In our ideal imagination someone would always be able to give us an exact game plan to achieve our dreams, full of steps we know exactly how to do.
That kind of recipe would be comforting and make us more confident.
I can’t give you that.
But what I can give you is a mental picture of the five key phases that make a scientific discovery happen. It’s just one of six core components of my scientific discovery framework (you can read about that here).
Equipped with a mental picture, it will be easier to see where you’re losing momentum and look for ways to fire up your progress.
Let’s dive into this “discovery cycle”.
The five evolution phases of scientific discovery, in order, are:
Question. It all starts with having an unanswered question about the world that needs to be answered. Discovery always begins by actively asking an unanswered serious question. Serious questioning is about generating compelling questions and then choosing one to go out and answer.
Ideation. Next you must form an idea about what might be the answer to your question. Productive ideas are ones that we can chip away at through real-world tests and investigations. Ideation is the process of generating productive ideas and narrowing it down to one idea you move forward on.
Articulation. Productive ideas don’t investigate themselves. You’ve got to put it in a format that lets you determine your idea’s ability to correctly answer your serious question. Transforming something from an idea to a real-world process, procedure, gadget, or systematic concept is articulation.
Evaluation. Now that you’ve articulated the idea you think might answer your question you need to put it to the test. Compare your concept against real examples. Observe and probe your data. Run your model and see if it breaks. That’s the heart of evaluation.
Verification. If your idea survives your evaluation (and most of them won’t) then it’s time to open your idea to deep challenges from others. It’s not a scientific discovery until other people have independently confirmed that your idea answers your starting question and that the way you articulated the answer holds up. Personally, I think two separate independent verifications plus your initial investigation are ideal because good things come in threes.
And that’s the discovery cycle in a nutshell.
The scientific discovery cycle is a human learning algorithm for scientific discovery.
You may move back and forth between scientific discovery phases as you make mistakes and learn new things. That’s normal. But in the end, if you discover something new, you will have evolved through all the phases at some point in the process.
Talking with other scientists, I’ve learned that how long you’ve worked with science (not a project) affects which phase is more likely to trip you up.
People new to science tend to get stuck on the question phase.
They don’t know what a good science question looks like. If this fits you, learning more about creativity, filling your knowledge gaps, and becoming more skilled at asking deep questions and mining published papers can help.
People who have some experience working with science, but haven’t spent a whole career on it, often struggle with the articulation phase.
They’ve got ideas, but they don’t know how to put them in productive testable forms. If this sounds like you, reading up on rapid prototyping, building mental models, and techniques like work sprints can help.
People who have made a career out of working in or around science frequently run out of ideas and struggle with ideation.
They may feel like everything’s been done. Or that every idea is bound to fail (or get ignored) anyway. Sometimes they can’t imagine better solutions than the good solutions they already know. If this describes you, then looking into techniques to get around the Einstellung effect or how to think of more “subtractive solutions” might help.
Those are the three main phases where most individuals get stuck and lose momentum in the scientific discovery cycle: the question, ideation, and articulation phases.
Just to be thorough, if the evaluation phase is where you struggle try things like practicing Fermi questions, toy model techniques, or “why not?” counter-thinking. Verification problems are usually about convincing others to engage with your proposed discoveries enough to test your ideas in a public forum. Learning better communication skills can make the difference.
Most scientific discovery projects must pass through all five evolution stages—question, ideation, articulation, evaluation, and verification—to succeed.
Knowing which stage you’re stuck in can point you toward techniques to help you get past an obstacle.
And being clear on where you are in the discovery cycle can tell you what not to do, like getting lost in brainstorming hacks (ideation) when what you need are strategies to create a new metric to measure something (articulation).
Use this discovery cycle framework like a teacher who points out where you are in your project and what needs more work.
Simply put, a mental picture of the scientific discovery cycle is your ultimate personal coach.
Reflection Question
What phase are you in on a discovery project you are working on, or planning, and what’s keeping you from moving to the next stage?
Bernadette K. Cogswell, “A mental picture of the scientific discovery cycle is your ultimate personal coach”, The Insightful Scientist Blog, September 24, 2021.
[Page feature photo: Photo by DeepMind on Unsplash.]
To make a scientific discovery you need a plan not a map
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.]
On the influence of tracking the evolution of your ideas on the pace of discovery.
Have you ever moved, or had a big change in your situation, and when you started sorting through everything you wondered why you kept it all?
I have been looking through all the handwritten paper notes I scanned just before I left England (…more than 1,476 sheets of notes!…).
They are red, purple, and green block notes. Mysterious half-sentences jotted down in equally bright felt tip pen.
They are small Moleskine pages stained with decaf coconut flat whites. Coffees bought at the chain Pret a Manger for my morning tram ride to work in England.
And they are neatly laid out calculations on blank pages. Carefully crafted while sitting at my temporary desk. Each time anxiously listening for the buzz of wasps through the open window in high summer in a building with no air conditioning.
Why did I keep them all?
Because I believe in the value of tracking the evolution of your ideas.
I think it can emphasize when you are harping on the same old theme. It can point out when you have failed to try something different. And it can highlight when you have made progress over the course of time.
All of this evidence can speed up the pace at which you gain new insights, and hence the pace of discovery.
Tracking ideas can also remind you of the reality of how you actually arrived at some inflection point in your progress.
And it can pinpoint when you suddenly veered into promising territory. (In the lean innovation and startup world, this same concept is called a pivot point in product development.)
In principle, tracking the evolution of your ideas speeds up discovery because we have bad memories
We have very selective memories.
I won’t quote a bunch of psychology literature here since most of us will recognize from experience the existence of the following ideas.
How many times have you argued with a family member or colleague about something they say they don’t remember happening?
Psychologically we do have selective memories, a result of “selective attention”. We only retain some things as important enough to remember and other things we ignore.
Have you ever debated with a family member or colleague and they loop back to the same argument?
Sometimes, no matter how many times you state your case from a different angle, they keep coming back to the same point. A point you think you’ve already rationally and calmly explained to them is no good.
Our brains do literally have a thinking pattern, called “einstellung”, in which they get stuck on a particular loop that is more accessible in our memory. Our brain can’t get past that idea to try other solutions or take other lines of thought.
Another trick our mind plays on us is to engage in something called “sunk cost bias”.
This is the belief that items we have invested our personal time and money in are more valuable than they actually are.
So once you’ve latched on to a particular train of thought (or your colleague with the “crazy theory” has) the more time you spend on it, the more convinced you’ll be that it’s valuable.
(Unfortunately, we also have a mental predisposition to believe that more complex theories are more likely to be true than simpler ones).
The point is, our minds are not perfect repositories and mirrors.
Our memories don’t capture in exact detail everything that happens to us.
And our minds can’t reflect back to us precisely what need, when we try to recall a set of events or information.
But science is full of discoveries that were driven by personal events and private internal themes.
These themes kept driving the discoverer to make certain idiosyncratic and, it turns out, progressive choices at different points along their path. (To see an example of this at work in someone other than our beloved Albert Einstein, see the link on the discovery of high-temperature superconductivity in American Scientist below).
In some cases, these discoverers were aware of these themes in their choices, but at other times they were not.
So imagine how powerful it would be if you could see these themes, as they play out.
Powerful why?
Because being able to see the evolution of your ideas and themes would give you the ability to change themes at will. It would also allow you to recognize nontraditional inputs, linked to the theme, that might also push you toward discovery.
Hoping to recognize your evolution and thematic drivers by chance is bound to be slower, a sort of random walk. In contrast, doing so with intent is an efficiency-driven algorithm.
Being holistic, tracking the evolution of ideas mobilizes and harmonizes environmental forces to speed up discovery
Not only would knowing your own intellectual history and ancestry help you make discoveries faster, but a realistic picture of how discoveries are made would enable powerful social forces to come into play.
At the level of policy, having a clear awareness of what it takes to make a discovery would allow more supportive policy making decisions. This means knowing how long, by what actual means, with exposure to what themes and ideas, and according to what personal choices a discovery was made.
At the group or organizational level, having an honest and holistic understanding of the scientific discovery process allows a group to better synchronize with discovery goals. It may highlight when bringing in a new person, a new department, or a new topical theme is useful. Or it can elucidate when new resources or more time are best given to the team already present to incubate discovery.
In practice, tracking the evolution of your ideas can be achieved through two activities
On a practical level, tracking the evolution of your thoughts requires two different mindsets to be at play (though not at the same time) as you move through your investigation process.
Let’s call them the “logging mind” and the “reflecting mind”.
(In the study of learning, related concepts are the “focused mind” and the “diffuse mode mind”, respectively).
These two mindsets naturally lead to two sets of activities to engage in during the investigation process, when you’re trying to track your intellectual heritage.
The first activity uses the logging mind and is where you record your exposure to various ideas, themes, individuals, sources, and activities.
I have alternately logged these things on sticky notes, in notetaking apps on my phone, in spiral notebooks, and on block notes, over the years.
In the last two years I have started to record, along with a one-sentence reference to each item, one of two additional tags added to the item.
Take for example the cryptic block note, “Network Analysis”.
The first tag might be a place, such as “Chicago conference on CEvNS”. (Or tags might be simpler like “Nashville, TN” or “Schipol Airport”).
The second tag might be a date such as “F.11.22.2018”. (The “F” stands for Friday. I use M, T, W, R, F, S, and U for the days of the week).
I find the combination of these two tags and a note allow me to bring up in my memory, by association, what I was doing, how I came in contact with the item, and why it struck me as important.
(Sometimes I can rely on just the date tag, if it’s memorable enough. For example, around the date I moved U.S. states or countries, birthdays, holidays, and very sad family events stick with me.)
This associative thinking mode is actually much more reliable than a chronological one.
Research has shown that our minds are especially good at recalling visual-spatial information—such as places. (This is famously used in the “memory palace” or “method of loci” technique by world champion memory athletes).
So for the conference tag example above, upon seeing the item, I might even be able to remember:
where I was sitting (the lobby of the University of Chicago Physics Department building eating a Starbucks snack),
what I was wearing (a much loved fuchsia and burgundy flannel shirt with a favorite pair of Italian Murano glass earrings),
the internal conversation I was having (about using network analysis of publications on a scientific topic to inform community white papers and roadmap documents), and
what had just happened that made me jot down the note (interviewed researcher Andrey Rzhetsky about an article he co-authored using network analysis to track the efficiency of group discovery in science).
The second activity uses the reflecting mind and is where you record your reactions and responses to the investigation process and the items recorded in the logging mind activity.
For example, keeping a research journal and “freewriting” about what you are thinking at regular intervals can work. Just be sure to include personal details, such as what is going on in your life and environment. And note your personal reactions towards events and evidence (a “reflecting mind” activity).
You’ve also seen how piecing together a train of thought, which is what you do with the “reflecting mind”, can lead you to an awareness of what is affecting your work and what themes are driving your process.
For example, I shared with you the Netflix-driven incidents that honed my working definition of scientific discovery in another post (“Don’t Curate the Data”, see link below).
That train of thought came to me after reading a bunch of philosophy literature.
Feeling dissatisfied with what I had read, I found myself unable to purge the language and ideas others had used and move in a different direction.
To get past this kind of einstellung, I made a lateral move. Instead of reading more I watched TV.
I browsed according to what themes called to me—craftsmanship, a sense of honor, nobility, care, handcraft, and diligence—and which I felt defined the spirit of scientific discovery.
These new spark points were not enough for an operational definition testable in the lab, but they were enough to guide me toward different themes.
I was very diligent about capturing my thoughts on block notes at the time. So, I was able to recognize the old themes that were causing me dissatisfaction—categorization, thought, chronology—and consciously turn toward new themes that I wanted to include—quantitative, applied, craftsmanship.
Then I actively based my new efforts on that mental shift.
Within two weeks I had generated my own new definition of scientific discovery that I have not come across elsewhere in the literature, after six months of trying to come up with something new. (And I am working on putting together historical case studies that illustrate the merits and shortcomings of this definition, for publication in a peer-reviewed journal).
But without being able to look at my point of origin, even if only at one turn in my path, I would not have been able to consciously make this mental shift.
This kind of clear-sighted awareness and finesse is what more discoverers need to help them make smart choices and shift their thinking when the situation calls for it.
By analogy, tracking the evolution of your ideas is making visible an invisible maze
I have seen many versions of how to track the evolution of your ideas.
I’m still working on finding my own best way, which supports my intention of becoming a Maestra of scientific discovery and the scientific discovery process.
Sometimes trying to find our way toward a discovery feels like an invisible maze where we encounter many dead ends, or end up right back where we started.
By keeping a record of our thoughts and influences we make the maze visible.
And we give ourselves an aerial view of our point of origin and the paths we have traced out in our minds and with our actions.
Knowing your point of origin and where your thoughts have wandered can help speed you toward undiscovered territory, by showing you the paths less travelled.
Interesting Stuff Related to This Post
Gerald Holton, Hasok Chang, and Edward Jurkowitz, “How a Scientific Discovery Is Made: A Case History”, American Scientist, volume 84, July to August, pages 364-375 (1996), freely available on Researchgate from one of the co-authors at https://www.researchgate.net/publication/252275778_How_a_Scientific_Discovery_Is_Made_A_Case_History.
Bernadette K. Cogswell, “Point of Origin: On the influence of tracking your ideas on the pace of discovery”, The Insightful Scientist Blog, November 29, 2019, https://insightfulscientist.com/blog/2019/point-of-origin.
[Page feature photo: An aerial view of the maze at Glendurgan gardens, built in 1833, in Cornwall, United Kingdom. Photo by Benjamin Elliott on Unsplash.]
It’s tempting when we talk to others about our ideas to only want to share the good stuff. To only share the things we think are logical, sound reasonable, maybe only the things we think (or hope) will make us seem smart and focused. But this tendency to re-frame our real experiences and distill them into nice little stories we can tell people over coffee or a beer can be a dangerous setback to getting better at a new skill set.
Trying Too Hard to Look Good
Why? Because sometimes we are so busy trying to think about how to tell (or should I say sell) others on what we’re doing or thinking that we scrub our memories clean of the actual messy chain of events that led us to come up with the polished version. That messy chain, and every twist, turn, and chink in its construction, is the raw knowledge from which we can learn about how we, or others, actually accomplish things. I’ll call it “the data.”
So this fear of how others will perceive our process is one thing that gets in the way of having good data about our process. We start to curate the data to make ourselves more acceptable to others.
But we need this data to gain a meaningful awareness of what we actually do to produce a certain outcome. This is even more important when we try to figure out how to reproduce a mental outcome.
Maybe you came up with a winning idea once, but now you’re not sure how to get the magic back. Or maybe you want to pass your strategy on to a younger colleague or friend, but don’t really know what you did. Maybe you’re hoping to learn from someone else who succeed at thinking up a breakthrough solution, but they say “I really don’t remember what I did. It just sort of came together.”
Which brings us to a second thing that works against having access to good data about our own interior processes and patterns. Memory.
Mining Memory is a Tricky Business
We all know we don’t have good memories, even when we are trying hard (studying for tests in school, or trying to remember the name of every person in a group of ten new people you just met are classic examples). Memory is imperfect (we have weird, uncontrollable gaps in what we retain). Memory is selective (we have a tendency to be really good at remembering what happened during highly emotional events, but not during more mundane or routine moments). Memory is pliable (the more we tell and retell a version of something that happened to us, the more likely we are to lose the actual memory in place of our story version).
These tricks of memory not only frustrate us when we try to observe and learn from ourselves, but also when we try to learn from others.
There have been lots of interviews with famous scientists who made discoveries asking them about how they did it. But their self-reported stories are notoriously unreliable or have big gaps because they, like us, are subject to the fickle whims of memory and the hazards of trying to tell your own biography one too many times. Mining memory for useful insights is a tricky business.
So memory and lack of awareness (or mindlessness) cause us to lose access to the precious data we need to be able to see our behaviors and patterns from a larger perspective in order to learn from them and share them.
When I first started learning about scientific discovery, recognizing these pitfalls of bad memory and mindlessness caused me a lot of annoyance. I would think of a great example of a scientific discovery, such as a discovery that shared similarities with an area or question I wanted to make discoveries in. I’d think, “Perfect! I’ll go read up on how they did it, how they discovered it. What were they reading, what were they doing, who were they talking to?” But of course, answers to those questions wouldn’t exist!
Maybe the discovery was of limited interest so nobody bothered to ask those questions and now the discoverer had passed away. Or maybe the discovery was huge and world changing but the histories told about it tended to re-hash the same packaged myths—like Newton and the apple falling inspiring ideas about gravity, or Einstein taking apart watches from an early age leading to picturing little clocks when working out the effects on time of traveling near light speed in special relativity. Part fact, part fiction, these stories leave hundreds of hours of more mundane moments, links in the mental chain, unilluminated. Good data that could guide future generations gets lost, sacrificed on the altar of telling a whimsical story.
So when I sat down in September of 2018 to start trying to work out a more modern definition of scientific discovery—something pragmatic that you could use to figure out what to do during all those mundane moments—I kept thinking about how to better capture that process of obtaining insights, as you go.
That’s when I realized we already have the methods the problem is we always want to curate the story told after the fact. And rather than curating the data that make it into the story (i.e., creating an executive summary and redacting some things), we end up actually curating the source data itself (i.e., never gathering the evidence in the first place). In other words, rather than just leaving out parts of the story, we actually tune out to parts of the story as we are living it, so that we literally lose the memory of what happened all together.
But that story is the raw data that fields like metascience and the “science of science” need to help figure out how scientists can do what they do, only better. And as scientists we should always be the expert on our own individual scientific processes. The best way to do that is to start capturing the data about how you actually move through the research process, especially during conceptual and thinking phases. Capture the data, don’t curate the data.
A Series of Events
Let me give you a real life example to illustrate. As I said, I sat down to try to come up with a new definition of scientific discovery. I’m a physicist by training. Defining concepts is more a philosopher’s job, so at first I had a hard time taking myself and any ideas I had seriously. I got nowhere for three months; no new ideas other than what I had already read. Then one day a series of events started that went like this:
I read a philosophy paper defining scientific discovery that made me very unhappy. It was so different than my expectation of what a good and useful definition would be that I was grumpy. I got frustrated and set the whole thing aside. I questioned why I was studying the topic at all. Maybe I should stick to my calling and passion, physics. I read when I’m grumpy, in order to get happy. So I searched Amazon. I came across a book by Cal Newport called So Good They Can’t Ignore You. It argued that passion is a bad reason to pursue a career path, which made me even grumpier; so grumpy I had to buy the book in order to be able to read it and prove to myself just how rightfully disgruntled I was with the premise.
Newport stresses the idea of “craftsmanship” throughout his book. I was (and still am) annoyed by the book’s premise and not sold on its arguments, but “craftsmanship” is a pretty word. That resonated with me. I wanted to feel a sense of craftsmanship about the definition of scientific discovery I was creating and about the act of scientific discovery itself.
I didn’t want to read anymore after Newport. So I switched to watching Netflix. By random chance I had watched a Marie Kondo tidying reality series on Netflix. Soon after, Netflix’s algorithm popped up a suggestion for another reality series called “Abstract: The Art of Design.” It was a series of episodes with designers in different fields, like architects, Nike shoe designers, theater and popstar stage shows set designers, etc. It pitched the series as a behind the scenes look at how masters plied their craft. Aha, craftsmanship again! What coincidence. I was all over it (this was binge watching for research, not boredom, I told myself). I was particularly captivated by one episode about a German graphic designer, Christoph Niemann, who played with Legos, and whose work has graced the cover of The New Yorker more than almost any other artist. The episode mentioned a documentary called “Jiro Dreams of Sushi.”
Stick with me. Do you see where this is going yet? Good, neither did I at the time.
So I hopped over to Amazon Prime Video to rent “Jiro Dreams of Sushi” about a Japanese Michelin rated chef and his lifelong obsessive, perfectionist, work ethic regarding the craft of sushi. At one point the documentary showed a clip of Jiro being named for his Michelin star and they mentioned what the stars represent: quality, consistency, and originality. Lightbulb moment! Something about the ring of three words that summed up a seemingly undefinable craft (the art of creating delicious food) felt like exactly the template I needed to define the seemingly undefinable art of creating new knowledge about the natural world.
So I started trying to come up with three words that summed up “scientific discovery”. Words that a craftsman could use to focus on elements and techniques designed to improve their discovery craft ability. There were more seemingly mundane and off-tangent moments over a few more months before I came up with the core three keywords that are the basis of the definition I am writing up in a paper now.
The definition is highly unique, with each term getting its own clear sub-definition that helps lay out a way to critically examine a piece of research and evaluate it for its “discovery-ness”, i.e., its discovery potential or significance. It’s also possible to quantify the definition in order to try and rank research ideas relative to one another for their discovery level (minor to major discovery).
It’s a lot better idea than some of the lame generic phrases that I came up with in the early days, like “scientific discovery is solving an unrecognized problem ” (*groan*).
On an unrelated track at that time, I was reading Susan Hubbuch’s book, Writing Research Papers Across the Curriculum, and had come across her idea that you create a good written thesis statement by writing out the statement in one sentence and then defining each keyword in your statement using the prompt “By <keyword> I mean…”. So then I took the three keywords I had come up with and started drafting (dare I say crafting?) their definitions in order to clarify my new conception of “what is scientific discovery?”
So that’s the flow…my chain of discovery data:
Reading an academic paper led to disgust; disgust led to impulse spending; impulse spending brought in a book that planted the idea of craftsmanship; craftsmanship led to binge-watching; binge-watching led to hearing a nice definition of something unrelated; the nice definition inspired a template for how to define things; and simultaneously reading a textbook suggested how to tweak the template to get a unique working definition down on paper.
How do I know all this? I wrote it down! On scraps of paper, on sticky notes, in spiral notebooks, in Moleskines, in Google Keep lists, Evernote notes, and One Note notes (I was going through an indecisive phase about what capture methods to use for ideas).
I learned to not just write down random thoughts, but also to jot down what inspired the thought, i.e., what was I doing at the moment the thought struck—reading something, watching something, eating something, sitting somewhere, half-heartedly listening to someone over the phone…(Sorry, Mom!)? Those are realistic data points about my own insight process that I can use later to learn better ways to trigger ideas. (And, no, my new strategy is not just to watch more Netflix.)
Make a Much Grander Palace of Knowledge
Instead of trying to leave those messy, mundane, and seemingly random instigators out, I made them part of my research documentation and noted them the way a chemist would note concentrations and temperatures, a physicist energies and momenta, a sociologist ages and regions.
And then I promised myself I wouldn’t curate the data. I wouldn’t judge whether or not impulse book buying is a great way to get back on track with a research idea, or whether or not Marie Kondo greeting people’s homes with a cute little ritual is a logical method of arriving at a template to devise operational definitions. I wouldn’t drop those moments from memory, or my records of the research, in order to try and polish the story of how the research happened. I’ll just note it all down. Keep it to review. And maybe share it with others (mission accomplished).
Don’t curate the data, just capture the data. Curation is best left to analysis, interpretation, and drawing conclusions, which require us to make choices—to highlight some data and ignore other data, to create links between some data and break connections among other data. But think how much richer the world will be if we stop trying to just tell stories with the data we take and start sharing stories about how the data came to be. The museum of knowledge will become a much grander palace. And we might better appreciate the reality of what it is like to whole-heartedly live life as a discoverer.
Bernadette K. Cogswell, “Don’t Curate the Data”, The Insightful Scientist Blog, August 2, 2019, https://insightfulscientist.com/blog/2019/dont-curate-the-data.
[Page Feature Photo: The gold dome in the Real Alcazar, the oldest used palace in Europe, located in Seville, Spain. Photo by Akshay Nanavati on Unsplash.]
