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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I ask this about much of the media I see. In this spirit, I want to share how I’m thinking about and using AI in The Science of Explanation.

First: Are Explanation Skills Still Needed?

It’s easy to imagine AI being the world’s best explainer, capable of explaining almost anything at any level. This leads to a pivotal question: Are explainers and explanation skills really needed?

The answer is yes, and let me explain why.

First, ideas must work between people. There is a big difference between having knowledge and sharing it with clarity. The need to share ideas will remain in conversations, meetings, presentations, and more.

Second, explanation is a social activity. The best explanations depend on the human ability to sense the needs and capabilities of the audience. These signals, like body language, emotion, attitude, and context, help us adjust our message in real time.

When explanations work, they create social alignment and a shared sense of reality and context. This is what makes explanations work. Claude can provide answers, but it has no clue about the human signals on the other side of the screen. The feedback loop is missing.

Humans have to make communication work for other humans, and that’s the challenge. How do we overcome it? By understanding how the mind works.

Now let’s get into the nuts and bolts:

How I use AI

It seems fitting to start by asking Claude and ChatGPT how I use them.

Claude

ChatGPT

This rings true. AI, for me, is like having a smart research assistant who is always there to analyze and challenge what I’m writing. It forces me to constantly revise, reconsider, and reframe. I find this back-and-forth to be productive, even if it doesn’t know how I feel.

Perhaps the most valuable feature of AI is infinite patience. I can ask hundreds of dumb, probing questions, and it is happy to keep responding.

I Write the Drafts

I’ve published two books and written scripts for hundreds of explainer videos. I don’t claim to be an amazing writer, but my voice and style have become distinctive over time, and I value that deeply.

Today, I write the drafts of everything I publish. Once the draft is in place, AI helps me refine, but not replace, what I write. AI notices inconsistencies and contradictions that I sometimes miss.

What it doesn’t know is you, dear reader. That’s up to me.

Learning Humility

The Science of Explanation is a new perspective for me as a layperson. I am not only learning about scientific discoveries, but also how to write about science in a way that’s both clear and trustworthy.

Claude, in particular, has been a helpful partner. It consistently pushes me to write in terms of what will pass muster in the scientific community. It will ask:

You’re making this claim, but not backing it up with evidence. Where is the research that supports it?

I wish every person on the web had to answer this question before publishing.

AI has also helped me write from the perspective of scientific humility. This means acknowledging the limits of science and the findings of scientific experiments.

For example, there is a big difference between saying “This is true” and “This is likely under specific circumstances.”

It’s been a challenge to find the balance between simplicity and accuracy, and AI has become a sandbox for experimenting with the nuances of scientific explanations.

Problems

I’ve been bothered by a few consistent issues. The first is ChatGPT’s confidence in providing quotes. Here’s a real example:

WTF? ChatGPT called it a quote and put it in quotation marks. But then can’t find it? This is… not optimal.

Saving Time with Image Production

I’ve drawn thousands of original visuals for Common Craft videos, which all share a specific style. However, they are time-consuming to produce. I have to ask: Is drawing the best use of my time?

ChatGPT has been a decent partner in quickly creating images in Common Craft style. When it works, it’s useful.

Here’s an example of my drawings in Common Craft style (not AI)

Over time, I taught ChatGPT to use Common Craft style as a template for new drawings. I outlined the specific attributes in text and then attached examples.

Here are examples created by ChatGPT:

Videos

Making animated videos is second nature to me. I’m sure AI could help, but my process gives me ultimate control, and that’s something generative AI lacks.

The videos in my posts are created by me, without AI. The visual series below is designed and animated in PowerPoint, for example. Watch a video.

NotebookLM as an Offboard Brain and Media Creator

One of my favorite AI tools is NotebookLM, by Google. It’s different because it only focuses on the materials, or “Sources,” you provide to it.

For example, I could upload 50 PDFs (or YouTube videos, audio files, etc.) to a Notebook and ask NotebookLM to summarize them and provide relevant quotes. Then, the results link back to the exact passages in the materials I shared.

Today, I have a Notebook that tracks every post on The Science of Explanation. NotebookLM reads the posts and allows me to ask questions and see the overall content from a new perspective.

What blows my mind is the “studio” features, which turn the Notebooks into media.

AI Made This Video

The video below was generated by NotebookLM, based on Science of Explanation posts. The tool offers a “paper craft” option, which feels like it was inspired by Common Craft style.

The script is too long, the visuals are too cluttered, and nothing moves. But for a video made in a few minutes, it’s impressive.

You’ll PROBABLY Get What You Want

ChatGPT’s drawings can be unpredictable. Just when I think a style is defined, it goes off the rails. While writing this, I asked ChatGPT to draw Fiber Man. Here was the output:

Nope. Not even close. I pushed further:

On the next try, it did better. I said nothing about “Cognitive Load,” but it’s an accurate inference:

Here’s how I think of generative AI: You’ll probably get what you want. The models are “probabilistic,” meaning that the results are interpretations and not determinations, like a calculator.

Looking Ahead

AI is probably going to change society in unpredictable ways, both good and bad. It could cure cancer and then wipe out jobs. I’m worried about the existential threats, but I have no way to alter the course of these new technologies.

What I can do today is focus on something even more fascinating: the human mind, and how it works. In this focus, I am using AI consistently, but all the while, I’m reminded that it’s not a person with feelings and a brain. It can never feel joy, embarrassment, or the feeling of human connection. When it comes to communication and explanation, humans are what truly matters.

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In recent posts, I’ve focused on those limits because they define the mechanics of our mind. And there is a lot more to know. Before moving ahead, let’s do a quick review.

A Review:

The goal: Every communicator wants to be understood. But too often, explanations fail to connect. Information is ignored or not remembered.

The big questions: Why does that happen? What can we do about it?

The source of answers: Decades of scientific research have led to an understanding of what happens in the mind when an idea is understood and remembered.

Why does this matter? If we understand the mechanisms, we can design communications to work with the mind rather than against it.

We’ve made some initial steps on a bigger path that looks like this:

World → Bubble → Attention → Gauntlet (multiple steps)→ Memory

An Example of Using Cognitive Science

Your job is to get people to remember a ten-digit number after seeing it briefly. You have a choice:

1. Provide the ten-digit number in a string: 3458560274

2. Create chunks of digits like a Social Security Number: 34-585-60-274

Because you’re aware of cognitive science, you know that #2 will likely get the best results because the human mind responds best to information in chunks. This is an example of working with the mind that has been understood since the 1950s.

We all have default settings that control how we manage incoming information. The Science of Explanation is being built on multiple insights like the one above.

The World and the Bubble

The path we’re on will help us see every step information takes on the way into the mind. It goes from awareness (you hear someone speak) to memory (you remember what they said). What seems simple is actually a fascinating look at how our minds process information.

The path begins with awareness of the world around us. Because we have human senses, our experience is uniquely human. What we see, hear, taste, smell, and touch appears to be the only experience possible. But it’s not. My dog and the bird outside have different experiences that presumably feel natural to them.

Every animal on earth experiences life inside a bubble that represents the boundary of what can be experienced. Our bubble, the human version, is limited by what our senses can detect. It defines what is available to us, or what becomes a signal at all.

Big idea: Our senses define what signals we can detect from the world around us.

Some Signals Matter More than Others

Imagine reading your favorite book on the couch. You don’t think about it, but you’re surrounded by signals, like music in another room, the hum of the refrigerator, spinning laundry, a dog at your feet. They’re all part of your bubble, but fade into the background. Then two things happen:

• The dryer alarm goes off

• Your phone buzzes

These enter your bubble at about the same time. You roll your eyes and try to keep reading, but your mind is stuck on the phone and asks: Who is it? What if it’s important? In this competition for your attention, the phone wins and the dryer fades into the background, the loser.

This moment shows something important: signals are constantly arriving in your bubble, but most are not processed. The phone was different; it won your attention, and that set it up for becoming usable information.

Big idea: Attention is competitive. The winner gets processed, the loser fades.

Managing Signals

Attention is essential because it’s like a gatekeeper of the mind. By understanding how it works, we can find ways to earn it and hold it when we communicate.

A key idea is that attention has hard limits. Our minds can’t focus on multiple streams of information at once. For example, we can’t listen fully to two conversations or watch TV while reading a book. You can think of it like a bottleneck that allows only one signal at a time.

The question becomes: how does the mind select signals? Or more specifically, what signals reliably earn our attention? Research has shown that two factors matter:

• Signal strength - We initially pay attention to what is loud and clear around us. Read more

• Meaning - We are tuned to focus on what is meaningful, like our safety, goals, relationships, etc. Read more

The buzzing phone earned attention in the previous example because it was loud and was connected to meaning. The dryer alarm? Not so much.

Big idea: Attention is limited and filtered based on signal strength and meaning.

Olivia and the Gauntlet

Earning attention is only the first step. Most signals that win attention are still forgotten. Somewhere on the path, they were challenged and lost.

In previous posts, I introduced the story of Olivia, who is distracted and trying to remember her doctor’s advice in the exam room.

In Olivia’s story, we know the outcome: she remembered to eat more fiber, and it became a healthy part of her lifestyle. She forgot all the other advice.

The doctor’s advice to eat more fiber is a survivor. It earned attention and then faced multiple challenges in order to be remembered. It successfully ran the gauntlet of the mind.

The question is: Why? Why did that advice make it through while others didn’t?

The Next Phase

Soon, we’ll step into the gauntlet and look at specific mechanisms that are backed by science and show what an idea faces on the path to memory. We’ll look at context, working memory, mental models, and more.

We’ll follow a path that shows how to design explanations that work with the mind.

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When we’re absorbed in a book, a problem, or a movie, the world fades away. Yet, a sudden noise, a loved one’s voice, or a buzzing phone can still break through.

Some signals grab attention. What holds it is meaning: anything tied to our goals, identity, or immediate concerns. A job offer or a diagnosis carries a different kind of meaning than a definition in a dictionary.

A Prediction Problem

For explainers, this creates a challenge. Attention is fragile. Your audience is distracted, tired, and overwhelmed. The only way to hold their attention is to connect your message to what matters to them.

But meaning varies. Each person’s background, goals, and biases shape what they notice. The same message can resonate with one person and miss another entirely.

This turns explanation into a prediction problem: what will matter to this audience?

We can’t know for sure; we can only make informed guesses. When we’re right, the message connects. When we’re not, it falls flat. One way to improve those guesses is to look for patterns in how people tend to think and what they value.

The Vibes Connection

In the first year of this newsletter, I was fascinated with a big question: Why are we the way we are? At the time, the newsletter was called The Vibes Project.

Most people intuitively recognize that humans are different in common and recognizable ways. In a single group, you might find people who are outgoing, shy, self-conscious, sensitive, awkward, or composed.

We see and feel these differences and similarities, but have no vocabulary for them. I set out to research the dominant ones and look for ways to describe them as opportunities for more meaningful connections.

Over time, I proposed that there are four “vibes” that represent different social orientations. They are:

• The Natural Vibe – effortlessly cool and composed, can be aloof

• The Analyst Vibe – curious and rational, can be awkward

• The Feeler Vibe – sensitive and empathetic, can be overwhelmed

• The Performer Vibe – trendy and dramatic, can be intense

While I believe these archetypes are helpful, they are not yet a scientific taxonomy. I see them as a useful model for thinking about how to connect with others.

I’m excited to bring Vibes back into the fold because they work best with a practical focus, like better explanations.

Attention + Vibes

Vibes offer a practical way to make educated guesses about what will capture and hold someone’s attention. With a little planning, we can look for patterns that provide clues about what matters to them.

It’s important to keep in mind that Vibes are normal parts of being human. There is no hierarchy or spectrum, just ways that people are.

Kerry from Work

There is some chance that you read the list of Vibes above and thought of someone specific. Let’s suppose that you did, and Kerry from work came to mind as a Performer. She’s a friend who loves attention and putting on a show. She’s outgoing, in tune with mainstream culture, and comfortable being in the spotlight.

Now, let’s suppose Kerry is starting a new position and learning a lot of new processes. Your job is to explain those processes and ensure that she remembers them. You have a choice:

• Explain them as you would to anyone

• Explain them to Kerry, a likely Performer

By considering her Vibe, you can look for ways to connect your message to what may be meaningful to her. The question becomes: What’s meaningful to a Performer?

The answer depends on the individual, but Performers are often sensitive to visibility, feedback, and social response. As such, your explanation could reflect how the processes are viewed by others.

• Along with the facts: “Here is how to complete the process successfully.”

• You include a bit of color: “This process is especially important because leadership reviews it every week.”

The key is thinking about your audience before you start to explain. Take a beat and think about what they are likely to find meaningful. With or without the Vibes I’ve proposed, this exercise will help you consider what’s likely to earn and hold their attention.

The Conference Speaker

Vibes can also apply to groups in some situations. Imagine being a speaker who performs at a variety of conferences. How can you earn and hold attention?

Conferences are often attended by people with something in common. They use a specific product, have similar interests, education, etc. Again, explanation is a prediction problem. What will work for this audience?

• Conference 1 is a conference of engineers who are likely to have traits of the Analyst vibe. As such, they are likely to respond to accuracy, logic, and rigor. By starting your presentation from this perspective, you have a better chance of engagement.

• Conference 2 is a meeting of therapists whose work involves empathy and interpersonal communication. They may respond from the Feeler perspective and be attracted to human stories, vulnerability, and emotion. Your first few slides show that you understand their interests and what meaning to them.

In these examples, you can see that planning is key. Every audience is different, and Vibes offers a simple model for thinking about what will earn and hold their attention.

The Bottom Line

If attention is the gate, meaning is the key. Your job is to figure out which keys will get a response from your audience and help your message get through the gauntlet.

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Quick Video Explanation

The video below is a 2.5-minute summary of the article below. Some will find the visuals and animations easier to grasp than text.

What Do We Know So Far?

Last week, we looked at the work of David Broadbent, who was one of the first researchers to use scientific experiments to understand the mind. His work on attention established a few big ideas:

1. The mind applies filters. Some information is filtered out of our experience before we are aware of it. In a room with multiple conversations, the mind automatically ignores most of them.

2. The mind has a bottleneck. When channels of information compete for attention, only one can pass through at a time. It’s impossible to process two conversations at once. You have to listen to one, then the other, to remember anything.

3. Signal strength matters. The mind selects channels based on physical characteristics first; the clearest, loudest signals get attention. The person in front of you easily gets your attention.

What makes these findings so powerful is their reach. This isn’t an anecdote or special case. This is something fundamental about the human mind: we evolved mechanisms to manage overwhelming amounts of information.

But these early findings were also incomplete. Broadbent did important work, but it was only the beginning.

A Knob or a Door?

Broadbent saw attention as all-or-nothing. If a signal didn’t get attention, it was blocked, like a door that is either open or closed.

Anne Treisman, a cognitive psychologist at Oxford, noticed something about Broadbent’s experiments using dichotic listening. Subjects who were focused on one channel could remember fragments from the other channel. This was a hint that attention wasn’t all or nothing. It was a matter of degrees.

Treisman showed, through similar experiments, that attention is more like a volume knob that can be turned up or down. The competing signals don’t go away; they just appear at lower volume, which she called “attenuation”.

Why does this matter? Because it jump-started a new idea: some types of information can break through focused attention. By studying what breaks through, we can understand how the mind prioritizes attention.

An Improved Method

Treisman also saw a problem with the design of Broadbent’s experiments. She asked: How do you know that someone is truly paying attention to something in the lab?

She solved this with a method invented by Colin Cherry called “shadowing,” which forced subjects to repeat what they heard in headphones in real time.

This was huge. By forcing them to speak in real time, she could ensure their attention was consistently held on a single channel. This created a control for the experiment. With this control in place, she could introduce other audio signals and measure which ones broke through attention.

“I Heard My Name”

Dichotic listening combined with the shadowing method created a reliable model for research. Soon enough, she noticed something fascinating.

While subjects repeated what they heard in one channel, she introduced a variety of different inputs in the other ear. These attenuated signals were often ignored, but some reliably broke through attention and could be recalled later.

For example, she noticed subjects could recall hearing their own name while focused on another channel. This “own-name effect” became well studied in cognitive science, and her work provided the model that explains it: The person’s name, in a secondary channel, was not ignored by the mind, but sensed in the background.

The big idea is simple: Some information can break through focused attention.

Meaning Matters

Treisman’s work inspired other scientists to continue testing the idea that our attention is flexible and can be refocused. This work established that meaning matters. Information that is meaningful for the subject, like hearing their name, can break through.

Researchers found that certain categories of information reliably capture attention, including:

• Signals of potential threat

• Information related to current goals

• Unexpected or novel information

• Emotionally charged words

These findings have real power because they are default settings we all share. Even if we’re focused on something else, our minds can be distracted when meaningful information is introduced. This is as true in the office as it is on the savannah.

Want a real-world example? Think about what happens when your phone buzzes. It breaks through your attention almost every time.

Attention is Universal, and Personal

We all pay attention to what is clear and obvious around us. But that attention can be refocused when we hear something meaningful.

The challenge is that meaning is not consistent across people. We all have experiences, personalities, and biases that impact where our attention is naturally focused, and what kind of information can break through it.

The Lessons for Explainers

As we’ve discussed, attention is required. Start by reducing distractions, making your channel the only one available, and making sure it’s clear.

Anne Treisman’s work adds another layer. If you want to ensure that you earn attention, understand that some information can break through focused attention. By attaching your message to what is meaningful to your audience, you have a better chance of earning their attention.

It’s up to you to identify what information will connect to their sense of threats, goals, novelty, and emotions, for example. What can break through?

Olivia’s Experience

Now we have a clearer picture of what Olivia experienced in the doctor’s office. As we saw in previous posts, she faced multiple distractions and assumed she could remember all the advice from her doctor. It was an illusion.

Only one piece of advice made it into her long-term memory: eat more fiber.

Why? It started with attention. Her doctor provided a clear signal full of meaning. The fiber advice broke through everything else because it surprised her, represented a threat to her health, and connected to her goal of living a long life.

The Gauntlet and Attention

Attention is the gatekeeper of the mind. Nothing has a chance to enter without first earning it. In the last few posts, we’ve established a few big ideas from the science of attention.

1. Attention happens without our control. The mind automatically filters what we encounter.

2. When signals compete for attention, we can’t process them at the same time. Multitasking is a myth.

3. Our attention can be influenced by meaningful signals. We automatically shift attention to things that matter to us.

4. Everyone’s version of meaning is different, which means we have to anticipate what kinds of signals will break through to our audience.

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In last week’s post, we followed the story of Olivia, and I made a handful of claims about the role of attention and how the mind manages it. They include:

• The mind has limits and evolved ways to deal with what we experience

• We don’t passively receive information; we select and filter it

• What makes it through the filter is often predictable

In the doctor’s office, Olivia was overwhelmed with distractions, internal thoughts, and advice from her doctor. And her mind reacted predictably: it selected some information for long-term memory (eat more fiber to reduce cholesterol) and forgot others (use the treadmill more).

This scenario sets up the driving question of the series: why was the cholesterol information so sticky? What can it teach us about explanation and communication?

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The Power of Attention

Attention is the gatekeeper of the mind. Without it, few things can enter. By understanding the science of attention, we can predict what kinds of explanations get through the gate and possibly into long-term memory.

A Note About Science

For most of history, our thoughts, emotions, and ideas were impossible to measure or observe directly. Without this ability, it was difficult to claim that a subject like attention could be scientifically proven.

This started to change in the 1950s, when psychologists started to design new kinds of experiments. For example, they brought subjects into a lab, placed headphones over their ears, and then provided different audio in different ears. Then, they asked the subjects what they could recall from the audio.

This was called “dichotic listening”, first devised by Colin Cherry, who noticed a phenomenon he called “The Cocktail Party Effect”, a reference to our innate ability to focus on one conversation among many.

Signals in the Noise

One of the most famous researchers was Donald Broadbent, a psychologist at Cambridge, who served in World War II. While serving in the Royal Air Force, he noticed something troubling. Radar and communications operators had to juggle multiple channels of information and often missed important signals in those channels. He wanted to know why.

He asked: When competing channels appear, how does the mind interpret the information? Where is attention automatically directed? Finding answers could help the RAF design more effective radar systems.

Broadbent’s Experiments

In his 1958 book, Perception and Communication, he described experiments that showed what happens when we manage multiple channels at the same time.

Broadbent placed headphones on subjects and played different audio in different ears simultaneously. For example, three digits in one ear, the different digits in the other. Then he asked the subjects to recall what they heard.

This simple setup allowed him to test and measure how the mind manages attention. By keeping one ear’s audio the same, and changing the audio in the other, he could test what subjects could recall.

Bottlenecks and Working Memory

At the time of his experiments, it was not known how the mind manages incoming information. Does it pay attention to everything equally, or does it automatically choose one channel over another?

Dichotic listening provided a strong foundation for answering this question. There were two likely outcomes:

1. If the mind pays attention to everything equally, the subject would recall a mixture of digits from the two channels.

2. If the mind chooses one channel, then the next, they would recall them as two distinct groups.

Time after time, subjects recalled the digits as two distinct groups. They focused on one ear, remembered the digits, and then focused on the other ear. This was compelling proof that the mind can only focus on one channel at a time.

Big idea: The mind has a built-in bottleneck. Only signals from one channel can go through at a time. Everything else fades away.

This was an early version of cognitive science. Broadbent’s work showed that the invisible mechanisms of the mind could be measured and observed. His work was later improved upon, but the big ideas remain.

Mind as a Filter

Broadbent figured there must be some logic behind how the mind manages attention. When we look around a room, we automatically tune into some things and not others. Why? What earns our attention first?

In Broadbent’s experiments, he changed the physical characteristics of the sounds in one ear and not the other. For example, one ear may hear digits at a higher volume or at a location farther away. This helped him establish a new idea: attention processes physical characteristics first. The closest, loudest signals earned attention.

Big Idea: The mind filters information as it arrives. Before we know anything about the meaning of the information, our minds assess the physical characteristics and choose what has the best signal strength.

This is known as selective attention, and it’s now part of the foundation of cognitive science.

Olivia’s Experience

As Olivia sat in the exam room, her mind was at work in the background. Since she could only pay attention to one thing at a time, it automatically filtered her experience based on what was closest, clearest, loudest, etc. She noticed the baby crying next door.

When the doctor arrived, Olivia’s mind switched to a different mode of attention. Despite everything else happening around her, she focused on each piece of advice. and attempted to remember everything: treadmill, salt, fiber, weight.

Despite her best efforts, two weeks later, only one idea remained: eat more fiber. It had a strong signal strength, earned her attention, and started the journey through the gauntlet.

Lessons for Explainers:

1. Attention is required. Earn it as quickly as you can.

2. Processing happens one channel at a time. Make sure your channel is the only one.

3. Signal strength matters. Be clear, visible, and easy to hear.

The Next Question

Why did fiber make it through? What was it about that information that allowed it to survive while other advice didn’t? Next week, we’ll meet the woman who built on the work of Broadbent, improved his experiment design, and contributed to the foundations of cognitive science.

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Catch up: Introducing The Gauntlet - A New Series

Meet Olivia

Olivia, a health-conscious mom of two, sits in her doctor’s waiting room. She recently had a physical and blood test and awaits the results. She shifts in the chair and goes through a mental checklist.

“I wonder what the blood tests will show? What meds do I need to renew? The doctor is late, which means I’m running late. Oh, I have to remember the suspicious mole. Wow, that baby next door is not happy!”

Olivia has always been data-oriented and willing to put in the work to improve. She has a good memory and feels in control of her health.

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Finally, the doctor enters, and they walk through her health and the blood test results. At the end, the doctor provides recommendations and explains them by representing their knowledge in a form that works for her.

“You’ve gained a little weight. Not bad, but keep an eye on it. Try to walk more on the treadmill, and maybe try smaller portions. The mole is fine. Oh, and maybe reduce your salt intake for your blood pressure.”

Meanwhile, the baby wails. She’s running late. She’s being bombarded with distractions and new information. The doctor continues.

“Your cholesterol numbers have gone up and can lead to heart disease. You should eat more fiber because fiber helps remove bad cholesterol.”

Olivia nods and thinks to herself, “Weight, blood pressure, and now cholesterol. This sucks, but I’ve got this. It’s time take control and follow everything the doctor said.”

What Olivia doesn’t realize is that her feeling of control is mostly an illusion. Try as she might, much of the doctor’s advice is likely to be forgotten or ignored.

This isn’t because she’s lazy or in disbelief. Rather, it’s because her brain, like every human brain, has limits. It simply cannot do everything, and evolved ways to deal with incomplete or overwhelming amounts of information that appear in her sensory bubble.

In the background, her mind is processing the doctor’s advice without her awareness or control. It’s looking for what information should be amplified versus ignored. Multiple ideas are hoping to survive in the midst of distractions and incoming information. What will make it through to her long-term memory?

Challenge 1: Limited Attention

When multiple signals enter her bubble, her mind needs to be selective. To her, it feels like she’s getting it all, but in the background, her mind is filtering what gets her attention. Nothing can pass through the gauntlet without it.

Attention happens so quickly that we don’t have to think about it. Every waking moment, our eyes, ears, and other senses pick up signals that our mind evaluates. Most of those signals stay in the background, while others become part of our consciousness.

Let’s consider another example…

Scenes from an Intersection

Imagine sitting in a car at a stoplight. You glance at the red light for a second. That’s attention. Your phone buzzes, and it’s your partner. You scan the message. That’s attention. Then you jump when the horn blares behind you because the light is green. The horn demanded your attention and earned it easily.

In this example, we see how attention moves quickly and comes in many forms. By reading these words, you are controlling your attention (called top-down processing). When you stub your toe, your attention is demanded (bottom-up processing).

Attention is our first cut at evaluating reality, and the mind processes it immediately.

What Governs Attention

Attention isn’t neutral or the same for everyone. Instead, it’s biased and highly dependent on each person’s experience and goals. It generally recognizes and processes things that are:

• Threatening (horn)

• New or Novel (horn)

• Goal Dependent

• Emotionally relevant (partner)

These priorities are ancient parts of us that evolved from our need for survival. They work in the office just as they did on the savannah.

Back to Olivia’s Experience

Weeks after the appointment, Olivia is motivated and eating a high-fiber diet. She can imagine the fiber sticking to cholesterol and removing it.

The big question: what made this advice so sticky?

The answer starts with attention. It’s the price of admission, and every idea requires it. Cholesterol got attention because it matters to Olivia. It was new information that felt like a threat to her goal of living a long life, so it lives to fight another day in the gauntlet.

Keep in mind that attention alone is not enough. It’s only one step in the gauntlet. Later, we’ll see the same idea face new challenges on the way to long-term memory.

Let’s Zoom Out

Olivia is just one data point. Other patients may hear the exact same information and come away with different memories and goals. Why? Because our attention reflects what matters to us as individuals. Someone who is anxious about asthma may not see cholesterol as a big problem.

In all these scenarios, the mind works in a predictable way: It has limits, chooses what gets attention, and amplifies what matters.

The Lesson for Explainers

We all want our explanations to be remembered and used. For that to happen, they have to run the gauntlet of the mind, and attention is the first gate.

But here’s the problem: you can’t force attention. You can only earn it. Before your audience has made conscious decisions about your ideas, their minds have already evaluated your initial points. Your job is to align and build upon those evaluations.

This means the opening of any explanation is critical. You’re not simply persuading the audience, but appealing to a part of their biology that responds to the signal in the noise. If nothing in your first few sentences signals relevance to your audience's goals or sense of threat, their mind has already moved on.

What You Can Do

Before your next explanation, ask one question: Why would this feel urgent or relevant to this audience or person right now? Then lead with that. Not background. Not context. Focus on the thing that makes it matter to them. Threat, novelty, goals, emotions.

Olivia’s doctor did this when he said, “Your cholesterol numbers have gone up and can lead to heart disease.” Her mind saw this as a threat to something she cares about deeply: her longevity.

Next Up…

We’ll look closely at the science of attention and what mechanisms are at work when the mind encounters and evaluates new information.

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When we learn from an explanation, it feels natural and satisfying. But behind the scenes, your mind is doing a lot of work that you don’t realize. Information is being amplified, filtered, and discarded without conscious effort.

By understanding the mechanics at work and what helps information become memorable, we can craft explanations that are optimized for making the trip.

The upcoming series will focus on the invisible workings of the mind and the default settings we all share. We’ll follow an idea as it tries to survive in the mind, from awareness to being remembered, and what’s required for true understanding.

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A Matter of Constraints

As humans, the world around us is overwhelming. Sights and sounds, for example, are too rich with information for our minds to take it all in. So, we are selective. Our minds automatically filter, organize, and discard what we perceive.

When an idea feels meaningful and memorable to us, it’s a survivor. It got attention, competed with other ideas, faced memory limits, was influenced by emotion, and more. Making it through the entire process is rare. Most of what we experience never gets attention or is discarded quickly.

The question becomes: how do we craft explanations that survive?

Real Limits

Let’s do a simple test that illustrates a default setting we all share. It’s based on something well-known in cognitive science.

Read the numbers below and try to remember them:

347846790

Look away from the screen and say them aloud.

Now try again with the same number of digits, but organized differently:

654-196-327

Again, look away from the screen and say them aloud.

For most people, it’s easier to remember the digits that are in “chunks” compared to a long string. This isn’t related to intelligence or effort. Instead, it’s a factor of simply being human. Our minds are tuned this way.

Why Does This Matter?

Consider how numbers appear around you every day, like phone numbers, license plate numbers, and social security numbers. They are all presented in chunks instead of strings.

This isn’t a coincidence but cognitive science at work. A psychologist named George Miller first published this phenomenon in 1956, and helped establish the idea that we have hard limits regarding how many things we can remember at once. This basic idea has stood the test of time. If you want people to remember information, present it in chunks.

I find this fascinating because it’s not about personality, experience, or learning, but a default setting of the mind. We evolved with minds that are tuned for efficiency.

We’ll cover this idea in more detail later.

Working with Mechanisms

The example above is meant to illustrate a big idea: our mind is biased and always at work, whether we know it or not. And today, we have a solid understanding of the invisible mechanisms that make it happen. We can make accurate predictions about what gets our attention, how information is filtered, and what happens when something is remembered, or not.

And here’s the key: if mechanisms are predictable, then explanations can be designed to work with them.

My Goal with the Series

It’s clear to me that the field of cognitive science has a classic explanation problem: the most useful information has limited reach because of how it is communicated. I want to help solve the problem by applying my expertise as an explainer.

In the past, I was more focused on the explainer and taught the practice of repackaging an idea to be more understandable. Now I’m establishing a new perspective that’s focused specifically on the audience and what’s happening in their minds, so that explanations have better chances at survival.

The Gauntlet is a big step toward making cognitive science as real, useful, and engaging as it deserves to be.

What to Expect: The Gauntlet of the Mind

Starting with the next issue, we will follow a specific idea from being communicated to being remembered as knowledge in someone’s mind. I’m calling this path The Gauntlet because the path to knowledge is full of challenges that must be overcome.

Figures like this will be our symbol of an idea that is trying to survive the constraints while facing one challenge after another. Multiple ideas will run the gauntlet, and most will not make it.

We all want to be understood by others. But it’s not easy. By understanding the fundamentals, you can start to see opportunities that were not apparent before, and I’ll be your guide through every step.

Know someone who would love this series? Send them to ScienceofExplanation.com

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Watch the short video above to understand this idea more clearly.

Why does this matter?

Because explanation is a social activity. When we explain, we build on our knowledge and experience to make an idea clear. But that’s not enough. To share it, we have to create a representation of that knowledge that lives in the real world as words, drawings, data, etc.

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Then the real challenge begins: ensuring our representation enters another person’s bubble, where they can transform it into their knowledge. This happens every day, but we don’t notice. By understanding the mechanics of this process, we can find new opportunities to understand and be understood.

Hidden Depth

This simple idea represents decades of research in cognitive science. The video above sets the stage for the Science of Explanation to explore what we currently understand, and how it connects to being a better explainer.

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Earth teems with sights and textures, sounds and vibrations, smells and tastes, electric and magnetic fields. But every animal can only tap into a small fraction of reality’s fullness. Each is enclosed within its own unique sensory bubble, perceiving but a tiny sliver of an immense world.

Yong’s sensory bubble, which is based on the work of German biologist Jakob von Uexküll (1864–1944), who called it “Umwelt,” is a foundational part of the science of explanation. Here’s why:

We all live in sensory bubbles that we cannot escape. Everything we know and do happens inside the bubble and is subject to our biology, experience, and knowledge.

The wall of the bubble acts as an interface to the “real” world outside of it. It’s not a physical boundary, but an always-on process that constantly translates reality from our senses into something useful to our minds. And only a fraction of “true” reality, what our senses can perceive, can make it through the wall.

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I think of it like this: Whatever “true” reality is made of, we initially experience it as information. A rose has shape, color, smell, and taste that can be represented with data. The data lives outside the bubble, in true reality. When we experience a rose, that information comes into the bubble, and needs to be transformed into something we can use.

So the brain quickly processes the rose. Without thinking about it, we recognize what it is and how it works. We make predictions about the scent and apply existing knowledge that serves as a shortcut. This process happens constantly in the background and represents our version of reality. Information comes into the bubble, and we process it immediately.

Is the rose real? Yes. But our experience is not the genuine article, but one version among millions.

Among the Human Bubbles

While human bubbles are mostly alike in terms of the interface, each of our bubbles is unique on the inside. We all have lived experiences, learning styles, accumulated knowledge, and personalities that make us different. When new information arrives in the bubble, we differ in what we do with it. We assign different forms of meaning or relevance. We pay attention to different aspects of ideas.

The exact same rose can be experienced in very different ways, depending on the person. A rose expert can notice things the novice misses.

This is why the bubble metaphor is useful for thinking about explanation and communication. When we explain ideas, they must travel between bubbles in a form that’s useful. That’s a real challenge, because we can’t know what it’s like in someone else’s bubble. We have to assume that every bubble is unique. What can we do?

The Mind in the Bubble

Within our respective bubbles, there is a brain that functions similarly across people. Thanks to cognitive science, we now have a grasp on how information from outside the bubble becomes knowledge within it.

Imagine a doctor telling you, “Eat more fiber because it can lower cholesterol.” A few weeks later, you’re on a high fiber diet and proud of your progress. Your doctor’s words became knowledge in your mind. How did that happen? Why those words and not others?

Human brains process information in predictable ways. Cognitive science has identified the patterns we use to filter and evaluate information as it comes into the bubble. When your doctor spoke, you automatically paid attention, grasped the gist, assigned meaning, and encoded it for later use. Remembering to get milk on the way home? That didn’t make the cut.

The key for explainers is to understand those patterns so we can design explanations to fit them. When we want information to become knowledge, we can think about each step of the pattern and how to optimize. We can ask: what ingredients must be present for this to work inside someone’s bubble?

For example, attention is an essential step. Without it, the idea dies quickly. For your explanation to succeed, you’ll need to consider how to get and hold attention. This is a part of cognitive science that we’ll explore later.

Bubble as a Tool

I will be referring to sensory bubbles from now on. The bubble metaphor allows us to think about three situations in context:

• Our bubbles: what we experience as reality

• True reality: the world outside of our bubble

• Other people’s bubbles: how our communication works in other bubbles

The driving question, starting now, is: how can I explain so that information is useful inside someone else’s bubble?

We now know the basic ingredients that help turn information into knowledge across diverse bubbles. We understand the power of context, attention, encoding, and more. And by thinking in terms of bubbles, we have a way to consider what will work for each person in our audience.

Next: A Brief Explainer Video

Next week, I’ll share a brief post with a video that brings this idea to life. I hope you’ll watch it and let me know what you think.

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To explain successfully, there has to be a sense of shared reality that creates the baseline for evidence and truth. We explain what we believe is real. Unfortunately, this sense of reality is not evenly shared. What seems obvious to one person can be vastly different for another. When baselines diverge, explanation becomes more difficult.

As we get into the science of explanation, it’s important to discuss the baseline problem. Do we share the same reality? What does that even mean? For this, we’ll need to get into a bit of philosophy.

Sometimes you hear people say, “We never experience true reality.” This statement always bothered me because it sounds like our daily lives are just illusions or figments of our imagination. It seems preposterous to claim we’re living in a land of make-believe.

Now that I understand this statement from a different perspective, I find it fascinating and a foundational idea. Our experience of reality feels like the only reality, but it’s not.

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A Rose By Any Other Name

Imagine a single rose. It’s red, smells like a rose, and has sharp thorns. This rose is very much a part of reality. However, the colors you see, the smells you detect, and the feeling of a thorn’s tip seem like reality to you; a reality shared by all humans.

Now think of my dog, Piper, sniffing the same rose. To her, the rose is not red at all. Her vision is dichromatic, meaning it uses only two colors: blue and yellow. Her sense of smell is thousands of times stronger than yours. The scent is a cacophony.

Now we can ask: Who is experiencing “true” reality?

The answer is neither. The difference between Piper and you is not the rose, but how your senses and brain process it. The rose has physical properties that are part of “true” reality, but the color and smell you both experience are perceptions in your mind and not properties of the world. You and Piper can never experience the true version of the rose.

Our Biological Limits

The same thing happens between people. Two people can witness the same event, hear the same words, or read the same sentence, and walk away with vastly different experiences that feel real. Direct access to true, objective reality is out of reach.

Why? Because we’re all limited by our senses. The very idea of a rose is based on how it looks, smells, and feels to us. We have no other way to understand it. Our experience is just the human version, and it’s easy to mistake it for reality itself.

This matters because we don’t just observe reality, we use our senses and our minds to experience a version of it that’s unique to us as humans, and as individuals. Each person experiences reality through filters that reflect a lifetime of learning.

When we assume our experience is reality itself, we assume everyone else shares the same baseline. This assumption makes misunderstanding inevitable. We talk past one another, argue about facts that feel obvious, and struggle to explain things we understand clearly. The problem isn’t intelligence or information, but a mismatch of baselines. We automatically work from the reality we know.

How Do We Solve the Baseline Problem?

The solution to this problem is bigger than one article. But the answer lies first in recognizing that we all work from different baselines. Our entire lives are spent existing in a reality that is unique to us, yet feels real and shared.

The key, going forward, is thinking about how to craft explanations that create a workable starting point, one that we can reasonably assume is shared between us and our audience. We’ll do that by considering how the mind filters and evaluates incoming information.

A Powerful Metaphor

I was recently inspired by the 2022 book An Immense World, Ed Yong. In it, he outlines the incredibly diverse ways that animals perceive reality. Like Piper and the rose, it inspired me to think about what is real.

One quote from the book’s introduction stood out and helped me see an approach to solving the baseline problem. Yong writes:

Earth teems with sights and textures, sounds and vibrations, smells and tastes, electric and magnetic fields. But every animal can only tap into a small fraction of reality’s fullness. Each is enclosed within its own unique sensory bubble, perceiving but a tiny sliver of an immense world.

This is true for humans, too. We all live in a sensory bubble that represents our version of reality, our baseline of what feels real to us. This sensory bubble metaphor has real power in analyzing communication and explanation. In the next post, we see how our bubbles influence our understanding and what it takes for information to move between bubbles.

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