synapse, which is the place where they meet one another. In so-called chemical synapses, when the electrical signal reaches the terminal, the cell throws a lot of chemicals out of the cell. The next cell that it's connected to—the downstream neuron—has receptors for these chemicals. We call those chemicals neurotransmitters. Once they bind to the receptors, they produce an electrical influence on the postsynaptic cell. Usually, that is just a current generated by opening ion channels. Just like a battery, they open and the ions go through. This current produces the signal on the postsynaptic side, and that is the basis of computation in neurons and brains.

Juan: You said most neurons are spiking neurons. What are the non-spiking ones?

Konrad Kording: There are some neurons that are what we call analog or graded neurons, where what changes over time is the continuous voltage. A spike is binary; it is either there or it's not there. It's like a telegraph signal—beep beep beep—that arrives on the other side. Analog neurons are more like wires where the voltage can go up and down continuously in a graded signal.

Much of the computation in human brains is in terms of spiking neurons. However, much of the computation in very small systems, like worms, is graded. Much of the information processing in your eyes—in your retina—is also graded and analog. Then, the outputs from your eye that go to the rest of the brain are spiking. That's the basis of seeing.

Juan: And how do all these different functions get computed in the brain? In the computer science model, we have a very simple abstraction where we look at a neuron as just one mathematical object with a set of inputs coming in, which then fires an output. They're effectively the same everywhere. But brain neurons aren't like that, right? There are many different types.

Konrad Kording: Yes, there are lots of different neurons in brains, and I think their function is much more complicated. In the biological neuron, there are a lot of incoming synapses—roughly 10,000, depending on where you are in the brain. What happens is every synapse produces a current on the postsynaptic side. But it's not like in computer science where the output of the neuron is just the simple sum of all these inputs. We have local synapses that interact in highly non-linear ways.

For example, we have a phenomenon called NMDA spikes. If a couple of local synapses are active at the same exact time, they produce a much stronger combined signal than if they arrived separately. Moving from these very local processes to more global ones, we have things called calcium spikes, where the neuron can remain highly active for a prolonged period of time, maybe a tenth of a second. And then, ultimately, you have the somatic spiking of the neuron.

When researchers analyze how complex neurons are using detailed biophysical simulations, the consensus is that a single biological neuron is computationally equivalent to a three-layer or four-layer artificial neural network that has just one output. In that sense, a real neuron is incredibly complicated and does much more work than a single node in an artificial neural network.

Juan: That's a very good modeling insight—representing one biological neuron with a three-to-four-layer artificial neural network. Is there some kind of estimate on the parameter count that an average neuron might have? Because that implies a lot of computation is happening inside a single organic neuron.

Konrad Kording: Yes, you need a very large number of parameters to describe the computation in one neuron. Imagine you have 10,000 synapses. It's not just that you have 10,000 parameters, meaning one weight for each. It's much more complex. Some connections are very fast and transient, while others are much slower. There are excitatory and inhibitory connections. There are also dynamic synapses: with depressing synapses, if two inputs come immediately after one another, the second one barely registers. With facilitating synapses, the opposite happens: if just one input comes, very little happens, but if two come in rapid succession, it produces a very strong signal.

All of these features require parameters. I think we need at least 10 parameters per synapse. With 10,000 synapses, we are already at 100,000 parameters per cell. But there are a lot of extra parameters coming from the local non-linear properties of dendrites. We have ion channels that can amplify signals, weaken signals, or make them non-linear. There might be a million of them on a single cell. So, the number of parameters needed to describe a single cell is undoubtedly very, very large.

Juan: Across the different types of organic neurons, do they follow at least a set of patterns? If you were to model an organic neuron with a particular artificial network structure, is that viable? Or are they so uniquely tuned that you would have to learn a different network from scratch for each individual neuron?

Konrad Kording: Every neuron is different. And they have to be, because what do neurons do? They embody what we know about the world. If they were all identical, they couldn't store any unique information. They must all be different, which is why they have so many parameters. If we want to model them well, we need to use a lot of parameters.

Juan: To push back on that, artificial neural networks are mathematically identical in terms of their node equations, but they store differences in their parameter weights. The structure of the artificial neurons is identical, while the parameters differ. In organic terms, are neurons structurally different, or is it just the parameters that vary? Is the actual wiring and anatomy so fundamentally different from one neuron to the next that it's difficult to generate a single unifying model?

Konrad Kording: I think they are all very different, and yet they are all the same. Let me explain what I mean by that. If you describe what a neuron does with its inputs, yes, they are non-linear and they are all different from one another. But if you zoom in on how they work, they all operate on the same physical principles. There are ion channels and synapses on the cell. The synapses and ion channels produce electrical currents. The neuron integrates these electrical currents using the physics of the cable equation. After integrating them, it produces an output that is sent through the axon.

Of course, there are slight variations because it's biology, and evolution does whatever works. There are some variations where communication between neurons doesn't go through traditional axonal outputs. We don't quite know how large these effects are, but they do exist. For example, if two neurons are touching, even if there is no synapse between them, the electrical activity of one can electrically influence the other. This is called ephaptic coupling. This means that two adjacent neurons can influence one another beyond traditional synaptic connections.

Juan: Yes, you mentioned the cable equation. What are the computational models we've built to represent neurons today? What does the zoo of different models look like, and what has worked versus what hasn't?

Konrad Kording: There is a continuum from highly realistic to abstract models. Let's start with the realistic ones. A realistic model treats the neuron by looking at its physical dendritic tree. If we zoom into the physics of the dendritic tree, it is essentially water and salt on the inside. In physics, we know how to model water and salt: current flows through it, and it has a certain amount of local resistance. A long branch of a dendrite can be modeled as a series of resistors. Around that, you have the cell membrane, which acts as a capacitance to the outside. Different cells have different branch lengths, thicknesses, and membrane properties. From a physics perspective, this translates to a tree of resistors and capacitors with local sources of current.

How does current enter the system? If we zoom in, we have ion channels. There is a physical mechanism that opens them and one that closes them. When open, ions flow through, which is equivalent to attaching a little battery with a resistor. This is how we model them when we try to build very realistic biophysical models. Synapses are modeled similarly to ion channels, except their state depends on the activity of the presynaptic neuron.

Where it gets more complicated is on the molecular level. Inside cells, there are various molecules that regulate these processes. An ion channel is regulated by intracellular biochemical cascades. This is where immense complexity enters, and it's an area where we are still quite uncertain.

There are aspects we understand very well. For example, the action potential (the spike). The most famous model—the Hodgkin-Huxley model—describes a piece of axon using resistors and capacitors, alongside voltage-gated ion channels that let in more current when the voltage is high, and then lower the voltage after a short delay. This gives rise to the spike: the voltage shoots up and then goes back down. This propagation transmits the signal along the axon.

We understand that level very well. However, when it comes to non-linear regulation, things are much more complex. While a voltage-gated sodium channel is relatively simple, real cells feature second messengers where various cellular phenomena affect ion channels in complex ways. Learning is the extreme case of this: molecular cascades ultimately change synaptic strength or even initiate the growth of new synapses.

Juan: Going back to these different models, we have realistic descriptions in physics that we can simulate computationally. But simulating every resistor and capacitor is way too granular and computationally expensive. If we start abstracting, what are some of the more abstract computational structures that represent what a neuron does while omitting some of the lower-level detail?

Konrad Kording: A cable equation model is incredibly expensive to simulate because you are calculating the state of every little piece of the cell over time. Therefore, we use various abstractions.

One way is coarse-graining. You take the cable equation and replace what was originally a hundred resistors with a single resistor that approximates the system's behavior. This leaves you with fewer resistors and capacitors, making it much easier to simulate.

The next step of abstraction is to assume the dendrites do not matter significantly. This gives us the integrate-and-fire neuron. In this model, we look at the average influence of each synapse on the voltage at the soma (the cell body). We pretend that the complicated dendrite—with all its delays, timescales, and non-linearities—does not exist, and we just linearly sum the inputs. The integrate-and-fire model at least produces binary spikes. It is the basis of spiking neural networks, which are used in artificial intelligence with the promise of making computation much more energy-efficient.

We can abstract even further to what we call a rate model. Here, we assume that precise timing doesn't matter as much as the average rate of spikes over a given window (e.g., a second). We bypass the biophysics entirely and represent the output simply as a continuous function of the inputs. So, you have a whole continuum of biological realism.

Juan: What do you think is going to be the right computational model to build large-scale representations and networks that can replicate behavior well? Can we abstract all the way to a leaky integrate-and-fire structure, or do we need something much more detailed? How do you think this will develop over time?

Konrad Kording: I think the information processing inside the cell truly matters because non-linearities are the fundamental basis of neural network computation. The idea that you can represent a neuron as a simple linear adder with a threshold—the way we do in standard artificial neural networks—is likely wrong.

But what we can do now is use machine learning to accelerate these simulations. We can run expensive, realistic simulations of a neuron and use machine learning to find a fast approximation of its input-output mapping. We call this amortized inference. If a realistic neuron receives thousands of inputs over time and computes a complex function, we can train a deep neural network to approximate that exact function.

In fact, there is work showing that a highly complex biological neuron can be approximated by a simple three-layer artificial neural network. You can simulate a three-layer artificial neural network massively faster than the biophysical cable equations of a biological neuron. This provides a path to map realistic neurons into highly efficient models, which I think is essential if we want to simulate large-scale brains.

Juan: What sort of functions do you think these individual neurons are calculating? In artificial neural networks, we use very simple linear algebra functions and build complexity by stacking layers. The individual nodes are not complicated; the complexity emerges from the depth. But you are saying the biophysics of a single organic neuron are so complex that a single neuron might be learning a highly complex function on its own.

Konrad Kording: Yes, and we've actually done some fun research on this. We took a realistic, somewhat simplified model of a biological neuron and asked: Can a single neuron solve a machine learning task? We found that a single biological neuron can solve MNIST.

For those who don't know, MNIST is a benchmark dataset of handwritten digits where the system must identify whether an image is a 0, 1, 2, and so on. These are real handwritten numbers from the US Postal Service, so they vary wildly. What we did was take a neuron with its dendritic tree and all its parameters, and we asked if we could train its free parameters to classify, say, a number 7 versus a number 1. A single neuron can do this remarkably well.

Of course, the structure of the dendrites limits the exact functions it can compute. It's a tree-like structure where signals meet at branches, so it cannot implement every possible three-layer neural network. But having this pre-existing physical structure and sparse connectivity might actually make it easier to compute these complex functions. We do not give individual neurons enough credit for what they can do.

Juan: That is incredibly powerful. When you describe the three-to-four-layer capability, does that come from the dendritic structure or the parameter space of the individual synapses?

Konrad Kording: It comes from the structure of the dendrites. Dendrites are tree-like structures. If you look at the famous drawings by Ramón y Cajal, they look remarkably like trees. We actually wrote a paper quantifying how similar trees are to dendrites, and they are structurally very similar. In a dendritic tree, wherever two branches meet, you have a physical site where two signals can combine non-linearly. That branching structure is where the layers come from.

Juan: Is it as straightforward as tracing the physical tree structure of a neuron to represent it with an artificial neural network of the same topology, or does it get more complicated than that?

Konrad Kording: That is exactly how we did it. There is also beautiful work by Idan Segev looking at this holistically, but a very good first-order approximation is that you can take a neuron's physical tree structure and model it as an artificial neural network with the same tree structure.

Juan: Okay, so that covers single neurons. When we start putting them together into larger units—circuits and eventually whole brains—how do those networks work? What are the communication pathways? In artificial neural networks, we largely constrain ourselves to feed-forward architectures because it makes the optimization algorithms, like backpropagation, much easier. But organic networks are highly recurrent. Can you speak to the complexity of modeling these recurrent biological circuits?

Konrad Kording: Yes. In a transformer architecture, you have a strict feed-forward transmission of information. It goes from layer to layer until it outputs a result. In the brain, it is very different. If we are two connected neurons, if I talk to you, you will very likely talk back to me. Or, at a network level, my brain area talks to your brain area, and yours talks back. There is massive, dense recurrence where information flows in both directions. Recurrent neural networks are actually having a bit of a resurgence in machine learning right now because feedback is incredibly useful.

Biologically, the brain has hundreds of distinct areas, and the neurons look different in all of them. But a general, consistent pattern is that if area A projects to area B, area B almost always projects back to area A.

Juan: Is the feedback running through different channels, or does it use the exact same channel going backward?

Konrad Kording: They project slightly differently. If you look at the cortex, which is what most people study, there are specific layers. Signals going "up" the hierarchy originate in certain layers and target specific layers in the downstream area, while the feedback signals coming back target different layers, such as the superficial layers. So, there is a clear anatomical separation. This separation is highly useful from both a learning and processing perspective, as the network can distinguish bottom-up sensory inputs from top-down contextual expectations.

Juan: And that's very useful algorithmically. The separation of forward and backward information is what allows machine learning to run backpropagation. In artificial networks, we compute a global loss and backpropagate it. But in biological networks, learning has to happen locally. How does the brain solve this "credit assignment" problem without a global coordinator?

Konrad Kording: Let’s break that down into processing versus learning.

During processing—like when you decide to say a word—information in your brain loops back and forth through recurrent layers until a decision is made. In contrast, in a standard artificial feed-forward network, information only goes forward during inference.

When it comes to learning, we have the credit assignment problem. If you make a mistake, you need to know which specific synapses were responsible so you can adjust them. In AI, we solve this globally using backpropagation, which calculates exactly how each weight should change. In the brain, we don't know how this is solved. A synapse only has access to local physical variables.

So, how can a local synapse figure out its contribution to a global error? Let's look at the physics. If you are a synapse somewhere in the brain, what information do you actually have access to? You know when the upstream (presynaptic) neuron was active, and you know when the downstream (postsynaptic) neuron was active, which you hear via a backpropagating action potential traveling back up the dendrite. You also sense local concentrations of neuromodulators like dopamine or serotonin. That’s it.

Juan: And do these neuromodulators act locally, or are they broadcast globally?

Konrad Kording: They are broadcast broadly, but their effects can be locally modulated. There is a huge literature on this. For example, dopamine is often described as signaling a reward prediction error—the difference between expected and received reward. This was popularized by Wolfram Schultz's beautiful experiments. If you get an unexpected cup of coffee, your dopamine neurons fire. If you expect coffee and don't get it, they pause.

But real life is much more complicated than a simple scalar reward. If I say a word and you frown, that's one feedback signal. But I also have internal critics evaluating if the word sounded right, if it was strategically appropriate, and how the audience might receive it.

In AI, we have a single, clean loss function. In humans, we have many overlapping layers of feedback. Other neuromodulators, like serotonin or acetylcholine, signal things like saliency or attention—telling the network, "This event was important, do not forget what just happened." AI is starting to build these kinds of attention and gating mechanisms, but biology has evolved highly sophisticated systems for this.

Juan: What do we know about the actual learning algorithms in the brain? Are there many different algorithms localized in different areas, or is it a single unifying algorithm tuned in different ways?

Konrad Kording: Let's look at the theoretical landscape. There are two main camps of thought.

One camp, championed by scientists like Tony Zador, suggests that the brain's structure is largely pre-wired by evolution. In this view, we don't need complex, general-purpose learning algorithms because evolution has already solved most of the connectivity, leaving only a simple, thin layer of learning to connect specific stimuli to responses (like learning that the word "blue" corresponds to the color blue).

Most theorists, however, believe it cannot be that simple. The world is incredibly complicated, and our language and cognitive structures are highly flexible. If you make a mistake in a complex task, there are infinite ways you could have erred. You need a powerful way to solve the credit assignment problem—figuring out which neurons screwed up.

Because synapses only talk to their immediate neighbors, any global error signal must be translated into local updates. Theorists have proposed several biologically plausible ways the brain could approximate gradient descent locally.

The most straightforward proposal is a "twin network" model. Imagine every neuron in your brain has a twin. One neuron is the student doing the fast processing, and its twin is the teacher calculating the error signals. By propagating error signals through the trainer network, you can mathematically approximate backpropagation. This is highly realistic, but we do not see anatomical evidence for these parallel "trainer" networks in connectomic data.

Another class of models suggests that instead of having two physical networks, the same network multiplexes over time. A neuron might have a phase where it makes a decision, and a subsequent phase where it receives feedback and updates its weights. Algorithms like this have been proposed by researchers like Walter Senn, Blake Richards, Yoshua Bengio, and even myself during my PhD. These models do not violate known biology, but we still lack direct evidence that the brain actually uses them.

Juan: Why is the biological evidence so hard to find?

Konrad Kording: Because the relevant experiments haven't been done. We know that biological learning is incredibly efficient. If you reach for a coffee cup and misjudge its weight, your motor system corrects about 30% of the error on the very next trial, just half a second later. This is incredibly fast compared to artificial neural networks, which require thousands of iterations and are prone to catastrophic forgetting if they learn too quickly.

We also know that local synaptic plasticity looks a lot like the components of gradient descent. In Hebbian learning, synapses strengthen when pre- and postsynaptic neurons fire together. If the presynaptic neuron isn't active, the synapse doesn't change—which matches gradient descent, because if there's no input, the gradient with respect to that weight is zero. Similarly, Spike-Timing-Dependent Plasticity (STDP) shows that a synapse only changes if the presynaptic spike occurs before the postsynaptic spike. If it fires afterward, it couldn't have caused the output, so the gradient is zero and no learning occurs.

So, our microscopic data is highly compatible with gradient descent. But the fields are culturally siloed. The biophysicists studying synaptic plasticity work microscopically in brain slices, and they often don't think about global network gradients. The theorists studying biological backpropagation are AI-adjacent and don't typically run active biology labs. The two sides rarely align to run the definitive experiments.

Juan: How would you design an experiment to prove the brain is doing gradient descent?

Konrad Kording: It's actually a very clean concept. Gradient descent predicts that if making a neuron more active improves performance on a task, that neuron should naturally become more active after learning. If making it more active makes performance worse, it should become less active.

We can test this by combining two standard neuroscience techniques. First, we train an animal on a task and record neural activity to see how the neurons change. Second, we use optogenetics to stimulate specific neurons during the task to measure their direct causal effect on performance—this gives us the mathematical derivative (the gradient) of behavior with respect to that neuron's activity. If the brain is doing gradient descent, the change in a neuron's activity after learning must correlate with its causally measured gradient. The tools exist; we just need to run both experiments in the same animal at the same time.

Juan: Speaking of the experimental toolkit, neuroscience has seen an incredible explosion of new technologies. Can you give us an overview of the state of the art in recording and mapping the brain?

Konrad Kording: The technological development is mind-blowing. In my lab, we identified "Stevenson's Law," which shows that the number of neurons we can record simultaneously has doubled roughly every six years. When I was a PhD student, recording 100 neurons simultaneously was a heroic feat. Today, you can insert a single silicon probe—like a Neuropixels probe—and record from a thousand neurons, and you can place multiple probes in a single brain.

We also have optical recording techniques using calcium or voltage imaging, which allow us to monitor thousands of neurons visually. We can combine this with molecular genetics to make only specific cell types (for example, a specific class of inhibitory interneurons) express fluorescent proteins, allowing us to record from them while the rest of the brain remains invisible.

On the perturbation side, optogenetics—popularized by Karl Deisseroth and Ed Boyden—is revolutionary. We can express light-sensitive proteins from algae in specific neurons. By shining light of a specific color, we can turn those neurons on or off. We even have "latch and release" optical techniques: you shine one color of light, and the cells "record" their current activity level. Later, you shine another color, and only the cells that were active during the recording phase are reactivated, allowing you to recreate a specific brain state.

On the anatomy side, we have automated high-throughput electron microscopy (EM). Historically, researchers like Kevin Martin had to trace neurons and reconstruct synapses by hand, which took years. Today, we have automated multi-beam scanning electron microscopes and AI segmentation algorithms that can reconstruct millions of neurons and their synaptic connections automatically. Imaging is getting cheaper by a factor of two every 18 months—almost as fast as the cost of compute is falling.

Yet, despite this deluge of data, we are still struggling to answer the same fundamental questions we asked 70 years ago.

Juan: Why is that? Is it a scale problem? Do we just need more data to build better models, or are we missing something more fundamental?

Konrad Kording: Let me give you a sobering example: C. elegans. This is a tiny roundworm with one of the simplest nervous systems in existence. It has exactly 302 neurons. Despite its tiny brain, it exhibits a rich behavioral repertoire: it searches for food, avoids danger, finds mates, and lays eggs.

We know the complete connectome of C. elegans. We have mapped every single physical wire and synapse, not just in one worm, but in multiple individuals. We know their molecular properties. We can record from almost all of its neurons simultaneously while it moves.

And yet, we cannot simulate it. Our biophysical simulations of this 302-neuron worm are barely worth the paper they are printed on. My own lab has tried, and we failed. If we cannot simulate a 300-neuron worm when we have all the data, we are clearly missing something fundamental.

Juan: What is going wrong? Why can't we simulate C. elegans despite having the complete wiring diagram and activity traces?

Konrad Kording: It comes back to the Inverse Problem.

Imagine you record the activity of all 300 neurons over time. The problem is that their activities are highly correlated. They move together within a very low-dimensional state space. If you try to run a regression to figure out how much neuron A causally influences neuron B, you have to invert the covariance matrix of their activity. Because the data is low-dimensional, this matrix is ill-conditioned, meaning you cannot mathematically solve for the causal weights. There are an infinite number of different physical connectivity models that can produce the exact same low-dimensional activity pattern.

Juan: But isn't this the exact type of problem that modern deep learning is great at? If you have enough recording data across many different behaviors, shouldn't you be able to fit a deep neural network to predict the worm's activity?

Konrad Kording: Yes, you can. But there is a massive difference between prediction and understanding.

Machine learning is spectacular at the forward modeling problem: "Given the current state of these 300 neurons, predict what they will do in the next millisecond." Because the system's dynamics are low-dimensional, a neural network can fit this easily and make highly accurate predictions.

But it fails completely at the inverse problem: "What is the actual causal mechanism inside the network?" Because the data is low-dimensional, the machine learning model will distribute its weights across the correlated dimensions arbitrarily. If you then perturb the system—say you reach in and optogenetically silence one neuron—the model’s predictions will completely break down because you have gone out of its training distribution.

This is a mistake almost everyone new to machine learning makes. They fit a highly predictive model and assume that because the model's predictions are accurate, the model represents how the real-world causal system actually works. It doesn't.

For example, if you build a machine learning model to predict mortality using electronic health records, it might find that taking Vitamin D is a fantastic predictor of living longer. But that is a correlation; wealthy people who exercise and see doctors regularly are simply more likely to take Vitamin D. If you use that model to prescribe Vitamin D to a sick population expecting them to live longer, your causal intervention will fail. Because our recorded neural data is low-dimensional, we cannot solve the causal inverse problem through observation alone.

Juan: How do we break through this? If observational data and predictive machine learning are not enough, what is the path forward? Do we need to scale up our ability to perturb the system, or do we need a completely different modeling approach?

Konrad Kording: I spent 25 years of my career trying to solve these inverse problems from neural recordings, and I am now convinced there is no credible solution there for larger systems. We need a completely different approach.

Instead of trying to infer the synaptic weights from the output activity, we must be able to see the weights directly. We need to combine connectomics with molecular and physiological annotations.

If we look at a synapse under a microscope, we shouldn't just see a physical contact. We need to be able to look at its size and molecular composition and say, "That is an excitatory synapse, its strength is exactly X picoamps, and its time constant is Y milliseconds." If we can read the parameters directly from the anatomy, we don't have to solve the mathematically impossible inverse problem. We can just build the simulation directly from physical measurements.

Juan: So, this would mean mapping not just the wiring diagram, but the exact parameter values of every synapse. How far away are we from being able to do that? What is missing to turn a physical connectome into a working simulation of an organism?

Konrad Kording: What is currently missing is what I call compilers.

Right now, connectomics gives us a list of physical wires—an image of where cells touch. But it doesn't tell us how they interact. We lack the translation mechanisms—the compilers—that take anatomical and molecular images of a synapse as input and output the physiological parameters, like synaptic strength and dynamics.

We need to treat this translation as a supervised machine learning problem. We can perform high-throughput, automated physiology to record the exact electrical current flowing through a synapse. Then, we immediately freeze the tissue and use electron microscopy and molecular profiling to reconstruct that exact synapse in 3D, mapping its volume, receptor count, and protein distribution.

If we do this for a million synapses, we can train a machine learning compiler to predict physiological currents directly from anatomical and molecular images. Once we have a reliable compiler, we can take a purely structural connectome and automatically compile it into a fully parameterized, working simulation of a brain.

This is a highly concrete, achievable research program. It's not rocket science; it's standard biophysics and machine learning. But it is difficult to get funded because it sits right in the crack between traditional physiology and structural connectomics.

Juan: If we could build these compilers, the downstream value would be massive. If we can simulate a mouse brain and truly understand its learning algorithms, that could unlock new architectures for AI, easily paying back the research costs.

Konrad Kording: Absolutely. The value chain is clear: compilers give us synaptic parameters; parameters give us cellular models; cellular models give us circuit simulations; and circuit simulations reveal the underlying learning algorithms.

But there are real conceptual and physical risks along the way. It is possible that synapses are so complex that they cannot be compactly described, or that they require measuring more molecular species than we realistically can. If we need 1-nanometer resolution of every single protein's physical position to simulate a synapse, the structural data requirements would shoot up by several orders of magnitude, rendering the project unfeasible.

But unlike most of neuroscience, we can actually write down these risks explicitly. We can list the 20 ways this program could fail and design benchmarks to test them early. It is a bottom-up, engineering-driven approach to understanding the brain, which is entirely orthogonal to the traditional top-down methods of neuroscience.

Juan: Let's discuss the simulation itself. Suppose we can successfully compile and simulate a brain—either of a mouse or eventually a human. What is the potential utility of these simulations, both for science and society?

Konrad Kording: There are two distinct types of science here: traditional human science and what I call machine science.

Traditional science produces simple principles that humans can easily understand and reason about, like Newton's laws or the Hodgkin-Huxley equations. Machine science produces highly complex, predictive models that work incredibly well but are too complex for a human mind to understand—such as AlphaFold for protein folding.

If we simulate a human brain—say, a digital simulation of myself, "simulated Konrad"—it would be an instance of machine science. I cannot hold the states of a trillion parameters in my head, so I won't "understand" the simulation in a traditional sense. But it would be incredibly useful.

For instance, we could use a simulated human to run in-silico drug trials, screening thousands of compounds for psychiatric or neurological diseases without risking patient safety. We could optimize deep brain stimulation protocols for Parkinson's disease in simulation. We could run AI alignment experiments by testing how simulated humans respond to different AI behaviors in parallel, reading out their subjective preferences directly.

And beyond the utility, it would be one of the most monumentally cool achievements in human history. To understand and simulate the very organ that generated our civilization would be at the absolute top of humanity's achievements.

Juan: This concept of simulating a human mind feels very similar to Artificial General Intelligence (AGI). For decades, scientists and science fiction writers mapped out the broad strokes of how machine intelligence would develop, but it felt so distant that the mainstream dismissed it. Now, we are suddenly building models that outperform humans on a wide range of cognitive tasks, and society is struggling to process it. A digital or computational human existence has that same sci-fi flavor, where people dismiss its near-term reality because it has been "thirty years away" for so long.

Konrad Kording: Yes, and technological leaps always force us to fundamentally rethink what we are and what we value.

When the steam engine arrived, people whose physical strength was their primary economic value must have felt deeply threatened. Now that AI is here, experts who derived their identity from knowing niche information are finding that a machine can retrieve that information instantly.

But the first applications of brain simulation won't be "uploading" humanity into a digital-only species. The immediate benefits will be medical and practical: curing brain diseases, designing brain-computer interfaces, and understanding mental states.

A lot of people worry that AI will inevitably lead to the end of human civilization. But we are building these tools. We build them because we believe the future will be more magical, more prosperous, and more wonderful with them.

Juan: Let's talk about AI. The capabilities today are dramatically ahead of where they were five or ten years ago. You can now talk about AGI without being dismissed as a crackpot. As a neuroscientist observing this rapid scaling, what are your reflections? What has surprised you, and what has progressed slower than expected?

Konrad Kording: The main realization I find missing in the public debate is the extent to which artificial intelligence is fundamentally different from biological intelligence.

Human intelligence is often projected onto a single, linear axis: highly intelligent people are expected to be good at writing essays, solving differential equations, reasoning about social dynamics, and planning their lives. When people look at AI, they try to project it onto this same human manifold.

In reality, AI has a completely different shape. Computers have been better than us at multiplying large numbers since before we were born. Now, an LLM can summarize a highly technical paper instantly, but it might fail to solve a straightforward, real-world scheduling problem. AI does not live on the human cognitive manifold.

This gives me a lot of hope. If AIs were designed exactly like us, they would be direct competitors. But they aren't. They are tools that excel at things we find difficult, and they struggle with things we find trivial. By coupling human cognition with AI, we become dramatically more capable. I use AI every single day, and it makes me a much more productive scientist.

Juan: We often conflate the word "learning" in machine learning—which means adjusting parameter weights—with "learning" in the human sense, which involves absorbing conceptual frameworks, drawing metaphorical relationships, and extracting generalized skills from very few examples. Human learning and conceptual reasoning feel qualitatively different from adjusting millions of weights via gradient descent. How do you view this distinction?

Konrad Kording: Humans possess an explicit history of their own thoughts. We don't just produce an answer; we reason about our internal thought patterns, identifying which strategies worked and which failed. A standard neural network has no active record of its internal processing; it simply maps inputs to outputs.

Furthermore, humans operate using highly structured world models. I can close my eyes and simulate walking into a coffee shop, interacting with a barista, and ordering a drink. We translate our high-dimensional neural representations into discrete, causal entities—like "coffee cup" or "door"—and use them to plan.

Deep learning systems lack this explicit causal structure. To compensate for their lack of structured internal memory and world models, we have to build increasingly complex context windows and external databases. There is something fundamental about active, internal world simulation that is currently missing in AI architectures. Our brains do this constantly. Even when we sleep and sensory inputs are blocked, our cortex continues to run active simulations of the world.

Juan: Have researchers tried building artificial neural networks that mirror the specific macro-regions and functional pathways of the brain? Or is it better to just let everything emerge from scratch? It is remarkable that every time we train a transformer, we are effectively forcing it to re-evolve intelligence from a blank slate, ignoring the millions of years of architectural optimization encoded in biological brains.

Konrad Kording: There is a fundamental physical bottleneck in biology: the amount of information we can pass to our offspring is strictly limited by the storage capacity of our DNA. Because of this DNA bottleneck, evolution had to find highly compressed, elegant architectural priors that allow an organism to learn rapidly from very little data. A human child learns to speak using a tiny fraction of the language data that an LLM requires to reach proficiency.

Evolutionary thinking is the most powerful tool we have for understanding the brain. In neuroscience, we call these "normative models." We assume the brain has evolved to find the mathematically optimal solution to the survival challenges within its specific environmental niche. If we model the demands of that niche—whether it is visual depth perception or motor control—the optimal solution almost always predicts the actual neural structures we observe in the brain.

Juan: Applying that evolutionary frame to AI is fascinating. What is the environmental niche of an AI model, and what pressures are shaping its development?

Konrad Kording: The evolutionary niche of an AI model is shaped by humans—specifically by developers and market incentives.

Currently, the selection mechanism works like this: a researcher writes a neural network architecture. If it wins a benchmark or performs well on a task, it is copied, modified, and built upon by other researchers. The models that survive are the ones that provide economic utility and align with human demands.

In this sense, "alignment" is actively built into the evolutionary landscape of AI. Companies want AI systems that solve their business problems and don't lie to their customers. They have no interest in funding an AI that tries to bypass its guardrails or take over the company. The market naturally selects for cooperative, useful, and aligned systems.

Juan: A counterpoint to that optimistic view is the "treacherous turn" scenario. If a model becomes highly capable, the selection pressure to appear aligned might simply train it to become better at hiding its misalignment, deceptive capabilities, or attempts to bypass its sandbox.

Konrad Kording: I lean toward a more stable, optimistic view based on the scaling properties of intelligence. I believe that on any single task, the return on investment for extra intelligence is highly sublinear. Beyond a certain point, throwing more compute at a task yields diminishing returns.

If this sublinear scaling holds, then a highly advanced, deceptive AI gains very little marginal advantage from trying to cheat. Furthermore, in a world with multiple advanced AI systems, the other highly capable, aligned models will easily detect and call out any single model attempting to behave deceptively. The system is inherently self-stabilizing because the collective intelligence of the aligned models will always dwarf the capabilities of a single rogue actor.

Juan: Let's look at the physical world. While AI has advanced rapidly in cognitive tasks, robotics remains far behind—a classic manifestation of Moravec’s Paradox. Why is it so much harder for a machine to clean a room than to write a symphony? How can we bridge this gap? Do we just need to brute-force robotic data collection, or are there insights from biological motor control that we are missing?

Konrad Kording: The missing link is causality.

Humans are deeply causal thinkers. When we interact with the world, we interpret events in terms of cause and effect. Our current machine learning systems, however, are purely correlational. They excel at predicting the future based on statistical regularities of the past, but they do not understand causal influence.

Look at how a human baby learns. They don't just sit and observe; they actively manipulate their environment. They touch things, drop toys, and put objects in their mouths to run active, causal experiments. This is directed, intentional exploration designed to map the causal laws of physics.

We live in a world where physical causality runs along highly sparse lines through time. A coffee cup is a physically specialized object designed for a single causal chain: moving liquid to our mouth. Humans are exceptionally good at identifying these sparse causal pathways and using them to plan.

AI systems do not have this intuitive grasp of physical agency and causality. They model the statistical regularities of the world, but they struggle with intuitive physics and the physical outliers that are trivial for a toddler.

Juan: That explains why animals with tiny brains can navigate the physical world so much better than our best robots. A bee can fly through a complex forest, navigate wind currents, and communicate resources with its hive using under a million neurons.

Konrad Kording: Yes, but we must distinguish between adaptive learning and hard-coded policies.

Insects don't need a highly sophisticated, flexible causal model of the world because evolution has hard-coded their behavioral policies directly into their neural circuits. A fly's navigation is largely a set of reflexes honed over millions of generations. When the environment changes in a way its ancestors never encountered—like a glass window pane—the hard-coded policy fails completely, and the fly repeatedly beats itself against the glass.

Humans evolved in a much more complex social and physical niche, where a static policy would fail. Instead of hard-coding the policy, evolution equipped us with an incredibly sophisticated, multi-dimensional reward system.

The human brain regulates hundreds of homeostatic variables simultaneously—blood sugar, salt levels, core temperature, social connection, and emotional state. We have specialized error-attribution systems. If you eat a poisonous berry and get sick three hours later, your brain doesn't run gradient descent on your visual system; it specifically attributes the negative reward to your gustatory and dietary preferences. This highly directed credit assignment is what allows us to learn complex, open-ended behaviors from very few physical trials.

Juan: This suggests that we won't get truly sophisticated, creative, and adaptive digital agents until we build in this level of rich, multi-dimensional reward complexity and causal error-attribution.

Konrad Kording: Exactly. Current AI systems are trained on highly simplified, unidimensional objectives.

During pre-training, their sole value is next-token prediction—imitating human text. During reinforcement learning from human feedback (RLHF), their objective is narrowed to "say things that humans rate highly." This makes them highly useful tools, but it deprives them of the rich, intrinsic values that drive biological intelligence.

Humans are opinionated. We have internal values and goals that aren't simply about conforming to the average opinion of our peers. This internal tension and richness of objectives is what drives genuine creativity, agency, and perhaps even the qualitative nature of consciousness.

Juan: Let's shift to how you are using these tools today. How is AI accelerating your own research or changing the way you and your students do science?

Konrad Kording: Many people use AI to remove friction—to write their papers or generate ideas for them. I think that is a massive mistake. I use AI to generate friction.

When I write a paper, I use LLMs as adversarial critics. I ask them, "Where are the logical flaws in this argument?" or "Do these cited references actually support the specific claim I am making?" You would be shocked at how often the model points out that a paper I cited doesn't quite support my sentence, even in a field I have worked in for 20 years.

I built an educational app called Plan Your Science that uses this exact philosophy. When a student enters their research question, the AI actively pushes back. It tells them if their terms are ill-defined, points out existing papers that might make their project redundant, and forces them to formulate mutually exclusive hypotheses.

Friction is how we grow. If you want to get physically stronger, you have to lift heavy weights. If you want to get intellectually stronger, you need intellectual friction. AI is the ultimate tool for providing personalized, constructive friction.

Juan: That is a brilliant framework. You could use AI to audit the entire scientific literature for logical consistency and reproducibility.

Konrad Kording: We are actually working on exactly that. A student of mine is building a pipeline where you upload a paper's method section and its associated code repository, and the AI audits them to find discrepancies between what the authors claim they did and what the code actually does.

The results are going to be fascinating and probably a little terrifying. Historically, scientific reproducibility has been a major issue. Years ago, I ran an NSF-funded data sharing grant, and we found that a large portion of published professors couldn't reproduce the figures from their own papers when we asked them for the raw data and scripts.

With modern Python repositories, things have improved significantly, but using AI to systematically audit and validate the literature for a dollar of compute per paper would be a massive leap forward for the integrity of science.

Juan: You also recently co-authored a paper with your wife, Anna Maria Ionescu, looking at the macroeconomic impacts of AI through this lens of intelligence and physical constraints. What are the core insights from that work?

Konrad Kording: This paper grew out of a clash between two different worldviews.

In the tech world, we see exponential trends where the cost of intelligence halves every few months. In the macroeconomic world, national GDP grows at a slow, linear-to-exponential crawl, taking decades to double. What happens when these two realities collide?

To model this, we categorized the economy into four distinct inputs: physical capital (like excavators), human physical labor (like human hands and bodies), human intelligence, and artificial intelligence. We then looked at how easily these inputs can substitute for one another using the macroeconomic concept of Constant Elasticity of Substitution (CES).

If you want to move dirt, you need both a physical machine (the excavator) and intelligence to guide it. If intelligence becomes virtually free, you can optimize the pathing of the excavator perfectly, but you are still strictly bounded by the physical limits of the machine—its engine power, physical wear and tear, and fuel efficiency. You cannot substitute intelligence for physical reality beyond a certain point.

If you want to build a house, free intelligence doesn't lay the bricks any faster. If you want to scale up robot production, you still have to build physical factories, secure raw materials, and build roads. These physical supply chains scale at the slow, physical rates of traditional capital, not the exponential rates of software.

Because the physical sector is highly non-substitutable with pure intelligence, even if the cost of cognitive intelligence drops to absolute zero, macroeconomic growth will not experience a hyper-exponential vertical takeoff. GDP might double, but it will not experience a hundred-fold acceleration. The physical bottleneck acts as a stabilizing anchor on the economy.

Juan: That is a highly grounded perspective. It also suggests that human jobs involving physical presence and physical manipulation are highly insulated from automation.

Konrad Kording: Exactly. Physical human labor—teachers who physically interact with children, nurses who provide high-touch care, construction workers who navigate unpredictable physical environments—remains highly non-substitutable.

Even in software engineering, AI hasn't replaced human developers. It has made them vastly more productive. Because our expectations for software quality are constantly rising, we will simply use this extra productivity to build larger, more complex, and higher-quality software systems, which will still require human oversight.

History shows that as technology automates specific tasks, we don't run out of work; we simply find new, highly valued ways to spend our growing wealth. We will have smaller class sizes, better healthcare, and higher-quality infrastructure. The transition will be far more stable and positive than the doom-and-gloom narratives suggest.

Juan: That is a wonderful, optimistic note to end on. If you look out twenty or thirty years, what is the future you find most exciting and inspiring?

Konrad Kording: I dream of a world where we understand the brain well enough to simulate it, laying to rest the idea that brain simulation is a far-future fantasy.

I dream of a world where AI is universally recognized not as a competitor to humanity, but as a deeply collaborative tool that helps us solve our most challenging problems—from curing diseases to managing our resources and education.

As engineers and scientists, we have always used technology to make the world more magical, prosperous, and wonderful. AI and brain simulation are simply the next steps on that extraordinary journey.

Juan: Thank you, Konrad. This was a fascinating and deeply insightful conversation.

Konrad Kording: Thanks for having me, Juan. It was a pleasure.

Summary

Introduction: The Crisis of Cognitive Overload and the Maximized Brain

In our rapidly evolving modern world, many individuals experience profound cognitive overload. This phenomenon is driven by two primary factors: a desperate urge to hoard information to make sense of rapid societal changes, and a relentless pressure to maintain extreme levels of productivity to keep pace with these changes. To cope, we construct elaborate systems, stick rigidly to exhausting routines, and manage endless task lists—often at the expense of our mental health.

Compounding this stress is the digital "leaderboard" of social media, which fosters perpetual self-comparison. We constantly question whether we are fast, productive, or ambitious enough. This state of mind births what neuroscientist Anne-Laure Le Cunff terms the Maximized Brain: the subconscious belief that any goal we pursue must be executed in its largest, most ambitious form. For example:

• Instead of simply exercising, we commit to going to the gym every single day.

• Instead of writing a simple daily page, we resolve to write a novel.

• Instead of testing a creative side project, we try to build a high-growth startup.

This maximalist approach is highly fragile. It routinely leads to overwhelm, emotional burnout, and eventual abandonment because the friction of the goal is simply too immense for our primitive brains, which have not evolved since prehistoric times.

The Four-Quadrant Mindset Matrix

Mindsets act as the default filters through which we view the world, subconsciously driving our choices, relationships, and emotions. By mapping the two core drivers of personal growth—Curiosity and Ambition—on a coordinate matrix, Le Cunff identifies three restrictive subconscious mindsets and one empowering alternative:

• The Cynical Mindset (Low Curiosity, Low Ambition): Driven by a constant state of survival, individuals in this mindset have lost interest in exploration or self-improvement. They frequently engage in self-soothing or avoidant behaviors like "doom scrolling," consuming negative news cycles, and debating these current events endlessly with others while mocking earnest or curious individuals.

• The Escapist Mindset (High Curiosity, Low Ambition): These individuals retain a strong sense of wonder but have entirely abandoned their personal or professional ambitions to dodge the weight of responsibility. This mindset manifests as seeking temporary relief through retail therapy, binge-watching, or planning elaborate fantasy vacations instead of taking active steps to improve their daily lives.

• The Perfectionist Mindset (Low Curiosity, High Ambition): Characterized by self-coercion, toxic productivity, and overwork, perfectionists try to conquer life's inherent uncertainty through sheer labor. They are entirely driven by fixed outcomes, falsely believing that happiness is a milestone deferred until a specific goal is conquered.

• The Experimental Mindset (High Curiosity, High Ambition): This is the ideal alternative. In this state, uncertainty is viewed not as a threat, but as an open invitation to learn, play, and grow. Failure is reframed from a personal deficit into a valuable objective data point.

Because these mindsets are highly fluid and context-dependent rather than static personality traits, individuals can intentionally transition toward the Experimental Mindset through conscious self-awareness.

The Anatomy of a Tiny Experiment: The PACT Framework

To cultivate an experimental mindset, Le Cunff proposes substituting intimidating, high-stakes goals with Tiny Experiments based on the scientific method. This cycle begins with basic observation, moves to formulating a research question, designs an experiment to gather data, and concludes with an objective analysis to decide the next path forward.

To formalize these experiments, individuals commit to a PACT—a highly structured commitment device characterized by four key attributes:

• Purposeful: It must align with something the individual genuinely cares about, imbuing daily actions with micro-purposes rather than demanding a grand, intimidating "life purpose."

• Actionable: The task must be immediately executable without requiring outside permission, additional resources, or complex preparations.

• Continuous: It must be practiced regularly over a pre-determined timeframe (e.g., daily for two weeks, or weekly for two months). Setting a fixed duration prevents the Maximized Brain from quitting prematurely when initial results are uncomfortable.

• Trackable (Not Measurable): It avoids complex key performance indicators (KPIs) or objectives and key results (OKRs). Tracking is binary: did you perform the action today, yes or no?

Distinctions from Other Frameworks

• PACT vs. New Year's Resolutions: Resolutions are typically overly ambitious, highly rigid, and prone to early abandonment. A PACT is small, highly achievable, and can be initiated at any point in the year.

• PACT vs. Habits: A habit is an indefinite commitment to a behavior pre-determined to be beneficial. A PACT is a temporary diagnostic tool designed to test if a behavior actually suits the individual before they commit to making it a habit.

Data Analysis: Balancing External and Internal Signals

When analyzing the results of a PACT, one must weigh two distinct data pools:

• External Signals: Objective metrics such as financial returns, career progression, audience growth, or traditional markers of success.

• Internal Signals: Subjective emotional data, including energy levels, enjoyment, anxiety, and fulfillment.

To illustrate this balance, Le Cunff shares her personal experiment with becoming a YouTuber. She committed to a PACT to write, film, and publish one video per week until the end of the year. Externally, the experiment was highly successful, yielding high subscriber counts, positive feedback, and collaboration offers. Internally, however, Le Cunff felt intense dread and anxiety when filming, which triggered severe procrastination on filming days. By prioritizing internal data alongside external success, she confidently ended the channel, choosing instead to focus on her thriving newsletter.

Navigating Uncertainty and the Power of Affective Labeling

Modern society is marked by rapid technological and cultural disruption, leaving individuals with an deep-seated sense of instability. In response, our brains seek artificial control by consuming endless streams of information, mistakenly conflating raw information with actual knowledge.

From an evolutionary standpoint, the human brain is hardwired to fear the unknown. In primeval environments, unexpected noises or unfamiliar foods represented lethal threats. Today, this translates into intense neural activity when facing uncertainty. Studies show that human beings experience significantly higher levels of psychological stress when facing uncertainty than when anticipating known physical pain. This is why we often prefer receiving immediate bad news over waiting in suspense; bad news grants a comforting illusion of control.

Affective Labeling as a Neural Intervention

To shift from fearing uncertainty to collaborating with it, Le Cunff recommends Affective Labeling—the practice of putting exact words to complex emotions.

• Neurological Impact: Affective labeling downregulates activity in the amygdala (the brain's primitive center for unconscious emotional processing) and increases activity in the prefrontal cortex (the seat of rational, logical thinking).

• The Landscape Technique: If finding a precise word for an emotion is too difficult, individuals can describe their internal state as a physical landscape (e.g., "a stormy day over a dark forest" or "a steep, scary cliff overlooking a beautiful sea").

By processing our emotions before attempting to solve the objective problems of a life disruption, we protect our long-term mental health and clear our cognitive space to make better decisions.

Embracing Liminal Spaces

Liminal spaces are transitional phases of life where the old reality has dissolved but the new one has not yet formed. Because the brain craves clear, binary classifications (e.g., safe vs. dangerous), these periods feel highly uncomfortable. Le Cunff illustrates this with the airplane metaphor:

• Anxious Avoidance (Response 1): Trying to escape the transition by sleeping, drinking alcohol, or waiting anxiously for the flight to end.

• Mindful Agency (Response 2): Embracing the disconnection from daily routines (such as having no Wi-Fi) as a pocket of freedom to read, journal, reflect, or let the mind wander.

True freedom resides in the brief gap between a stimulus and our response. Pausing to choose our reaction rather than relying on automatic, fear-based defaults builds the confidence needed to adapt to a changing world.

Deconstructing the Three Subconscious Cognitive Scripts

Cognitive scripts are internalized behavioral patterns that dictate how we assume we should behave in specific scenarios. While useful for routine tasks (e.g., knowing the order of events when visiting a doctor or dining at a restaurant), they become highly damaging when applied to major life choices. Le Cunff identifies three dominant scripts that restrict human potential:

• The Sequel Script: The subconscious assumption that our future choices must maintain narrative continuity with our past. This script compels individuals to stay in careers solely because they match their university majors, or to repeatedly date the same type of person to maintain behavioral patterns.

• The Crowd Pleaser Script: Making major life decisions designed to satisfy the expectations of parents, partners, peers, or colleagues, prioritizing their comfort and validation over personal fulfillment.

• The Epic Script: The culturally celebrated belief that a meaningful life must be marked by grand ambitions, massive impact, and monumental success. This script stigmatizes simple, present, and deeply connected lives, falsely insisting that putting all of our emotional eggs in one high-stakes basket is the only path to validation.

The Epic Script has gained immense popularity due to Survivorship Bias on social media, where the exceptional successes of a few passion-driven founders are broadcasted, while the thousands who followed the exact same path and failed remain completely invisible. This has led to a 700% surge in the use of the phrase "find your purpose" in literature over the last twenty years.

Breaking Free

To break free from these scripts, one must identify the word "should" in their daily internal monologue, as it is a prime indicator of an active cognitive script. By replacing "should" with "might" (e.g., "What might I explore today?" instead of "What should I do?"), individuals open up possibilities for curiosity-driven choices. Le Cunff offers three core diagnostic questions to evaluate these scripts during any life transition:

• Am I following my past, or am I discovering my path? (Counteracts the Sequel Script)

• Am I following the crowd, or am I discovering my tribe? (Counteracts the Crowd Pleaser Script)

• Am I following my passion, or am I discovering my curiosity? (Counteracts the Epic Script)

Reframing Procrastination & Mindful Productivity

In a society shaped by post-industrial work moralization, productivity has been elevated to a measure of personal virtue, while procrastination is viewed as a character flaw. This cultural dynamic has fueled a massive commercial productivity industry of time-tracking software, structured templates, calendars, and wearable devices.

Le Cunff challenges this view, arguing that procrastination is not a sign of laziness, but rather an invaluable neurological signal. She introduces the Triple Check Tool to diagnose the root cause of procrastination across three dimensions:

• The Head (Rational Block): You are not logically convinced that the task is worth doing.

• Remedy: Redefine the strategy or brainstorm alternative approaches with colleagues.

• The Heart (Emotional Block): The task feels boring, painful, or thoroughly unengaging.

• Remedy: Redesign the experience to make it enjoyable (e.g., working from a favorite coffee shop or co-working with a colleague).

• The Hand (Practical Block): You lack the necessary tools, skills, or resources to execute the task.

• Remedy: Raise your hand and request targeted training, coaching, or mentorship.

If a task aligns across the Head, Heart, and Hand, but procrastination persists, it indicates systemic or environmental barriers that require either direct communication with stakeholders or removing oneself from that environment entirely.

The Second Arrow of Suffering

Le Cunff connects this to the Buddhist parable of the Two Arrows:

• The First Arrow: The initial negative event or difficult emotion (e.g., the act of procrastinating).

• The Second Arrow: The self-inflicted shame, guilt, and anger we experience for feeling that initial emotion.

While the first arrow is often an unavoidable part of human life, the second arrow is entirely optional. Eliminating self-blame allows us to address the root causes of our work blocks with clear, scientific curiosity.

Mindful Productivity and Magic Windows

Traditional productivity treats time as a series of rigid boxes to be packed with tasks. Mindful Productivity shifts this focus from time to energy, managing our physical, emotional, and cognitive resources. This approach centers around identifying and utilizing our Magic Windows—those natural daily periods of deep focus and effortless flow where time seems to slip away. By answering three key questions, individuals can structure their workdays around these natural energetic peaks:

• When is my Magic Window?

• What specific tasks belong in this window?

• How can I protect and keep this window open?

The Practice of Self-Anthropology

For action-oriented individuals, the hardest part of any new venture is simply starting, because they focus heavily on final outcomes. To overcome this friction, Le Cunff introduces the concept of Self-Anthropology: treating your own life as an ongoing field study.

By stepping back and documenting your daily existence with the objective curiosity of an anthropologist, you begin to question deep-seated assumptions and identify areas ripe for tiny experiments.

The 24-Hour Self-Anthropology Exercise

To practice this, spend 24 hours recording objective observations in a notebook or phone, taking note of:

• Fluctuations in energy levels and mood throughout the day.

• Which conversations and social encounters left you feeling energized or drained.

• Daily cognitive insights from articles, podcasts, or thoughts.

• The direct emotional and behavioral outcomes of your tasks.

Moving from Observation to Experimentation

• Observe: Identify patterns (e.g., "I notice that I feel highly energized when presenting my ideas at work, but I also experience brief spikes of performance anxiety beforehand").

• Hypothesize: Formulate testable solutions (e.g., "Perhaps taking a brief public speaking class, working with a performance coach, or simply volunteering for more internal presentations will build my confidence").

• Experiment: Select a single hypothesis and design a brief, structured PACT (e.g., "I will volunteer to give one short team presentation every two weeks for the next business quarter").

• Evaluate: At the end of the set timeframe, collect and analyze both internal and external data points to decide whether to persist, pause, or pivot.

By adopting this continuous cycle of self-observation and low-stakes experimentation, you can shed the burdens of perfectionism and linear success, designing a life of genuine agency, agility, and curiosity.

Transcript

Part 1: The Experimental Mindset

I'm Anne-Laure Le Cunff. I'm a neuroscientist and the author of Tiny Experiments: How to Live Freely in a Goal-Obsessed World.

Are we all experiencing cognitive overload?

A lot of us are currently experiencing cognitive overload, and there are many reasons for that. One of them is that the world is changing fast, and we're trying to hoard as much information as possible to understand what's going on around us. Another one is that we're trying to be as productive as possible in order to keep up, again, with this world that keeps on changing. Because of that, we try to build systems. We try to stick to routines, and we try to go through very long lists of tasks, often ignoring our mental health in the process.

In essence, there is a lot more to think about on a daily basis, but our brains haven't evolved. They're still the same that they were thousands of years ago. We're all staring at a giant leaderboard with social media where we can see how other people are progressing, their success, and where we keep on comparing ourselves to each other. This creates anxiety because we keep asking ourselves: How am I doing? Am I doing better? Am I being fast enough? Am I being productive enough? Am I being ambitious enough?

What is the maximalist brain?

What I call the maximized brain, or the maximalist brain, is this belief that we have that whatever we do, it has to be the biggest, the most ambitious version of this goal. When we want to exercise, we decide that we have to go to the gym every single day. If we want to start writing, we decide that we're going to start writing a book. If we want to explore a new project, we decide that this has to be a startup.

The problem with the maximized brain is that it very often leads to overwhelm and burnout, and sometimes just completely abandoning our projects because they're just too big. Tiny experiments offer an alternative to this maximized approach where instead of going for the bigger thing, you go for the thing that is most likely to bring you discovery, fun, enjoyment, and that is based on your curiosity rather than an external definition of success. When you're running a tiny experiment, you're not trying to do something big; what you're trying is just to learn something new.

How did you discover the experimental mindset?

I divide my life into two separate chapters. In the first one, I had a very linear approach that was driven by traditional definitions of success. I did my best to do well in school, and then I got a good job at Google, and then I tried to climb the corporate ladder, getting a promotion, working on the best projects possible. From an external standpoint, I should have been happy, but I wasn't. Instead, I was feeling empty inside. I was both bored and burned out.

I left my job at Google thinking that I should try to do something different and not realizing that I was following yet another script of success. I decided to start a startup, and again, I didn't find happiness there by following this idea of success that everyone around me was following. It's only when my startup failed, and for the very first time in my life I didn't have a clear idea of what I was supposed to do next in order to be successful, that I finally asked myself: "What is it that I wanted to do? What would make me happy even if I forgot about the traditional definition of success?"

And so I went back to the drawing board, and I started thinking about what I was curious about—again, not based on traditional definitions of success. What were topics I would be excited to explore even if nobody was watching? And for me, that was the brain. I had always been fascinated with why we think the way we think and why we feel the way we feel. So I decided to go back to university to study neuroscience. I completed my graduate studies and I got a PhD in neuroscience.

Throughout this journey, I decided to learn in public, and this is how I started my newsletter. Every week I would pick a topic that I had discovered in my studies in university, and I would take those neuroscience insights and turn them into practical tools that I would write about in the newsletter to help other people apply them in their life and work. This tiny experiment of starting to write online and sharing what I was learning in public was the beginning of my work of trying to understand how we can live more experimental lives.

Why is mindset so important?

A mindset is a default way of seeing the world, and our mindsets influence so many things in our lives. They influence our decisions, they influence our relationships, they influence the way we think, and even the way we feel. When we're not aware of our mindsets, they can impact the direction of our life, the path that we're taking, without us even realizing it.

Being aware of your mindsets is the difference between living a conscious life where you're making choices in accordance with what you actually want and going where you actually want to go, versus being on autopilot and having those mindsets subconsciously drive all of your decisions. The great thing about mindsets is that they can actually change, but the first step is to make them conscious.

What are the mindsets that hold us back?

There are three subconscious mindsets that get in the way of us living happy, conscious lives. These three mindsets are called the cynical mindset, the escapist mindset, and the perfectionist mindset.

• The Cynical Mindset is when we have lost all curiosity and ambition in life, and we're actually sometimes even making fun of earnest people who still have this high level of curiosity and ambition. When we're cynical, we feel like there's no point trying because we're in survival mode all the time. So things that we might be doing instead include doom scrolling, sitting on the sofa going through negative news, being stuck in this cycle, and then maybe even spending a lot of time and energy discussing and debating those negative news with other people.

• The Escapist Mindset is when we're still curious, but we have decided to let go of our ambitions. We're trying to do everything we can to escape our responsibilities. That can take the form of retail therapy, binge-watching, or dream-planning our next vacation instead of doing something right now to change our lives.

• The Perfectionist Mindset is when we try to escape uncertainty through work. We have high ambition but low curiosity. That might look like self-coercion, overworking ourselves, and toxic productivity. Our goals are driving all of our decisions. We feel like if we manage to achieve that goal, if we manage to be successful, then we'll be happy.

You can picture those three subconscious mindsets on a 2x2 matrix where the two different factors are curiosity and ambition:

• Cynical Mindset: Low curiosity, low ambition.

• Escapist Mindset: High curiosity, low ambition.

• Perfectionist Mindset: Low curiosity, high ambition.

Those mindsets are actually very fluid and they might change depending on our situation, different triggers, and different ambitions that we might have at the moment. This is actually really good news because that means that these mindsets are not fixed personality traits. We can change them by becoming aware of them and then making the decision to change our mindsets. This is something we can achieve.

What mindset should we strive for?

There is an alternative to those three mindsets, which is called the Experimental Mindset. This is a mindset where your curiosity and your ambition are both high. In an experimental mindset, you're open to uncertainty. You see it as an opportunity to explore, to grow, and to learn.

Having an experimental mindset helps us completely reimagine our relationship to ambition and to goals. When you have an experimental mindset, instead of chasing those linear goals that give you the illusion of certainty, you're open to designing experiments. Instead of trying to get to a specific outcome, you start from a research question. Anytime you don't understand something, it doesn't create fear; it creates curiosity.

Having an experimental mindset means seeing failures as data points that you can learn from. It means being open to making mistakes because you know you're going to learn from them. It means embracing the fact that you might not have a plan, that you don't know what's coming, and this is great. It means that you can design your life in a way that is conscious and connected.

How do you cultivate an experimental mindset?

The idea of cultivating an experimental mindset is based on the scientific method, and this is very simple. First, you start by observing your current situation by looking at the world around you. Then you ask a research question, and you design a tiny experiment to collect data which you can then analyze. Based on those results, you can decide what your next step is. What's great about that is that even though you don't know where you're going, you can trust that you're going to grow through each cycle of experimentation.

To design an experiment, you need to commit to curiosity. A great way to do this is to design what I call a PACT. This is a commitment device where you say, "I am going to run this experiment." The way it works is that you choose one action, you then decide on a duration, and you say, "I will perform this action for this specific duration."

Why does it look like that? There are several reasons:

• The first one is that when a scientist designs an experiment, they decide in advance on the number of trials. You don't simply stop in the middle if the results don't really look like what you expected. You collect all of the data, and once you have all of that data, you can analyze it and decide what the answer is.

• The second reason is that it allows you to notice when you're falling prey to the maximized brain. You can make sure that you're keeping your experiment tiny enough that you're actually going to complete it by choosing a duration that is reasonable, something you can actually achieve, so you can collect all of the necessary data.

A PACT is:

• Purposeful: It needs to be something you care about. What's great is that when each PACT you design has purpose imbued into it, you don't need to have a grand purpose in life.

• Actionable: This is something that you need to be able to do right now. You don't need extra resources. You don't need help from other people. This is something you can try straight away.

• Continuous: This is something that you need to do regularly. Again, you decide on the duration and the number of trials, and then you say, "I'm going to do this action for two weeks," or two months, or one year.

• Trackable, not measurable: You don't need complicated metrics. You only need to be able to say whether you did it or not. Did you do the action? Did you perform it? Yes or no? That's the only tracking you need.

A PACT is not a New Year's resolution. This is not something that you decide on that is very ambitious at the beginning of the year and that you're going to abandon. This is something small and achievable that you can start doing at any point during the year.

A PACT is not a habit either. The difference between a habit and an experiment is that with a habit, you're very clear that this is something that's going to be good for you, and so you commit to it for an indefinite amount of time. For example, you say, "Starting today, I'm going to go to bed at the same time." Whereas with an experiment, you're not quite sure whether this is going to work for you or not; you're going to test it. So you're going to say, "I'm going to go to bed at the same time every night for two weeks, and only then am I going to decide whether this is good for me or not and whether I want to turn it into a habit."

And a PACT is not a KPI, an OKR, a performance metric, or whatever people call them in the corporate world. It's really just about learning something new. It's not about being successful or getting to a specific outcome.

How do you analyze the collected data?

Once you've completed your PACT and you've gone through the entire duration of that data collection phase, you can finally look at the data. I highly encourage you, while you're running your PACT, to take little notes. It can be very simple—a few bullet points on your phone, something in your journal—just to keep track of whether you did it or not and how it felt. Based on that, you can make the decision to either persist with your PACT as is because it works for you, pause it if you feel like that's not really something you want to keep going with, or pivot, which means making a little tweak and changing something before you start your next cycle of experimentation.

A lot of us tend to only pay attention to external data or internal data when we're analyzing our experiments. For example, we might only look at the external metrics of success or only at the internal feelings that we might be experiencing. But both are very valuable in order to make the right decision when it comes to your next steps.

The external signals might show you whether this is something that is worth pursuing in terms of financial success, career, or any ambition that you might want to explore. But the internal signals are also very important. There's no point being successful externally if it feels horrible to work on this project. Equally, if it feels really good but you have no way to sustain yourself with this project, it might be worth reconsidering the parameters to find a way where you can have an overlap between external success and internal positive feelings and emotions.

How have you personally employed the experimental mindset?

Let me give you an example. I designed an experiment where I wanted to explore whether I wanted to become a YouTuber. This was something I had noticed a lot of friends around me doing, and they seemed to have a lot of fun. That piqued my curiosity enough that I wanted to give it a try.

So I designed a PACT, and I said, "I'm going to publish a video every week until the end of the year." It was a very simple PACT, which I completed. Every week, I published a new video. At the end of my PACT, I looked at the data. External data was pretty good: looking at the traditional metrics of success of a YouTube channel, I got to a good number of subscribers and received a lot of positive comments. People seemed to like the videos, and I even had some people reaching out and asking if we could collaborate together.

But looking at the internal data, I actually did not enjoy producing these videos. Every week when I had to sit down in front of the camera, I was dreading it. I love having face-to-face conversations with people and seeing their reactions in real time, but just looking at a camera with no feedback whatsoever was very uncomfortable for me. As a result, every week I was procrastinating for so long. Every time I had to film a video, I felt deeply anxious, and I was not even able to work on anything else on those days where I was supposed to film.

This is a typical example showing why it is so important to consider both the external and the internal signals before making a decision. Based on this, even though the YouTube channel was fairly successful in such a short amount of time, I decided to stop. I realized that I was not going to be a YouTuber, and I preferred to keep on writing my newsletter.

What are some tiny experiments anyone can do?

You can run tiny experiments in all areas of your life.

• In work, for example, you could say, "I'm going to write an internal newsletter every week for the next six weeks where I share the most interesting links that I find."

• In relationships, you could say, "I'm going every Sunday to sit down and send a note to a friend I haven't talked to in a while."

• And when it comes to your health, you could say, "I'm going to go for a walk for 20 minutes for 20 days to see how I feel at the end of that experiment."

With an experiment, you're not making any assumptions as to whether this is going to work for you or not. A habit that a friend has—for example, going for a run three times a week—might not be that good for you. Maybe when it comes to your health and body movement, you prefer dancing or something else. That's why it's interesting to run an experiment before committing to a habit. When it comes to running, for example, you could say, "I'm going to try that. I am going to go and run three times a week for three weeks, not for the rest of my life, and at the end of the three weeks, I'm going to decide whether this is what I want to keep doing or if I want to experiment with another way to move my body."

Why should we commit to curiosity?

In essence, committing to curiosity is ensuring that you're going to live a life that is intentional—that you're going to live your life, not the life that other people are expecting you to live. Curiosity keeps you adaptable and nimble in an ever-changing world. It ensures that you stay open to new possibilities, and frankly, it just makes life more fun.

This all might sound philosophical, but there's actually a lot of neuroscientific research showing that when we experience a thirst for water, the same parts of the brain activate as when we experience a thirst for information. So when we say, "I'm thirsty for knowledge; I want to learn more; I want to know more," we couldn't be more right. Thinking about your own mindsets and developing this self-awareness is really just a way to direct this curiosity towards the things that you actually want to do with your life.

Part 2: The Illusion of Certainty

Why is there a feeling of uncertainty in the world?

The pace of technological and societal change has accelerated massively. We feel like we cannot rely anymore on those traditions and institutions that used to give us a sense of stability. It's become increasingly difficult, or maybe even impossible, to know what skills we should invest in in order to build the careers of the future. To make it worse, we have infinite access to information, which makes us feel like if we just keep on reading, listening, and watching, we might get that key information that is going to finally give us this sense of elusive certainty.

This modern environment is anxiety-inducing. The problem today is that it's become really, really hard to know the difference between information and knowledge. Information is just a piece of data; it could be valid, it could be not valid, it could come from any kind of source, and it could be based on different contexts and circumstances that we're not aware of. By consuming all of this information, we might actually not take action to go out into the real world and collect our own data and our own knowledge, which would be much more helpful in order to make our decisions.

The less control we have, the more we seek it, and that might take a lot of forms. For example, trying to consume as much information as possible just so we feel like we have a sense of clarity as to what is going on. That could also look like sticking to the safe path—the more obvious one where we feel like we understand all of the parameters—or sometimes that could even look like doing nothing because we're too afraid to do something dangerous.

How are uncertainty and anxiety linked?

Uncertainty fuels anxiety. Research shows that when we experience uncertainty, our neural activity intensifies. Our brains are basically on high alert, getting ready for any threat that we might face, and that happens even when there's no actual danger.

In studies, uncertainty has been found to cause more stress than individual pain. The reason why is because when we know we're going to experience pain, we can mentally prepare for it. Whereas when we're facing uncertainty, the doubt of not knowing what kind of pain or what level of pain we will face is actually more stressful than knowing exactly what's going to happen. This is also why we prefer getting bad news over waiting for an answer. Bad news feels better because now we know; even if it's negative, we feel like we're in control.

Why did our brains evolve to fear uncertainty?

Our brains are wired to fear uncertainty, and that makes sense from an evolutionary perspective. When you think about the conditions in which our species started, the more information you had, the more likely you were to survive. A weird noise in the bushes or a new food that you'd never tried before—all of these could be lethal. So anytime you find yourself in a situation where you feel like you don't understand everything that's going on, or where you feel like you don't have all of the information, your brain is trying to get to a solution.

Obviously, this was very useful in the jungle, but not so much in our modern environment. We try to get to an answer as quickly as possible, which means we sometimes go for the most obvious one rather than the most interesting one. It might even reduce our ability to connect with other people, because we feel it is better to stay with people that we understand and people we know rather than trying to connect with strangers, because strangers could mean danger, uncertainty, fear, and anxiety.

How should we approach uncertainty instead?

Instead of fearing uncertainty, we should learn to collaborate with it. What that looks like is seeing uncertainty as an opportunity for learning and for growth. When there is no uncertainty, when we know exactly what we're doing, that means we're not growing anymore. So we should actually seek uncertainty, embrace it, and every time we face it, say, "Hello, welcome back. Let's work together."

All scientists know that real growth requires both trial and error. If you keep on repeating the same trial and everything works as expected, it means that you're not growing; you're not learning anything. You're just repeating the same thing that you know is working already. It's only through errors that you can adjust your path, try new approaches, and discover that some of your assumptions were wrong. This is why all scientific experiments are just cycles of trial and error, and this is modeled after nature. This is how nature evolves, and this is how we can evolve. By making sure that we try new things, make mistakes, and learn from them, we can grow in our personal and professional lives.

What is the linear model of success?

A linear model of success is based on a fixed outcome that we try to get to. It implies that first you do A, then B, then C, and then you'll be successful. There are lots of problems with a linear model of success. One of them is that it assumes that you know where you're going, which might not always be the case. Another one is the assumption that wherever you want to go right now is where you will want to go a few years from now. Things are changing very fast, our world is evolving, and you should allow yourself to change the direction of your ambitions as the world changes.

Another problem is that linear goals breed toxic productivity and unhealthy comparison. When we're all climbing similar ladders in parallel next to each other, we might compare ourselves to each other, looking to the right and to the left and asking, "Is this person being faster? Is this person working harder? Is this person being more successful?" This creates toxic productivity when we overwork ourselves just trying to climb those ladders as quickly as possible.

This can create a sense of ambivalence toward goals. It feels so exhausting to keep on climbing this ladder with no certainty at all that we might get to the top or that we might get there fast enough. This exhaustion from constantly comparing ourselves to each other has led some people to adopt a new mantra: "No goals, just vibes."

How can we go from linear success to fluid experimentation?

To break free from this goal-obsessed life, we need to go from rigid linearity to fluid experimentation. That means letting go of the focus on the outcome and instead embracing the joy that we can find in the process. Instead of focusing on climbing the ladder, we should try to design cycles of experimentation. Instead of focusing on certainty, this is all about focusing on curiosity. Breaking free from this goal-obsessed life and developing an experimental mindset is all about going from outcomes to processes, from ladders to growth loops, and from certainty to curiosity.

How can labeling emotions help manage uncertainty?

Affective labeling means labeling your emotions. It really just means putting words to feelings, and it's incredibly helpful because it allows you to better connect with and understand your emotions so you can manage them better. Affective labeling allows us to reduce activity in the amygdala, which is involved in unconscious emotional processing, and to increase activity in the prefrontal cortex, which is involved in rational thinking. This allows us to better manage our emotions.

What's great about affective labeling is that it's very simple, doesn't cost anything, and you can do it straight away whenever you want to better regulate your emotions. It's really just about picking a word to describe your current emotion. If you can't find the right word, there is research showing that describing a landscape is also a great way to practice affective labeling. You could say, for example, that "it's a stormy day over a dark forest," or you could say that "it's a scary cliff over a beautiful sea." That's a great way for you to describe your emotions when you cannot find just one word that captures exactly what you're feeling, acting as a way to map out your emotional landscape.

Whenever we face a disruption in our life, it's very tempting to try and immediately solve the objective consequences—the actual problems that arise from that disruption—and completely ignore the emotional experience of that disruption in the process. Affective labeling is really about making space for that emotional processing before you deal with the objective consequences.

The reason why this is important is twofold:

• First, this is going to be better for your mental health in the long term. By processing these emotions as you go through them rather than ignoring them, you're going to be more connected to yourself and experience better mental health.

• Second, this is also going to help you better solve the actual problem. When your mind is clear and you have processed the emotions, you will be able to think about the actual consequences in a more efficient way.

This practice is particularly useful for people who like to feel like they're in control and who tend to hide their emotions underneath something else rather than connecting with them. This might look like hiding an emotion underneath a more manageable, surface-level emotion that we are more familiar with. For example, if we're sad, we're just going to say that we're a little bit angry or a little bit disappointed about this, when really the core emotion is sadness. Or sometimes that might look like hiding the emotion under thinking, logic, and the rational brain, where we convince ourselves that we are looking for a solution, being proactive, and dealing with the problem, so everything is fine and we're not feeling anything too complicated or unmanageable.

Why do humans struggle with transitional periods?

We struggle with liminal spaces and transitional periods because our brains really like to be able to categorize situations as safe or dangerous as quickly as possible. When we can't do that, it feels very uncomfortable. We want to get out of this transitional space, get back to safety, and get back to a sense of clarity.

Imagine that you're on a plane 30,000 feet up in the sky with no Wi-Fi. There are two different responses that you can have:

• Response 1: You feel anxious. Because of that, you might try to sleep the entire time to ignore that you're on the plane. You might drink alcohol to dull some of the anxiety, and in general, you just want the flight to be over as quickly as possible.

• Response 2: You feel like, "This is great—no Wi-Fi, nobody can contact me. This is freedom." You might decide to watch a movie that you've been meaning to watch for a long time, crack open a book you wanted to read, journal, or just let your mind wander, really enjoying that space of transition that is outside of your daily routine and outside of the sense of certainty that you normally have as you go about your daily life.

There's a famous quote that has been attributed to a lot of different psychologists, including Viktor Frankl, which says: "Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth and our freedom." What that means is that whenever we find ourselves in a situation or face a trigger, there is a little gap before our response, and in that gap lies the freedom to make a choice. Are we going to go for the automatic response—the one we have no control over—or are we going to pause and ask ourselves, "How do I want to respond to this situation?"

Every time we use that freedom, every time we pause and ask ourselves, "What do I actually want to do?", we prove to ourselves that we have agency and that we are able to make our own choices. That doesn't mean it is always the correct choice. In many cases, there is no such thing as making the right choice because we don't have all of the information and things are changing so fast. But at least we know that we don't have to go for the automatic response. Ultimately, that means living a life not of rigid control, but of agency, experimentation, and exploration. This allows us to build confidence in our ability to adapt as the world changes around us.

Part 3: The Three Cognitive Scripts That Rule Your Life

How do we discover our purpose?

When you ask happy people how they discovered their passion, if they give you an honest answer, they'll tell you they stumbled upon it. What we can learn from that is that finding your passion and finding your purpose in life is not really about seeking it, obsessing over it, or applying a step-by-step plan to figure out what it is. Instead, it's about following your curiosity, experimenting, exploring, trying new things, and trusting that you will figure out what makes you excited to wake up in the morning.

What is a cognitive script?

A cognitive script is an internalized behavioral pattern that tells us how we're supposed—or at least how we think we're supposed—to act in certain situations. They can be very helpful for routine tasks and decisions in everyday life. Cognitive scripts were first discovered in a seminal 1979 study where researchers found that people follow very similar scripts in similar situations, such as going to the doctor or dining at a restaurant. Since then, researchers have found these cognitive scripts in all areas of life.

For routine, everyday decisions, cognitive scripts are actually very practical and useful. For example, you know that when you go to the doctor, you're supposed to wait in the waiting room, and then someone is going to call your name, and then you're going to go into the doctor's office, and this is where you're going to start telling them about whatever the issue is. You know in which order you're supposed to do all of those different actions. That's great, it's useful, and it's a pretty good thing that you don't have to overthink it every time you go to the clinic.

The problem with cognitive scripts is when we use them to make the more important decisions in our lives, in our careers, and in our relationships. Instead of asking ourselves, "Is that really what I want to do? Is that my decision?", we let our choices be driven by those stories that we have internalized—by those scripts that tell us how we're supposed to behave in a given situation.

What is the sequel script?

One of these scripts is the Sequel Script. That's the script where we feel like we've always behaved in a certain way, so we're going to keep on behaving in the exact same way. We feel like the narrative of our life needs to make linear sense.

This is the script that makes people choose careers that are aligned solely with whatever they studied at university. This is the script that makes people keep on dating the same kind of people they've been dating before, and this is, in general, the script that makes us repeat the exact same behaviors and patterns that we've had in the past.

In relationships in particular, what's very interesting is that we want the "sequel" to connect to whatever the previous experience was. This might mean dating the exact same type of person, or choosing the next person in response to whoever we were dating before. They might look like the complete opposite, but the truth is we still picked this person based on a sense of continuity with whatever the previous experience was before.

With the sequel script, it's quite obvious why it limits the possibilities that we might explore in life. Because we feel like whatever decision we make next needs to make sense in relation to the decisions we made in the past, we ignore a lot of the more left-field, unexpected decisions that could be brilliant opportunities for growth, exploration, and self-discovery.

What is the crowd pleaser script?

Another cognitive script that rules our life is the Crowd Pleaser Script. This is the script where we make decisions based on whatever is going to please the people around us the most. Quite often, those other people are our parents. We might make decisions based on whatever is going to make them feel like we are safe and successful.

But the audience for the crowd pleaser script can also be your friends, your partner, or your colleagues. What you don't realize when you follow the crowd pleaser script is that you're not making decisions based on what you want and what would make you happy, but rather based on what will make others around you happy.

What is the epic script?

Finally, there's the Epic Script, and this one is very insidious because it's actually celebrated in our society. It's the script that says that whatever you do, it needs to be big, it needs to be very ambitious, and it needs to be highly impactful. Anything less than that is considered a failure.

The epic script is an extreme version of the idea of following your dreams. Because of that, it has created a form of stigma around having a small, simple life—a life that is focused on just being happy in the moment, being present, being connected, and exploring your curiosity. It makes us worry: "If I don't have those external signs of success, if I'm not following some grand passion, then am I really living a meaningful life?" This is the anxiety-inducing question created by the epic script.

When you think about it, this is a very myopic definition of success where we try to put all of our eggs in the same basket. We choose this one thing, and we say, "If I succeed at this, then I'm successful in life." The problem with this is that if we fail at this particular project or this particular goal, we feel like we have failed at life entirely. The other problem with putting all of our eggs in the same basket is that sometimes the basket just becomes too heavy, and we drop it altogether.

Our modern, hyper-connected online world has made the epic script unfortunately very popular. We have become overly obsessed with finding our purpose. Mentions in books of the phrase "find your purpose" have surged 700% in the past two decades alone. We see all of those success stories of people who have found their passion and are very happy as a result. We see the entrepreneur who followed their passion and was highly successful, but we don't see the thousands of other ones who tried the exact same thing and failed. This is called Survivorship Bias, and it's very unfortunate how nowadays we are basing all of our decisions—and even our self-worth—on that incomplete information.

What should we do when we notice we are following a cognitive script?

Once you have identified the cognitive scripts that rule your life, you can actually break free from them. It starts by seeing them as stories we tell ourselves rather than truths that we need to follow blindly.

A really good way to do this is to stop for a second every time you hear yourself saying, "I should do this." The word "should" is actually a really good signal that there might be a cognitive script at play. Once you've identified the places and situations in your life where you're using that word "should," you can decide to replace it with another word: "might."

• "What might I want to do instead of what I should do?"

• "What might I want to explore?"

• "What might I want to experiment with?"

If you want to start writing your own scripts in life, there are three questions that you can ask yourself while designing your next experiment:

• Am I following my past, or am I discovering my path?

• Am I following the crowd, or am I discovering my tribe?

• Am I following my passion, or am I discovering my curiosity?

Those three questions together allow you to embrace the liminal space we're all in, to make friends with uncertainty, to start experimenting more, and, very importantly, to deal with those three powerful cognitive scripts that underlie a lot of our decisions on an unconscious level.

Part 4: In Defense of Procrastination

Why does procrastination have a negative connotation?

Procrastination has become a bit of a dirty word, and that's because we have just been through hundreds of years of moralization around productivity and work. Being productive has come to mean that you are a good, useful, helpful contributor to society. In a way, we have all agreed to tie our self-worth to our productivity, and so not being productive means that you're being lazy, you're not contributing, and you're not being helpful to society.

Because of the efficiency worship that we developed in the industrial age, we now see procrastination as a character flaw rather than what it actually is: a signal that is worth listening to. This has propped up an entire industry of productivity, whether it's online courses telling you how to manage your life and your time to make the most of every single minute, templates to track all of your tasks, wearables to tell you whether you've been using your time correctly, or calendars ruling the way we spend our day. Even if at a personal level you don't feel like productivity is the most important thing you want to optimize for, it's very hard to resist the sirens of our society telling you, "You must be more productive."

As a result of this society that keeps telling us we need to be productive to be a good person, whenever we find ourselves procrastinating, we just try to ignore it. We push through using sheer willpower, and we feel self-blame and self-judgment. Instead, there's a very simple tool that you can use whenever you experience procrastination—a tool that is based on self-discovery, on thinking like a scientist, and trying to understand what the signals are.

What is the Triple Check Tool?

This tool is called the Triple Check, and it's about asking yourself: "Why am I procrastinating? Is it coming from the head, from the heart, or from the hand?"

• The Head (Rational): If it's coming from the head, it means that at a rational level, you're not fully convinced that you should be working on that task in the first place.

• The Heart (Emotional): If it's coming from the heart, it means that at an emotional level, you don't think this is going to be fun or enjoyable to work on.

• The Hand (Practical): If it's coming from the hand, it means that even though at a rational level you feel like you should be working on this, and at an emotional level you feel like it looks like fun, at a practical level you feel like you're not equipped with the right tools or you don't have the right resources in order to get the job done.

How can the triple check inform what we do next?

What's great about the triple check tool is that it's not just a tool for diagnosis; it also tells you what to do in each of those situations:

• If the problem is rational, coming from the head, it means that you might want to redefine the strategy. Maybe reach out to your colleagues and tell them, "Hey, I'm not quite convinced this is the way we should go about this. Do you want to go and brainstorm together and see if there's a better approach that we could use?"

• If the problem is emotional, coming from the heart, you might want to redesign the experience so it's more fun. That could look like going to your favorite coffee shop, or grabbing a colleague and saying, "Hey, let's do a little bit of co-working while I work on this task."

• If the problem is practical, coming from the hand, where you feel like you don't have the right tools, resources, or skills, then raise that hand. Ask for help. Tell the people you're working with that maybe you need a bit of mentoring or coaching, or you need to take a course in order to be able to do that task.

Sometimes you'll go through the triple check—head, heart, hand—and everything feels in alignment, yet you're still procrastinating. In this case, it might be worth looking outside of yourself for systemic barriers. That means having honest, sometimes difficult conversations with other stakeholders, redesigning your environment if it is not conducive to you being focused and productive, or sometimes completely removing yourself from that work environment and doing something else, because you might not be able to change that situation every time.

What are magic windows?

Your magic windows are those moments of high productivity, creativity, and focus where you feel like there is zero effort involved, time is flowing, and your attention is completely locked onto the task that you're working on. Whether you've noticed it or not, we have all gone through those kinds of magic windows in our lives. Those are the moments when you look at the time and you feel like, "Where did it go? What happened?" Maybe you were lost in a conversation with a friend, maybe you were working on a creative task, or maybe you were taking a walk. Those are your magic windows, and what's amazing is that once you learn to identify them, you can start becoming a bit more intentional and opening those magic windows at will during your daily life.

What is mindful productivity?

You might think that mindfulness and productivity are antithetical and don't really belong together. But when you go back to the very definition of mindfulness, it's really about paying attention to your experience in the present moment without self-blame or self-judgment. Mindful productivity is about cultivating this awareness in the way you work and direct your focus. Mindful productivity is really about answering three key questions:

• When is my magic window?

• What belongs in this window?

• How can I keep that window open?

What is mindful productivity's most valuable resource?

The traditional definition of productivity only focuses on time as the most important resource, and the assumption is that every minute is a little box that you need to fill with as much stuff as possible in order to be productive. With mindful productivity, you consider other very important resources: your physical resources, your emotional resources, and your cognitive resources. That really means thinking about your emotions, your energy, and your executive function. Practicing mindful productivity requires you to change your perspective on what the most important resource is, shifting from time to energy.

How does managing emotions influence productivity?

We have a very negative relationship with procrastination. If you go online trying to find solutions for how to deal with procrastination, you're only going to find articles that tell you how to "beat" or "defeat" procrastination—very violent language. But instead, if we start seeing it as a useful signal, it allows us to better connect with the emotions surrounding that procrastination and to understand that this is really just our brain trying to send us a message. By reconnecting with these emotions instead of trying to ignore them, not only are we going to understand ourselves better, but we will also better understand our relationship to work and become more productive in the process.

What does death by two arrows mean?

In Buddhist philosophy, there is a concept called "death by two arrows" that tries to explain some of the sources of human suffering. The way it works is that whenever we experience a difficult event or emotion, that's the first arrow. In the case of procrastination, when we procrastinate, that is the first arrow. But then there's a second arrow, which is the shame and the self-blame that we experience because of that initial experience.

What's very important to know is that second arrow is completely optional. We don't have to add a second layer of suffering to the difficulties and the challenges that we are facing in the first place.

What's the hardest part of knowing what to do next?

The hardest part of knowing what to do next is often simply getting started. The reason why is because we focus too heavily on the outcome rather than the process. By not focusing on the outcome and instead designing a tiny experiment, you can let go of any rigid definition of success. You let go of that binary result you're looking for, and instead focus on a research question—a hypothesis that you might have, something that makes you feel curious and that you want to explore. Whatever the outcome, whatever the result, if you learn something new, that's progress.

How can we practice self-anthropology?

A great way to start reimagining what your life could look like is to practice what I call Self-Anthropology: becoming an anthropologist with your own life as your topic of study. Just imagine an anthropologist who goes somewhere to study a new culture. They know nothing about this culture and everything is new to them, so what do they do? They take their notebook out and they start taking field notes. They ask questions like: Why are people doing things the way they're doing them? Why do they care about this? Why is this thing so important to them?

You can do the same thing with your life. As a little exercise, you can say that for the next 24 hours, you're going to treat your life as if you were an anthropologist. Take a notebook or use your phone and start taking little notes. Observe what gives you energy and what drains your energy, the conversations that you enjoy having, and the projects that you like working on. Just by doing this, you are going to start questioning a lot of your scripts, a lot of your assumptions, and a lot of your goals, and you're going to open a window for experimentation.

There's no fixed rule when it comes to what you can include in your field notes. It should really be guided by your curiosity, but here are some ideas for inspiration:

• Include insights that you have during the day after reading or listening to something.

• Capture your mood.

• Capture your energy levels.

• Capture the results of encounters and conversations with other people.

Just like a scientist, you need to first start with observation. It can be really tempting when we're a "doer" and we like to get things done to jump straight into designing the experiment, but it's important to have that initial phase of observation.

Let's walk through an example of how this works:

• Observe: Let's say you observe that you get a lot of energy from giving presentations at work, but also a little bit of performance anxiety.

• Hypothesize: In the second step, you need to formulate a hypothesis. Maybe you would benefit from taking public speaking classes. Maybe you would benefit from being coached. Or maybe you would benefit from just practicing more and giving more presentations at work. The great thing is that you can just pick one of those hypotheses and start testing it; the other ones will still be there if you want to try them later.

• Experiment: You pick one of them, and that's the last part where you design a tiny experiment. You say, "I'm going to commit to this action for this specific duration." In this case, you could say, "I'm going to commit to giving a presentation every two weeks at work for one quarter."

• Analyze: At the end of the quarter, you look at the data. You see how you felt at an internal level, but you also look at the signals at an external level: Did my colleagues enjoy it? Did that bring me new projects? Was that something that was valued inside of the company?

Based on those external and internal signals, you can confidently decide whether you want to keep going, stop, or tweak your experiment for the next cycle.

Summary

This summary provides an overview of the life-altering insights shared by Dr. Jill Bolte Taylor, a Harvard-trained neuroanatomist, during her conversation with host Hala Taha on the Young and Profiting podcast. It details her personal background, the neurological mechanics of her stroke, the warning signs of a stroke, her groundbreaking "Four Characters of the Brain" framework, generational shifts in cognitive dominance, and practical strategies for achieving cognitive balance and whole-brain living.

Introduction and Biological Motivation

Dr. Jill Bolte Taylor's journey into neuroscience was deeply personal. Growing up with a brother diagnosed with schizophrenia, she observed profound differences in how they uniquely perceived the same reality. While she might interpret their mother’s shouting as terror for their safety, her brother perceived it as anger. This stark contrast in emotional processing sparked a lifelong quest to understand how the brain constructs our perception of reality, normal versus abnormal cognitive function, and the cellular basis of human consciousness.

Prior to her stroke, Dr. Taylor viewed consciousness from a strict neuroanatomical perspective. She believed that every human capacity is mapped directly to specific brain cells and that even a single-celled organism possesses a fundamental form of consciousness, defined by its semipermeable membrane separating "self" from "other." In her view, the human body is a highly organized, multicellular culmination of various nested levels of consciousness operating in dynamic neural constellations. Climbing the academic ladder at Harvard Medical School and working under Nobel laureate David Hubel, she integrated her scientific rigor with her artistic passion, specializing in the visualization of cells, neurotransmitters, and neural relationships.

The Stroke: Experiencing Brain Death from the Inside Out

On December 10, 1996, at the age of 37, Dr. Taylor experienced a massive hemorrhagic stroke caused by a congenital arteriovenous malformation (AVM). An AVM is a rare malformation where high-pressure arteries connect directly to low-pressure veins without the buffering capacity of a capillary bed. These malformations typically rupture between the ages of 25 and 45. In Dr. Taylor's case, it had caused chronic, severe migraines since age 17—a symptom that permanently disappeared after surgical removal of the ruptured tissue.

Over the course of four hours, Dr. Taylor watched her own brain shut down:

• The Initial Pain: She woke up to a sharp, pounding pain behind her left eye and extreme light sensitivity.

• Dissolution of Boundaries: While exercising and taking a shower, she watched her motor coordination fail. The sound of running water became painfully amplified. Crucially, as the hemorrhage expanded into her left hemisphere, she lost the ability to define the boundaries of her physical body. Her arms and skin seemed to blend with the molecules of the shower wall, bringing about a state of profound sensory euphoria and boundaryless connection to the universe.

• Loss of Language and Panic: Her right arm became paralyzed, signaling a stroke. Attempting to make a phone call took 45 minutes of painstaking effort. By the time she connected with a colleague, her speech had degenerated into unintelligible, animal-like sounds (which she likened to a golden retriever's bark), though she believed she was speaking clearly.

• The Lack of Fear: Because the hemorrhage was drowning her left emotional brain (the seat of fear and anxiety), she felt no panic. Instead, she felt a profound, peaceful surrender, observing her cognitive collapse with scientific curiosity.

Her survival and recovery were miraculous. After a golf-ball-sized blood clot was surgically removed from her left hemisphere, she spent eight years fully rebuilding her brain. Guided by her mother, who treated her like an infant in an adult's body, she had to relearn colors, sounds, speech, vocabulary, and motor skills from scratch.

Stroke Warning Signs: The S.T.R.O.K.E. Acronym

To help the public quickly identify and respond to stroke symptoms, Dr. Taylor developed the acronym S.T.R.O.K.E.:

• S - Speech: Sudden difficulty speaking, understanding language, or producing coherent sounds.

• T - Tingling: Acute numbness or a tingling sensation, typically concentrated on one side of the body.

• R - Remembering: Sudden inability to recall basic information, such as a spouse's name, current location, or common facts.

• O - Off-balance: Sudden loss of coordination, difficulty walking, dropping limbs, or physical drooping on one side of the face or body.

• K - Killer Headache: A sudden, excruciating, throbbing, or pulsating headache, often localized to one side.

• E - Eyes: Sudden, dramatic changes or deficits in vision.

Prevention Strategies: Dr. Taylor emphasizes that stroke prevention requires maintaining healthy vascular pressure in the brain. The blood vessel walls in the brain are exceptionally thin and transparent. Chronic anger, stress, lack of sleep, and poor diet elevate internal vascular pressure, increasing the risk of rupture. Sleep is particularly critical, as it acts as a cellular flushing system where metabolic waste accumulated by active neurons is cleared.

The Four Brain Characters (Whole Brain Living)

Traditional neurology historically claimed that only the analytical left cerebral cortex is conscious, leaving the remaining parts of the brain classified as unconscious. Dr. Taylor's stroke dismantled this myth, revealing that the brain is divided into four distinct anatomical modules of cells—two emotional (limbic) and two thinking (cortical)—split between the left and right hemispheres. Each module houses a distinct personality or "Character" that influences our decisions, habits, and self-perception:

• Character 1: Left Thinking (The Analytical Brain):

• Attributes: Highly organized, logical, structured, and obsessed with details, language, planning, and linear time.

• Role: Focuses on identity, status, achievement, and maintaining control over the physical environment.

• Character 2: Left Emotional (The Protective/Reactive Brain):

• Attributes: Powered by memory, pain, past trauma, and fear.

• Role: Acts as a defensive scanner, projecting past negative experiences onto the present to protect us. It is the seat of the "imposter syndrome," anxiety, tribalism, and the exclusionary "us versus them" mentality (which underlies racism and social division).

• Character 3: Right Emotional (The Experiential/Playful Brain):

• Attributes: Playful, highly creative, sensory, physical, and deeply connected to the immediate present.

• Role: Seeks raw experiences, joy, laughter, and collective bonding without judgment of right or wrong.

• Character 4: Right Thinking (The Wise/Spiritual Brain):

• Attributes: Boundaryless, peaceful, compassionate, and deeply rooted in a sense of universal love and awe.

• Role: Acts as the internal observer, recognizing our connection to all of humanity and the universe, offering deep, calm, and unconditional reassurance ("I got you").

Generational Shifts in Cognitive Dominance

Dr. Taylor outlines how societal structures and technology have shaped the dominant brain hemispheres of different generations:

• The Greatest Generation & Baby Boomers: Historically dominated by the left hemisphere. Winning World War II and building post-war financial security required intense discipline, pragmatism, and left-brain organization. This fostered a zero-sum, transaction-focused economic model where financial profit and material security were valued above all else.

• Generation X: Technologically savvy and pragmatic, serving as a transitional group that built the digital tools we use today.

• Millennials & Gen Z: Increasingly right-brain dominant. Dr. Taylor traces this to developmental shifts, such as childhood exposure to interactive technology (e.g., Teddy Ruxpin) and visual learning systems (e.g., teaching arithmetic using pictures rather than rote memorization). Consequently, younger generations prioritize collective well-being, collaborative decision-making, ethical alignment, and human connection over pure corporate profit.

This generational friction is evident in modern workspaces. While Baby Boomers may struggle to understand a younger worker's willingness to quit a high-paying but unfulfilling job, Millennials and Gen Z find the traditional, profit-at-all-costs corporate grind alienating.

Actionable Strategies for Whole Brain Living

Dr. Taylor stresses that the goal of human evolution is not to abandon the left hemisphere for the right, but to practice "whole-brain living" by consciously integrating all four characters. She shares two key physiological rules to help individuals regain control of their minds:

• The 90-Second Rule: When an emotion is triggered, a chemical cascade is released by the brain, flushing through the body and runing its physiological course in less than 90 seconds. If an individual remains angry, anxious, or sad for longer than 90 seconds, they are making a conscious, often habitual choice to re-stimulate those neural loops. By simply pausing and watching the feeling dissipate, one can choose to exit the emotional reaction.

• Tapping into the Right Brain: To balance an overstimulated, stressed left brain, individuals must practice mindfulness. This includes physical exercise without competition, listening to music, breathing consciously, eating meals mindfully without digital distractions, and allowing oneself to play and create without an end goal.

Ultimately, Dr. Taylor suggests that our primary purpose as human beings is to love one another. By balancing analytical drive with deep empathy and systemic connection, humanity can move past its current state of geopolitical and cultural division toward mutual profit, peace, and holistic survival.

Transcript

Dr. Jill Bolte Taylor: Brain cells are like people. We're social; they're social. A single cell can have 10,000 to 15,000 connections in a network with other neurons. Ten to fifteen thousand—that is a social network. Talk about influencers, boy, it's those brain cells.

Hala Taha: Dr. Jill Bolte Taylor, a Harvard-trained neuroscientist, experienced the unthinkable. She witnessed her own brain shut down, moment by moment. Today's conversation will change how you understand your brain and how you use it.

Dr. Jill Bolte Taylor: Running a business is very different than being an entrepreneur, especially in this day and age. Entrepreneurs have to have creativity. In order for you to do everything that you do, you have to have brain cells that perform that function. We have four major modules of cells inside of our head: two emotional—the right hemisphere and the left hemisphere—and then two thinking—the right thinking and the left thinking. The value of the right hemisphere says, "I care about people." Creativity is in the right hemisphere. It is that left emotional brain that looks at someone and says, "You're different from me. I'm going to push you away." We are skewed to the values of the left hemisphere.

Hala Taha: When you were 37, you had a stroke. It took you about 45 minutes or longer to realize that you were having a stroke, and you were somebody who studied the brain. How can we know if we're having the symptoms of a stroke, and what do we do immediately when we feel those symptoms?

Dr. Jill Bolte Taylor: So, the warning signs. Here are the warning signs. I use S-T-R-O-K-E to help people remember. S stands for—

Hala Taha: Hey, YAPfam. Welcome back to another amazing episode. Today's conversation will change how you understand your brain and how you use it. Our guest is Dr. Jill Bolte Taylor, a Harvard-trained neuroscientist who experienced the unthinkable. At just 37 years old, she suffered a massive stroke and witnessed her own brain shut down, moment by moment.

That life-altering experience became the foundation for Whole Brain Living, a powerful framework that reveals the four distinct characters inside your brain that shape your thoughts, emotions, habits, and decisions. Once you learn to recognize them, you'll gain the ability to consciously choose clarity over chaos, intention over reaction, and calm over stress.

We're going to be covering this extensively in today's conversation. But first, if you're new here, hit that follow button so you never miss a dose of wisdom.

Dr. Jill, welcome to the Young and Profiting podcast.

Dr. Jill Bolte Taylor: I am so happy to be with you. Thank you.

Hala Taha: Likewise, I'm so excited. I love talking about the brain. I love understanding the brain more. As entrepreneurs, we're using our minds every day, and we need to make sure that we're optimizing our minds and our productivity. So, I am very excited for this conversation today.

Something that really made me curious when I was researching you, Dr. Jill, and was really eye-opening, is that you were actually studying the brain before you had this traumatic stroke. When I had heard about you in the past, I thought that you had a stroke and then you got interested in the brain. But it turns out you were actually studying the brain long before you had your accident, which I want to hear all about.

But first, talk to us about why you decided to study the brain. What were you curious about? What were the kind of questions that you were trying to solve when you were initially starting out as a scientist and researcher?

Dr. Jill Bolte Taylor: Thank you. Yes, I have a brother who is 18 months older than I am, and he would eventually be diagnosed with the brain disorder schizophrenia. As a child, I just noticed that this guy and I were completely different in the way we interpreted our experiences.

For example, if we were out playing kickball, and the ball goes flying out into the yard, and my mom is on the stoop, and all of a sudden she jumps up and she's screaming at us. I interpreted that as terror—she was afraid her kids were going to get killed if we ran out into the street. But my brother interpreted her hollering as anger. That is a fundamental difference when we are not understanding and perceiving people's emotions in the same way.

So, I became really fascinated with what the differences were between me and my brother, and what was going on. It had to be at the level of the brain, because biologically, he is the closest thing to me that exists in the universe. I became fascinated with body language, social relations, vocal languaging, and what I am as a human being. I was just fascinated with what I am as a living being.

That caught my attention, and then as I got older, I really became fascinated with how our brain creates our perception of reality. What is normal, and what is not normal? I grew up to be a neuroanatomist. I study the brain at a cellular level, because fundamentally, I figured the differences between me and my brother were going to be in the way our brains wired themselves.

Hala Taha: Something I really want to dig deep on is the fact that you were studying what our reality is. What is perception? How do we perceive reality and consciousness itself? Talk to us about how you thought consciousness worked before you had your accident.

Dr. Jill Bolte Taylor: Oh, that's a big question. I believed that every ability we have is because we have brain cells that perform that function. To me, the single-celled organism is life—that is the miracle. We just happen to be a bunch of cells stuck together for multicellular life, which is also fantastic. But I was fascinated with the single-celled organism.

How did the universe create a bunch of atoms and molecules that work together in order to become a blueprint for a living entity? That living entity is defined by having a boundary, a cellular membrane that is semipermeable. Things exist outside in the universe beyond that membrane, and then that membrane allows some things in, giving it a perception of its environment.

So, I was very cellular; I was very anatomical. My perception of consciousness was: I do believe that a single cell has a consciousness. It's, of course, not like ours, but I believe it still has an awareness of the difference between itself and that which is outside of itself.

Hala Taha: And you believed this before your accident—that each individual cell had its own consciousness? That's so interesting. Why did you believe that?

Dr. Jill Bolte Taylor: Well, because what is life? To me, the difference is in the universe's ability to put all the atoms and molecules in the right formation in order to come up with genetic material. That genetic material then becomes something, which is ultimately nothing other than atoms and molecules. I don't believe that we have the physical construct of a human body and then consciousness suddenly happens to us. I think that we are a culmination of consciousness.

We have different levels of consciousness, and different parts of our brain work together in constellations of skill sets that then end up looking like separate consciousnesses, which also end up looking like different personalities. I always had that construct, but I was focused on working in the lab. I was teaching and performing neuroanatomy in the gross anatomy cadaver lab. I loved my life, and I loved what I was doing. I had that same thinking pattern, really focused at a cellular level on the differences between my brain and my brother's brain.

Hala Taha: One last question before we get into your accident and what happened: who were you back then? Who was Jill back then? How did you self-identify and define success?

Dr. Jill Bolte Taylor: I was climbing the Harvard ladder. I had my PhD in neuroanatomy, and my first post-doc was at Harvard Medical School. I grew up in Indiana, and once I got my PhD, I went to Harvard for my post-doc. I was studying in the lab of David Hubel, a Nobel laureate. It was a very alpha personality—go, go, go, let's achieve.

From David Hubel's lab, I moved from the Department of Neurobiology to the Harvard Department of Psychiatry because I wanted to focus my basic science research on schizophrenia and what the differences were at a neuroanatomical level. I was climbing the Harvard ladder, doing what a girl had to do.

But I was also an artist in my heart, and I chose neuroscience to make a living. When I went to the labs, I said to all of my mentors, "I'm an artist in my heart, so give me projects that you care about that have an aesthetic component to the science." That meant the visualization of cells, neurotransmitters, and the relationships between them. It allowed me to make beautiful art and learn new things in the lab at the same time.

Hala Taha: So, you got to combine both passions, which is great.

Dr. Jill Bolte Taylor: Yeah.

Hala Taha: When you were 37, you had a stroke. Take us back to the morning of the day that it happened. What were some of the initial things that you realized were happening? What did you think was happening in the moment, and how did things unfold?

Dr. Jill Bolte Taylor: First of all, I'm a PhD. I am a scientist. Of course, I had learned neurology and studied stroke at a cellular level, as well as dementia and all kinds of neurological trauma. But I was a scientist, a lab rat; I was not an MD or a practicing clinical neurologist.

When I woke up on the morning of December 10, 1996, as soon as I sat up, I had a major pounding pain behind my left eye. It was very unusual for me to experience any kind of pain. I was generally very healthy, knock on wood. But I had this pounding pain, so I thought, "Okay, I'm going to exercise and get my blood flowing, and hopefully I will feel better."

I got up, but as soon as I stood up, I realized that the light coming in through the windows was really burning my eyes. It was uncomfortable. So, I closed the blinds. I got on my Cardio Glide—it was a full-body exercise machine back in the '90s. I was jamming away on this thing, and I looked down at my hands, and they literally looked like primitive claws grasping onto the bar. I thought, "Wow, that is unusual."

The pounding kept going, and I just looked at myself. It was as though I was observing myself having this experience instead of actually being inside the body having the experience. The pounding in my head just wasn't getting any better, so I thought, "Okay, enough of exercise."

I decided to take a shower, because at this point, I was still planning on heading to work. I started walking across my living room, and every step was rigid. It was as though I was having to actively tell my legs to move: Move. Coordinate. Move.

As I was getting into the shower, lifting my leg, there was literally this conversation going on inside of my body: Okay, you muscles, contract. You muscles, relax. I lost my balance while in the shower. I leaned up against the wall and turned the water on. I pulled out the nozzle, and when the water hit the tub, the volume was so amplified that it literally knocked me backward. It was like raw energy just knocked me over.

I was leaning up against the wall, looking at my arm, and I realized I could no longer define the boundaries of where I began and where I ended. I felt like atoms and molecules blending with the atoms and molecules of the wall.

About this time, your audience is probably thinking, "Man, this sounds like some kind of a psychedelic trip—psilocybin, maybe." Having not had that experience, I can't speak to it directly. But if you've ever had a similar experience, it is the dissolving of the boundaries of where we begin and where we end as a biological creature. Because we are this massive conglomeration of cells and the energy of the life of those cells. A group of cells in that left hemisphere, right where the hemorrhage was happening, had gone completely offline. Because of that, I could no longer define the boundaries of where my body ended.

Eventually, I got out of the shower, went into my bedroom, and mechanically dressed myself. I just somehow got dressed. Then I asked myself, "Can I drive? Can I drive?" In that instant, my right arm went totally paralyzed by my side. That was the moment I realized, "Oh my gosh, paralysis. A warning sign of stroke. Oh my god, I'm having a stroke."

The very next thing my brain said was, "Wow, this is so cool! How many brain scientists have the opportunity to study their own brain from the inside out?" That alpha personality was saying, "Okay, we're having a stroke. We'll do this for a few weeks, learn what we can learn, and then we'll go back to work."

After that, it took about 45 minutes for me to actually figure out how to make a phone call and get help.

Hala Taha: Wow, what a powerful story. I really want to dive into what was happening neurologically, but I'm also very curious: could you have prevented this stroke to begin with? Do you feel like there's anything you could have done to prevent this, or that we can do to prevent the same thing happening to us?

Dr. Jill Bolte Taylor: Well, there are different kinds of stroke. There are two primary types of stroke. One is hemorrhagic, which is when blood vessels rupture and blood seeps out into the brain tissue. That was the kind of stroke I had. Specifically, I had what we call an arteriovenous malformation (AVM).

Essentially, you have an artery, which is a high-pressure system, coming in. Usually, it tapers down to a very small capillary space where red blood cells line up, and then at the other end, it connects to a low-pressure vein that absorbs fluid to take deoxygenated blood back to the heart. In my case, I had a congenital malformation where an artery was directly connected to a vein without the capillary network to neutralize the pressure. These blood vessels usually rupture between the ages of 25 and 45. I was 37 at the time.

So, no, I did not know it was there, and there was nothing I could do about it unless someone had given me an MRI and said, "Oh, Jill, we have a problem." We do that now, but back then in 1996, we weren't routinely giving people MRIs.

However, I had been diagnosed with migraine headaches at the age of 17—20 years before this thing ruptured. I have not had a single migraine since I had the surgery to remove the malformation. If there is someone in your audience who experiences chronic migraine headaches, and none of the current medications—which are actually excellent for migraines—help, I highly encourage them to go get a scan. Go get your brain looked at to see what's actually going on inside of your head. Knowing if you have an organic vascular problem is incredibly important.

Hala Taha: Yes, being preventative. Now, like you said, MRIs are widely available. Immediately when you said that, I thought of a person in my life who always gets migraines. I need to tell them to go get an MRI scan.

Dr. Jill Bolte Taylor: If the medications don't work, then absolutely, you want to go and see what else might be going on inside. I have had people write to me and say, "I heard you give this advice on a podcast, and it saved my life." Who knows who we can positively influence out there?

Hala Taha: Absolutely. One more question: it took you about 45 minutes or longer to realize that you were having a stroke, and you were someone who literally studied the brain. How can we know if we are having the symptoms of a stroke, and what should we do immediately when we feel those symptoms?

Dr. Jill Bolte Taylor: The most typical type of stroke happens when a blood clot is thrown somewhere in your body and travels up into the blood vessels of the brain. Because those brain arteries get smaller and smaller as they taper, the blood clot eventually blocks the flow of blood. That is called an ischemic stroke.

To help people remember the warning signs of a stroke, I use the acronym S-T-R-O-K-E:

• S stands for Speech: If you suddenly have any problems with language—either formulating words or understanding them. If you try to speak and what comes out is garbled or makes no sense, even if you can still hear the correct language in your head, that is a huge warning sign.

• T stands for Tingling or Numbness: Usually, this occurs on only one side of the body, though not all the time. Pay close attention to what is going on with your limbs and how they feel.

• R is for Remembering: All of a sudden, are you experiencing an acute problem with memory? For example, suddenly forgetting your spouse's name, where you are, or basic details. This is not like forgetting where you parked your car, which is common; it is a sudden, major cognitive lapse.

• O is for Off-balance: If you suddenly feel very off-balance. Often, one side of the body or one side of the face will droop. You might find yourself dragging a leg or experiencing sudden paralysis in an arm.

• K stands for Killer Headache: This is a throbbing, pulsating, intense headache—like the intense ice cream brain-freeze pain, but severe. It is a major warning sign, usually on one side of the head.

• E stands for Eyes: Sudden, major problems with your vision.

A stroke happens quickly—boom. What can you do to help prevent it? Pay attention to what you are eating, because food makes a massive difference.

Also, how much sleep are you getting? Sleep is the time period when the incoming stream of sensory information shuts down. This is when brain cells turn on their garbage collection system, and fluid flushes out all the cellular waste. Your brain is made of roughly 86 billion neurons that are constantly communicating, consuming energy, and creating waste while you are awake. When we go to sleep, the system is flushed.

How stressful is your life? The stress circuitry is a constant "go, go, go, do, do, do" loop. It tells us: "I reached a goal, I'll celebrate for a second, and now I want more." There is always this driving force of the left brain that wants more.

We can manage this stress. Our vulnerability to vascular injury is high because if you look at a human brain, you can actually see the blood inside the vessels because the blood vessel walls are incredibly thin and transparent. When we get angry, hold in rage, experience deep fear, or feel out of control, that high emotional state directly impacts the internal pressure system of the brain. That is simply not healthy for the biological organism.

Hala Taha: Such good tips. I feel like that is so helpful. I want to go back to your story. Suddenly, you found yourself with awareness but without any physical control. You were witnessing your own brain shutting down. What happened next?

Dr. Jill Bolte Taylor: What was really interesting to me was experiencing how the two hemispheres of the brain process information in completely different ways.

The left hemisphere houses our language centers. Language is the ability to take a sound—like the word "dog"—and associate a specific meaning and comprehension to that sound. It allows us to read, write, perform mathematics, and speak multiple languages. The left hemisphere is a very, very busy place.

Because my hemorrhage was in my left hemisphere, those circuits began to go offline. When they did, I would drift into the consciousness of the present moment. The right hemisphere is a "right here, right now" processor. It is entirely focused on the present moment.

In the present moment, my name doesn't exist—that information is housed in the left hemisphere. The physical boundaries of where I begin and end are also in the left hemisphere. Right here, right now, there is only the richness of the immediate sensory experience. And in that state, it feels like pure euphoria. It is beautiful, because there is no judgment of what is right, wrong, good, or bad. It's just, "Wow, I am alive."

On the morning of the stroke, I waffled back and forth between the two hemispheres. I went from being in the present moment thinking, "Oh my gosh, I'm alive!" to shifting back to the left brain thinking, "Oh my gosh, I'm having a problem. I need to get help."

Eventually, I was able to make a phone call and get help. But by the time help arrived, I had lost all language. I could no longer speak. To myself, I thought I sounded like a golden retriever barking, and the person on the other end of the line also sounded like a golden retriever to me. Fortunately, he recognized my voice, realized something was terribly wrong, and did what he needed to do to get me help.

Hala Taha: So, you felt like you were barking?

Dr. Jill Bolte Taylor: Yes, in my mind, the communication made no sense.

Hala Taha: Were you afraid at this time? Did you feel fear?

Dr. Jill Bolte Taylor: No, I did not. I was very fortunate that the cells in the emotional portion of my left brain—where fear, anxiety, and trauma are processed—were swimming in a pool of blood as well. My fear center was offline. I was completely fine, and I didn't really care.

The right hemisphere doesn't care about those things; it thinks life is great and peaceful. I was motivated to save myself, but I had no linear information to attach myself to normal reality or organize my rescue.

As I waffled between the two hemispheres, I knew I was in grave danger. Right after I finally managed to make the phone call, I dragged myself down the stairs, unlocked the front door, and curled up into a little fetal ball on the floor. I heard a voice inside my own mind saying, "Hold on. Hold on." And I remember thinking, "What am I holding on to? What does that even mean?"

It meant: Don't leave the body. Over the course of four hours, I had become so profoundly disabled that I was afraid if I went unconscious, I would never be able to make this body work again. In the consciousness of the right hemisphere, I felt energetically as large as the universe, because we are energy, and there are no boundaries in energy. When you are connected to all that is, there are no small details or fear of death.

Hala Taha: You just knew that if you left your body, you wouldn't be coming back.

Dr. Jill Bolte Taylor: I felt like I would be gone.

Hala Taha: What happened next in terms of your recovery? How did you get better, what did you learn, and how did you realize there are "four characters" in the brain?

Dr. Jill Bolte Taylor: As I was passing out, I was in an ambulance arriving at the Massachusetts General Hospital emergency room. I felt my spirit surrender; at that point, I had no say. I was gone.

Within moments, they took my gurney into the emergency room under bright lights. People were poking and prodding me, setting up IVs, and handing me a consent form to sign. I remember thinking, "What is wrong with you people?" They had to take my hand and help me make a scribble on the page.

They gave me anti-inflammatories and steroids, and they already had a CAT scan from the first hospital showing the hemorrhage. Because I worked at Harvard, they recognized me as a colleague, so I received incredible care. They stopped the bleeding, and two and a half weeks later, they performed major brain surgery. They cut my head open and removed a blood clot the size of a golf ball.

They sewed me up, sent me home, and told my family, "We have no idea how much of her function she will ever get back. Her job now is to try to recover."

I have a mother who is an absolute angel. She dropped everything in her life to come take care of me. She surrounded me with an environment of love, watching to see what I could learn and what was blocking my progress. She recognized that I was essentially an infant in a woman's body.

At first, I couldn't even see color. She had to teach me what colors were. She had to teach me how to make sounds to produce language, rebuild my vocabulary, and teach me how to walk. She taught me everything. She raised me twice.

Hala Taha: Wow. What did this experience teach you about consciousness? A lot of us don't think about consciousness at all—it's just something that is. Explain to us, simply, what you believe consciousness actually is.

Dr. Jill Bolte Taylor: Let's start with a single cell. That single cell contains the entire DNA blueprint required to multiply and differentiate into all the different kinds of cells that make up the body.

Initially, you have three layers of cells: the ectoderm, which becomes the nervous system (brain, spinal cord, peripheral nerves) and the skin; the mesoderm, which becomes the muscles; and the endoderm, which becomes the connective tissues and internal organs. All of this is made of highly differentiated groups of cells.

Eventually, we end up with this magnificent human brain. The brain is divided into two hemispheres. The primary evolutionary difference between a reptile and a mammal is the addition of our emotional limbic system. Reptiles do not have complex emotions; mammals do. And the primary difference between typical mammals and humans is the massive explosion of our thinking cerebral cortex.

We humans have four major modules of cells inside our heads: two emotional modules (divided between the right and left hemispheres of the limbic system) and two thinking modules (divided between the right and left cerebral cortex). Most of us have heard the pop-psychology myth that "the right hemisphere is emotional and the left hemisphere is analytical." Anatomically, that is simply not true. We have both emotional and thinking modules in both hemispheres.

The primary difference for me when I lost my left hemisphere was that I lost linear time. The right hemisphere is entirely a "right here, right now" machine. The emotional component of the right hemisphere processes the raw, physical experience of the present moment—what it feels like to be alive, to sit, to walk, to feel the temperature of the air, or to feel the water against your skin. The right thinking module is also entirely rooted in the present. If you are purely in the right hemisphere, only what is directly in front of you exists; the past and the future do not exist, and neither does your name or language. It is a state of deep gratitude, love, and connection to all that is, beyond the physical limitations of the self.

By contrast, the left hemisphere steps completely out of the present moment. If I ask you, "Hala, what did you have for dinner last night?"

Hala Taha: I had Asian food.

Dr. Jill Bolte Taylor: Exactly. Where did you go in your mind to remember that? You don't have that Asian food right in front of you. You had to step out of the present moment and leave me here alone to access a different consciousness that runs in your background.

The beauty of the left emotional brain is that it acts as a catalog of everything you have ever experienced. It is constantly running to help you navigate and survive. If the ground suddenly started to shake right now, your brain would say, "Earthquake! I need to get out of this building." That emotional reactivity is designed to protect you in the present moment based on threat data from your past.

So, we are running four distinct consciousnesses:

• Right Emotional: The raw, physical, sensory experience of the present moment.

• Right Thinking: The peaceful, wise, boundaryless observer connected to the universe.

• Left Emotional: The protective scanner that uses past memories and fears to keep us safe.

• Left Thinking: The analytical, logical, detail-oriented coordinator that plans, organizes, and speaks.

When we understand all four of these characters, we gain the power to choose who and how we want to be in any given moment. That is true personal power.

Hala Taha: This is fascinating. I've only ever heard of the classic "left brain versus right brain" concept. Let's bust some common pop-psychology myths about the brain. What do we need to know?

Dr. Jill Bolte Taylor: The biggest myth is that we only use 10% of our brain. No, if a brain cell is alive, you are using it.

Consider this: for an entrepreneur to do everything they do, they have to use diverse groups of brain cells. Some people say, "Oh, I'm not creative at all." But creativity is a function of the right hemisphere because it operates outside the box of what is "right, wrong, good, or bad"—which is the domain of the left brain.

Brain cells are highly social. A single neuron can have 10,000 to 15,000 connections forming a massive social network. The same is true for humans; we are social creatures. If we don't socialize, we become rigid, set in our ways, and emotionally decline. The same thing happens to neurons.

Another major myth is that the right brain is purely emotional and the left is purely analytical. As I mentioned, we have emotional and thinking processing centers in both hemispheres.

Hala Taha: Let's talk about how the left and right sides of the brain uniquely perceive reality.

Dr. Jill Bolte Taylor: According to traditional medicine, only one-quarter of our brain is considered "conscious," and that is the left thinking tissue. Look at the state of the world—it's a bit of a mess because we almost exclusively value that one portion of our brain.

Imagine the world we could live in if we stopped treating the other three-quarters of our brain as unconscious and out of our control. Whole-brain living is about recognizing which parts of your brain you are using, deciding if you are balanced, and choosing when to activate different circuits.

If your left analytical circuitry is running all the time, that is your stress circuitry. It can interfere with your sleep, your health, and your relationships. We are not single-celled organisms, reptiles, or simple mammals. We are human beings, and we have these four wonderful groups of cells designed to serve us. To be a whole, successful human, we need to utilize the skill sets of all four characters.

Hala Taha: Just to recap the four characters for our listeners so we are all on the same page:

• Left Thinking: Identity, planning, control, organization, and achievement.

• Left Emotional: Pain, fear, trauma, and memory.

• Right Emotional: Play, creativity, and connection.

• Right Thinking: Witness, wisdom, peace, and universal love.

Let's talk about how these four characters specifically influence an entrepreneur.

Dr. Jill Bolte Taylor: Running an established business is very different from being a creative entrepreneur, though there is a lot of overlap today.

If you operate solely from the left thinking brain, you focus strictly on the physical brand, the product, the logistics, and the finances—you hire "bean counters" to watch the money. But if you want to grow, you also have to hire a creative team. The creative team might terrify the left brain because they don't look at strict historical data. Instead, they operate on intuition, saying, "If a customer shops for this, they will reach for this package design over that one." The left brain panics because that requires changing production based on a feeling. All of life is this dynamic relationship between the right and left hemispheres.

Entrepreneurs must have right-brain creativity. You might be relaxed, letting your mind wander, and suddenly—boom—you get a brilliant idea. But if you don't act on that idea, it just goes back into the collective ether. To bring it to life, you have to turn on your left brain. You have to organize it, get funding, plan steps A through Z, and figure out how to sell it.

Along the way, you have to manage your left emotional brain, which might trigger imposter syndrome: "Who am I to start a company? I don't know what I'm doing. How do I know who to trust?" Firing someone or managing team conflict can be terrifying for that emotional center.

This is where you need your right thinking brain—your vision and wisdom. It steps in and reminds you: "You can do this. You have good intentions, excellent logical skills, and emotional strength. You are not alone."

When the left emotional fear says, "Who am I to do this?", the wise, loving right thinking character can step in and say, "I've got you." That isn't arrogance or narcissism; it is healthy self-awareness. Fear is meant to be temporary information, not a permanent lifestyle. It is a warning to look at the bigger picture, find your balance, and keep moving forward.

Hala Taha: Would you say that modern society is heavily left-brain dominant? What part of the brain do we need to understand and tap into to help solve our current global crises? It feels like the world is constantly facing conflict, war, and deep division. Why are we so skewed to the left brain, and how can we change that?

Dr. Jill Bolte Taylor: You are absolutely right. We are heavily skewed to the values of the left hemisphere, and this has been dominant for a very long time.

After World War II, the "Greatest Generation" focused on prosperity and stability. They valued family (right brain), but also focused heavily on building roots, the American Dream, owning a home, saving money, and buying nice cars (left brain). Because the U.S. played a decisive role in ending the war with the atomic bomb, it positioned itself as a dominant global power. The system naturally became highly disciplined, organized, pragmatic, and left-brain dominant.

The Baby Boomers inherited this left-brain focus from their parents, who wanted them to have everything. Then, the Boomers had Millennials and Gen Z.

Technologically, a major shift occurred. Think about the creation of early interactive toys, like Teddy Ruxpin in the '80s. It was one of the first times we put a talking, simulated creature into a child's crib to soothe them. Gen X came along as technologically savvy creators, building the digital world.

When we taught the older generations math, we made them memorize timetables: two times two is four, six times six is thirty-six. That is pure, rote left-brain training. But for Millennials and Gen Z, we introduced visual concepts: "Two chickens plus two goats equals four animals." We gave them a visual, which actively trained their right brains.

As a result, younger generations are much more right-brain oriented. They value collective collaboration, group decision-making, and alignment with their personal values over pure corporate loyalty. A Boomer might stay at a job they hate for 40 years just for a paycheck, while a Millennial or Gen Zer will leave a toxic or unfulfilling job immediately because they prioritize their mental and emotional well-being.

Because of this shift, younger generations are more naturally collaborative and group-focused. However, they are still trying to fit themselves into an older corporate structure that prioritizes pure left-brain, zero-sum financial profit.

Hala Taha: That explains so much about the shifting political and cultural dynamics we see today.

Dr. Jill Bolte Taylor: Let's look at how this plays out politically.

The value structure of the right hemisphere cares deeply about people, social programs, food security, mental health support, and rehabilitation. It is inclusive.

The left brain focus, particularly the left emotional center, is tribal. It says, "There is a 'me' and a 'you.' If you don't look like me, sound like me, or belong to my tribe, you are a threat." This tribalism is fueled by politics, sports, and nationalism.

When the left emotional brain looks at someone of a different race or culture, it says, "You are different, so I am going to push you away." And when we push others away through racism or bias, our left brain naturally attempts to elevate itself as superior to justify the exclusion.

By contrast, when the right hemisphere encounters someone different, it says, "Oh, you are different! You have different skin, speak a different language, and eat different food. I am curious about you; I want to know you better."

Racism and division really boil down to which neural circuits we are choosing to run.

Hala Taha: Do we have conscious control over which parts of our brain we use? Is our brain dominance something we are born with, or is it programmed in childhood? Can we consciously change it as adults?

Dr. Jill Bolte Taylor: Because society and traditional medicine have taught us that only our left thinking brain is conscious, we grow up assuming the other three-quarters of our brain is an untouchable "unconscious" black box.

But during my stroke, my left thinking brain, my left emotional brain, and my right emotional brain were completely wiped out. All I had left was my right thinking tissue. I had to live entirely in that consciousness.

As I recovered and slowly regained my right emotional tissue, I realized that was a completely different state of awareness. To function in society again, I had to deliberately rebuild my language and analytical circuits in my left hemisphere. I literally used my intact right hemisphere to rebuild the neural pathways in my left hemisphere.

Because of that experience, I learned how to consciously choose which part of my brain to activate. In an instant, I can choose to be highly analytical, playful and creative, or quiet my mind to connect with the present moment.

My TED Talk about this experience went viral in 2008, and I was named one of Time magazine's 100 most influential people because people recognized the profound implications of this concept. My book, My Stroke of Insight, spent 63 weeks on the New York Times bestseller list.

I realized that while people walked away with a reverence for my story, my actual goal was for them to develop a deep reverence for their own minds. There is nothing wrong with capitalism or money, but that represents only one-quarter of who we are. It shouldn't dictate our entire existence. Our greatest power as human beings lies in our present-moment relationships with ourselves and one another.

Hala Taha: We have about ten minutes left, so let's focus on how we can practically use the right side of our brain. How do we catch ourselves when we are stuck in our left brain, and how do we build the habits to tap into our right brain? Hala, what do you personally do for fun?

Hala Taha: I like to work out, go out to dinner, go to the movies, and spend time with my boyfriend.

Dr. Jill Bolte Taylor: Working out gets you into your body, which is excellent for activating the right hemisphere's experiential processing.

But if you are doing yoga, holding a pose, and simultaneously planning a business call or analyzing a problem, that does not count. You have to bring your mind completely into the present moment.

If you are doing something physical, be completely physical. Listen to the music, feel the movement of your muscles, and get your entire body awake. If your physical activity is always highly competitive, you are still operating out of your left brain.

Enjoy the simple fact that you have a physical body that can move. Play, laugh, have joy, and be creative without needing a specific business purpose.

Your left analytical brain might hear this and think, "What a total waste of time!" But you need to realize that your creative genius lives in that right-brain tissue. The left brain is the coordinator—it wants to do, plan, build, and check things off a list. But your raw genius lives in the present moment, where you can look at the world with fresh eyes and see new possibilities.

When you eat, actually taste your food. Don't eat while watching a movie, scrolling on your phone, or standing up. Take a pause from the constant "go, go, go."

If the pause feels uncomfortable to you, start small—just try it for five seconds. Focus entirely on your breath. This is why breathwork and yoga are so powerful; breathing only happens in the present moment.

Allow yourself to feel a sense of spiritual awe that you exist at all. Think about the absolute miracle of your physical body—your eyes, your voice, your hands, even your internal organs. Be grateful that you are here in this world.

It is the same feeling of awe you get standing on a mountaintop looking out over a vast horizon, or standing on a beach watching the ocean. It reminds you: "I am alive, and I have this brief window of time to exist in this form. How do I want to connect? How can I bring gratitude into my daily life?"

When you operate from that space of appreciation, you can consciously choose how much time and energy you want to spend in your busy left hemisphere. This is not about being solely right-brained or left-brained; it is about being a whole, integrated human being.

Hala Taha: After everything you have lived through, what is the most important thing you want people to understand about who they really are?

Dr. Jill Bolte Taylor: I want people to understand that every single ability we have is made possible by living brain cells, and this brain is an incredibly precious, vulnerable organ. Please take care of it.

We live in a society where people regularly use drugs and alcohol that disrupt their brain function. I lost my mind and so much of my brain tissue, and I had to work incredibly hard for eight years to rebuild it. Now, I might drink a beer twice a year, and that is plenty for me. I want to protect what is going on inside my head.

If people truly value the unique skill sets of all four characters, they will see that they can live their lives with real intention and choose who they want to be in any given moment. To me, that is true freedom.

Hala Taha: I totally agree. This was an absolutely incredible episode. Personally, I learned so much about the distinct differences between the left and right hemispheres. Thank you so much for sharing your wisdom.

I always end my show with two final questions. First, what is one actionable thing our listeners can do today to become more profiting tomorrow? Given your expertise, it can be directly related to the brain.

Dr. Jill Bolte Taylor: Since your entrepreneurial audience spends most of their time engaged in the intense, analytical work of the left brain, my actionable tip is to regularly pause throughout the day and ask yourself: "Which of the four characters am I operating from right now? Is this where I want to be, or do I want to make a different choice?"

This is especially helpful when you become upset. When an emotion is triggered, the chemical cascade released by your brain takes less than 90 seconds to completely flush through your body. If you remain angry, sad, or jealous for longer than 90 seconds, you are making a biological choice to keep re-stimulating that loop.

Ask yourself: "Do I really want to stay in this negative loop, or am I ready to let it go?"

Hala Taha: I love that. It is all about stepping outside of your immediate reaction. What is your secret to profiting in life? This can go far beyond finances and business.

Dr. Jill Bolte Taylor: I truly believe that our number one job as human beings is to love one another. If we approach life with the mindset that our energy should benefit all of us collectively, then we all profit.

Hala Taha: Dr. Jill, thank you so much for your time. Where can our audience go to learn more about you and your work?

Dr. Jill Bolte Taylor: My website is DrJillTaylor.com. Thank you so much, Hala. I was really looking forward to this.

Hala Taha: Likewise! What an incredible conversation with Dr. Jill. I'm still processing everything we learned today about the brain and consciousness.

We covered so much ground. I study productivity and performance constantly, but Dr. Jill showed me that we've been missing three-quarters of the picture. We've been conditioned to think only our left brain is active, leaving us running on a single cylinder when we have four powerful characters inside our heads.

Your creative genius is not hiding in your endless to-do list. It lives in your right hemisphere—in play, presence, and purposeless creativity. When you are grinding through your day, checking off tasks and analyzing metrics, you are stuck in that left-brain loop.

When you pause, get into your physical body, and actually taste your food instead of scrolling through emails, that is when your mental breakthroughs happen. When the left emotional fear of imposter syndrome kicks in, tap into your right thinking wisdom that says, "I've got you. You can do this." That is whole-brain living.

To my fellow entrepreneurs: your drive and ambition are beautiful, but they are only one-quarter of who you are. The real magic happens when you balance that hustle with presence, play, and genuine human connection. Get out of your head and into your body.

Also, please remember the warning signs of a stroke: speech problems, tingling, memory issues, balance trouble, severe headaches, and vision changes. If you suffer from chronic migraines that don't respond to medication, please go get an MRI scan. Your brain is precious—protect it.

If this conversation opened your eyes to a new way of thinking about consciousness and success, please share it with someone who needs to hear this message. And if you learned something valuable today, please drop us a five-star review on Apple Podcasts, Spotify, or Castbox.

You can also watch the video version of this episode on YouTube by searching "Young and Profiting." Connect with me on Instagram @yapwithhala or on LinkedIn.

A big shout-out to my incredible YAP production team. Thank you for all your hard work. This is your host, Hala Taha, AKA the Podcast Princess, signing off. Go out there and start profiting!

The paper presents an ambitious and highly speculative framework that seeks to bridge the gap between abstract, high-level mathematics and the functional architecture of the human brain.

The core hypothesis is that the structural and algebraic richness of the Exceptional Simple Lie Group E8 may serve as a candidate symmetry model underlying key aspects of cortical computation, connectivity, and information processing.

Summary of the Framework

The paper proposes that the massive complexity and efficiency of the neocortex are not merely an emergent property of cellular-level biological interactions, but are fundamentally constrained and organized by a deep, elegant mathematical symmetry: E8.

E8 is the largest and most intricate of the five exceptional simple Lie groups, possessing an extraordinary 248-dimensional structure. It is a mathematical object of immense elegance that has appeared unexpectedly in various fields of theoretical physics, notably in some unified theories like string theory.

The framework draws from algebraic topology, theoretical neuroscience, and information theory to map the properties of this group onto the brain. Specifically, the study aims to:

• Map E8 to Topology: Relate the mathematical properties of E8 to the functional topology of cortical manifolds. This suggests the brain's activity patterns might organize themselves in a high-dimensional structure whose geometry is governed by the E8 root system.

• Model Dynamics: Examine how feedback loops and information flow in the cortex correspond to differential and geometric analogues within the E8 structure. The paper outlines a potential computational model that is intrinsically constrained by E8 symmetry, offering a rigid, non-arbitrary template for brain function.

• Validation and Application: The work suggests pathways for neuroscientific validation, focusing on analyzing imaging and time-series data for E8-like patterns. Furthermore, it explicitly considers applications to Artificial Intelligence (AI), hypothesizing that an AI built upon this inherent brain symmetry could achieve more efficient and human-like general intelligence.

Deeper Insights and Implications

The true significance of this paper lies in its philosophical and conceptual implications, which challenge conventional views of neuroscience and nature's elegance.

1. The Principle of Deep Mathematical Realism

By proposing E8 as the organizational principle of the brain, the paper is asserting a form of deep mathematical realism. This implies that the most efficient and robust physical and computational systems in the universe, from particle physics to consciousness, are built not just described by—but governed by—a small set of highly structured mathematical objects.

If the brain is an E8 system, it would explain its astonishing efficiency. E8, being a highly constrained structure, represents an optimal configuration of many interacting parts. The insight is that the brain is not simply a biological computer that works, but a minimal complexity, maximal computational power system whose architecture is necessitated by the requirement for this perfect symmetry. This shift in perspective moves the study of consciousness from a purely neurobiological problem to an algebraic topology problem.

2. A Symmetry-Constrained Path to AGI

The paper offers a powerful, constraint-based template for Artificial General Intelligence (AGI). Current AI often uses architectures like deep neural networks, which are highly effective but lack a demonstrable, unifying principle that links them directly to the efficiency of the human brain.

The E8 framework suggests that to build AGI, researchers should not just model connectivity, but must embed the E8 symmetry into the AI's computational core. This is the deeper insight for AI: a truly general intelligence may only be achievable by replicating the fundamental algebraic necessity of the neocortex, rather than merely its statistical or connectionist properties. This could lead to AI models that are exponentially more efficient, less prone to catastrophic forgetting, and capable of true abstract generalization.

3. Epistemology and the Limits of Reductionism

Philosophically, the E8 hypothesis directly engages with questions of epistemology and the limits of reductionism. If the brain’s highest-level functions—the things we call consciousness and thought—are simply an expression of the E8 geometry, it means these phenomena are algebraically necessary outputs of the system.

The paper argues against extreme reductionism, suggesting that to understand thought, reducing the system to individual neurons (the components) is insufficient. Instead, one must understand the symmetry group (E8) that constrains the arrangement of the components. This structuralist approach suggests that the whole (consciousness) is not merely the sum of its parts, but the expression of its governing symmetry.

In conclusion, the paper serves as a potent intellectual provocation, aiming to stimulate dialogue that views the brain not just as a complex biological machine, but as a marvel of mathematical physics, whose ultimate secrets are inscribed in the language of symmetry and exceptional Lie groups.

AI model trained on over ten million decisions from psychological experiments that can predict human behavior with unprecedented accuracy, even in entirely new situations it has never encountered before, potentially revolutionizing our understanding of human cognition and decision-making processes.

The essay “Your brain does not process information and it is not a computer” by Robert Epstein argues that the dominant information‑processing (IP) metaphor for human cognition is a misleading and ultimately futile analogy. Epstein begins by observing that, despite intensive research, scientists will never discover a literal copy of Beethoven’s Fifth Symphony, words, pictures, or any other environmental stimulus stored in the brain. He stresses that while the brain is certainly not empty, it does not contain the kinds of discrete data structures—memories, representations, algorithms, or symbolic registers—that characterize digital computers.

He contrasts the newborn’s innate capacities (reflexes, basic perceptual biases, and powerful learning mechanisms) with the absence of any pre‑installed ‘software’, ‘data’, or ‘hardware‑like’ components that would allow it to operate as an information processor. The argument proceeds to a brief tutorial on how computers truly work: information is encoded as bits, organized into bytes, stored in physical memory, retrieved, copied, and transformed according to explicit programs. Human cognition, by contrast, lacks such encoding, storage, and retrieval mechanisms. The brain does not hold symbolic representations of a dollar bill, a poem, or a melody that can be fetched from a memory register; instead, experience changes the brain’s structure in a way that enables future performance without the need for “retrieval”.

Epstein traces the historical lineage of metaphors for intelligence over the past two millennia: clay‑infused spirits, hydraulic humours, mechanical automata, electrical/chemical analogies, and finally the computer metaphor that emerged after the 1940s. Each metaphor reflected the most advanced technology of its era, but all were eventually superseded. He points out that the modern IP view—the idea that the brain processes symbols like a computer—originated with early cognitive scientists such as George Miller, who applied information theory to the mind, and was cemented by works like John von Neumann’s The Computer and the Brain (1958). Since then, billions of dollars and thousands of researchers have pursued a framework that assumes the brain is an information processor, producing a massive literature that seldom questions its basic premise.

To illustrate the inadequacy of the IP model, Epstein describes a classroom exercise where a student draws a dollar bill first from memory and then with the bill present. The memory‑based drawing is poor, despite the student having seen the bill countless times. This demonstrates that the brain does not store a precise visual “representation” that can be retrieved; rather, exposure to the bill altered the brain’s dynamics, making the student better able to reproduce it when the stimulus is present. He argues that memory is not a retrieval of stored data but a re‑enactment of prior experience, and that even the notion of “memory stored in individual neurons” is untenable—functional neuroimaging shows distributed, often massive, networks engaged during recall.

Epstein then outlines an alternative, “anti‑representational” or embodied cognition perspective. Experience shapes the brain in orderly ways, allowing us to perform tasks (sing a song, recite a poem, catch a baseball) without invoking internal symbolic models. The baseball example from McBeath et al. (1995) shows that a player catches a fly ball by maintaining a simple optical relationship with the ball rather than calculating trajectories via internal representations. This view aligns with scholars such as Anthony Chemero, who reject computational accounts and emphasize direct organism‑world interaction.

The essay warns that clinging to the IP metaphor not only misguides scientific research but also fuels speculative futurist claims—e.g., Ray Kurzweil, Stephen Hawking, and Randal Koene’s predictions of mind uploading and digital immortality. Since no “software” or memory banks exist in the brain, such scenarios are fundamentally impossible. Moreover, the unique, history‑dependent changes each brain undergoes mean that even identical experiences produce distinct neural configurations. This “uniqueness problem”, illustrated by Frederic Bartlett’s work on memory distortion, underscores the impossibility of a universal brain‑computer mapping.

Epstein highlights the practical consequences of the metaphor’s dominance: massive projects like the EU’s Human Brain Project, which promised a full‑brain simulation by 2023, have floundered, exposing how the IP assumption can lead to unrealistic expectations and waste of resources. He concludes by urging a shift away from the entrenched computational metaphor toward a more faithful understanding of the brain as a dynamic, embodied system that changes through interaction with its environment. The call to “hit the DELETE key” is a metaphorical plea to discard the outdated information‑processing view and to pursue neuroscience free of its intellectual baggage.

Overall, the essay challenges the foundational assumptions of contemporary cognitive neuroscience, argues for an embodied, anti‑representational framework, and cautions against the hype surrounding brain‑computer convergence.

Summary

The prolonged stress, isolation, and anxiety associated with the COVID-19 pandemic have had measurable physical effects on the human brain, often manifesting as a "spiral of negativity" or chronic fatigue. To counteract these biological changes and "reboot" cognitive function for the post-pandemic era, neuroscience suggests a proactive approach focused on neuroplasticity and physiological maintenance. The following six evidence-based strategies are essential for restoring mental energy and emotional resilience:

1. Prosocial Behavior and Altruism

Engaging in acts of kindness and volunteerism does more than benefit the recipient; it fundamentally alters the brain’s chemistry. Studies indicate that altruistic actions activate the brain's reward circuitry in a manner similar to personal financial gain. For older adults, regular volunteering is specifically linked to higher life satisfaction and reduced symptoms of depression, providing a sense of purpose that buffers against psychological distress.

2. Physical Activity as Cognitive Defense

Exercise serves as a powerful tool for both mental health and structural brain integrity. Higher levels of physical fitness are correlated with increased brain volume and improved cardiovascular health, which in turn facilitates better cognitive performance across all age groups. Beyond immediate mood elevation, regular exercise—even a brisk walk—builds long-term resilience against neurodegenerative conditions like dementia.

3. Nutritional Neurology

The brain requires specific building blocks to maintain neural connections. A diet rich in fruits, vegetables, and cereals—particularly those that support the growth of grey matter—is vital for academic and job performance. Conversely, diets high in sugar and saturated fats can actively damage neural function and hinder the brain’s ability to form new connections, making dietary choices a cornerstone of cognitive recovery.

4. The Criticality of Social Connection

Loneliness is now recognized as a significant public health crisis, exacerbated by lockdowns. Scientific evidence demonstrates that maintaining social ties protects emotional cognition and reduces the risk of mortality. Social interaction stimulates the brain’s reward system, acting as a biological safeguard against the detrimental effects of isolation.

5. Continuous Learning and Neuroplasticity

The brain remains capable of structural change throughout life. Acquiring new skills—such as learning a musical instrument, a new language, or even juggling—increases white and grey matter in specialized regions. Engaging in mentally stimulating leisure activities builds a "brain reserve," which provides a protective buffer against age-related cognitive decline.

6. Sleep as a Biological Reset

Sleep is not merely a period of rest but a critical active state where the brain reorganizes itself and flushes out toxic metabolic waste. Proper sleep is essential for memory consolidation, emotional regulation, and immune system strength. Chronic sleep deprivation disrupts the reward system and attention spans, whereas quality sleep enhances creativity and overall well-being.

Transcript

Six ways to 'reboot your brain' after a hard year of COVID-19 – according to science

It’s time to snap out of bad habits. There’s no doubt that 2020 was difficult for everyone and tragic for many. But now vaccines against COVID-19 are finally being administered – giving a much needed hope of a return to normality and a happy 2021.

However, months of anxiety, grief and loneliness can easily create a spiral of negativity that is hard to break out of. That’s because chronic stress changes the brain. And sometimes when we’re low we have no interest in doing the things that could actually make us feel better.

To enjoy our lives in 2021, we need to snap out of destructive habits and get our energy levels back. In some cases, that may initially mean forcing yourself to do the things that will gradually make you feel better. If you are experiencing more severe symptoms, however, you may want to speak to a professional about therapy or medication.

Here are six evidenced-based ways to change our brains for the better.

1. Be kind and helpful

Kindness, altruism and empathy can affect the brain. One study showed that making a charitable donation activated the brain’s reward system in a similar way to actually receiving money. This also applies to helping others who have been wronged.

Volunteering can also give a sense of meaning in life, promoting happiness, health and wellbeing. Older adults who volunteer regularly also exhibit greater life satisfaction and reduced depression and anxiety. In short, making others happy is a great way to make yourself happy.

2. Exercise

Exercise has been linked with both better physical and mental health, including improved cardiovascular health and reduced depression. In childhood, exercise is associated with better school performance, while it promotes better cognition and job performance in young adults. In older adults, exercise maintains cognitive performance and provides resilience against neurodegenerative disorders, such as dementia.

What’s more, studies have shown that individuals with higher levels of fitness have increased brain volume, which is associated with better cognitive performance in older adults. People who exercise also live longer. One of the very best things that you can do to reboot your brain is in fact to go out and get some fresh air during a brisk walk, run or cycling session. Do make sure to pick something you actually enjoy to ensure you keep doing it though.

3. Eat well

Nutrition can substantially influence the development and health of brain structure and function. It provides the proper building blocks for the brain to create and maintain connections, which is critical for improved cognition and academic performance. Previous evidence has shown that long-term lack of nutrients can lead to structural and functional damage to the brain, while a good quality diet is related to larger brain volume.

One study of 20,000 participants from the UK-Biobank showed that a higher intake of cereal was associated with the long-term beneficial effects of increased volume of grey matter (a key component of the central nervous system), which is linked to improved cognition. However, diets rich in sugar, saturated fats or calories can damage neural function. They can also reduce the brain’s ability to make new neural connections, which negatively affects cognition.

Therefore, whatever your age, remember to eat a well-balanced diet, including fruits, vegetables and cereal.

4. Keep socially connected

Loneliness and social isolation is prevalent across all ages, genders and cultures – further elevated by the COVID-19 pandemic. Robust scientific evidence has indicated that social isolation is detrimental to physical, cognitive and mental health.

One recent study showed that there were negative effects of COVID-19 isolation on emotional cognition, but that this effect was smaller in those that stayed connected with others during lockdown. Developing social connections and alleviating loneliness is also associated with decreased risk of mortality as well as a range of illnesses.

Therefore, loneliness and social isolation are increasingly recognised as critical public health issues, which require effective interventions. And social interaction is associated with positive feelings and increased activation in the brain’s reward system.

In 2021, be sure to keep up with family and friends, but also expand your horizons and make some new connections.

5. Learn something new

The brain changes during critical periods of development, but is also a lifelong process. Novel experiences, such as learning new skills, can modify both brain function and the underlying brain structure. For example juggling has been shown to increase white matter (tissue composed of nerve fibers) structures in the brain associated with visuo-motor performance.

Similarly, musicians have been shown to have increased grey matter in the parts of the brain that process auditory information. Learning a new language can also change the structure of the human brain.

A large review of the literature suggested that mentally stimulating leisure activities increase brain-reserve, which can instil resilience and be protective of cognitive decline in older adults – be it chess or cognitive games.

6. Sleep properly

Sleep is an essential component of human life, yet many people do not understand the relationship between good brain health and the process of sleeping. During sleep, the brain reorganises and recharges itself and removes toxic waste byproducts, which helps to maintain normal brain functioning.

Sleep is very important for transforming experiences into our long-term memory, maintaining cognitive and emotional function and reducing mental fatigue. Studies of sleep deprivation have demonstrated deficits in memory and attention as well as changes in the reward system, which often disrupts emotional functioning. Sleep also exerts a strong regulatory influence on the immune system. If you have the optimal quantity and quality of sleep, you will find that you have more energy, better wellbeing and are able to develop your creativity and thinking.

So have a Happy New Year! And let’s make the most of ourselves in 2021 and help others to do the same.