The primary driver is a lifelong fascination with our differences. I love trying to understand humans and why we are the way we are.

Before attempting to understand our differences, I realized I needed to better understand what we have in common. That’s what I’ve covered in 32 posts since January 2026: How we process information and why some of it ends up as a memory. Here’s where we are today:

Sensing → Attention → Context → Gist → System 1 and System 2 → Working Memory → Chunking → Cogntive Load → Understanding

I realize this is not sexy or provocative. But it’s in service of something bigger and more focused on our differences that is yet to come.

For now, thank you for reading and sharing. I hope that newsletter has fed your fascination as it has mine.

Do Analogies Work? How Do We Know?

A well-placed analogy can feel like a shortcut. Once we understand one phenomenon, we can start to see connections to others. As we discussed last week, our minds collect patterns that help us understand the world around us. Those patterns are interconnected, and analogies are one way those connections become useful.

In the 1980s, two researchers asked: Can learning the relationships in one situation help us understand a different situation?

Mary Gick and Keith Holyoak conducted fascinating experiments to help answer this question.

Study participants were told two stories that included a solution to a problem. The first was a story about a military general who wanted to capture a fortress. No single road could carry the entire army, so he divided the army into smaller groups on smaller roads that converged at the target.

The second story was about a firefighting team. Their big hose didn’t work to stop the fire, so they used multiple smaller hoses.

Once the participants had seen both stories, they were asked:

What do these stories have in common?

Researchers were testing to see if people saw the pattern of convergence. The researchers grouped participants into three categories based on their responses:

1. Good: captured the convergence pattern clearly.

2. Intermediate: captured part of it.

3. Poor: missed its key features.

The takeaway: Not everyone sees the same kinds of connections. Some people see patterns; others just see teamwork or military exercises.

This provided a foundation for another experiment and a new challenge.

Here, the participants were told they needed to use radiation to destroy a tumor without harming the surrounding tissue. A single strong beam would be harmful to tissue; a weak one would not reduce the tumor. What could be done?

The researchers were testing to see if the participants saw the medical story as analogous to the military story. Both divide forces and then converge to beat the enemy.

The driving question: Will the participants use their knowledge of the military story to solve the medical problem?

The results were stark. People who saw the convergence pattern were much more likely to use it in a different situation.

• Of the participants in the “good” category, 91% solved the radiation problem (10 of 11 people)

• Of the participants in the “intermediate” category, 40% solved the radiation problem (4 of 10 people)

• Of the participants in the “poor” category, 30% solved the radiation problem (9 of 30 people)

Why Does This Matter?

First, when we see a connection, it’s easy to assume that everyone else sees it too. They don’t. Instead of assuming, make the important connections clear. Walk through the pattern so they can see what is happening. Show them which relationships matter and why.

Second, this kind of knowledge is transferable. Once the audience sees and can describe the pattern, they are more likely to apply it later. Show them how the pattern works in other situations, and they’re more likely to remember it in the future.

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• Your understanding of your bed may relate to sleep, sex, laundry, and reading.

• Your understanding of physics may relate to curiosity, academics, math, and work.

• Your understanding of democracy may relate to politics, laws, freedom, and history.

How we understand the world around us is highly dependent on what we’ve experienced and learned over a lifetime. It’s our personal context.

As we’ve seen in previous posts, information that arrives to us isn’t filed away like a card catalog. What we know is not merely a collection of facts that we can summon when needed.

We interpret new information using what we already know. Our existing knowledge and experience help shape what we notice, how we understand it, and what we remember.

That’s what I mean by “constructed by us.” The things we learn and remember are a combination of the new information and our existing knowledge, including context.

A Classic Example

In 1977, Roger Schank and Robert Abelson published a book called Scripts, Plans, Goals, and Understanding. In the book, they provided a clear example of how patterns guide our understanding. It’s called The Restaurant Script.

When you enter a restaurant, you don’t have to start from scratch. You’ve seen the pattern many times:

Enter → get seated → order → eat → pay → leave

The steps are simple and easy to take for granted. But within that pattern, there are roles and expectations. You understand that ordering food leads to the server delivering it. When you’re done, you expect the check.

Often, reality matches our expectations and things flow smoothly. The pattern is reinforced. Other times, we see something new and may revise our expectations.

These patterns are one way how the mind organizes information. They build up over our lives and are interconnected with other patterns. They sometimes get updated, fade to the background, or are combined.

Our restaurant pattern informs what we do at a coffee shop or bar. Researchers often refer to these patterns as “schemas.”

A “schema” is organized knowledge about a familiar kind of object, event, or situation, including relationships and expectations. A script, like the restaurant example, concerns how a familiar sequence of events usually unfolds.

Schemas prevent us from having to understand every situation from scratch.

Note: I’m using the term “pattern” as a shortcut for these schemas, scripts, etc.

When Patterns Break

Part of learning and building knowledge is updating our patterns. We often experience situations where we have expectations that don’t match reality.

Let’s consider the restaurant example again. The pattern is familiar:

Enter → Sit down → order → food arrives → eat → pay → leave

Now let’s imagine going to a cafeteria for the first time.

You enter the restaurant with expectations and see that something is different. Instead of a host, you see a line of people, trays, a food counter, and a checkout.

You don’t have to start from scratch to understand this new context. Your restaurant pattern gives you a starting point for adaptation. You know what to expect at a restaurant (pay, eat, leave), but still need to figure out how to do it.

In this way, patterns give us a head start in adjusting to a new situation. When things change, or we learn something new, our patterns can update to include new situations and connections.

In this case, your next cafeteria visit may feel more normal.

How Patterns Become a Problem

Our expectations are powerful. When new information arrives, we recognize the similarity and may assume the connection is stronger than it is. We try to connect it with an existing pattern because that’s more efficient.

A key idea to remember: the mind is constantly trying to fit new information into what we already know.

Sometimes, this creates a problem. We easily see connections that aren’t there. We assume we understand the new information because we can see it through the pattern we already have.

An Example Misunderstanding

We’ll assume you are used to working with gas burners on a stove, and the pattern is clear:

Turn up the dial → more gas flows → more heat is produced → pan heats faster

A dial set to “high” heats the pan faster than one set to “low”.

This creates an expectation that involves energy and temperature: heat arrives quicker via a higher setting.

Now imagine experiencing a thermostat for the first time. You want the room to warm quickly and see a familiar-looking dial. So the pattern kicks in: you expect a similar outcome: a higher setting leads to faster heating.

So you turn the thermostat to the maximum setting, expecting it to heat faster. The context and your existing knowledge provided a plausible explanation that felt correct to you. It fit into an existing pattern.

Here, the familiar pattern led to a mistaken expectation. In a basic on/off heating system, the thermostat sets the target temperature, not the heat output. Choosing 90 instead of 75 won’t make it reach 75 any faster. It will keep the heating on longer.

What Happened?

Your existing pattern led you astray. The situation, context, and mechanism looked similar. You had a similar experience in the past. Yet, you were wrong. The problem was not the reality of thermostats, but expectations you brought to the table.

This is a common way that we misunderstand. We’re biased to see a new idea through the lens of an existing pattern.

This is a big challenge for communicators. Everyone has patterns and expectations that are invisible. Part of clear communication is anticipating the audience’s assumptions (dials lead to faster heating) and finding ways to update their patterns.

This relates to the Curse of Knowledge. Often, our patterns feel so obvious and normal that we are drawn to leaving them out. We assume everyone sees the same patterns. Do I really need to explain it?

What Can Help?

Do a bit of mental homework before you speak. Imagine the audience having patterns that they will use to understand your message. Ask yourself:

• What patterns are they likely to have?

• How will my ideas confirm or challenge those patterns?

• What can I do to help them update their existing patterns?

Your audience doesn’t simply receive your message. They interpret it using their own experience and expectations. You can’t fully predict that interpretation, but you can make the important relationships explicit and give people opportunities to check their understanding.

How?

Don’t assume you share the same patterns. Build context. Make connections visible. Show causal relationships. Discuss the mechanism of the dial and the difference between heating faster versus longer. This can help them construct a new pattern, or update an existing one.

Coming Soon

In the background, something essential is also playing a role: our ability to reason. The idea of reason is bigger and more pivotal than it seems. We’ll dive into it soon.

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1. What is it?

2. Why does it work as it does?

3. Where else does this work?

4. What does it help me do?

Neuroscientist Tommy Blanchard described it this way:

One way of testing for understanding is to test if the person would know what would have happened if something was different. Being able to answer counterfactual questions shows the person has something that goes beyond surface level memorization, since it requires reasoning based on a deeper structure of the phenomenon.

Clear enough. Rote memorization rarely represents understanding. What’s more important is seeing the big picture of how things fit together.

The Problem

Understanding is often hard work. It uses up attention, working memory, and other mental resources. We don’t always invest the effort needed to examine how something works. We easily jump to conclusions that make us feel that we understand.

Put simply: we fool ourselves into thinking we understand more than we do.

This isn’t a character flaw or lack of intelligence but a natural way the mind processes information. Our minds seek resolution or fit, and often, we convince ourselves that a subject is resolved without a full understanding.

“Of course I know how X works; I do X every day!”

This is a problem for communicators because once someone feels that a gap has been filled, they may see little reason to keep looking. “I understand that now” is a convenient way to head off any further exploration. And often, it’s wrong.

How Do We Know?

In 2006, researcher Rebecca Lawson conducted a series of experiments that illustrated the difference between what we think we understand versus what we actually understand.

She asked research subjects (experts and non-experts) to rate their knowledge of how bicycles work on a scale from 1-7, with 7 being thorough knowledge. The initial averages showed their confidence:

• 4.6 and 4.2 for non-experts

• 5.4 for experts

Lawson then asked subjects to draw the missing pedals, chain, and frame along with the chain’s connection to the rest of the bike. The image below is from her 2006 paper: The science of cycology: Failures to understand how everyday objects work:

She also asked multiple-choice questions about the correct positions of various bike parts. This image is also from her paper:

The results were surprising.

44% and 60% of the two non-expert groups made at least one drawing error, compared with 9% of experts.

From the paper:

What is striking about the present results is that so many people have virtually no knowledge of how bicycles function. They do not appear to understand a simple set of causal relations (turning the pedals turns the chain that spins the back wheel that drives the bicycle forward); therefore, they do not know how turning the pedals moves a bicycle.

Another Study

At Yale, Leonid Rozenblit and Frank Keil conducted a similar experiment in 2002, which they shared in the paper The Misunderstood Limits of Folk Science: An Illusion of Explanatory Depth.

In this study, the researchers asked 16 undergraduates to rate their understanding of devices in simple terms. The devices included a speedometer, flushing toilet, quartz watch, and more. The students then had to write a detailed explanation of how the devices work. After generating their explanations, they could then re-rate their understanding.

Consistently, the students lowered their level of understanding of the device after attempting to explain it. This led the researchers to conclude that having to explain concepts and mechanisms forces people to reckon with what they understand, or not.

Rozenblit and Keil coined a new term for this phenomenon: The Illusion of Explanatory Depth

The Illusion of Explanatory Depth

The studies above provide an example of human nature. We easily confuse familiarity with understanding.

In the case of the bicycle, there is little need to understand the mechanics at work. We pedal; the bike goes forward, and it seems like that’s all there is to know. It feels like understanding.

Consider something common like a zipper. We all use them successfully and assume we know how they work. But do we?

You’d expect someone with a moderate understanding of a bicycle to know the mechanics and how the parts relate. But many participants made mistakes. Their familiarity created an illusion of understanding.

The experiments showed that there were real gaps in understanding how bicycles and other items are structured and the mechanics that make them work. Those gaps only appeared when subjects were asked to explain them.

Ask yourself: what familiar parts of daily life do I assume I understand?

Why Does This Matter?

Our minds are tuned to solve mysteries and feel the rush of understanding. In this quest, we easily convince ourselves that we understand more than we do. Relying on familiar patterns and assumptions saves us from analyzing every situation from scratch.

First, check your understanding. If you need to explain something, don’t depend on assumptions. Write down what you know about the mechanisms and relationships between ideas and check them against the facts.

As a communicator, part of your job is to ensure that your understanding is accurate and that the audience shares a similar understanding of the subject. Everyone needs to be on the same page.

How?

Don’t assume everyone understands. The people confidently nodding their heads may feel as if they understand, but it could easily be an illusion. Build context. Make the connections visible. Show the causal chain.

By taking the time to discuss the relationships between ideas and the mechanisms at work, you can create a stronger foundation for related ideas. This gives them an opportunity to adapt their existing knowledge and be open to new information.

Next Up

Soon we’ll look at how the mind organizes what we know into patterns that help us see connections and make predictions, and why those same patterns can sometimes lead us astray.

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You’ve probably heard this phrase, often attributed to Albert Einstein:

“If you can’t explain it simply, you don’t understand it well enough.”

It’s not clear that he ever said it, but it captures a compelling idea nonetheless. To me, it brings up a couple of big questions:

• What does it mean to understand something well?

• How does that understanding translate to a simple explanation?

Like all things regarding the mind, there is hidden complexity because you can’t see understanding happen. It’s a very personal phenomenon.

However, there are four elements of understanding that I think are important.

Four Elements of Understanding

The elements below should apply to most things you can imagine. A person, idea, machine, process, object, animal, etc. That’s why this is powerful.

This is my own framework and is not based on a specific study, researcher, etc. However, it is informed by research that we’ll cover soon.

The four questions below can test the limits and reach of your understanding.

1. What is it?

   You should be able to identify it. You should recognize it and distinguish what makes it unique among similar items. A bicycle is a form of transportation. It is different from a motorcycle and a unicycle.

   

2. Why does it work as it does?

   You should be able to recognize the connections and circumstances that explain why it works or what is happening. The human pedals, the chain transfers energy to the back wheel, and the bike moves forward. There is often a system or process to understand.

   

3. Where else does this work?

   You should be able to apply your understanding to other examples and explain what they have in common. By understanding a bike, you can explain a garage door opener in similar terms: A chain is transferring energy from the motor to the door. Understanding a bike opened your mind (and the door).

4. What does it help me do?

   You should be able to apply your understanding to something specific, like solving a problem. If you observe that the pedals turn, but they don’t drive the back wheel, you know to check the chain. Your understanding of the system helped you see a missing connection.

The Test: My Dog Piper

If these four elements are indeed helpful, we should be able to test them. We’ll start with my dog, Piper, an 8-year-old Bernedoodle. Let’s try understanding something Piper does: whining at the door.

1. What is it?

   Piper is whining at the door.

2. Why does it work as it does?

   Whining could mean many things. To understand, I consider the context. What are the relationships and circumstances that explain what’s happening? I think about her normal routine and the last time she went out.

3. Where else does this work?

   If I understand that Piper whining at the door may mean she needs to go out, then what does it mean when she whines by her food bowl? When did she eat last? Is it her normal mealtime? This new context provides meaning and clues as to what she needs.

4. What does it help me do?

   My understanding of her whine helps me decide what to do next. If she whines in multiple contexts, I can make predictions and test my interpretation. I can decide to take her out and observe her actions, which extends my understanding.

Here we see that Piper’s whine alone doesn’t tell me exactly what she needs. The meaning comes from the situation and context, which help me interpret her whining and take action. In context, I can form a plausible interpretation and check it with observation.

How This Leads to Simpler Explanations

This post started with a quote:

“If you can’t explain it simply, you don’t understand it well enough.”

This relates to a fundamental idea in communication: you, the explainer, must understand the subject yourself. The four questions above can expose what parts of the subject you don’t yet understand. Filling these gaps can improve your explanations.

To explain how pedaling drives the back wheel, you need to understand those connections yourself.

Further, the four questions can help you evaluate what needs to be explained. A useful explanation doesn’t have to answer all four. You can ask: What is this audience missing? What part of this concept needs a better explanation?

An audience who understands the parts may need to see how they work together. An audience who understands how the parts work together may need to know how to apply that knowledge.

Your Turn

Pick a subject and apply the four questions. Consider how your answers help you fill a gap, see a new perspective, or identify a new way to explain the subject.

1. What is it?

2. Why does it work as it does?

3. Where else does this work?

4. What does it help me do?

I’d love to see your interpretations. Reply in the comments…

Leave a comment

Also, here’s Piper, not whining.

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Sensing —> Attention —> Context —> Gist —> System 1 and System 2 —> Working Memory —> Chunking —> Cogntive Load

Note: The path above isn’t linear. There aren’t hand-offs at each stage. Many of the processes happen simultaneously and interact with one another.

That being said, the path sets the stage for processing something we need to solve problems: understanding.

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A Shift in Focus

We’re now entering a new phase with a new question:

What does it mean to understand an idea?

Think about times when you’ve had an aha! moment where ideas that seemed separate suddenly came together. Consider:

• Understanding how the pipes work under your sink

• Seeing the magic of compound interest

• Figuring out how to back up a trailer

Something changes in these moments. You may have understood the individual facts, but a gap remained: how those facts fit together and relate to one another.

The pipes under your sink become a system. Money, time, and interest become a process. The confusing feeling of backing a trailer becomes predictable.

The facts haven’t necessarily changed, but their relationships have become clear.

This is an important part of what we mean by “understanding.” The individual facts are helpful, but understanding often emerges when we grasp the bigger picture and the connections that make it work.

The big idea: Facts alone don’t necessarily produce understanding.

For our audience to understand something new, we must focus on how things relate, why they work as they do, and what to expect when things change.

In the next few posts, we’ll look at how the mind makes this possible. We’ll come back to chunks from a new perspective, look at the idea of schemas that help us organize related information, and mental models that help us reason about how something works.

We’ll see that memory preserves more than individual facts. It also preserves relationships between ideas.

These mechanisms help explain something fundamental about the mind and the role of explanation: new ideas don’t arrive in an empty mind. We understand them by connecting them to what we already know.

As we’ll see, this ability is extraordinarily powerful… and can often lead us astray.

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In this story, we know the conclusion. The doctor’s advice to eat more fiber made it into her long-term memory and became a part of her lifestyle.

The question is: why did this specific piece of advice survive when so many other things were forgotten?

The Setup

Before going into the doctor’s office, Olivia wasn’t thinking about cholesterol, heart disease, or healthy eating.

The results from her blood test changed the context.

In milliseconds, her attention focused on the doctor, whose words seemed to matter more than they did before. Her family history, her existing knowledge, and the situation all added meaning. She saw the gist quickly: this is a threat to my health.

Most of this happened automatically and with little effort. Her mind was operating in what Daniel Kahneman called System 1: fast, error-prone, and constantly scanning.

In the moment, it felt like all the doctor’s advice was being remembered, but it wasn’t. Most of what she heard was being forgotten because the human mind has hard limits. Most of what enters our awareness never becomes a lasting memory.

Working Memory, Context, and Chunks

As the doctor recommended losing weight, consuming less salt, using the treadmill, eating smaller portions, eating more fiber, etc., her mind was also on the clock and the crying baby next door.

Her working memory was under pressure and her pupils dilated.

Like most people, she can only hold and manipulate roughly four “chunks” of information in working memory. In order for “eat more fiber” to be remembered, it had to compete for working memory resources.

But “eat more fiber” had an advantage.

From the moment Olivia heard her cholesterol numbers, they connected with information she already had: her family history of heart disease, what she had learned about cholesterol, and the health problems of family members.

The treadmill or smaller portions didn’t have this history.

Taken individually, the details of heart disease are complicated, but she didn’t have to construct them from scratch. Instead, years of experience meant the idea existed as a larger concept in her mind: the risk of heart disease.

If someone with no knowledge of heart disease heard the exact same information about cholesterol, their mind would have seen a lot of details instead of a concept.

The chunks they need to understand the big picture had not yet formed. Their mind and working memory would be overwhelmed, making everything harder to remember.

Advice with Good Explanations

At the same time, her doctor could have used technical jargon and data to explain the specifics of heart disease. Instead, he used clear language and familiar examples.

The information was still complex, but the doctor’s explanation reduced the extraneous cognitive load. By lowering the cognitive load, Olivia could focus on the meaning instead of decoding technical language.

A Productive Combination

The incoming advice to eat more fiber was met with Olivia’s existing knowledge and experience to create a potent combination.

The cholesterol numbers meant something to her because context and existing chunks were already in place. When new information connects to existing chunks, the mind can process and organize more efficiently.

But that’s not all.

On the ride home, she thought through the doctor’s advice and made a plan to stop and buy high-fiber food.

Here she was engaging in a different kind of thinking that is slower and more deliberate. This is what Kahneman called System 2 thinking.

She wasn’t hearing advice in real time, but thinking through it, connecting it to her life, making decisions, and creating a long-term plan.

This type of thinking is powerful because it provides new opportunities for new information to connect to existing knowledge and become established in memory.

The Survivor

Up to now, “eat more fiber” survived because a combination of factors worked together.

• It captured attention.

• It connected with meaningful context.

• It fit into knowledge Olivia already possessed.

• It was presented with reduced cognitive load.

• And later, Olivia actively thought about it and used it to make a decision.

Other pieces of advice, such as using the treadmill or reducing salt, had fewer of these advantages. They were easier to lose.

What survives the gauntlet tends to be what the mind finds meaningful and has the capacity to keep.

A Challenge for You

The next time you need to explain a complicated subject to a person or audience, think about the bullets above as a guide that’s not simply advice or intuition, but a picture of how the mind processes information.

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Today I think of my experience in terms of cognitive load and the pressure math put on my working memory. Below, we’ll see why.

A Quick Refresher

We recently discussed how the human mind can work with about four “chunks” of information at a time. Four numbers, four concepts, four names. Once our working memory is occupied, it can’t manage more information without making mistakes.

Cognitive load is the total pressure placed on working memory. The big idea: if we can understand the sources of the pressure, we can manage cognitive load.

In the most recent post, I explained two types of cognitive load:

• Intrinsic load: Some things are naturally complicated. Quantum physics has a high cognitive load for the layman; basic addition has a low cognitive load for most people.

• Extraneous load: Presentation matters. When a diagram is poorly designed, or sentences have too much jargon, cognitive load increases.

Below, we’ll look at a specific study, replicated multiple times, that shows cognitive load in action, and why it’s useful for communicators.

Meet John Sweller

In the late 1980s, John Sweller, an educational psychologist, was studying problem solving in mathematics.

He noticed something fascinating: a specific math problem could be presented in multiple ways, and student success seemed connected to presentation style. The complexity of the problem itself didn’t matter as much as how it was being presented.

At the time, the mainstream view of learning math was based on practice. Learners are presented with a problem and learn by attempting to solve it. This trial-and-error process, it was thought, was how math was learned.

One of Sweller’s most famous studies challenged this idea and discovered something fundamental about the mind.

The Experiments

Sweller gathered two groups of novice math students who were tasked with solving a specific algebra problem.

Group 1 was provided the problem with no solution. They had to work through trial-and-error to get the answer.

Solve for x:

3x/2 + 5 = 11

Group 2 was provided a solved equation. They could see how the pieces fit together, what led to the solution, and why.

Start: 3x/2 + 5 = 11

Subtract 5 from both sides: 3x/2 = 6

Multiply both sides by 2: 3x = 12

Divide both sides by 3: x = 4

Check: 3(4)/2 + 5 = 6 + 5 = 11 ✓

The big question: What group performed better in the future?

Sweller’s work and subsequent experiments showed something new: The students who learned via the solved equation (or “worked example”) performed better on similar problems than those who had to find the solution.

This challenged decades of learning theory. The question was why.

Working Memory and Math

Sweller proposed a new idea that led to Cognitive Load Theory (CLT) and explained the difference between the two groups using working memory.

Group 1 was provided a problem to solve, and they searched for a solution. The trial-and-error approach put a lot of pressure on working memory, which was used for experimenting rather than creating chunks of knowledge.

The student’s cognitive resources were used for solving the puzzle of the equation rather than understanding the process.

Group 2 was provided the solved equation. Here, the process was laid out in steps, with no trial-and-error required. Instead of using working memory to solve a puzzle, they could use it for understanding the logic, which led to chunks forming more easily.

And in the long run, the formation of those chunks is what helped the students who saw the solved equation do better on similar problems.

A Surprising Measurement of Cognitive Load

Cognitive load seems like a theory that exists mostly in models and diagrams. But in reality, there is a physical response that makes it measurable and therefore more scientific.

The big idea: when cognitive load goes up, our nervous systems respond in a way that makes our pupils dilate. This is called the task-evoked pupillary response.

Researchers Kahneman and Beatty designed studies that asked subjects to remember strings of numbers and then recall them. During this process, they measured their pupil dilation.

The results were fascinating. The subjects' pupils grew as numbers were added and shrunk as the subjects recalled them. It’s not hard to imagine working memory being filled up and then relieved.

So what? This discovery gave cognitive load theory a more concrete foundation. As we’ve seen before, when an invisible mechanism in the mind can be linked to a physical reaction, the idea becomes more valid.

This review of the research by Pauline Van der Wel and Henk van Steenbergen is helpful in understanding the specifics.

Why This Matters: Show Your Work

When we communicate, we ask the audience to understand what we say. Cognitive Load Theory tells us that a new idea may look like a puzzle to the audience. Their mental resources are used for fitting pieces together instead of remembering the big ideas.

Instead, we should consider showing them the finished puzzle and then the steps it took to assemble it. This helps remove the extraneous load and allows their working memory to be used for something more productive: developing new chunks that can be employed in the future.

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You arrive home with a new prescription that you need to start taking immediately. You remove the bottle from the bag and stare at the label. The stakes are high. If you don’t follow instructions, you could have serious side effects.

The label seems familiar, but it takes time to decode it all. In this simple example, you are confronted with two problems that must be overcome.

The first is the complexity of the subject. A new prescription comes with complexity that’s intrinsic to the task. You’ll need to understand instructions, dosage, schedules, and refills. How difficult this feels depends partly on what you already know.

The second complexity is HOW the information is presented. A prescription label is legally mandated to contain specific information, which results in small text on a tiny bottle. You spend mental effort simply trying to read the words instead of understanding the ideas. This load is extraneous and is separate from the content.

This combination puts your mind under pressure. Think of working memory as a truck that can carry a limited load. If the load is bigger than the capacity, things start to fall off. The same is true with the mind. When we try to load too many things at once, it starts to make predictable mistakes.

Researchers refer to this as cognitive load. Every waking moment, our minds are being loaded with information that we try to understand.

Chunking and Cognitive Load

In recent posts, we learned about chunking: with practice and repetition, our minds start to process ideas in groups instead of individual items. Instead of processing thunder, lightning, and rain separately, we process it as a “storm.”

These chunks are how the mind works with limited resources. They compress detailed information into a smaller size that is easier to process. In some ways, this is what it means to “process” information. Our minds are chunking machines that are always looking for ways to organize information into meaningful groups.

The problem is that chunking requires free working memory. If the mind is under pressure due to complexity, there aren’t enough free resources for creating new chunks. So we read a dense scientific paper, but don’t remember any of it. New chunks were not created.

Big idea: High cognitive load = more difficult formation of chunks

Existing Chunks Also Relieve Pressure

Let’s imagine a scientific paper on dark matter in space. It’s written by PhDs for fellow researchers. As a non-scientist, you try to read it, but soon give up. The cognitive load of the academic language is too high; chunks don’t form. Working memory is under too much pressure.

Now consider that paper in the hands of a nuclear physicist. Dark matter is a different field, but this person’s academic training has led to chunks related to physics, math, academic language, notation, and more. Those chunks help reveal connections and context that are invisible to the novice.

In this example, the existing chunks of the nuclear physicist helped reduce the cognitive pressure and free up working memory. This is why what an audience already knows matters as much as what you’re trying to teach them. If you can predict what chunks are in place, you can lower the load and free up more processing.

Big idea: More existing chunks = lower cognitive load

This subject is pretty complicated. Let’s see if I can decrease the cognitive load with visuals.

Low Cognitive Load

When information feels familiar and is clearly presented, cognitive load is reduced, which frees up resources for processing and forming new chunks.

High Cognitive Load

When information feels complex and is difficult to read, cognitive load is high. There aren’t enough resources to form new chunks.

Bring It All Together

When learning something new, our minds use working memory to process the information. We can hold about four chunks at once, and the system is subject to overload and predictable mistakes. Cognitive load is the pressure placed on working memory. That pressure can grow or shrink depending on two factors:

• Intrinsic load: Complexity is all around us. This “load” represents how much processing is required to grasp the complexity of a subject, relative to the learner’s existing knowledge. Quantum physics has a high cognitive load for the layman; basic addition has a low cognitive load for most people.

• Extraneous load: Presentation matters. This load relates to how much processing is required to grasp how the information is presented. When a diagram is poorly designed, or sentences have too much jargon, load increases.

The lesson: Manage the complexity of the subject you're communicating and make the presentation easier to consume.

But another mechanism plays a powerful role. The chunks connection:

• When we know a subject, our existing chunks help reduce cognitive load by using compressed versions of ideas instead of details. This frees up resources.

• When we encounter complexity, it uses up our cognitive resources so we aren’t able to form new chunks.

Why Cognitive Load Matters

First, cognitive load isn’t just a handy metaphor. Decades of research have explored the limits of working memory and how it affects learning. We’ll look at that research soon.

For now, cognitive load gives us a practical way to think about clear communication and turns explanation into two jobs:

1. Start by considering what your audience already knows. Their existing chunks determine how much complexity they can handle.

2. Remove unnecessary load. Strip out anything that makes the audience work without helping them understand.

Good explanations can’t eliminate the complexity in every subject. But they can ensure that the audience’s limited capacity is used on the overall subject and not deciphering complex language, bad diagrams, and poor presentation.

Next, we’ll take a look at the science behind these ideas.

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Since January of 2026, I’ve published 24 posts with this goal. Most of those posts focused on science: the researchers and experiments that led to a greater understanding of how we think and learn.

Many posts ended with a “Tips for Explainers” section where I applied the research to the real world of communication. For example, we learned the science behind why context is more powerful when it comes first.

Now that 6 months have passed, I’m taking stock of what we’ve learned about explanation as we follow the path an idea takes toward memory.

DISCLOSURE: The points below were distilled and written by an AI tool called Gemini Notebook (formerly NotebookLM). It looked at all 24 posts and distilled these points, with edits and videos by me. Details on the tool below.

What Science Has Taught Us About Explanation So Far

1. Preparation and Audience Strategy

• Treat explanation as a “prediction problem”: People don’t share knowledge directly. Everything we communicate is a representation of what we know. How we represent our knowledge, and how it is perceived by others, is the basis for explanation failure or success. This requires making accurate predictions about what will work for others.

• Acknowledge unique “Sensory Bubbles”: Everyone perceives a unique version of reality based on their biology and experience. Successful explainers work to create a shared baseline so they aren’t talking past their audience.

• Avoid the “Curse of Knowledge”: The more you know, the harder it is to imagine not knowing. Don’t assume your audience shares your context; make the invisible visible for them.

  • Source: Inside the Sensory Bubble

  • Source: The Baseline Problem

  • Source: What Happens When We Explain (Original video included)

2. Earning Initial Attention

• Nothing is possible without first earning attention.

• Lead with relevance: In your first few sentences, signal why the information is urgent or relevant to the audience’s goals, threats, or emotions.

• Prioritize signal strength: The mind filters physical characteristics first. Ensure your explanation is loud, clear, visible, and easy to hear to break through the internal “bottleneck” that limits what we can perceive.

• Reduce competition: The mind can only process one channel at a time. Try to make your message the only available signal to prevent multitasking.

• Set the tone immediately: An audience gets the conceptual “gist” of a scene in about 100 milliseconds. This initial impression happens before you even speak. Think about your first impression.

  • Source: How the Mind Decides What to Notice

  • Source: The Science of Attention

  • Source: The Limits of Attention at the Doctor’s Office

  • Source: Your Mind Gets the Gist in 100 Milliseconds

3. Building Context and Meaning

• Provide context BEFORE content: Sequence is critical. Providing context first can help an audience remember twice as much information as providing it later.

• Lower the “cost of understanding”: Understanding a new idea comes with costs: time, mental effort, etc. Give your audience a reason to stay engaged by focusing on context, meaning, and big ideas more than details.

• Connect to personal context: When we learn, we take in raw information and construct understanding that’s based on our knowledge and experience. This personal context is how we find meaning and make sense of the world around us. The best explanations consider the personal contexts of the audience and what is meaningful to them.

• Fill the gaps: Often, the key to explanation is building context that bridges the gap between the big idea and the personal context of the audience.

  • Source: How Context Leads to Understanding

  • Source: The Science of Context - How Do We Know?

  • Source: Your Mind on Context

4. Working with Mental Limits

• Respect the “Magic Number Four”: Working memory is limited; when isolated, the mind typically only holds about four chunks of information at once.

• Use “Chunking”: Group related ideas together into small, related packages. This allows the audience to think through multiple complex ideas simultaneously.

• Translate for novices: Experts think in highly compressed chunks. To be effective, experts must provide the details that allow novices to build their own chunks.

• Encourage “Slow Thinking”: Design your explanation to move past the fast, intuitive reactions of System 1 and survive the deliberate reflection of System 2, which is where understanding becomes knowledge.

  • Source: The Science of Working Memory: How Do We Know?

  • Source: Two Ways of Thinking, Two Explanation Challenges

  • Source: We Think In Chunks

  • Source: We’ve Been Studying “System 1” All Along

We’re Just Getting Started

The list above is incomplete because I continue to research and learn about how the mind processes information. We haven’t yet encountered ideas like reasoning, mental models, encoding, and more.

My plan is to keep following the path an idea takes as it becomes a memory. Once that path is complete, it will become a powerful framework for thinking about communication and explanation. I call it the gauntlet of the mind.

But there is another level to this. I am also fascinated with neurodiversity and how we can account for minds that process information differently. Once we have a neurotypical path, we can start to think about how to account for differences we encounter in everyday life.

About My Use of Gemini Notebook

I’m a user of an AI tool called Gemini Notebook (formerly called NotebookLM) by Google. Unlike ChatGPT or Claude, it only knows about what you provide it. For example, you can upload a dozen PDFs to a Notebook and use the chat interface to explore what’s in them.

I created a specific Notebook for collecting links to all Science of Explanation posts. This way, I can ask it to analyze and summarize what I’ve written.

For this post, I made this request:

It dutifully got to work and produced the points you see above, all based purely on the content I provided.

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The driving question: what does it take for an idea to be remembered in the future? As you’ll see below, our minds evolved efficient ways to store incredible amounts of information in small packages.

This is a short post. Be sure to watch the video at the bottom.

Chunks and Working Memory

Working memory, as I discussed recently, has hard limits. We can remember about four items at a time without making predictable mistakes. Beyond 4-5 items, our memory becomes unreliable.

But here’s the thing… working memory is flexible. As we learn and develop expertise, our minds become more efficient. We automatically compress related information into a single package, or “chunk,” that’s easier to store and retrieve.

When learning to drive a car, it seems like a group of individual parts: steering, acceleration, braking, parking. But with experience, the parts become compressed into a single idea, or chunk, we call “driving” that’s easier to access in the future. Driving only takes up one part of working memory. This is why we can drive and have a conversation at the same time.

Chunking Machines and Expertise

In the background, our minds are chunking machines, constantly recognizing related ideas and chunking them into more efficient packages. Once something like “weather” or “shopping” becomes a chunk, it takes up less space in working memory, allowing us to think through multiple complex ideas at the same time.

This is one of the secrets of expertise. When someone has deep experience in a subject, they’ve developed thousands of chunks that relate to their field. They think and debate in chunks that compress the details and create room for more chunks.

This is why experts are often difficult to understand. They’re so used to thinking in chunks that they leave out the details novices need to understand.

We don’t consciously generate chunks. They are a by-product of practice and experience. They form through repetition and develop over time. The most capable people in any field have likely put in the time and effort to develop a wide variety of useful chunks.

The Science of Chunking

The original video below is under 3 minutes and explains how we came to understand the role chunking plays in how we think. The experiment in the video has been replicated many times over and reveals a fundamental mechanism of the human mind. Once you see it, you won’t be able to unsee it.

The study in the video: Chase, W.G., & Simon, H.A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55-81. LINK

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A Quick Test

Imagine that you’re driving a car in the middle of nowhere and low on gas, maps, and cell signals. You’re lost and stop at a farmhouse where a kind gentleman gladly gives you instructions.

You listen carefully and assume you can remember everything.

“It’s easy. Turn left out of my driveway and at the next stop, turn right and then go until you see a big red barn. After the barn, there is a road on the left called ‘Smith Rd.’ Turn left on Smith and go for about 3 miles, and you’ll see the gas station on the right.”

By the time you get to the barn, your gas is getting lower, and you start to question your memory.

• What was the name of the road after the barn?

• Do I turn left or right?

• How many miles to the gas station?

This is working memory in action.

You didn’t fail. This isn’t about intelligence, but capacity. You encountered one of the built-in limits of the human mind. We cannot remember everything at once.

The question becomes: how many things can we remember? If we knew that data point, we could use it in designing communication. This is the story of researchers who searched for, and found, that number.

The Science Behind Working Memory

George Miller was a cognitive psychologist at Harvard in the 1950s. Over years of research, he noticed a pattern in the data regarding human memory. Across experiments and papers, the number seven kept appearing.

In the introduction to his most famous scientific paper, he wrote:

The persistence with which this number plagues me is far more than a random accident. There is, to quote a famous senator, a design behind it, some pattern governing its appearances. Either there really is something unusual about the number or else I am suffering from delusions of persecution.

Miller ran his own experiments and also synthesized existing research from other labs. Across very different experiments that asked subjects to remember sequences or count objects at a glance, the same number kept appearing: seven.

His 1956 paper “The Magical Number Seven, Plus or Minus Two” is one of the most cited papers in psychology because it popularized the idea that humans remember information in “chunks” and provides evidence that the mind can reliably remember a specific number of chunks: seven, plus or minus two.

The Problem with the Magic Number Seven

Miller was honest about the limitations of his findings and the rough-estimate nature of the number seven.

The problem: Miller wasn’t focused on working memory as we know it today. His experiments were not designed to isolate working memory.

For example, his subjects were allowed to use memory hacks, like mentally repeating the numbers, to increase their scores. This mental repetition is called “rehearsal”, and it makes it difficult to measure working memory by itself.

Miller was right about the power of chunking, but his numbers needed another look.

Seven or Four?

Psychologist Nelson Cowan at the University of Missouri studies working memory. Starting in the 1990s, Cowan’s lab published multiple papers about how to isolate and measure working memory.

In 2001, he reviewed existing studies that had controlled for rehearsal, combined them with his own laboratory research, and published a response to Miller's famous paper. It was called:

The magical number 4 in short-term memory: A reconsideration of mental storage capacity

Cowan wasn't just controlling for rehearsal. He identified three strategies that inflate working memory performance beyond its raw limit: rehearsal, long-term memory chunking, and sensory memory assistance.

The research pointed to something remarkable: when working memory is isolated, it can be measured with more fidelity. Across multiple sources, he found that the human mind can remember about four chunks of information at a time. Anything over four causes predictable problems.

You may wonder: how does one measure such a thing?

How to Measure Working Memory

At any moment, our minds are doing multiple things. I’m typing and reading these words, but I’m also listening to music, smelling coffee, etc. It feels normal.

What is not normal is the opposite: isolation. Preventing the mind from doing something natural and specific is much more difficult. But that’s precisely what researchers do to measure working memory.

Remember the subjects above who were “rehearsing” by mentally repeating the numbers in order to remember them? This is something we do naturally and mostly unconsciously. We have an “inner voice” that’s always helping.

The problem is that the inner voice is always on when we’re trying to remember something. The question becomes: how do you prevent people from rehearsing what they are trying to remember?

The answer: by distracting it. An example experiment where researchers are measuring working memory:

• Subjects hear a set of numbers to remember

• Then they are instructed to immediately count backward, aloud, for a period of time. This prevents them from rehearsing.

• Then, they are asked to recall the numbers they heard.

In this experiment, it’s possible to get a clearer picture of working memory because backward counting occupies the same brain function as rehearsing, without helping memory.

This methodology is used in a variety of research. Cowan reviewed these studies along with his own research and proposed that the more accurate number of chunks we can remember is four.

His influential paper kick-started a new direction in working memory research that continues today.

Olivia at the Doctor

This is why Olivia remembered the fiber advice and forgot the treadmill. Her working memory was limited, and the doctor’s recommendations had to compete.

She was distracted and hearing a lot of information at once:

She didn’t realize it, but some of that information had a distinct advantage. Because she already had knowledge about cholesterol and heart health, the doctor's new advice about fiber attached itself to an existing mental structure.

Rather than becoming another separate item competing for space, it became part of a larger chunk.

The treadmill didn’t have the same foundation or meaning. It became a separate item in her mind competing for limited space.

What this Means for Explanation

First, the obvious: your audience can easily be overwhelmed. Don’t squander the attention you’ve earned with long lists or multiple sub-points. The magic number four can serve as a guide for how much to say at once.

Second, think about your points in terms of chunks of related ideas. Whenever possible, connect new ideas to concepts your audience already understands. Existing knowledge creates larger chunks, making new information easier to hold in working memory.

Third, working memory is like a competition. Some information makes it through, and some doesn’t. What will help your idea survive?

As we’ll see in the next issue, the idea of chunks is more fascinating than it appears. It’s a metaphor that provides a vivid way to think about how we learn and understand the world around us.

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Earlier in this series, I told the fictional story of Olivia at the doctor’s office. The doctor had a lot of recommendations, but she only remembered one: eat more fiber.

The driving question: Why did “eat more fiber” make it through to her long-term memory and not the other ideas?

Earning Olivia’s Attention (and Action)

When the doctor made recommendations for Olivia, her mind was engaged because her health matters to her (attention). Her existing knowledge (context) helped her evaluate what was said. This created a substrate for memory to form, but this is only the beginning. Another kind of thinking will have to do the heavy lifting.

Two Systems, Two Challenges

This quick, intuitive sort of thinking is called System 1. Every moment, System 1 rapidly interprets what we experience. It’s effortless, automatic, and makes predictable mistakes. By simply being alive, we use System 1 thinking, a capacity we share with many animals.

The reality is that it can’t process everything. Our attention is narrow and constantly shifting. The challenge: In order to think about anything at all, it must first get our attention.

The second sort of thinking, System 2, is slower and deeper. This is where we make a conscious effort to think something through. We apply reason and knowledge to construct an understanding of what we have experienced. We reflect and decide.

But it has hard limits, too. The challenge: Too much information or too many inputs can easily overwhelm System 2.

Back to Olivia

When Olivia heard the doctor’s recommendations, she assumed she’d remember to use less salt, have smaller portions, walk on the treadmill, and eat more fiber. But that didn’t happen because her mind can’t do it all.

During the drive home, Olivia reviewed the visit and one idea stood out: eat more fiber. Why? It had meaning, built on her personal context and context provided by the doctor.

This sense of meaning led to her buying high-fiber foods. This deliberate reflection is System 2 thinking, and it can create action and long-term memories.

The other recommendations didn’t make the cut. This wasn’t necessarily a conscious decision, but a matter of competing priorities and a limited ability to process everything.

The Thing About System 2

System 2 is different because it requires effort and resists being used.

In Thinking Fast and Slow, Daniel Kahneman wrote:

System 1 is gullible and biased to believe, System 2 is in charge of doubting and unbelieving, but System 2 is sometimes busy, and often lazy.

Instead of simply experiencing the world around us (System 1), we take a step back and ask why or how it happened (System 2). This is a normal part of how we think, and we all do it differently. In fact, many researchers consider System 2 to be distinctly human.

Having captured Olivia's attention and been deemed meaningful, the fiber advice became available for more deliberate thought, the kind of conscious reflection that System 2 makes possible.

A Theory with Ancient Roots

Systems 1 and 2, often called Dual Process Theory, are influential in cognitive science because they are a useful framework for thinking about the mechanics of the mind.

I think of it in terms of the question driving this project: why are we the way we are? Could our differences be partially based on how we engage these types of thinking?

Interestingly, this theory is not new. The idea that human thinking involves both fast, automatic processes and slower, deliberate ones has philosophical roots going back to ancient Greece. It became part of cognitive science in the 1970s and gained widespread awareness from Kahneman’s book Thinking Fast and Slow.

Why This Matters

In the context of explanation, Systems 1 and 2 have real power because they show that explanations can fail for two very different reasons.

Sometimes explanations never earn enough attention to be considered. Other times they earn attention but overwhelm the audience before understanding can develop.

System 1 and System 2 help explain these challenges:

Please note that this framing is conceptual and remains controversial. The brain does not fit so neatly into systems and categories. Further, thinking doesn’t happen in a pipeline, but in a progression.

Nevertheless, there is compelling evidence that dual process theory captures important patterns in how we think.

What to Expect

Now that the basics of System 1 are in place, the elements of System 2 will become the priority.

We’ll see the specific researchers and experiments that support the big ideas of System 2, like working memory, cognitive load, and more. As always, we’ll be solving a mystery: why did “eat more fiber” make it through?

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The post below is a capstone that brings together a number of ideas under one umbrella: System 1 thinking, made famous by Daniel Kahneman.

You’re Not Always In Control

Let’s start with a famous illusion, called the Müller-Lyer illusion. Ask yourself: which horizontal line is longer?

If you’re like most people, the top one appears longer. The moment you saw them, your mind automatically made a guess.

Now let’s look at them again:

The lines are indeed the same length. Here is what’s fascinating: measuring them doesn’t change your perception of the lengths. The illusion continues to make them look different, even if you know, rationally, that they are the same.

Another is the Kanizsa Triangle. Our minds automatically fill the gaps and see a triangle that does not exist.

Why Does this Matter?

These illusions show that what we see and what we know are not always the same thing. Much of what we experience happens automatically, before conscious thought has a chance to weigh in.

The illusions above are simple examples of how the mind constructs experience. In the blink of an eye, attention is captured, context is applied, and a gist begins to form. By the time we start thinking consciously, much of the work has already been done.

Where We’ve Been

For months, I’ve studied the path information takes when it enters the mind and why only a fraction of it becomes stored in memory.

• The research led me to learn about the sensory bubble that we all live within, and how, by simply being human, we have limits on what we can experience.

• I learned about the science of attention and how it is earned.

• I studied the powerful role that context plays in our perceptions.

• Last week, I covered gist processing and how an idea or concept forms in milliseconds.

These all happen simultaneously when something new arrives in our bubble and sets the stage for deeper processing.

And in some cases, the information makes it through and has a chance of being stored in memory. The question is: Why? Why do some ideas make it through?

Daniel Kahneman and the Linda Problem

Daniel Kahneman won the Nobel Prize in 2002 for his work in fusing psychology and economics. He and his collaborator, Amos Tversky, conducted experiments that showed how the mind makes decisions under pressure or uncertainty.

What they found upended the dominant beliefs about rationality and how we interpret information. For example, they showed that the human mind uses mental shortcuts (heuristics) that lead to predictable errors in judgment.

A famous experiment, called The Linda Problem, shows this clearly. In the study, participants were given the following description of a fictional woman named Linda:

“Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.”

Participants were then asked which of the following two statements was more probable:

• Option A: Linda is a bank teller.

• Option B: Linda is a bank teller and is active in the feminist movement.

85% to 90% of participants chose Option B. It appears to make logical sense. But it’s a mistake.

The probability of “B” can never be higher than “A”.

Why? Because “B” violates a fundamental law of probability: the probability of two events occurring together (in conjunction) can never be greater than the probability of one of the events occurring alone.

Why the Linda Problem matters

In this example, we see how context and gist shape our thinking. The Linda Problem shows that people do not reason directly from information; we reason from the meaning we construct from that information.

We quickly think: “Linda is an outspoken activist, therefore it’s more probable that she’s a feminist bank teller.”

The mistake feels reasonable because the description of Linda creates a compelling story. Rather than evaluating probability directly, we rely on the gist we formed from the details. The story feels right, so the answer feels right.

Kahneman showed that these errors happen in a wide variety of situations. We constantly rely on rapid judgments to make sense of the world, and while they are often useful, they can also lead us astray in predictable ways.

The Big Picture: Systems 1 and 2

In his groundbreaking book, Thinking Fast and Slow, Kahneman described two ways we think:

• System 1: Immediate, automatic, pre-rational (fast)

• System 2: Rational, effortful, logical (slow)

This framing provides a powerful way to think about the science of explanation and the gauntlet of the mind. I’ll refer to it often going forward because it’s one of the most influential frameworks in cognitive science and useful way to think about how information travels in the mind.

We’ve Now Covered Most of System 1

The big ideas we’ve covered up to now are usually considered part of System 1; the “fast” form of thinking in terms of attention, context, and gist. The snapshots from System 1 generally work, but are prone to errors and mistakes. This reality will ripple through the rest of the research.

Next up: System 2

When new information makes it through System 1, it faces new challenges in System 2, where it must survive with working memory, cognitive load, mental models, encoding, and more. This is the slow part of thinking, and there’s a lot to cover.

If System 1 acts as the gatekeeper, System 2 acts as the builder. One determines what gets through. The other determines what becomes knowledge. That’s where explanation really lives. We’ll continue to follow an idea through the gauntlet and look at the science behind what we know and how we know it.

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Previously, we discussed it in terms of attention, what earns it, and why. But there is an even faster, more automatic part of this scene: you quickly get the “gist” of what’s happening. You automatically sense what’s there before you think about it or what words describe it.

The same is true when you view a work of art, notice someone’s emotions, or hear a sound. You very quickly form an idea or concept. You get the gist, and it’s an essential part of how new information makes it into your memory.

Gist processing feels obvious, but has real scientific weight. For example, we know it takes about 100 milliseconds for us to get the gist of a scene. That’s a blink of the eye.

How do we know?

Mary Potter was born in Lebanon and spent her entire career, over 50 years, at MIT, studying cognitive psychology. She studied under Jerome Bruner, who was influential in developing the idea of constructivism and how we make meaning.

She wanted to know: when does meaning start to form? Her work showed that it happens faster than anyone thought possible.

Imagine this simple scenario:

You are shown a drawing of a chair. What’s happening in your mind at the moment it appears? It’s easy to assume that we think in words and that the word “chair” must be the first thing that comes to mind.

This is not the case.

Potter conducted experiments in 1975 and showed that our minds form a quick understanding of the concept before we can think of the right word. It takes about 600 milliseconds to get the concept and 900 milliseconds to get the name.

Understanding is upstream of language. We understand first.

Just the gist, ma’am

Potter went on to do more experiments that showed exactly how quickly we can understand and comprehend a concept.

She conducted studies where people viewed a screen where 100s of random images appeared for only fractions of a second. A child playing, two men talking, a car on a road. The images switch so fast that it’s difficult to remember anything. She called this Rapid Serial Visual Presentation (RSVP).

Potter then presents a challenge. She provides a button and says, “There is a picture of a picnic in the images you’ll see. Press the button if you see it.”

Then, she changed the frequency of the images. In some experiments, the images, including the picnic, were displayed more slowly, like every 300 milliseconds. In others, they were switched in 100 milliseconds.

Over multiple experiments, she showed that people could reliably tap the button when the picnic image was on the screen for 100 milliseconds or more. That’s long before the mind considers language or analysis. The gist is there first.

The role of context

We know that context matters and plays an essential role in understanding what we experience. It matters in gist processing, too.

When Potter had subjects view the images with no added instructions, they could remember very little. Why? There was no context.

By providing them with the word “picnic,” Potter provided context that played on their existing knowledge. Like the “doing laundry” example from last week, the initial context set the stage for more attention and better recall.

Context is what holds everything together. The subject’s existing knowledge of “picnic”, “party,” or “chair” provides a foundation for forming a quick understanding of a concept.

Again, we see that context, especially when provided first, makes understanding faster, easier, and more reliable. Without it, the mind has less foundation for understanding.

What this means

We like to think we’re in control. We decide what to look at, how to think about it, and what it means. It feels like we’re making a conscious effort.

It’s an illusion.

Long before we have an opportunity to analyze, consider, or name something, our minds have done a lot of heavy lifting.

Without realizing it, we move our eyes, sense the world around us, and understand concepts before our minds are conscious of what’s happening. It all happens so fast and so automatically that we take it for granted.

It is, for us, reality. But within that reality, the mind is moving with amazing speed.

What we experience is initially understood in about 100 milliseconds. If we sense something meaningful, we pay attention. If we pay attention, the information has a chance to be remembered.

The lesson

Your audience begins forming an understanding before you say a word. In about 100 milliseconds, they sense what’s happening without realizing it. This means you’re making an impression before you know it.

Their initial perception matters, but it’s not destiny. Your job is to earn their attention, set the tone early, build context, and give them reasons to remain attentive and engaged.

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Of Course Context Matters

It seems obvious to say that context matters. We use it to make sense of everyday life. It’s the glue that connects what we know to what we’re experiencing.

For years, I’ve taught communicators to appreciate and use context to improve explanations. But something was always missing. My view of context was based on an assumption that it works. I had no scientific evidence to back up my claims.

Today, I have a new perspective. The power of context has been studied for many years. Not only is it real and necessary for comprehension, but understanding the science behind it can lead to better explanations.

Here’s the story behind how we know…

Bransford and Johnson

John Bransford and Marcia Johnson were at the forefront of the movement away from behaviorism and toward cognitive science. Their focus: memory and comprehension.

Their work together led to one of the foundational studies in cognitive science, and one of the most cited papers in the field, called Contextual Prerequisites for Understanding. Bransford was a lead editor of How People Learn, a landmark report published by the National Academy of Sciences, now considered a classic in education research.

The Big Picture

At the time, researchers were oriented around the idea that information moves between people like a transmission from a radio tower. The signal carries all the information needed for understanding. From this perspective, we are passive receivers of signals that arrive fully formed and are stored in memory.

What if, Bransford and Johnson asked, the signals are not complete, but more like raw data that has to be translated by us?

From this perspective, we play an essential role in turning the signals into something meaningful. How do we do that? With context.

Signal + Context = Meaning

This idea birthed a powerful new idea in cognitive science: our minds play an active role in translating signals into meaningful information. This is called constructivism: we actively build understanding by connecting new experiences to what we already know.

The challenge of explanation relates directly to this idea. An audience is made up of people with different contexts. How do we account for these differences?

The Studies

Bransford and Johnson designed a simple study to understand how context affects comprehension and memory.

Here’s how it worked:

Study subjects were divided into five groups that all heard the same recorded message. I’ve abbreviated a version of the original below. Read it carefully:

The procedure is actually quite simple. First you arrange things into different groups. Of course, one pile may be sufficient depending on how much there is to do. If you have to go somewhere else due to lack of facilities that is the next step, otherwise you are pretty well set. It is important not to overdo things.

As you can tell, the words are easy to read, but have no apparent meaning. What’s missing is context. Once it is provided, do the subjects remember more from the passage?

The Context

The required context can be summed up in two words: doing laundry. Bransford and Johnson provided this context to the five groups in different ways:

• Group 1: Heard the description once with no context

• Group 2: Heard the description twice with no context

• Group 3: Provided context (doing laundry), then heard the description

• Group 4: Heard the description, then provided context (doing laundry)

• Group 5: Provided an image of the objects in the description before hearing the description

5 Groups, 1 Winner

After the experiment, each group was asked to recall the 14 specific items in the description. The scoring was based on which group could recall the most items, and the winner was clear.

Group 3, who were provided context first, could remember about twice as many items as any other group. Over and over, Bransford and Johnson showed the power of context. When it is provided first, it helps people remember and comprehend what they heard.

Why Does This Matter?

This study has direct connections to better explanations because it shifted the thinking from “meaning is transmitted” to “meaning is constructed.” As explainers, we don’t transmit information. Rather, we provide what is needed for the audience to construct meaning.

The problem is that context is not always obvious or available. It often has to be supplied by the communicator, and therein lies the skill of the explainer. The best explanations account for the audience and use context as a tool for developing knowledge.

Three Lessons for Explainers

Imagine a presenter on stage, explaining a new idea. Your ability to understand and remember what they say depends on different kinds of context.

1. Context comes from two places. The first is the “communicated” context. This is information provided by the explainer that’s meant to help with understanding. The second is your “personal” context, based on your existing knowledge and experience.

2. The context types work together. Part of the presenter’s job is to communicate context: “This matters because…” and recognize that you’ll apply your personal context to the information. Understanding forms more easily when both are present.

3. Sequence matters. The experiment above showed that context only helped when it was provided first, before hearing the description. The presenter can increase their chances of being understood by communicating context first, and giving you an opportunity to identify if it’s meaningful.

Looking ahead…

For a while now, we’ve been focused on the very beginning of the Gauntlet of the mind, when information arrives. Soon, we’ll dive a bit deeper and look at how the mind processes information it finds meaningful.

*All visuals by Lee LeFever

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This post and the video below illustrate why I now believe the science is real.

Something to Prove

A few months ago, I was talking to a friend who is a physics Phd and a former physics professor. After describing my fascination with the mind, he said something that stuck with me: “That’s the thing about psychology, you don’t have to prove anything.”

In some contexts, he had a point. The work of Freud and Jung, for instance, is mostly theoretical and lacks empirical evidence. This was true with most of psychology up to the 1950s, when the mind was considered a black box.

The arrival of computers changed everything. By the 1960s, researchers saw the potential to create computer programs that mimic specific processes in the mind. A computer could be programmed, for example, to remember and forget numbers similar to the human version.

This set the stage for a revolution where researchers built ever-more accurate and sophisticated models of the mind. The models became so reliable that they could accurately predict specific mental processes like memory capacity, reaction times, and recall patterns.

Why was this a revolution?

The models allowed researchers to lessen their dependence on expensive and time-consuming human studies. Scientists could test hypotheses quickly using a computer model and then test the findings against humans to see if they match something natural in the human mind.

This loop, experimenting rapidly with computational models and then testing the results with humans, is a big part of the science in cognitive science. It led to evidence of real mental mechanics that can be understood and replicated. Cognitive science involves a lot more than these models, but they are a powerful and productive tool.

Are the models trustworthy?

The use of computational models is not unique to cognitive science. They are widely used and trusted across scientific disciplines.

The 3-minute video below explains how similar models are used in climate science, particle physics, and engineering.

Looking for a Foundation

I’ve intentionally taken some time to dive deeper into these ideas because I don’t think it’s sufficient to read the headlines and assume every finding is trustworthy. For me to feel confident in this work, I needed to figure out how the science is actually done, why it can be trusted, and how it connects to better explanations.

I still have a lot to learn, but feel that I’m now building on a more solid foundation.

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The post also included an example of an experiment by William Hick that set the stage:

Hick found something fascinating. Each time the number of lights doubled, reaction time increased by a precise amount. This held across people and situations. He had discovered something mathematical about the human brain. The reaction times were precise, predictable, and observable.

The pivotal phrase: “He discovered something mathematical about the human brain.”

This was the beginning of a revolution built on the idea that computers could be useful in understanding the invisible nature of the mind.

The Roots of the Cognitive Revolution

During WWII, a mathematician named Norbert Wiener was working on a new anti-aircraft weapon that could predict how to hit a moving target.

He noticed that the human mind constantly makes predictions and corrections without effort. Imagine shooting an arrow at a bullseye. We have a goal, take an action, observe the outcome, and correct if needed. This loop constantly runs in the back of our minds.

In designing his weapon, he noticed something remarkable: The machine needed to use the same loop: goal, action, outcome, correction. It didn’t matter if it was a machine or a human brain; the process was the same, and both used the same information.

This was the opening salvo in a revolution. Wiener was among the first to propose that natural processes of the mind (perception, prediction, etc.) could be represented via mathematics.

However, an element was missing: measurement. He had no way to quantify the theory. A few years later, Claude Shannon filled this gap with “bits” and helped create what became cognitive science.

A Powerful New Metaphor

As computers emerged in the 1950s, Wiener’s ideas and Shannon’s bits suddenly seemed deeply relevant to psychology. A powerful new metaphor was born: mind-as-computer.

In the 1950s, researchers Allen Newell and Herbert Simon were among the first to create computer programs that could solve problems using human-like reasoning. Their Logic Theorist in 1956 proved mathematical theorems that were thought to require human thought.

For the first time, a computer program was shown to mimic the invisible processes of the mind.

This work established a fundamentally new way to think about, test, and understand the mind using the rigor of mathematics and computer programming. The idea wasn’t to understand why the brain works as it does, but how to observe and measure what it does.

This new direction was seen as a threat to the dominant researchers in psychology.

The Cognitive Revolution

Like all revolutions, there are winners and losers. The Cognitive Revolution was no different.

Before the 1950s, psychology was dominated by the behaviorists. These researchers saw the mind as a “black box” that could not be studied scientifically because it was impossible to observe and measure things like emotion, memory, or understanding.

So, they focused on what could be measured: behaviors. Think of Pavlov’s dogs: ringing a bell (input) caused the dogs to salivate (output). The dog’s emotions or motivations weren’t relevant because they couldn’t be measured.

This started to change in the 1950s. Thinkers like Noam Chomsky challenged the power and authority of the behaviorists, like B. F. Skinner, by illustrating their limitations. At about the same time, the mind-as-computer metaphor arrived and showed that it made the mind testable and measurable.

This was a turning point in psychology. Behaviorism lost its dominance as cognitive science rapidly gained influence.

Hick’s reaction times, Wiener’s loop, Shannon’s bits, and Simon and Newell’s computer jump-started a powerful new direction.

Behaviorism remains a vital part of psychology, and its findings are still relevant. However, the prestige and respect of behaviorism declined and was replaced by researchers who showed the mind was not a black box, but something that could be studied with scientific rigor, math, and computers.

Mind as Computer

As it turns out, there are a lot of valid comparisons between the two. Consider the human versions of inputs, outputs, memory, storage, processing, etc.

The mind-as-computer metaphor didn’t claim the brain literally was a computer. It gave scientists a new way to model, test, and measure invisible mental processes.

As we’ll see in the next post, the mind-as-computer metaphor is one of the most enduring in all of science. What started in the 1950s continues today as researchers build and test ever more accurate and powerful models for understanding the mind by building computer programs that mimic it.

Up Next

Next week, we'll look closely at how it works, why this metaphor produced some of the most trusted findings in cognitive science, and why it matters for how we understand explanation today.

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Meet Claude Shannon

Let’s get this out of the way… Anthropic’s chatbot, Claude, is reportedly named after Claude Shannon. That should give you an indication of his contribution. His paper and book, A Mathematical Theory of Communication, is one of the most cited scientific papers of all time. One scientist later called it “the Magna Carta of the information age.”

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Shannon’s work is the very beginning of a thread that connects many of the ideas we’ll soon discuss.

The question I consistently ask is especially relevant here: how do we know? What makes the science of explanation scientific? Claude Shannon is a big part of that answer.

Messaging in the 1940s

Shannon was a mathematician who was concerned with messages traveling long distances, like a radio transmission from New York to London. The messages were often garbled, subject to interference, or failed completely. Shannon wanted to solve these problems, but there was no way to measure them.

Over time, he developed ideas that helped solve the messaging problem, but more importantly, established the basis for information technology.

He was the first to describe, in scientific terms:

• Capacity - How much information a channel can carry

• Error correction - How information can survive interference

• Compression - How to transmit information efficiently

This seems like a feat of engineering, and it is. But if you look deeper, you can see big ideas that are the roots of computer science, cognitive science, and more.

The Big Idea

Information, to me, seems like something we say or learn; a human phenomenon. For Shannon’s work, please avoid thinking in human terms. Our focus here is NOT about meaning or content. It’s about a way to measure any kind of information in any context. Think of it like measuring the volume of a liquid: which liquid doesn’t matter.

To start, Shannon needed to define information. What is it? This is a deeper question than it appears because it’s not concrete.

To him, information exists when there is uncertainty that needs to be resolved. Imagine a radio operator listening for a signal to make a decision. Information is what resolves the decision.

• Before the signal arrives: uncertain

• After the signal arrives: resolved

• What caused the change? Information

Shannon saw that signals can carry varying amounts of information. Some signals carry more information, some less. He wanted to quantify these differences.

How to Measure Information

When a signal is transmitted, the amount of information it carries is based on how rare or surprising it is. Here’s an example:

Imagine two weather forecasts. One says that it will rain in Seattle in November. That’s an expected signal that didn’t resolve anything for you. The signal carries little information.

The second weather forecast says it will snow in Miami in July. This is a complete surprise. The signal carries a lot of information because it was unexpected.

When a signal is already known (rain in Seattle), it carries less information because the probability is high. When it’s a surprise (snow in Miami), it carries more information because the probability is low. Surprising signals = more information.

This is the starting point: using probabilities to gauge the amount of information in a signal. If you know the probability, you can calculate and measure information.

A Formula and Bits

The genius of Shannon's work is finding a calculation that measures how much information any signal carries, based on its probability. He used the term “bit” to describe the units. When a signal has more bits, it carries more information. If you add up all the bits in a channel, you can test capacity, for example.

The formula he used is: Information = log₂(1/probability).

The key point is that it works. It reliably calculates information in terms of bits, which makes information measurable. This is the key to everything. Once information became measurable, the mind itself became scientifically measurable in a new way.

The Alphabet

Using Shannon’s formula, we can find the bits for each letter of the alphabet, which is based on the probability of the letter appearing in a sentence. The average number of bits for a random letter is 4.7 bits.

• The letter “e” is common and expected. It has about 3 bits.

• The letter “q” is rare and unexpected. It has 5-7 bits.

This means that the letter “q” carries about twice the information as “e” because it is much less likely to appear. In information systems, rare signals require more bits to represent efficiently. If you play Scrabble, this probably feels obvious.

Testing Humans

Shannon’s work also helped explain processes in the human mind. Let’s consider a real experiment conducted by William Hick, a British psychologist.

Imagine sitting in a lab in 1952 with a panel of lights in front of you, along with a set of buttons that correspond with each light. Your job is simple: when the light turns on, press the button for that light as quickly as possible.

First, you see a panel with two lights and two buttons. One of the lights flashes, and you quickly hit the button for that light. Easy and expected. Your reaction time is recorded in milliseconds.

Next, you see a panel with four lights and four buttons. One light flashes, and you hit the button.

Over multiple rounds, the number of lights doubles on each panel: You see four, eight, and sixteen lights. Each time, you race to press the button for the light that appears while Hick precisely records your reaction time.

Hick found something fascinating. Each time the number of lights doubled, reaction time increased by a precise amount. This held across people and situations. He had discovered something mathematical about the human brain. The reaction times were precise, predictable, and observable.

Back to Shannon

Hick’s experiment highlights the power of Shannon’s work. Each light represents uncertainty. Which one will flash next? Fewer lights mean more certainty, thus less information. This maps directly to bits calculated with Shannon’s formula.

• Two choices — one bit of uncertainty.

• Four choices — two bits.

• Eight choices — three bits.

• Sixteen choices — four bits.

Each doubling adds one bit. And each additional bit adds the same fixed amount of reaction time. The mind was processing information at a measurable rate, in bits per millisecond. This rate is part of how mental processing is measured today.

Shannon had given Hick a ruler. And the ruler fit the human mind precisely.

Why This Matters

Thanks to Claude Shannon’s work, information became measurable for the first time. Like mass and energy, the ability to measure it meant that it could be tested and used in experiments. Bits per millisecond works a lot like miles per hour.

Shannon gave psychologists a formal language for thinking about the mind as an information-processing system.

Next Up

Soon, we'll see how Shannon's bits provided a way for scientists to study the invisible mechanisms of the human mind by comparing it to a computer.

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A One-Way Street?

It’s easy to think of explanation as a one-way street. We understand something and then share it with others. In this scenario, the audience only receives information. They are not necessarily an active participant.

This assumption is incorrect.

Explanation is more of a two-way street. Why? Because an explanation is like raw data to the audience. It can be useful on its own, but to have real power, it needs to be connected with meaning in their minds. They decide, not the explainer.

How does that happen? Via personal context.

Meaning Comes from Context

Our minds are full of knowledge and experiences that create a sense of context that we try to apply to new information. When it arrives, we automatically look for how it connects to what we already know. If we have context, the information takes on more meaning. If we don’t, it can be forgotten.

This brief video helps explain the big idea:

Olivia at the Doctor

In this series, I’ve shared the story of Olivia, who is trying to remember advice from her doctor. She’s distracted, but is listening and assumes everything will sink in. Spoiler: she’ll forget most of the advice she hears.

One piece of advice made it into her long-term memory: eat more fiber to reduce cholesterol. The question is why? What was it about that point?

• First, the advice earned her attention. It was new information that felt like a threat to her health.

• Second, she already knows about the connections between cholesterol and heart disease. This personal context added meaning to the advice.

• Third, the doctor added more context by saying, “Your cholesterol numbers have gone up and can lead to heart disease. You should eat more fiber because fiber helps remove bad cholesterol.” This bit of logic made sense to Olivia.

In short, Olivia easily saw meaning in this information, and that is what helped it become memorable. The information from the doctor connected to her mind in a way that made it stick.

Using the Two-Way Street

It’s obvious that context matters in communication. Great communicators use it deftly to relate information with context and meaning. But that’s not enough. One way context can’t do it all.

We must also consider the audience’s existing context and prior knowledge. Our explanations have a much better chance of being remembered if we appeal to the context that exists in the minds of the audience. We want context to quickly snap into place, so that meaning is easy to see.

Give your audience a way to see meaning that goes beyond your words, and appeals to something deep inside their minds, and they are much more likely to remember what you say.

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We want to know: what is necessary for information to become a memory?

Two things should be clear, based on recent posts:

1. Everything we know about the world around us comes from our senses. If we can’t see, hear, taste, touch, or smell it, it’s not there. Read more.

2. We can only pay attention to a fraction of what we sense. What gets our attention is usually something meaningful. Read more.

Today, we’re adding a layer that helps answer an inevitable question: where does meaning come from?

Does a Broken Cup Matter?

Take a second and look around you. This is your current context, and it influences how you think. An office has a work context. The bedroom has a sleep context.

Our minds depend on context from the outside world to understand and adapt to what we experience. A knife in the bedroom means something different compared to a knife in the kitchen.

Think about an object, like a coffee cup, and imagine it being accidentally broken. On its own, it’s just a broken cup with little meaning. Who cares?

But to your family, the cup has meaning. It was your grandmother’s favorite, and seeing it broken was an emotional experience.

This simple example makes an important point: meaning comes from context. This is true at home, in the office, and in relationships. The object, idea, or person matters, but meaning comes from how they relate to the bigger picture.

The problem: context is often invisible.

Your First Party

Imagine the idea of “house party” being new to you. You have no expectations, and your first party is a formative and tiring experience. Your night is spent observing others and understanding the idea of “party”. It’s a lot to take in.

Without context, nothing in your mind tells you what to do or how to behave. So you have to build that knowledge from scratch, which is distracting. You notice that parties are convivial and include lots of people talking. Most of them are drinking alcohol and eating snacks.

You don’t realize it, but the event is being filed away in the back of your mind as a new context. Party = people, alcohol, and snacks. Context is slowly becoming clear.

Your Second Party

Your mind now holds the concept of “party” and creates a shortcut that prevents you from having to learn it all over again.

When you walk into the second party, the context clicks into place, and your mind automatically tunes into the items that you noticed previously. You scan the room for people you know. You notice the location of the bar and snack table.

You didn’t have to think, “Look for people and alcohol.” Your attention went there automatically. Why? Because the context informed your attention.

The “party” context focused your attention on what is meaningful. You didn’t even notice the sculpture by the door, or the bistro lights in the kitchen. Context focuses attention.

Why Does this Matter?

Much of what we experience is high in context. Our homes, vehicles, work, relationships, etc., are mostly stable. We know what’s meaningful and take this sense for granted.

We also encounter a lot that is low in context. We see headlines that have little meaning. We enter conversations without knowing what’s been said. We watch movies with no expectations. Finding meaning in these situations is hard work.

The role of the explainer is based, in part, on supplying the context needed for helping people see meaning. We recognize situations where the right context can make all the difference.

The Reality for Explainers

Explanations often fail because of a lack of context. Without it, your audience might remember facts and figures, but not understand the big picture. They see the mug, but not the grandma, and it’s confusing.

Many communicators end up skipping essential context and moving directly to content. Why?

The culprit here is the Curse of Knowledge. The more you know about a subject, the harder it is to imagine NOT knowing. The curse interferes with our ability to make accurate assumptions about what the audience knows, or not.

We assume the context is obvious because it’s obvious to us. So we skip it. We don’t make the context clear to others, and it remains invisible. When this happens, the audience has to work harder to find the meaning that will keep their attention.

I made this video to explain an interesting experiment that illustrates the curse.

The Solution

Think of your audience being like the person entering a party for the first time. They have no context and must build a sense of meaning.

As an explainer, your job is to help build the shortcut they need to find meaning. By explaining the idea of “party”, you can help them build expectations about the party, what matters, and why it matters. With this information, they can skip the costly building and enjoy the party for what it is.

These explanations do something important: they lower the cost of understanding. They provide the audience with context at the beginning so that meaning and relevance are clearer. When the cost is lower, learning feels easier.

As we’ll see, context is not an addition or a helpful ingredient in how we think. Rather, it is an essential part of how we perceive the world around us. It works seamlessly with our senses and how we pay attention. When information arrives in our bubbles, context is automatically there to help.

And we have a role to play in this by filling in contextual blanks.

Up Next:

As always, the driving question is: How do we know? What is the evidence that supports my claims about context?

There is real science about the role context plays in our minds, and we’ll be taking a closer look at how context helps ideas move through the gauntlet of the mind.

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