September 3, 2025
In Part 1, I talked about how time is our enemy and it is important to know the enemy. Time is not the clock. It is the progression of entropy. https://www.linkedin.com/pulse/responding-time-part-1-jonathan-boyd-mqurc
In Part 2, I looked at the idea that “now” is not a defined point. https://www.linkedin.com/pulse/responding-time-part-2-when-now-jonathan-boyd-otgic
Part 3 is about “entropy sprawl”. https://www.linkedin.com/pulse/responding-time-part-2-entropy-sprawl-jonathan-boyd-5q3lc
Part 4: Distributed Mitigation, the anti-dote to entropy sprawl. https://www.linkedin.com/pulse/responding-time-part-4-distributed-mitigation-jonathan-boyd-ws89c
Part 5: Distributed Mitigation Systems. https://www.linkedin.com/pulse/responding-time-part-5-distributed-mitigation-systems-jonathan-boyd-iwmuc
For distributed mitigation systems to work, they have to be implemented fast. Faster than the incident is devolving. We have to stay ahead of time. We need to put the system in place before entropy sprawl is out of control.
That speed only comes when teams instinctively understand the system and can start mitigating actions in parallel. Teams cannot pause to assemble the system step by step. They must already know it and be able to move at once. For teams to have that instinctive ability, they must be made up of individuals who can automatically understand and implement. The individual cannot treat each choice as a new decision. Inside the individual brain, things need to happen automatically.
The ski turn is a good example. A turn does not work if the movements are done one after another. The weight shift, the knee rotation, the ankle roll, the pole plant, and the pressure on the edge must happen together. For them to happen together, the observations, decisions, and actions have to be instinctive. The skier does not stop to decide to rotate the knee or shift the weight. Those adjustments happen automatically. That frees the skier to make higher-level decisions about direction and speed.
Instinctive ability lets the skier expand their “now.” They are not stuck managing the step they are in. They can look further down the slope, anticipate what is coming, and make choices with more of the future in view.
So what is this instinctive ability? How do individuals perform with automaticity? Well, from what I have learned, it is not one thing, it is a combination of factors largely in the brain. The formula is equal parts neural adaptation and schema recognition.
In order to explain these concepts. I would like to introduce some functional MRI studies that have explored these ideas. If you remember, fMRI lets us watch which areas of the brain “light up” when a person is learning or performing a task.
The first study I would like to reference is, “How Self-Initiated Memorized Movements Become Automatic: A FunctionalMRI Study”, by Tao Wu, Kenji Kansaku, and Mark Hallett (2004). https://journals.physiology.org/doi/epdf/10.1152/jn.01052.2003
Wu and colleagues (2004) asked people to practice finger movement sequences while in an MRI scanner. At the start, many brain regions were active: the prefrontal cortex, premotor cortex, posterior parietal cortex, anterior cingulate cortex, caudate nucleus, and cerebellum. These are areas linked to conscious planning, decision-making, and error correction. In other words, the “thinking brain” was running the show. Every step of the sequence was managed as a separate decision.
After training, the same sequences were performed much more quickly and with fewer errors. The fMRI showed that brain activity in those same planning areas had dropped significantly. No new region took over. The same network was still working, but it was now running more efficiently. The prefrontal cortex was no longer micromanaging each movement. The cerebellum and basal ganglia, which handle coordination and habit, carried more of the load. The change wasn’t an on-off switch, but a gradual decrease in reliance on conscious control.
The picture below shows the difference. “A” is before training, and “B” is after training. The decreased activity is clearly evident.
Another more recent (2020) and longer study by Eva Berlot followed people over five weeks of training, thousands of practice trials. https://elifesciences.org/articles/55241
Berlot’s study showed at first, the premotor and parietal regions were very active. As the participants got faster and more accurate, activation in those regions dropped. Importantly, they also found that the neural patterns for trained sequences reorganized early in training. The brain created a clearer “map” for those movements. Once that map was established, it stabilized and required less effort to use.
The results showed that learning is not just about the brain “lighting up more” or “lighting up less.” With practice, brain activity patterns become more efficient, more organized, and easier to tell apart. A trained sequence of movements develops its own clear pattern that looks different from an untrained sequence. Here is an illustration from the study:
This illustration is a way of showing how the brain can change as someone practices a skill, like playing piano keys, typing, or skiing. Each colored dot represents the activity of a small part of the brain (a voxel) during a task. At the start, the dots for trained tasks (red) and untrained tasks (blue) are all mixed together, meaning the brain is not yet specialized in how it handles the skill. With practice, however, several things can happen. The brain may recruit more areas to help (panel a), or it may become more efficient, with the same regions firing in a tighter, more consistent way (panel b). Sometimes the average activity shifts to a new place, as if the brain has moved the job to a slightly different region (panel c). In other cases, the brain reorganizes the same set of regions, reshuffling how strongly each one responds (panel d). Perhaps the most important change is pattern separation (panel e). Here, trained and untrained tasks form clearly different clusters, meaning the brain has built distinct codes for practiced skills.
The diagram shows that learning does not just mean “using more brain” or “using less brain.” It means the brain is reorganizing itself, making practiced skills more efficient, more stable, and easier to separate from unpracticed ones. This helps explain why an expert does not consciously think through every step, they are running on cleaner, more distinct neural patterns.
So basically what both of these studies show is that neural adaption is not using one part of the brain vs. another, its more about the same circuits running with less effort because the connections have been strengthened, signals move faster, and the patterns have been refined. Importantly, these studies also show that there is no magic moment that someone “gets it”. It is gradual changes in pathways due to repetition.
Imagine a path worn into a grassy knoll. One person walking on the grass does not create a path. It is formed gradually by many trips over the same path. Each pass subtly embedding a little more of the path.
The other insight I found interesting in the study is that everyone improved their performance. All of the participants were able to increase their speed through repetition and training. This challenges the idea that some people are not trainable. It also challenges the idea that someone is naturally going to perform better than a trained individual. The studies did show that people learned at different rates, and some of that may be influenced by natural ability, but the outcome was the same. Everyone improved. Everyone can learn. No one is naturally better than someone who has put in the training.
The second part of the formula for instinctive, automatic actions is schema recognition. A schema is a mental framework the brain uses to recognize a situation and respond to it without breaking it into separate steps. Instead of treating each decision as new, the brain recalls a familiar pattern and acts on it as a whole. A schema turns scattered details into a single, meaningful picture.
Early in my career I read an impactful book, Sources of Power, by Gary Klein. Klein was a psychologist who wanted to understand how people really make decisions in time-critical, high-stress environments. Instead of running lab experiments, he went into the field. He observed firefighters, soldiers, nurses, and other professionals who routinely had to make rapid choices with incomplete information. He collected stories, interviewed teams after difficult calls, and looked closely at how they explained their thought processes.
What he found challenged the traditional idea that decision-making is about listing out options and comparing them side by side. There is no time for that. Experts did something different. They recognized patterns they had seen before. That recognition gave them a “good enough” option right away. They then mentally simulated how that option would play out. If it looked workable, they acted. If it didn’t, they adjusted. Klein called this the recognition-primed decision model.
One of the examples in Sources of Power comes from a story Klein collected while studying firefighters. A lieutenant described leading his crew into a small house fire. The fire was in the kitchen, and the attack seemed routine. But within seconds, the officer felt something was wrong. The fire was too quiet. His ears felt hot. Without being able to explain why, he suddenly ordered his men out. Moments later the floor collapsed. The fire had been burning in the basement all along.
Later, Klein asked him how he made that decision. The officer said it wasn’t logic or a checklist. He just knew something didn’t fit. Klein’s analysis showed that the officer was using a recognition-primed decision. Years of experience had given him a schema of how kitchen fires normally behaved. This one didn’t match. The cues were subtle, the heat, the silence, the odd feeling, but they were enough to trigger a gut sense that things were wrong. His schema recognition gave him a quick option: pull the crew out. He mentally imagined the collapse and acted before it happened.
Klein also described a case from a hospital intensive care unit. A nurse was watching over a newborn in distress. On the monitors, the baby’s vital signs looked fine, heart rate, blood pressure, and oxygen levels were within normal ranges. But the nurse noticed the baby’s skin tone looked off and the breathing seemed too shallow. Without waiting for alarms or a doctor’s order, she called for immediate intervention. The team discovered that the baby’s endotracheal tube had come loose and was slipping into the stomach instead of the lungs.
When Klein interviewed the nurse later, she said she couldn’t explain it in a logical checklist. She had seen enough babies in trouble to recognize that the pattern of cues didn’t fit what the monitor was showing. Her schema told her something was wrong even when the technology said otherwise.
So far we have looked at how practice builds brain pathways and how experience builds schemas. These are both important for automatic actions. But they only work if stress does not overwhelm the system. Stress changes the way the brain works.
A study of firefighters gives us a good example:
Jeong H, Park S, Dager SR, et al. Altered functional connectivity in the fear network of firefighters with repeated traumatic stress. The British Journal of Psychiatry. 2019;214(6):347-353. https://www.cambridge.org/core/journals/the-british-journal-of-psychiatry/article/altered-functional-connectivity-in-the-fear-network-of-firefighters-with-repeated-traumatic-stress/06866B457BD8A7044B154A71D7181585?utm_source=chatgpt.com
Researchers scanned the brains of almost one hundred firefighters and compared them to people who had not faced the same experiences. They focused on the “fear network” of the brain. This includes the amygdala, which drives fear and emotion, and the prefrontal cortex, which helps control and calm those reactions.
The scans showed that firefighters had stronger connections between the amygdala and the insula. That connection was linked to more stress symptoms. It meant the fear system was firing more often. But the study also showed that when the connection between the insula and the prefrontal cortex was stronger, firefighters had fewer symptoms. That pathway helped regulate emotions and keep reactions under control.
Stress can make the emotional parts of the brain stronger, which makes people more reactive. But if the regulating system stays strong, people can still stay calm and use their training. This is why stress regulation is just as important as practice and schemas.
The formula is now clear. Automatic response = neural adaptation + schema recognition + stress regulation.
Sounds like a complicated process. How do you make sure the brain pathways are efficient, the schemas are built, and the stress system is regulated? How do you get an individual ready to instinctively decide and act?
Fortunately, all of the studies point to the same solution. Repetition. Reps and sets. Especially repetition under conditions that simulate real stress.
This is the idea of experiential learning. It is not reading a book or listening to a lecture. It is learning by doing. You put yourself in a situation and gain knowledge through the experience. The firefighter crawling a smoky hallway. The nurse responding to a critical patient. The skier falling again and again until the turn feels natural. Each pass through the situation leaves a trace in the brain. Those traces combine into pathways and schemas. Over time they become automatic.
Experiential learning is powerful because it involves more of the brain. You are not only thinking about what to do. You are seeing, hearing, moving, and feeling the consequences of your choices. That combination speeds up neural adaptation. It also builds stronger schemas, because the brain is linking real cues to real outcomes. When the same cues show up later, the schema is ready to run.
The firefighter stress study showed that stress changes brain connectivity. That means training has to include stress. If we only train in calm, sterile environments, the connections between regulation centers are never tested. The amygdala will still take over when chaos strikes. But when you put people into realistic, stressful scenarios, they practice regulating the fear response. They learn how to keep the prefrontal cortex online.
This is why simulations, drills, and scenario-based training matter. They give individuals the chance to experience stress in a safe, repeatable way. Every rep strengthens not just the motor pathways, but the regulation circuits as well. Over time, individuals gain the ability to stay calm, recall schemas, and execute automatic actions even when the environment is chaotic.
Repetition makes individuals better at every part of the formula. For neural adaptation, reps strengthen the circuits and make movements smooth. For schema recognition, reps build the library of patterns that the brain can call on in a crisis. For stress regulation, reps under pressure train the emotional system to stay in balance. Together, this means the individual not only acts automatically, but acts automatically in the right way under the hardest conditions.
So how many repetitions does it take? The studies show there is no magic number. Wu’s work showed that changes in the brain start after a few sessions. Berlot showed that they continue to improve over weeks and thousands of trials. Klein’s decision work showed that schemas build across a career. The firefighter study showed that stress regulation is shaped over years of repeated exposure.
It takes as many reps and sets as it takes for the action to feel easy. The skier knows they are automatic when the turn flows without thought. The firefighter knows they are automatic when they hear the cues and act. The nurse knows they are automatic when they recognize the pattern before the monitor shows a change.
Automatic ability is not about talent. It is about building enough repetitions until the brain no longer has to think through every step. It is about practicing under stress so the actions survive pressure. The formula works, but only if it is rehearsed until it becomes second nature.
For us to deal with the rapid progression of events, which is time, and handle a rapidly expanding incident, which is entropy sprawl, we need to act in a way that is automatic and parallel. That is the purpose of distributed mitigation.
Distributed mitigation is only possible when teams follow established processes. These processes are the distributed mitigation systems. They divide the work, create overlap, and give individuals the authority to act within their area. When everyone understands the system, the team can attack the problem from multiple directions at once.
For teams to perform like this, the individuals who make up those teams must already be capable of instinctive decisions and automatic actions. That is where the concept of “now” matters. Each individual has their own version of now, shaped by what just happened and what they expect next. When they are stuck in sequential steps, their “now” is narrow. When they have neural adaptation, schemas, and stress regulation, their “now” expands. They are free to look further ahead, anticipate the future, and make choices that keep the team in front of the problem.
In the next part, I will focus on how to take those prepared individuals and shape them into teams. We will look at how to train the team so that they can operate in flow, cover for one another, and carry out distributed mitigation.
Here is some further reading: