There’s a hidden catch to reading inspirational stories about individuals.
Reading them makes you feel humans can do anything.
Reading them can also make you feel like, any human but you can do anything.
It’s a paradox hidden inside reading the stories that has to do with the details. The more details we read the more real the achievements seem. But also, the more different the person and/or experiences that lead to them seem to be from who we are.
So, after reading an upbeat story about how Albert Einstein said something cool and then discovered something amazing, you can feel both inspired and demoralized at the same time.
The reason is because these stories focus on individuals more than capacities.
That makes sense because biographies and inspirational quote sites are selling peoples’ personalities as much as their ideas.
That’s not a bad thing. It’s just not helpful when you’re trying to learn practical discovery skills.
Instead, I want you to become the Einstein in your own story. So, we need to focus on things a little differently.
Let’s talk about human capacities and discovery.
If you read widely, you will see a lot of ideas about how to do something new.
You’ll see buzz words like innovation, invention, creativity, and insight.
Capacities are the mental and physical abilities and skills we have to get things done.
So, discovery capacities are abilities, that all people have, to discover new things.
I’ve identified four discovery capacities.
What makes each discovery capacity unique is that we are motivated to apply that ability for different reasons. So, we create different results depending on which capacity we use.
On the other hand, what every discovery capacity shares is that it gives us a way to create new things in science and technology.
Here are the four discovery capacities:
1. Innovation. You apply your capacity to innovate when you are motivated to improve the way something works. Better. Faster. Stronger. That’s all about innovation.
2. Invention. When you are motivated to build something useful you use your capacity for invention. An idea brought to life is the heart of invention.
3. Insight. Being motivated to get a more accurate understanding of the real world is when you deploy your capacity for insight. Insight gets rid of being wrong, being oblivious, and being confused.
4. Scientific Discovery. If you are motivated to explain the natural world then you will use your capacity for scientific discovery. What exists? Why does it exist? How does it work? Scientific discovery is finding the answers to those questions.
Although making scientific discoveries is our focus, all the capacities have a role to play in discovery. They build on and support each other.
For a long time, I held these two images in my mind. That and the famous photo of Einstein sticking out his tongue (which you might know too).
Making discoveries, through the lens of Einstein, seemed like something only mischievous, extraordinary people do.
But then I started hearing a phrase from one of my family members (they’re not in science).
It’s called “capacity-building”.
The United Nations defines capacity-building as, “the process of developing and strengthening the skills, instincts, abilities, processes and resources…to survive, adapt, and thrive in a fast-changing world”.
I realized that to make discoveries we need exactly that.
The skills and processes to survive, adapt, and thrive in a fast-changing information world.
That’s why I call them discovery capacities. Because strengthening them is a capacity-building exercise for those who want to make scientific discoveries.
The four discovery capacities all support each other.
Sometimes you need to build a new tool (invent) to fill a knowledge gap (insight). Or you may need to improve how a system works (innovate) to create space for new knowledge to be found (scientific discovery).
Luckily, we all start out with a basic ability to do all these things.
That’s because basic human survival requires the skill to observe, adapt, build, and learn.
As we grow, and make new learning choices, we may specialize or strengthen some capacities more than others. But we never lack, or lose, those discovery capacities all together, unless we suffer a catastrophic injury.
We can also learn and develop specific techniques to improve our capacity.
But just remember, you’re always improving on a basic ability you already have. Not starting from zero. You’ve already got what it takes. But what you’ve got can get better.
Simply put, making a scientific discovery is something anyone can do, not just a group of elite science performers.
Discovering new things is a set of core human capacities we are born with.
So, own it. Grow it.
Keep your discovery capacities strong through practice.
Become your own Einstein.
Reflection Question
Building up your discovery capacity is about practice. Practicing which discovery capacity annoys you the most and why? Which one excites you the most and why?
If I told you there was a study that found what actions you take for the next 10 minutes determines whether or not you will make a scientific discovery in your life…how would you spend that time?
How does thinking about the impact of what your doing right now on your discovery potential make you feel? Guilty, curious, confused, even overwhelmed?
Unfortunately, no such study exists. Instead, there are plenty of biographies analyzing how the Einstein’s of the world spent their time.
Don’t get me wrong.
Getting inspired by previous scientific discoveries and the stories behind them is wonderful motivation.
But it doesn’t tell you how to spend the next 10 minutes of your life to make your own discoveries. For that you need an action plan.
So let me share the scientific discovery framework that I’ve developed, which will give you a plan. It’s helped me see how discovery gets done and it will help you too.
There are lots of parts to scientific discovery, but they all fit together in a logical whole.
In a series of posts, I’ll explain my framework for connecting those parts and how you can prioritize your efforts to get moving on making a discovery.
This first post lays out the big picture of scientific discovery. Get ready for an information download! Stick with it. Don’t worry if it feels like a lot. Shorter follow-up posts will guide you. Jump in and out of the series anywhere – the posts are all standalone. You can take it all in as you have time.
Let’s get to it.
I’ve identified six core areas that power scientific discovery:
1. Discovery repertoire. The personal portfolio of techniques that you use to get science done is your scientist’s repertoire. There are four sections to your internal portfolio: how you think about your science (mindset), what tasks you know how to complete to get science done (activities), the recipes you have for combining outcomes with actions (skills), and what you know (knowledge). When you have a solid plan, but still don’t make progress on your science it means you need to strengthen a weak part of your repertoire.
2. Discovery capacities. Learning new things in science and technology is driven by four human capacities: innovation, invention, insight, and scientific discovery. Capacities get different results because they are driven by different motivations. Innovation motivates us to improve the way something works. Invention motivates us to build devices that will do something useful. Insight motivates us to change how we see the world. Scientific discovery motivates us to explain how the world works. Insight and scientific discovery are core capacities that build on each other.
3. Discovery vital qualities. The difference between a scientific discovery and regular scientific research is that a new discovery-level scientific finding will have at least one of three vital qualities: It will shift our perspective on the world (be radical), it will link knowledge to make a broader range of predictions about the world (be universal), and/or it will be new knowledge (be novel). Your work should have one of these qualities as an objective to aim for discovery-level science.
4. Discovery impact classes. Scientific discovery intuitively feels more high impact than regular science. That impact lies on a continuum from low to high, determined by how many vital qualities a discovery captures. Minor class discoveries possess only one of the three vital qualities. Major class discoveries possess at least two and legacy class discoveries must have all three. Science spans from regular research to legacy class discoveries on an incremental spectrum defined by these qualities. So, start small and build up to the big discoveries.
5. Discovery learning categories. Scientific discovery learns something new about the world. What you learn falls into three categories: something about an unknown object (object-type), something about the properties of an object (attribute-type), or something about how and why the world works the way it does (mechanism-type). Some categories are easier to make discoveries in because the learning curve is smaller.
6. Discovery evolution phases. Most scientific discoveries evolve through five phases, which I call the discovery cycle. First, you ask an unanswered question (question). Then you form ideas for an answer (ideation). You make those ideas into tests in the real world (articulation). You run the tests and evaluate the results (evaluation). And if the results repeatedly prove true then they become a scientific discovery (verification). Troubleshooting your scientific discovery progress is easier if you know what phase you are in because unique problems trip up scientists at each phase.
The framework I’ve developed lets you craft a scientific discovery action plan, troubleshoot your progress, and connect specific activities and techniques with the results you want to achieve.
The simplest starting point? Aim for a minor class, attribute-type discovery that is universal. That represents a baby step from current science to something new. And if you hit an obstacle check your insight in a systematic way and seek out techniques to boost you from one phase of scientific discovery to the next.
No matter where you start, be inspired by the scientific discovery stories of others, but don’t stay stuck in them. Discovery isn’t a sightseeing tour through known territory. It’s a push toward unknown territory.
Simply put, to make a scientific discovery you need a plan for how to tackle the unknown, not a map of the known.
Take Action
Once you’ve got a framework and a plan, spend the next 10 minutes taking action. You’ll be 10 minutes closer to making your next discovery.
Have you have ever been on the receiving end of a colleague, boss, or even stranger sitting next to you on a plane (ah, the good old days before coronavirus), asking you questions like,
“But what impact will your work have?”
“Can you study something more interesting/important?”, or even,
“Who cares?”
If so, then you have come up against a problem all researchers, scientists, and citizen scientists face: How to try and do the best possible, most high impact, most important science you are capable of as often as possible.
What you are aiming for is a scientific discovery. And what well-meaning acquaintances and strangers are asking for is the same thing.
But how do you do that? Trying to aim that high can seem overwhelming.
The Problem: Many existing definitions of scientific discovery are good for textbooks, but not for project planning, follow-through, or troubleshooting.
My Solution: Define “scientific discovery” so that you can achieve it with training and algorithms, and perform quantitative studies to probe it.
The purpose of this post is to lay out all the key components that will help us train ourselves to become better discoverers. You can see this framework drawn in my concept sketchnote below.
My hand drawn sketchnote of the “architecture of scientific discovery” showing all the important parts of the process. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
There is a lot of ground to cover, so in this post I am going to give short descriptions of everything in the big picture.
In the following 22 (!) posts in the series, I will drill down into each of the 18 parts in more detail, with examples taken from science history.
Now let’s jump in…
Group 1:
Discovery Capacities
Scientific discovery is just one of four human capacities for discovery in science and technology.
Let’s zoom in on the discovery architecture picture I’ve drawn above.
A close-up of my sketchnote showing the four discovery capacities at the core of the discovery architecture. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
As human beings, we have the capacity to discover new things in science and technology. These discovery capacities form the main part of the structure and they house all our abilities and knowledge about science and technology.
These discovery capacities fall into four types—insight, invention, innovation, and scientific discovery. The main differences between types are their results and our reasons and motivations for pursuing a discovery.
So let me give you a definition of insight, a definition of invention, a definition of innovation, and a definition of scientific discovery.
Insight
Definition of insight:
“Insight” is refining the accuracy of your perspective of the real world.
Getting a more accurate perspective, or “achieving insight,” is accomplished in one of three ways. You can add something new to your perspective that you were not aware of before. You can correct something that you misperceived. Or you can clarify something that you only vaguely understood.
Motivation: “I want to…change how I see the world.”
Invention
Definition of invention:
”Invention” is building a machine or process that creates a previously unobtainable result.
Creating a previously impossible result, or “inventing something new”, is brought about by focusing on three aspects of what you build. You want to build something that has not been built that way before. You want to build something that does what it was built to do. And you want what you built to create something that a machine or process like it has not created before.
Motivation: “I want to…build a device that will do something useful.”
Innovation
Definition of innovation:
”Innovation” is improving the functionality of a process or device.
Refining how things work, or “innovating”, is really about making things work better more easily. You can make something function more efficiently. You can make something run faster or with fewer resources. You can make something more likely to produce what it was designed to produce. And you can make something produce a higher quality version of what it was designed to produce.
Motivation: “I want to…improve the way things work.”
Scientific Discovery
Definition of scientific discovery:
”Scientific Discovery” is finding the evidence, interaction, and causes of things that exist in the natural world.
Learning something new about nature, or “making a scientific discovery,” relies on three things. You must acquire knowledge. You must demonstrate that the phenomena exist using evidence and statistical or logical analysis. And the knowledge you acquire must include one or more element of the radical, the universal, or the novel (those are defined in the next section).
Motivation: “I want to…explain how the world works.”
In this framework “applied science” can be defined as a combination of mastering the capacities for invention and innovation, while “fundamental science” or “basic science” can be defined as a combination of mastering the capacities for insight and scientific discovery. By “science” I mean the physical sciences (e.g., astronomy, biology, chemistry, computer science, data science, engineering, geology, medicine, paleontology, physics, etc.), the social sciences (e.g., anthropology, economics, political science, psychology, sociology, etc.), and mathematics.
(Also, you might wonder why insight is listed as its own discovery capacity since it is integral to the other three discovery capacities, invention, innovation, and scientific discovery. This is true. However, it’s more useful to put it on an equal footing with the other three capacities. It’s easier to develop training protocols, algorithms, and quantitative metrics to explore discovery methods, all goals for developing this framework.)
Group 2:
Scientific Discovery Vital Qualities
There are three qualities any scientific study or research must have, or it can’t be called a “scientific discovery”.
The three scientific discovery qualities are the foundation on which we can build any kind of discovery.
A close-up of my sketchnote showing the three qualities that form the foundation of defining something as a scientific discovery. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
They are integral to recognizing discovery and generating discoveries.
These three essential qualities form the basis of what makes scientific discovery different from everyday scientific investigation and scientific research.
How is scientific discovery different from scientific investigation? Scientific discovery has a higher and more long-lasting impact on the evolution of science. So let’s define the vital qualities that embody that impact and enduring nature.
Radical
Definition of the radical quality of scientific discovery:
The “radical” quality of scientific discovery means that the new knowledge gained as a result of the discovery represents a meaningful shift in perspective from the previous state of knowledge.
Role in scientific impact and longevity: Scientific discovery is radical—it changes the perspective of science in one of three ways. Something can be added to what we know. Something can be rejected from what we thought we knew. Or something that we know can be changed. By impacting our scientific perspective, the radicality of scientific discovery opens up new avenues of research and creates or ends long-standing practices and beliefs.
Universal
Definition of the universal quality of scientific discovery:
The “universal” quality of scientific discovery means that the knowledge acquired as a result of discovery is valid and reliable and that the knowledge gained has predictive or descriptive power in a range of physical situations.
Role in scientific impact and longevity: Scientific discoveries have a broad impact because the new knowledge they bring has a range of application. The universal nature of a scientific discovery lies on a spectrum from “proximal” to “distal”. “Proximal” means that the new knowledge can be applied to a broad range of areas with few changes to its verified form. “Distal” means that the new knowledge discovered can only be applied to areas and phenomena closely or directly related to the area in which the discovery was made, or that to apply it to other areas requires a lot of translation. The scientific discoveries with the most direct universal appeal have the longest legacies.
Novel
Definition of the novel quality of scientific discovery:
The “novel” quality of scientific discovery means that the knowledge obtained through the discovery has not been previously shown to exist, in a reproducible way, by observation or experimentation.
Role in scientific impact and longevity: Scientific discoveries electrify areas of science because they bring something new to the table. And when those new elements are verified, they shape future research activities and ways of thinking.
The impact effects of scientific discoveries and their longevity, as forces that shape research practice, effort, and interest, are embodied in the three vital qualities at the foundation of the scientific discovery architecture. They give scientific discovery it’s je ne sais quoi factor that inspires the layperson and the scientist alike.
Group 3:
Scientific Discovery Impact Classes
The impact and significance of all scientific discoveries can be grouped into three classes.
The purpose of discovery is progress in some area (understanding, outcomes, effectiveness, and knowledge as we saw from the section on the four discovery capacities above).
The discovery classes represent broad categories that help identify the level of impact (or progress) that our discoveries are capable of achieving or fostering.
These classes, therefore, overarch the specific categories of scientific discoveries.
A close-up of my sketchnote showing the three discovery impact classes that overach all scientific discovery and describe a discovery’s effect and longevity. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
In particular, the discovery classes encompass three different levels of impact, from wide-ranging to narrow, as described in their definitions below, and as represented by the fact that the three domes of discovery impact classes are nested in my sketchnote diagram.
Minor
Definition of a minor impact class scientific discovery:
A “minor” class discovery meets the criteria for any one of the three vital qualities of scientific discovery—radical, universal, or novel.
Minor scientific discoveries are either radical, universal, or novel, but not all three at once. Therefore, they have an impact beyond ordinary scientific investigation, but their impact is limited.
Major
Definition of a major impact class scientific discovery:
A “major” class discovery meets any two of the three criteria for the vital qualities of scientific discovery—radical, universal, or novel.
Major scientific discoveries are either radical and universal, or radical and novel, or universal and novel, etc. They have two of the three vital qualities, but are missing the third one. As a result, their impact tends to be more wide-spread than minor class discoveries, but not as high impact as they could be if they embodied all three qualities.
Legacy
Definition of a legacy impact class scientific discovery:
A “legacy” class discovery meets all three criteria for the vital qualities of scientific discovery—radical, universal, and novel.
Legacy class discoveries are the full package—radical, universal, and novel. The impact of legacy class scientific discoveries is wide-ranging and long-lasting. These are the hardest scientific discoveries to achieve, but the ones with the greatest value.
Group 4:
Scientific Discovery Learning Categories
The types of scientific discoveries you could make can be grouped into three categories.
The learning categories are specific to only one of the discovery capacities, scientific discovery (they are not intended to be applied, by analogy, to insight, invention, or innovation).
These categories of scientific discovery divide the field of knowledge obtained through scientific discovery into three areas. These areas are determined by the kind of information you hope to gain, or your learning objective.
Object
Definition of the object type scientific discovery:
An “object” scientific discovery is acquiring knowledge about the existence of a new object in nature.
Learning Goal: Answers the question, “Does something exist?”
Attribute
Definition of the attribute type scientific discovery:
An “attribute” scientific discovery is acquiring knowledge about the characteristics, properties, and/or traits of an object or process in nature.
Learning Goal: Answers the question, “What is something like?”
Mechanism
Definition of the mechanism type scientific discovery:
A “mechanism” scientific discovery is acquiring knowledge about the causes, connections, interactions, and/or sequences of objects and attributes in nature.
Learning Goal: Answers the question, “How does something work?” and/or “Why does something happen?”
A close-up of my sketchnote showing the different types of discoveries you can make as central to the architecture of discovery. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
Let’s look at this part of the discovery architecture more closely, as shown above in my sketchnote drawing.
The scientific discovery learning goals are shown under the dome of the scientific discovery classes because they can fall under (i.e., be impacted by or represented in) all the classes of scientific discoveries.
Another way to think of it is that the scientific discovery classes are umbrella terms that cover all the categories or types of scientific discoveries you could make.
(Again, this is just the overview, in future posts I will talk about each of these in more detail and it will begin to make more sense as you see examples and further discussion.)
Onward to the last group I want to cover in this post…
Group 5:
Scientific Discovery Cycle
(Evolution Phases)
Most scientific discoveries must pass through five phases.
All of the above groups—the discovery capacities, the scientific discovery classes, the scientific discovery categories, and the scientific discovery qualities—form the main architecture of scientific discovery.
You can think of these like a very old and sturdy building, where every brick and design element of the structure is built up out of our application of the discovery capacities, classes, categories, and qualities and the knowledge, abilities, devices, and processes that we have created as a result.
There is one more important element in the overall architecture, and that is represented by the sun shown in the upper right hand corner of my sketchnote illustration.
A close-up of my sketchnote showing how the scientific discovery cycle illuminates all other aspects of discovery. (Artist: Bernadette K. Cogswell. For reprint permission, contact me.)
The sun represents the process that drives scientific discovery, or the “scientific discovery cycle”, which shines a light on all the other elements of the architecture so that we can become aware of them and manipulate them in the course of running our projects as scientists.
Below I summarize each of the five evolutionary stages of the scientific discovery cycle.
Question
Definition of the question phase of the scientific discovery process:
The “question phase of scientific discovery” is the stage in the process when the question to be answered, or problem to be solved, is explicitly defined.
Purpose of Stage: Define what you want to find, create, or explain.
Ideation
Definition of the ideation phase of the scientific discovery process:
The “ideation phase of scientific discovery” is the stage in the process when a possible solution or solutions is conceived of to answer the discovery question or solve the discovery problem.
Purpose of Stage: Come up with a guess for how you will find it, create it, or explain it.
Articulation
Definition of the articulation phase of the scientific discovery process:
The “articulation phase of scientific discovery” is the stage in the process when at least one proposed solution is put into a form that can be tested in the real world.
Purpose of Stage: Write an equation or description, or build or code a prototype, embodying your answer.
Evaluation
Definition of the evaluation phase of the scientific discovery process:
The “evaluation phase of scientific discovery” is the stage in the process when the testable solution is probed and its success in answering the discovery question, or solving the discovery problem, is assessed.
Purpose of Stage: Test your equation, description, code, or prototype to see if it answers your question.
Verification
Definition of the verification phase of the scientific discovery process:
The “verification phase of scientific discovery” is the stage in the process when the best available solution to answer the discovery question, or solve the discovery problem, is confirmed to be accurate and reliable by multiple independent analyses.
Purpose of Stage: Subject your “discovery” to public scrutiny and see if it holds up to testing.
Note that, the way I have defined it, the scientific discovery cycle is different from the scientific method.
The scientific method focuses on how to obtain valid and reliable information about the world. But it is not concerned with the impact of that knowledge.
The scientific discovery cycle (or scientific discovery process) is concerned with the impact of the knowledge obtained and its purpose is to obtain knowledge of a certain minimum impact level (namely, knowledge that is either radical, novel, or universal and, therefore, at least meets the standard for a minor class discovery).
The scientific method would be used to obtain relevant insight at various phases within the scientific discovery cycle (such as during articulation, evaluation, and verification).
Therefore, the scientific method is one set of activities in the scientist’s repertoire, which they can use to help them complete the evolution stages in the scientific discovery cycle. They are connected, but distinct.
Summary
Phew!
That brings us to the end of my overview of the architecture of discovery that I have built as a way to develop better discovery training protocols and quantitative methods to identify patterns and correlations in discovery processes.
If you are someone who loves algorithms or self-improvement, then this architecture and way of conceptualizing discovery and how to achieve it is for you.
So much good stuff to talk about!
I’m really looking forward to writing the rest of the posts in this series. Having these new words and concepts in my discovery arsenal has already helped me organize and conduct my research projects in a new way. And it makes extracting nuggets of insight from examples of scientific discovery much more productive.
To wrap up this post, let me give you the very, very short, bullet-list version of “the architecture of scientific discovery” that I covered in this post:
Scientific discovery is just one of four human discovery capacities—including insight, invention, and innovation—in science and technology.
There are three vital qualities—radical, universal, and novel—any scientific study must have, or it should not be called a “scientific discovery.”
The significance of scientific discoveries can be grouped into three impact classes—minor, major, and legacy—which range from low to high impact.
The types of scientific discoveries you could make can be grouped into three learning categories—object, attribute, or mechanism—based on the information gained.
Most scientific discoveries must pass through five evolution stages—question, ideation, articulation, evaluation, and verification—to succeed.
With all these categories and types, it’s easy to fall into the trap of seeing things as black and white, or just buckets to assign things too.
But the definitions and concepts I’ve come up with work well because they allow us to see scientific discovery as a continuum of insight, from narrow to broad, from low impact to high impact, from fundamental to applied.
Every scientific investigation, every research study, every time you use the scientific method, you are placing yourself somewhere on that continuum of insight. Discovery is always just a little way further along that scale. The question you should be asking yourself is not “Am I making, or trying to make, a discovery?” it’s “Where on the discovery spectrum am I working right now?”
So next time someone challenges you on your impact, dream brave and think (or tell them), “I’m working on some (minor/major) research questions right now, but my dream is to leave a legacy discovery for future generations to build on.”
And hopefully with this discovery architecture in your mental repertoire, you will someday be able to do just that.
Bernadette K. Cogswell, “The Architecture of Scientific Discovery: Overview of the Process – On how to define and categorize all aspects of scientific discovery”, The Insightful Scientist Blog, June 13, 2020, https://insightfulscientist.com/blog/2020/architecture-of-scientific-discovery-overview.
[Page feature photo: A quiet and quirky cabin sits among the mountains. Photo by Torbjorn Sandbakk on Unsplash.]
Have you ever watched a movie or TV show, or read a book, where at the end of the story the main character saves the day by doing something unbelievable? By unbelievable I mean that they do something completely out of character. This kind of ending can leave a bad taste in your mouth, as if the writers didn’t do their job in making us believe the character had changed enough to become a person who could behave that way by the end.
When I was working on my degree in creative writing, there was a phrase that summed up the problem:
“Once is an accident, twice is a coincidence, three times is a pattern.”
The idea is that people open up to the possibility that something is plausible by seeing relevant elements happen enough times that we decide a pattern is believable. It’s kind of a “conception through perception” game. If a character behaves in ways that build up to the ending then we consider the ending reasonable. But if we don’t see enough evidence then we find it hard to believe and the ending will seem like a cheap magic trick and a waste of our time (and money).
In my own experience, I’ve found it pays to be aware that this little rule of three affects not only how writers convince us of story endings, but also how we convince ourselves that some of our ideas merit pursuit.
That’s because deciding if a research idea is worth investigating is really about deciding if there’s enough of a pattern there to plausibly lead to an interesting ending…and hopefully that ending will be a scientific discovery.
So let’s talk about how to translate this magic of the number three from creative writing into research in a way that will help us decide if a research idea should move to the top of our to-do list or get shuffled to the back burner.
THREE…Essential Elements of an Idea
Most of our time as scientists is spent in the “articulation” and “evaluation” phases of scientific discovery. Meaning, we worry a lot about defining our ideas and assessing if they are useful, correct, and/or meaningful.
In starting on a research topic, it can be hard to formulate a clear awareness of what we mean by new ideas. And once we’ve jotted something down on paper, or typed it up, it can be difficult to decide if the idea seems worth focusing on. The tendency is to have conversations in your head about it and then put it on the mental back burner because of the feelings of “riskiness” that working on discovery-level science can bring up.
If you’re stuck with a sense that you “have an idea”, but that you couldn’t yet share that idea with someone in a three-minute sound bite then here’s something to try. You can write this down, type it up, do a voice memo, or some combo of all three. Whatever works for you. I’ll use pen-and-paper writing as my example since that’s how I prefer to work:
Write down the idea you are trying to get clear in your head as a one word prompt. Stick to one word, no phrases or sentences.
Spend a few minutes (no more than 15) just thinking about the idea behind your one-word prompt. Now, write down three more essential words that capture the heart of the idea. These new words should sum up the essential elements, features, behaviors, or requirements of your prompt word. Again stick to just three words, no phrases or sentences here either. But you must write down at least three words, no less.
Now create a list numbered one to three. For each number write down what you mean by each of the essential words. You can write in phrases or sentences here. But keep it to no more than 1-2 sentences per numbered item. Start each numbered item with the prompt “By <essential word> I mean…” You can spend up to one whole day to complete this list. But finish this whole exercise (steps 1-3) in 24 hours or less.
This little exercise can help you generate a clearer picture of your idea by forcing you to pick and choose what matters most to you and define it.
That’s where you as a scientist bring your best asset, your personal diversity, to the playing field. Don’t use other people’s words or definitions for this exercise. Set your phone aside. Don’t use Google. Don’t use textbooks or published papers. Just use what you’ve already got inside your head.
I cap the time you spend on it at 24 hours to keep you from overthinking it. The goal here is to make a rapid decision—“research this” or “shelve this.” You want to build momentum, not stall out in the graveyard of analysis paralysis.
The reason I say identify three essential words goes back to the accident-coincidence-pattern idea. Three words is a good sweet spot to help make abstract ideas more concrete. Think of it like triangulating a signal: getting three points of reference lets you narrow down and enclose your idea in a more well-defined area.
THREE…Sources of Information
At this point it’s helpful to get out of your own head and take a look at what other people are saying about your idea. In theory, you probably started out by reading the work of others or listening to someone speak, which helped spark the idea you are working through now. So you may already have some good sources to look over again.
The goal is to get three sources (by “source” I mean a written or spoken piece of work) you can compare against the idea you formulated in the previous exercise. You want to read them (or re-read them) and compare how you formulated your idea to how the author(s) or speaker(s) formulated it.
The most important thing is to find good quality sources to help evaluate your idea.
If you don’t know how to find or consider sources for their quality, here are some tips:
Look for good quality information, not good quality authors. That means you want sources that are complete, accurate and have minimal bias (or consciously acknowledged bias). Authors, writers, scientists, journalists, etc. are only human. No one produces good quality work all the time. Evaluate each information source individually; don’t just assume that famous names, or even people you know who usually do good work, put in that effort this time. We all have off days.
Value sources that speak most directly to the idea you are working through with real data and more references to explore. Be open to traditional (peer-reviewed published articles, monographs, academic books, etc.) and nontraditional (blogs, popular science outlets, podcasts, etc.) sources. Evaluate each source individually. I usually rank items with real data (even if it’s just a thoroughly explained personal example) and that reference other good quality sources I can freely access (no paywalls) more highly than ones that are tangential to my topic or only talk in general terms.
Try to get a good variety in your three sources. Make sure they are all by different authors or speakers. Try to get different perspectives in each one, i.e., the authors are from different fields, different career stages, different job sectors, are different genders, ethnicities, ages, nationalities, etc. The sources don’t need to tick all these boxes, but do the best you can. Try to ensure that you don’t rely too heavily on just one voice in the debate, which could cause you to repeat what’s already been done instead of trying something new.
Again, don’t over think this. I’d limit the time you spend on this to one week. Do the best you can with the information you have access to.
Once you’ve got these sources, spend some time reading them and noting the differences between how you articulated the idea and how they articulated the idea. You’re looking for similarities, differences, things they mention that you left out completely, and things you mention that they ignore (this last one is where scientific discovery lives).
THREE…Mental Examples
Now it’s time to move out of the “rainbows and butterflies” world and into the “bricks and mortar” world.
What I mean by this is that in the beginning we tend to be pretty excited, enthusiastic, and confident about our own ideas when they’ve only existed in our head. This is the “rainbows and butterflies” world. These feelings are a good way to generate momentum to get started on a project and they encourage “thinking.” But they’re not very helpful to encourage “doing.” Doing requires having a clear idea of what the next action is. That’s the “bricks and mortar” part. Rainbows and butterflies are inspiring, they captivate and focus our mental attention, but they are hard to hold in your two hands. With bricks and mortar it’s much easier to grasp how to start building something.
Applying your idea to examples is a way to get started on the bricks and mortar “doing” and to see if you’ve missed out on any major facets of defining your idea so that it’s open to scientific investigation. I like my three examples to cover three types (three is still the magic number!):
An example that fits your idea really well (an “exemplar”).
An example that doesn’t fit your idea at all (a “counter-example”).
An example where it’s hard to tell if it fits your idea or not (a “neutral example”).
Covering these three bases will encourage you to be deliberate and thoughtful and to assess your idea for its strengths (illustrated by the exemplar) its weaknesses (illustrated by the counter-example) and its limits and areas for improvement (illustrated by the neutral example).
You want to develop a more realistic understanding of what your idea is (you could tell someone about the exemplar in conversation as a way to help describe your idea) and to acknowledge its limits and shortcomings.
If the limits make the idea not useful, or the shortcomings show up for examples that are what you were trying to explain, then I find it’s best to go back and trying redefining my idea. Try changing up the essential words or changing their definitions until you have an idea that holds up better to this simple evaluation method.
THREE…Drafts
Now you’re ready to put your idea into a working definition that you can make a decision on.
I know, I know: all of that work just to get to what most people consider the starting point for research!
That’s why the tagline for The Insightful Scientist is “Discovery awaits the mind that pursues it.” Mental preparation and technique are a huge part of being a scientist and trying to make scientific discoveries. Learning processes and strategies to wield our mindset more effectively is one of the best ways to run a winning race in pursuit of discovery.
The point of all this mental preparation is to give yourself a clear picture of where your idea stands and the challenges and advantages to trying to investigate it. That is what gives you the ability to decide if it should move to the top of your to-do list or move to your mental back burner.
This last step ensures that you have something concrete to either (1) return to later if the idea doesn’t make the to-do list for now, or (2) act on right away if it does make your to-do list.
So set aside a day or two for this and type or write (no voice memos here) a formulation of your idea that is in complete sentences and includes both your prompt words, the essential words you identified, and their definitions. Keep the entire working definition to a minimum of one sentence and a maximum of 5 sentences (i.e., a paragraph). If you prefer word count goals, try for something in the 100 to 250 word range.
Write three drafts of your working definition:
First write a “rough draft” that just gets all the basic elements of your working definition (one word prompt, three essential words, definitions of those essential words) in there in grammatically correct language with proper spelling.
Then write a “second draft” that most likely changes some core features of the definition, like the essential words or their meanings, or adds on to clarify exactly what you mean.
Then write a “third draft” that tries to cut down on unnecessary words, overly complicated phrases, or overly technical words. Just include the essential in your definition, not the useful or the interesting.
Once you’ve got your third draft of your working definition it’s up to you to chart your own course and make a decision: are you going to research this idea or not? With all that mental preparation you’re in a much better spot to make a more thoughtful decision and you could explain that decision to someone else. Game. Set. Match.
Good Things Come in Threes
So that’s how I translated the idea of “Once is an accident, twice a coincidence, and three times a pattern” into a way of gathering information to decide what scientific ideas to pursue right now. In fact, I just used it last week to finally decide that one of the many working definitions of “scientific discovery” I have come up with over the last 8 months is worth putting into a paper to submit to the open access philosophy journal Ergo by later this year.
It’s important to point out that this general rule of three is not (necessarily) sufficient for a scientific investigation to be rigorous. That depends on the method being used. This rule of three is more about how to decide if fledgling ideas or flashes of insight from brainstorms are worthy of becoming methodical scientific studies. But as a general mental rule, especially if you’re feeling trepidatious, giving yourself a set of three (sources, examples, key words, ideas, sounding boards, etc.) can be an effective way to help you decide what makes the cut.
There’s another saying that also relies on the number three: “Good things come in threes.” In science accidents spark awareness, coincidences spark curiosity, and patterns spark discoveries.
So maybe there is power and magic to the number three.
Of course there’s only one way to find out if my anecdotal use of the number three will lead you to your own epic story of discovery: take a chance, roll the dice, and jump in with an open mind to try it out.
How to cite this post in a reference list:
Bernadette K. Cogswell, “Good Things Come in Threes”, The Insightful Scientist Blog, July 26,2019, https://insightfulscientist.com/blog/2019/good-things-come-in-threes.
Marshmallows are exquisite probes of the human psyche.
So, here’s a question: what relationship do marshmallows, tape, string, scientific discovery, and uncooked spaghetti all have in common? (And in case you’re wondering, this week’s feature photo is a bundle of uncooked spaghetti photographed from above.)
The answer to the question above comes from answering another question as posed in the book by journalist Warren Berger A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas. I came across Berger’s book while doing preliminary research for formulating my discovery cycle (which I’ll log entries on for each phase of the cycle I’m using in early 2019). It was a welcome reference entry for the first phase of the cycle, asking questions. Questions are what ignite the process of scientific discovery because they express and focus the desire to know more about something, inspiring us to act. It turns out that asking questions about how to ask questions was trickier to find information on that I thought it would be. It’s not something we spend much conscious time or effort on. We worry more about answering questions well rather than asking questions well.
So back to Berger’s provocative question, which was the following: “How do you build a tower that doesn’t collapse (even after you put the marshmallow on top)?” (p.120)
I had heard of this story (it’s often in the news), so when I came across marshmallows and kindergartners in Berger’s book, I assumed I already knew the punchline: if you are patient with a question and mull it over it will lead to more positive outcomes. It turns out I was dead wrong. When it comes to asking questions, patience is your friend. But when it comes to answering questions, instant gratification seems to be the way to go.
Here’s what the marshmallow tower studies have found: groups that engage in many trials throughout the allotted time, building, failing, and trying again, on average end up with taller structures. Kindergarteners jump right in to this approach, preferring a hands-on tactic and prototyping early and often to try and succeed. In contrast, other groups, like MBA students, spend the majority of their allotted time discussing how they should approach and try to solve the problem. This results in fewer actual attempts and on average shorter structures (or no successful structures at all!) as a result.
It seems then that Berger’s book not only discusses how and what kind of questions spark breakthroughs (which I’ll cover in a later log entry), but also how best to start trying to answer those questions: trial and error. If you’ve read many of my log entries on the site, you’ll know favoring trial and error and failure is fast becoming a recurrent theme. But it’s always good to have reminders. This is part of the intent of the ARTEMISvirtual reality software being built: to give you a way to build mental models of what you are trying to discover fast and often. And if you read much in the startup (like Eric Reis’ Lean Start Up), software (like Jeff Sutherland’s Scrum: The Art of Doing Twice the Work in Half the Time), or entrepreneurial arenas (like Jake Knapp’s Sprint: How to Solve Big Problems and Test New Ideas in Just 5 Days) then you will know that rapid prototyping to test out answers and learn by getting immediate feedback is all the rage right now.
“I speak without exaggeration when I say that I have constructed three thousand different theories in connection with the electric light, each one of them reasonable and apparently to be true. Yet only in two cases did my experiments prove the truth of my theory.”
(Thomas Edison, Harper’s Monthly Magazine, 1890)
He’s talking about theories, not experiments. Three-thousand-theories. As a theoretical particle physicist that really resonates by “quantifying” the “degree of try” it might take to even think up a good answer to a good question. Besides, maybe if there’s a marshmallow at the end of every attempt, I’ll get better at generating my own 3,000 theories to find the 2 that work. And if I’m smart, I’ll go after that marshmallow today and not wait until tomorrow.
The balancing act between theory and practice, qualitative insight and quantitative assessment, is a tough one. In my quest to develop a repertoire of skills and practices targeted at scientific discovery, theory and qualitative insight have dominated the body of literature I’ve read so far. Until I came across a magnificent pair of papers published by a group of sociologists and a theoretical biologist. Their goal was to analyze a tension often discussed among scientists: stick with tradition or pursue innovation?
In these recent papers, the authors devise a living map of “what is known”, represented as a series of nodes and links between them, on a network graph. They use biochemistry as their scientific use case; nodes represent molecules and links between nodes represent published connections between molecules. They do this using a massive network mapping of molecules and connections appearing in abstracts of published articles in journals—around 6.5 million abstracts. Ah, the glorious face of big data.
So, in this little microcosm of knowledge about discoveries in biochemistry, what can we learn about community-wide research strategies?
The first thing we learn is that there are techniques to map “what is known” and “how was it discovered” in a way that make them amenable to quantitative interrogation. This is no small matter because in these two papers the authors pursue two fascinating questions: (1) what balance does a scientific community strike between pursuing tradition and pursuing innovation as the knowledge network grows; and (2) what can be done to maximize the exploration of such knowledge networks?
The answer to the first question is given in the longer of their two sociology papers (heavy reading for a poor physicist, but worth every ounce of effort). As the knowledge network grows, research becomes more intensive and localized on already well-explored nodes and well-explored links, i.e., research favors tradition. Innovation, exploring or seeking new nodes and links, is marginalized and receives less attention. The authors connect this leaning in to tradition and leaning away from innovation to numerous factors, including some of the usual suspects like pressure to achieve high publication and citation rates for job security and job advancement.
In their second, shorter, paper they examine their newly quantified knowledge network from the perspective of maximizing discovery, defined as discovering new links and nodes in the network. They find that when the knowledge network is young the approach of tradition, a localized search moving outward from central nodes (important molecules), is efficient. But as the knowledge network grows this approach becomes more inefficient, even though this is the strategy that becomes more favored and represented in the published literature over time.
They suggest a number of policy remedies that would trickle down to individual discoverers by enacting change at the community level:
“Thus, science policy could improve the efficiency of discovery by subsidizing more risky strategies, incentivizing strategy diversity, and encouraging publication of failed experiments…Policymakers could design institutions that cultivate intelligent risk-taking by shifting evaluation from the individual to the group…[Policymakers] could also fund promising individuals rather than projects…Science and technology policy might also promote risky experiments with large potential benefits by lowering barriers to entry and championing radical ideas…”
[Rzhetsky et al., PNAS vol. 112, no. 47, p. 14573 (2015)]
As always though, I remain most concerned with how the individual can take action: how, with my own two hands and one mind, can I weave outward and affect change in the shape and size of the known web of knowledge, especially in my own field of neutrino physics? If I combine what I’ve read in these fascinating sociology papers with my thoughts in “A Good Map is Hard to Find”, then I formulate an idea: my own two hands and lone mind can make one PowerPoint.
Now, I’ve been invited to attend a workshop to discuss possibilities for discovering new physics in a newly observed reaction called coherent elastic neutrino nuclear scattering, or CEvNS (i.e., a neutrino bounces off the nucleus in an atom as if it were one solid unit, instead of bouncing off of one proton or one neutron in the nucleus). Workshops to produce agendas, devise long-term strategy, and draft roadmaps and white papers are ubiquitous in physics (and other sciences). It’s how communities foster consensus on “what to do next.”
To me, an agenda-setting, roadmap-writing workshop seems like the perfect time to field test the idea of a “discovery call”: a voluntary, open-science call to action to trial scientific discovery strategies. A “discovery call” is something you can talk about with colleagues, add to a website, or put on a PowerPoint slide. The discovery call I’ll be pitching is as follows: in physics, particles are analogous to molecules and particle interactions and mechanisms are analogous to connections between molecules. Can we build a network map of published trends in our area of interest, CEvNS, and consider new strategies to maximize our network coverage with minimal experiments? And can we take this a step further and build two other deeply analogous maps to use for comparison: one for neutrino neutral current interactions (i.e., where a neutrino bounces off another particle) and one for neutrino charged current interactions (where a neutrino bounces off of another particle, changing particle type in the process)? It would be a way to provide a roadmap with a greater degree of informed choice about how, and how well, we’ve explored a given microcosm.
It seems to me that we have an opportunity to leverage our own history to help point our compass toward discovery, and to be able to see where untried paths have been neglected but might now be the roads best taken. Perhaps today is the time to map what is known, with greater awareness and more practical purpose, so that tomorrow we can stand at discovery’s edge.
Interesting Stuff Related to This Post
Jacob G. Foster, Andrea Rzhetsky, and James A. Evans, “Tradition and Innovation in Scientist’s Research Strategies”, American Sociological Review, volume 80, issue 5, pages 875-908 (October 1, 2015).
Andrea Rzhetsky, Jacob G. Foster, Ian T. Foster, et al., “Choosing experiments to accelerate collective discovery,” Proceedings of the National Academy of Sciences of the United States of America (PNAS), volume 112, issue 47, pages 14569-14574 (November 24, 2015).
Bernadette K. Cogswell, “At Discovery’s Edge”, The Insightful Scientist Blog, September 21, 2018, https://insightfulscientist.com/blog/2018/at-discoverys-edge.
[Page feature photo: A dewy spider’s web in Golcar, United Kingdom. Photo by michael podger on Unsplash.]
I’m finding the most difficult (and intimidating) part of pursuing discovery to be coming up with new ideas, at least on days when my “systematic mind” is team lead. On these days I stick to knowns and try to refit combinations of knowns, or incrementally push ideas a little toward the boundary of the unknown parameter space. A gentle shift here, a nudge there, but nothing really addresses the underlying discovery level shifts needed. It’s as if I’m pushing the same pebbles around the table expecting an oil painting to appear.
On other days, systematic’s co-director “imaginative mind”, takes hold and I am overwhelmed with ideas, but drowning in the ability to sift and evaluate them. At those time it’s as if there’s a canvas in front of me wild with splotches of thought, but no clear scale against which to weigh the relative merits or value of each, a splotch at a time.
In essence, it’s impossible to hold these two perspectives in mind at the same time. But it’s also difficult to capture the outputs of each perspective in a way that lets me glide back and forth between them so that I can make meaningful progress on the crux of pursuing discovery: conceiving of something new and having the prudence to recognize that idea’s significance. Which sounds to me like it’s time to find a good tool to augment the process. As the saying goes, is there an app for that?
Certainly, there are mind maps, endless note taking software, pen and paper, LEGOs, clay, foam models, scientific visualization and more. But none of these are purely designed to foster human conceptualization, let alone human conceptualization about Nature, through the modality of science. In particular, as I’ve started to read more deeply into research on scientific discovery, and as I think back on my own experiences and difficulties in ideation and follow-through for truly novel ideas, I’m struck by how all the strategies revolve around mental models and reasoning skills; in other words, messy, qualitative, human thought, not structured, quantitative, human calculation.
When I then add to the mix the fact that I know this will need to be able to work with speed and to foster thinking upideas as much as thinkingaboutideas and workingwithideas … Well then, I conceive of a tool I’ve nicknamed ARTEMIS, Artificial Reality Tool for the Enhancement and Manipulation of Insight in Science, whose job it is to help you recognize the undiscovered, both in Nature (i.e., scientific discovery) and in your own understanding of Nature (i.e., scientific insight).
I envision this tool will work in virtual reality, where images, sounds, and direct hand manipulations will be the mode of operation; feeding your perceptual and sensory-motor mind as much as your cognition to aid in ideation and evaluation; moving language, computer code, and mathematical symbolics to the background, all of which are slower and cognitively more cumbersome. Most importantly I imagine it will run in different modes and allow you to enter your own research questions as various abstractions designed to trigger different innate reasoning skills linked to insight and discovery. And it will allow you to evaluate among options at times when you are a fountain of ideas or to find new streams of thought on days when the well of inspiration runs dry.
I already have in mind a few neutrino questions to use as test cases, to hone, refine, and infuse ARTEMIS with everything I learn in my pursuit of the process of scientific discovery. The power of virtual reality as a serious research tool remains untapped, especially in its ability to redefine the relationship between humans and computation.
In the act of discovery one can think loosely of three phases: (1) conceptualization, (2) calculation, and (3) interpretation. (I take here a different and more physics theorist-based view of the phases of discovery than philosophers, psychologists, or historians might.) Much, much work has been done on automation in physics, and tools abound for precision calculations. Interpretation is also receiving its due with the advent of numerous tools for scientific visualization. But conceptualization remains neglected. Perhaps because it requires augmenting human thought rather than human action. Whatever the case, I am designing ARTEMIS to fill that gap and serve as a tool on the path to scientific discovery.
And as for the name, perhaps a bit of a lucky chance that I could think up such a quaint acronym; for in pursuit of discovery, who better to have as a traveling companion than the Greek goddess of the hunt.
