The Neuroscience of Learning: Turning Friction into Flow

Jonathan Reams

The “Big Idea” Summary

Formal education taught us to treat errors as failures to be avoided, but the brain’s natural learning mechanism—Predictive Processing—relies on “prediction errors” as the raw material for growth; we must learn the “Aikido move” of welcoming these errors to update our internal models rather than defending against them.

There is a feeling I often encounter when I hear people talk about leadership workshops. It’s a mix of exhaustion and skepticism. It’s the feeling of information overload, or “cognitive indigestion” combined with the burden of figuring out how to turn knowledge into action.

These leaders have read up on the topic and listened to podcasts. They have sat through seminars that might have given an injection of insight or enthusiasm in the moment, but felt it fade once back in the office. They have been pumped full of concepts—Transformational Leadership, Servant Leadership, Agile, Lean, Teal—but they are starved for capacity to translate this into action. They know a lot about how leadership ‘should’ look, but in the heat of the moment, when the pressure rises and the “weather” turns stormy, they find themselves reverting to old, reactive patterns.

Why does this happen? Why is the gap between knowing and doing so persistent?

In previous essays, I outlined the limitations of trying to force a “conduit” model of learning onto a human system that wasn’t designed for it. We treat the brain like an empty bucket waiting to be filled with information. If we just pour enough data, information, or knowledge in, and give it the right ‘instructions,’ we assume wise actions and good decisions will naturally flow out.

But that is not how natural learning works. To better understand how to actually grow, develop or learn, we have so far in this series been looking at things like predictive processing, dynamic skill theory and explored how our performance can be affected by a dynamic balance between vulnerability factors and resources.

I’m going to take a moment now and get into some neuroscience, because understanding the why changes how we approach the how.

The Brain as a Prediction Engine

For a long time, we thought the brain was a passive receiver. We thought the eyes took a picture, sent it to the visual cortex, and the brain said, “Oh, that’s a cup of coffee.”

Modern neuroscience, specifically the theory of Predictive Processing, suggests the opposite. It suggests that your brain is a proactive prediction engine. It is constantly modeling the world and projecting it outward. We don’t see the world as it is, but as we imagine it.

When you walk into a meeting, your brain isn’t just waiting to see what happens. It is running a simulation based on your past experiences, your biases, recent signals that give clues as to what to anticipate, and your current internal state. It predicts: “John is going to be defensive,” “This agenda is a waste of time,” “I need to protect my turf,” or fill in the blank with whatever variation of this is familiar to you.

You don’t just see the meeting; you see your prediction or a simulated model of the meeting.

This isn’t problematic in itself. Our brain’s predictive processing is super beneficial and efficient at most things that ensure our day to day functioning and even survival. It allows us to navigate complex environments without having to process every single pixel of data from scratch.

However, there are complicating factors that come into play at times. Part of the dominant materialist worldview includes the brain as the source of identity. It is seen to produce our subjective experience, our sense of being a person.

One result of this is that we can identify with our thoughts and from this, we can easily have a tendency to rationally justify the predictions the brain is making – despite any evidence to the contrary. This leads us to run up the ladder of inference (Chris Argyis’s lovely model), filtering out what doesn’t fit our predictions, or spinning the story to avoid facing unpleasant consequences.

The Gold is in the Error

So, how do we learn? How do we update the model?

We learn through Prediction Error.

A prediction error happens when reality (sensory input from the environment), clashes with your simulation. You predicted John would be defensive, but he smiled and asked for feedback. You predicted the strategy would work, but it failed.

Most of us were educated in the conduit or information transfer paradigm, the industrial model, to see error as failure. It’s a bad grade. It’s a sign that you didn’t study enough. We are conditioned to equate error with failure.

So, when we encounter a prediction error in leadership (a conflict, a mistake, a misunderstanding), our vulnerability factors spike. We feel ashamed. We get defensive. We try to explain away the error to protect our ego (our internal model of being a “Good Leader”).

We treat the error as noise to be filtered out.

But in Natural Learning, the error is not noise to rationalize away or tune out. It is the signal. It is the only raw material the brain can use to update its model. If you don’t have a mismatch, you don’t have the possibility for learning.

The Aikido Move

This is where the Learning Loop comes in. It is a structure designed to help us execute a kind of psychological Aikido.

In Aikido, you don’t block an incoming strike; you blend with it. You take the energy of the attack and redirect it.

When we experience a prediction error in leadership—a moment of friction, frustration, or surprise—our instinct is most often to block. We defend our position. We blame the context. We fit the square peg of new data to fit the circle of our old model. (“John is just manipulating me”).

The Learning Loop invites us to make an Aikido move. Instead of blocking the error, we welcome it. We say, “Huh. That didn’t go how I expected. Interesting.”

The Goal sets the compass so we know what we are looking for.
Distinctions tune our attention to notice the subtle signals we usually ignore.
Experimentation is us deliberately testing our predictions in the real world.
Reflection is where the update happens. We look at the gap between what we predicted and what happened, and we let that data update the model.

Cleaning Up the Signal

This brings us back to the “weather.” If your internal condition is turbulent—if you are vibrating with anxiety or the need to be right—you cannot perceive the prediction error clearly. You will interpret it as a threat.

This is why we distinguish between “Growing Up” (adding new, robust skills to meet the complexity of your context) and “Cleaning Up” (removing the noise and bias that clouds our perception).

You cannot effectively “grow up” if you haven’t “cleaned up.” If your internal signal is noisy, you will miss the subtle data points that the world in all its forms is offering you. You will keep predicting the same old storms and wondering why you’re always getting wet.

A Laboratory for Your Leadership

My aim here isn’t to turn you into a neuroscientist. It’s to invite you to become a better Bricoleur of your own development.

The “mad scientist” in me loves the theory of predictive processing because it explains so much about the mechanics that make us amazing and amazingly stupid at times. The bricoleur in me loves it because it validates the messiness of the process. It tells me that my mistakes are not evidence of my inadequacy; they are the friction required for traction.

We are designing Adeptify to be a laboratory, an experimental holding environment where we work with prediction errors on many levels and scales. I believe I’ve heard people in the agile community talk about failing fast. This allows them to learn fast. In order to build this type of learning as a habit, we first need to help you slow down the automatic processing just enough to catch the error, do the Aikido move, and update the model.

We want to help you tap back into the natural, fluid way of learning that you were born with, before you were taught that being wrong was dangerous.

The Reflection Loop

Catch the Surprise: Look back at your last 24 hours. Was there a moment where something didn’t go exactly as you expected (a “prediction error”)? Did you brush it off, or did you get curious?

The Aikido Check: When you feel resistance or friction in a conversation, what is your default move? Do you block (defend/argue) or do you blend (inquire/explore)?

Micro-Experiment: Tomorrow, try to consciously make one specific prediction before a meeting (e.g., “I predict this person will push back on X”). Watch what actually happens. If you are wrong, treat it as a victory—you just found data to update your model.