Simply put, they kill people and break things. That’s what they’re designed to do.
Stick with me. I’m laying this out because it will be the science example in upcoming posts on scientific discovery. So…
Having nuclear weapons around makes life dangerous.
You never know when someone might accidentally kill people and break things with one. You never know when someone might kill people and break things with one on purpose. It’s just a bad piece of technology.
Having them lie around instead of getting rid of them is bad.
But having a massive pile of the stuff that helps make them go boom, plutonium, makes a bad problem even badder.
Why?
Because even if you get rid of the current batch of nuclear weapons, all that extra plutonium makes it easy to build and stockpile them again.
If the world were more convenient we could get rid of all that plutonium, like putting leftovers down a garbage disposal. Done and dusted.
Too bad getting rid of plutonium is not that easy.
Once plutonium is made it’s currently impossible to get rid of it fast. That’s because plutonium can’t be destroyed in any safe and easy way. You have to wait for physics to run its course.
The plutonium used to make weapons is radioactive. That means it naturally disintegrates over time. Every isotope disintegrates at its own speed. For plutonium-239, the isotope most commonly used to make nuclear weapons, the time it takes for half of the plutonium you have to disintegrate away is about 24,000 years.
If the average person lives 100 years. That means it will take about two hundred and forty generations for half of the plutonium we’ve created for nuclear weapons to decay on its own and go away. We have no way to speed up that natural process with science and technology we have now.
That much stockpiled plutonium is equal, by mass, to about four hundred and twelve Fiat 500 Sport cars (what I happen to drive).
I love Fiats. But I don’t want the equivalent of 400 of those little Fiats sitting around, for two hundred and forty generations, when they are ultra-explosive killing people and breaking things machines.
Still, that’s the state we are in.
Let’s call it the “plutonium disposition problem”.
Luckily, lots of people realize having that much raw material to build nuclear weapons hanging around is a badder thing than just having nuclear weapons.
But discovering new ways to get rid of old plutonium is tough.
Many people have thought about how to deal with this extra plutonium. And get rid of the plutonium from any new weapons that get taken apart.
But plutonium disposition solutions have stalled out because of politics, money, and difficulty. One of the most popular solutions is to mix this plutonium with other things to make it less dangerous when it’s stored together in large quantities. Next put that mixture in special barrels, bury it in one or two underground facilities, and permanently seal it.
Then you wait for physics to work its magic and hope nothing goes wrong in the meantime, like natural disasters or someone breaking into the facility and treating it like a gold mine, only for plutonium.
But the ideal solution would be to discover a way to get rid of the world’s excess bomb plutonium within one human generation, using a non-self-destructive, sustainable, and ethical method.
I say within one generation because I think that’s the maximum amount of time any social agreement can be maintained, remembered, and honored. If all goes well. Longer than that and life is too unpredictable.
Simply put, nuclear weapons are bad and getting rid of extra plutonium prevents the problem from sticking around even after the last nuclear weapon is gone.
Plutonium may be a case of going from bad to badder. But it makes for a great real-world example of an area that desperately needs new discoveries to shift the balance.
In the first blog post in this series I gave an overview of what I call the “architecture of scientific discovery.” In that post (see link at bottom) I listed all the key elements and processes that influence achieving a scientific discovery.
In this post I want to dive deeper into how to define scientific insight so that you can always work towards becoming a more insightful scientist.
First, a quick reminder of how insight fits into the architecture of scientific discovery:
“Insight” is one of the four human discovery capacities. These capacities are what allow us to make progress in science and technology.
Every themed post in the architecture of discovery series, where I focus on one concept in the architecture, has four sections. For each core concept that fosters scientific discovery I talk about,
how to define it in a science context,
what role it plays in advancing scientific discovery,
how to recognize it in science examples, and
ways to use it in your own discovery project workflow.
So, this week let’s talk about “insight” in science.
How to define insight to make it useful for daily research work:
Gaining “insight” means improving the accuracy of your perspective.
Let me remind us of the basics of “insight” by repeating some key points from the first architecture of discovery post. A useful definition of insight in science is:
“Insight” is refining the accuracy of your perspective of the real world.
There are three ways you can get a more accurate perspective, or “achieve insight”:
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.
Another factor that distinguishes among the four human discovery capacities (insight, invention, innovation, and scientific discovery) is our motivation for pursuing a discovery.
In the case of insight, our motivation is: “I want to…change how I see the world.”
My definition of insight is heavily influenced by ideas from cognitive and therapeutic psychology. In cognitive psychology, I pull from the concept of “insight problems,” whose solutions are obtained through a sudden, all-at-once recognition of the problem solution. And in therapeutic psychology, I draw from the idea that insight leads to therapeutic breakthroughs, i.e., that mental awareness fosters a capacity to solve personal problems.
As you can see, although both of these definitions of insight work well in their respective contexts, and each echoes the other, it’s kind of hard to see how to apply them to become an insightful scientist.
So, I took some of the themes from those two fields and re-configured them into a meaning that lends itself to defining scientific insight. (Although, I think you could also apply the definition I’ve come up with to other areas, beyond science, too.)
Now, let’s discuss the three ways you can achieve insight in a little more detail.
Misperceiving Something
You can improve the accuracy of your perspective on the natural world…by correcting a faulty belief.
When I say “misperceiving” something, I mean that you have an inaccurate perspective on a piece of knowledge. In other words, you misunderstand something you already know.
What you misunderstand could be something you think you know about an object, the characteristics of an object, or the mechanisms behind how certain things work.
Until this faulty bit of knowledge is fixed, you are likely to make incorrect predictions, have a higher failure rate in your trial and error attempts, and build up a flawed picture of just how everything fits together. Getting rid of these faulty beliefs is key to getting on the right track for scientific discovery.
Being Unaware
You can improve the accuracy of your perspective on the natural world…by learning things you don’t know.
When I say being “unaware” of something, I mean that a piece of knowledge does not exist within your mental repertoire. That means, you simply don’t know something.
One of the most powerful tools in science is using “constraints.” Constraints are what you know have to be true, or be predicted, in order for a new piece of scientific knowledge to be consistent with everything else that we have already verified to be true about the natural world.
Being unaware of a crucial constraint can be costly. It can send you down the path of interesting, but ultimately useless, dead ends that cannot be directly used as a springboard for further scientific discovery. (Though mistakes, or “mis-fitting the data,” can have its uses. See link to my previous post, “Misfits Matter,” at bottom).
Also, if you don’t even know something exists—such as an object, characteristic, or outcome—then you can’t even know you are supposed to being pursuing its scientific discovery potential!
So, filling any gaps in your knowledge, by becoming more aware, is an important step in becoming a more insightful scientist and, hence, improving your likelihood of making a scientific discovery.
Feeling Vague
You can improve the accuracy of your perspective on the natural world…by understanding the what, why, and how of an aspect of the natural world.
When I say feeling “vague” about something, I mean that you can’t articulate your beliefs into a coherent and consistent chunk. When you have a vague understanding of something, you can’t explain what all the parts are, why they are there, and how they work together.
I use the term “coherent” because all the elements relevant for understanding something must be part of the descriptive chunk you create for it. In other words, all the pieces that you know about something have to “hang together” or you don’t have a good mental picture of what something is and how it works.
And I chose the term “consistent” because it means that none of the elements or connections you include in your mental chunk can contradict (i.e., falsify) each other.
It’s easy to know when you are vague about something, because you can’t explain it to someone else. And you dread or fear that someone will ask you questions about it!
Being vague suppresses our ability to gain insight and make discoveries because we aren’t working with a full and complete set of constraints to guide our predictions and efforts.
To sum it all up, gaining scientific insight and becoming an insightful scientist means correcting your misperceptions, clarifying vague perceptions, and filling your knowledge gaps about how the natural world works.
If you don’t work at becoming an insightful scientist with every project then you are at risk of creating bad science (or worse, harmful science).
But if you do put in the effort to become an insightful scientist, then you are more likely to be rewarded by making a scientific discovery.
So, next let’s explore why scientific insight and scientific discovery are so entangled.
What role insight plays in making a scientific discovery:
Insight infuses your research efforts with the three key qualities of scientific discovery.
If you have read the starting post in this architecture of discovery series (see link at bottom) you will remember that the three key qualities a scientific discovery must <italics> have are radicality, universality, and novelty.
(As a reminder: If research lacks all three of those qualities then it falls into the category of a scientific investigation. But if it possesses any one, two, or three of those qualities then it falls into the category of a scientific discovery).
Here’s a quick recap of what each of the three qualities of scientific discovery encompasses:
Radical means that the discovery causes a shift in our perspective.
Universal means that the discovery can be applied to a range of things.
Novel means that the discovery has never been demonstrated to be true before.
If the outputs of a research effort don’t contain at least one of those three key qualities, to some degree, then it isn’t impactful enough to be called a scientific discovery.
Strong connections exist between the definitions I just gave of the three qualities of scientific discovery and my earlier definition of scientific insight. Next, let me share some of the connections I see.
Interplay of Insight and the Radical Nature of Scientific Discovery
Radicality in scientific discovery is about shifting your point of view.
In other words, the radical quality of scientific discovery teaches us to see something in a very different way from how we saw it before.
Gaining scientific insight is how we achieve the radical perspective shifts needed for scientific discovery to occur at the individual level.
By becoming aware of new perspectives or information, your point of view changes. By correcting a faulty perception of the natural world, you gain a new view of how the universe operates. And by getting clarity on precisely how things work, your point of view becomes clearer and more focused.
Interplay of Insight and the Universal Nature of Scientific Discovery
Universality in scientific discovery is about understanding the extent to which your perspective of the natural world applies to a range of situations and objects.
Gaining scientific insight is how we recognize patterns in nature whose significance rises to the level of a scientific discovery.
Insight can allow us to recognize that various truths in science are more or less universal, by becoming aware of, clarifying, or correcting faulty perceptions of those truths.
For example, you cannot apply a piece of knowledge to a new arena if you are unaware that connections exist between two topics. And you cannot recognize a mechanism as more universally applicable if you only vaguely know how it actually works.
As you exercise your capacity for insight, you build up the necessary knowledge base to let you recognize universal patterns that will yield scientific discoveries.
Interplay of Insight and the Novel Nature of Scientific Discovery
Novelty in scientific discovery is about seeing and recognizing the undiscovered.
Gaining scientific insight is how we find new things that end up being scientific discoveries.
This is at the core of the idea that improving the accuracy of your perspective on the natural world, or developing scientific insight, is about becoming aware of new things.
When new pieces of knowledge are only new to you, then they stay in the realm of insight. But when they are verified and are new to the scientific community, then they move into the realm of scientific discovery.
Of course, the correspondence between insight and the three key qualities of scientific discovery is not cut and dried. I’m just pointing out that these things certainly interact.
So, being skilled at insight (i.e., improving the accuracy of your perspective on something) is crucial to also being able to recognize and foster the elements of the radical, the novel, and the universal in your own discovery process and research activities.
Now, abstractions and definitions are nice.
But examples and practicalities are also nice!
So I’d like to turn our attention first to some examples and then to some practical ways to use all this (in the last section).
How to recognize scientific insight using examples from science history:
Insight is visible when researchers discuss filling gaps or correcting misunderstandings in our common scientific knowledge.
The goal of presenting examples of working scientists struggling with insight is to help us recognize scientific insight in action, from stories of research or in research papers. For this post, I am going to pull out examples from the discovery of radioactivity in physics (see link to a nice overview by physicist and historian Allan Franklin at the bottom of this post).
Science History Example of “Insight”: Misperceiving Something
A good example of how misperceiving something affected the progress of discovery is the case of radioactive decay.
After Henri Becquerel had discovered the emission of unknown radiation from certain materials (quite by accident), physicists quickly identified new so-called alpha particles as what was being emitted. They also learned quite a bit about the range of energies with which such particles could be emitted from radioactive samples (known as energy spectra).
However, after the discovery of alpha radioactive decay (usually just called “alpha decay”), another new kind of radioactive decay was observed, involving what were identified as electron particles, by Walter Kaufmann. These electron particles were also emitted with a range of energies (and this was called “beta decay”).
As a result, a faulty analogy arose in the mind of many of the top physicists of the day that the energy spectra seen in alpha and beta decay experiments occurred by similar mechanisms.
This perception turned out to be false.
In fact, the analogous behavior in the alpha and beta decay spectra was caused by the existence of another fundamental particle, the neutrino, which had not yet been discovered!
The point here is that it took decades before physicists realized, and reconciled themselves to the idea, that they should be looking for an entirely new particle, not a way to explain beta decay using alpha decay.
The misperception that alpha and beta decay should be the same caused certain key experiments to be neglected, which slowed the pace of gathering the necessary information to come to the conclusion that new particles were at work and allowing a huge discovery (eventually leading to a Nobel prize in physics) to be made.
Science History Example of “Insight”: Being Unaware
Above, I just mentioned a good example of how being unaware affects scientific discovery—the accidental discovery of radioactive decay.
Becquerel would not have known about radioactive decay because he and other scientists had never observed it. So when he accidentally exposed a photographic plate by leaving it near a radioactive sample (producing a surprise image), he became aware of a new aspect of the natural world.
Of course, this was a discovery in and of itself. But more subtly, as I traced out in the previous example, knowing about radioactive decay and measuring its properties also became a next step in pursuing research into the existence of a new particle (in fact of many new particles). So becoming aware of radioactive decay opened up the knowledge pathway to later scientific discoveries.
By being unaware of the process of radioactive decay, physicists at that time were prevented from making new discoveries in many areas that rely on the decay process to become visible to experimental observation and investigation.
Science History Example of “Insight”: Feeling Vague
Lastly, a good example of how being vague can hinder scientific discovery, comes from what happened after physicists realized that the alpha decay and beta decay spectra were not the result of the same mechanism.
The best way to sum up a scientist realizing they had hit upon the problem of being too vague and lacking insight is to quote the words of the famous physicist Lise Meitner. When Meitner saw experimental confirmation of the beta decay spectra results she said, in a letter to fellow physicist Sir Charles Ellis:
“We have verified your results completely. It seems to me now that there can be absolutely no doubt that you were completely correct in assuming that beta radiations are primarily inhomogeneous. But I do not understand this result at all.” [Emphasis mine; as quoted in Franklin, p. 17.]
Meitner was an exceptional physicist. She had cranked through much of the theory. She was aware of all the experimental data. And she had contributed to the effort, by doing some of the experiments herself.
But with the acceptance of the beta decay spectrum, she hit a wall, as most physicists did, and realized that her understanding of the beta decay process was too vague to be able to create a coherent and consistent theory of what was really going on.
The vague understanding that physicists had at the time, of the details of beta decay, made it hard for them to successfully generate the insights necessary to discover the source of the anomalous behavior (a new particle).
So how can we put the definition of scientific insight I’ve given here to use and avoid some of these pitfalls and setbacks from science history?
Ways to add research activities targeted at insight:
Get clear on what you and others know for sure and put what you (don’t?) know in unfamiliar contexts and formats.
Some days we just want to think (or daydream!) about discovery, but other days we need to: Make. It. Happen.
So for this last section, I’d like to throw out a few ideas for how you can turn gaining insight into conscious research activities to help you pursue scientific discovery.
The most important thing to recognize in these ideas and this definition is that improving, honing, or strengthening your insight capacity will fundamentally strengthen your ability to recognize and integrate the three essential qualities of scientific discovery into your workflow. Insight is a lens through which you can hone your perspective on the world.
Activities to Help You Gain Insight
So, here’s a handy bullet list of activities that might help you gain insight:
Activities to fix “being unaware of something”: Ask yourself, “Am I missing information?” and “Do I recognize all the things I don’t know about my problem or question?” If the answer is yes to the first and no to the second, then you need to do some work in this area. Exploratory analyses are great to draw your attention to things you haven’t noticed. Make a graph and look for unexpected shapes in it. Put information in a table and look for empty entries you can’t fill in. Let someone who doesn’t know the topic ask you questions and note the ones you can’t answer. And the easiest trick—read more! Especially in places you don’t usually read.
Activities to fix “misperceiving something”: Ask yourself “Is it possible that what I believe to be true is wrong?” If yes, then you need to do some work in this area. Again the easy fix is—read more! Find out what other people have written about the topic that both agree and disagree with your beliefs. Another easy fix—listen to more people talk about the topic. Listen to seminars, TED talks, online courses, podcasts, speeches, and conversations. Other people will present things in a way that makes sense to them and may be a different perspective then you have of the topic. Also, try writing down a claims-reason-evidence chain (you can Google this idea from philosophy and argumentation). If your chain is faulty, weak, or broken, you will see it when you write it down.
Activities to fix “being vague about something”: Ask yourself, “Can I summarize the most important part of what I know in one minute or less and in one slide or less?” and “Does what I know explain at least 90% of the problem or question I am pursuing?” If it’s no to either question, then you need to do some work in this area. Approach your problem using the wrong method, on purpose, and learn why something doesn’t work. Create a mindmap, or bullet outline a state of the art or landscape analysis. Look for branches of the map and sub-headings of the lists that lack precise concepts or statements. Another option, sit down and write out one sentence defining something. Then create a list under it where you define every meaningful word using the form, “By <word in statement> I mean…” Work to be able to explain every part of your statement.
PUT IT IN ACTION:
If you find acronyms or sentences helpful for remembering or reminding yourself to do things then remember this tip for improving insight:
Always MUV your perspective toward better accuracy.
That will make you more insightful. MUV (pronounced like “move”) stands for the areas where you can improve that accuracy, if you Misperceive, are Unaware, or are Vague about something. It’s like the English expression “move the needle”. You are trying to move the needle on what you know (and the world knows!), by learning more and getting better at understanding the world every time you pursue discovery. So:
Become more insightful by improving the accuracy of your perspective on a question or problem.
Interesting Stuff Related to This Post
Ruben Laukkonen, Daniel Ingledew, and Jason Marcus Tangen, “Getting a Grip on Insight: An Embodied Measure of Aha! And Metacognition during Problem Solving,” preprint (PsyArXiv, May 28, 2018), https://doi.org/10.31234/osf.io/fyhwb.
Bernadette K. Cogswell, “Architecture of Discovery: Insight – How to define scientific ‘insight’ so that you can become an insightful scientist,” The Insightful Scientist Blog, July 12, 2020, https://insightfulscientist.com/blog/2020/architecture-of-discovery-insight.
[Page feature photo: A series of lenses grace the display case in a sunglass shop in Barcelona, Spain. Photo by Michel Oeler on Unsplash.]
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.]
First of all, I hope everyone is staying safe. I’m sending positive mojo out to all of my readers.
I just wanted to post a little update and a few thoughts on the future direction of this website.
I haven’t written for some months now. I am pleased to see that the second blog post written in a “tutorial” style, entitled “Good Things Come in Threes“, appears to have resonated with a broad spectrum of readers. It has now been viewed 298 times (come on 300!), across at least 20 countries and 6 continents. It has also somewhat miraculously begun to appear among the top ten search hits on Google for the phrase “good things come in threes”.
I’m honored that you each take the time to visit the site. And I truly, deeply hope that that post has helped fellow researchers, scientists, and discoverers out there to keep making progress.
I myself am in the process of re-training to re-enter the job market (hopefully early Fall 2020) as a data scientist. I was fortunately already unemployed and had hunkered down to re-train myself by spending a significant portion of my time in online courses (thank goodness for things like Coursera) before global events hit.
So I have not suffered as hard as many. My heart goes out to you all.
In the mean time, I have not forgotten (nor will I ever) that discovery awaits!
Discovery still awaits…
Around the last time I posted I had hoped to be able to put together a paper for submission laying out the framework I am using to synthesize scientific discovery related strategies, tools, and behaviors.
Unfortunately, I have not been able to access a sufficient number of historical case studies to get a publication worthy paper together yet (no access to many of the academic papers needed, no money left in savings to buy books out of pocket, plus in-person library access for interlibrary loan isn’t feasible at the moment).
However, I am finding that much of the framework I have conceived of for scientific discovery is useful in the field of data science (a profession devoted to the expert gathering, handling, and manipulation of large quantities of data to discover trends and insights).
I realize now, of course it applies! Scientific discovery covers all the sciences. Sometimes when you’re a novice it’s easy to lose sight of the big picture.
Since people seem to have found some of the processes I use useful (if I take the visitor interest in the post “Good Things Come in Threes” as an indicator) I have decided to go ahead and create a series of posts detailing my scientific discovery framework in the hopes that it will help you now (while I slog toward getting the paper done in the future).
These upcoming posts will cover my working version of the scientific discovery process, discovery categories, discovery classes, and how I’m using those definitions to guide day-to-day work.
You will also start to see things being illustrated with data science examples (rather than neutrino physics ones) as this is my current focus. Also, I have been working on building tools to help me become a more insightful data scientist and I will attempt to share these (including a simple trick called the “Question Pinwheel” to help generate more thoughtful starting research questions and a process manual for running a data science project from start to finish). It’s my hope that through this first data science pre-test I can lay the foundation to adapt it to a more general science template for how to move through my work with a more discovery-centered focus. Hopefully you will find some of my tools and ideas helpful too.
So stay tuned!
…and we should all dream brave.
I’d like to end with a sentimental reflection:
When I was about 12 years old I got into my first (and only) fight at school.
It was during an art class. The teacher stepped out for quite some time. A well-known school bully, recently transferred from another school, came up to me when I got up to wash some paint brushes out in the sink.
He said he had heard a rumor that I wanted to be a physicist. As a kid, that was considered such an odd thing that people often added it to my name when they introduced me: “Have you met yet? This is Bernadette-who-wants-to-be-a-physicist-when-she-grows-up.”
The bully asked me, “What’s a physicist?”
I explained it to him. He stared at me for a while, then crossed his arms, and told me that if my father had any sense he would beat me until I knew I was just supposed to grow up and cook for my family.
I took offense and let my anger get the better of me. The bully and I ended up in a full-on fist fight.
I lost.
He knocked me to the ground, sat on me, and punched me until someone told him the teacher was coming back (the other students stayed out of it entirely). Later on, in a school bathroom, I confided to my best friend that I was just relieved he hadn’t punched me in the face or I would have gotten a nosebleed and my Mom would have found out (cat’s out of the bag now).
I could draw a lot of things out of this memory for you. But I only care about one thing right now:
That moment showed me that what I thought was just being a little kid, with a little kid’s dream, was actually dreaming brave.
I eventually became a physicist, fulfilling the little kid’s dream I had from age 5. I also became a writer. And now I’m working on becoming a data scientist.
Each of us is a once-in-a-universe-occurence.
You are a powerful thinking machine, an un-repeatable collection of experiences and thoughts and actions.
No matter what happens, always use a part of yourself to dream brave. The course of progress depends on it.
For now my dream is to use everything I’ve learned about scientific discovery to get up to speed quickly as a data scientist and perhaps be able to contribute to some of the open source data science projects trying to gather data and insights to help combat Covid-19, the novel coronavirus. (For example, Google is hosting open source projects on Kaggle that could use researchers with backgrounds in data, statistics, and computation, no medical degree required. Feel free to check it out if you think you might have something to contribute.)
The discoveries we need are out there waiting for us. But we all know that discoveries can only be made by the minds that pursue them.
So take this bit of advice from Bernadette-who-wants-to-become-an-insightful-scientist-when-she-grows-up:
Stay safe. And dream brave. We need all the world-changing discoveries you’re going to make.
Bernadette K. Cogswell, “Dream Brave – On the future direction of the website and the global current moment”, The Insightful Scientist Blog, June 4, 2020, https://insightfulscientist.com/blog/2020/dream-brave.
[Page feature photo: A tattoo of the world on the arm of a man in Dayton, Ohio, U.S.A. Photo by Don Ross III on Unsplash.]
On why skills give the most value of all the elements in a scientist’s repertoire.
This is the fourth and final post in a four-part series. In an older post I defined “skills” as “procedures you use to channel caring into doing.” You can view that post here.
After I finished my first college degree I thought I wanted to be a screenwriter.
Action movies and sci-fi were just the thing to house my imagination and love of reading. Or so I thought.
I had just moved to a new state and had no idea where to start. And I had just finished a degree in psychology, where I studied the interplay of children’s acculturation with how they cope with school stress.
Writing Likert-type surveys taught me nothing about how to write a three-part story or how to hit the emotional plot points in a “hero’s arc.” And I couldn’t find a screenwriting class. (I’m old enough that back then the internet didn’t provide these things ready made in a MOOC).
So I signed up for the cheapest writing class I could find at a local university, while working three part-time jobs to make ends meet.
The class was called “Introduction to Writing Microfiction.”
Microfiction is where you write a short story in a minimal number of words, usually a few hundred words or less. I don’t remember a darn thing about how to write good microfiction after taking that class. But I do remember the notebook the instructor taught us to keep.
She advised us to buy a notebook we loved and a pen or pencil to match. On the cover we wrote the number of that notebook. For me that was “No. 1”. We earmarked the first 2 pages for a table of contents, to fill in as the notebook filled up. And after that we wrote anything and everything in it.
Our writing instructor had us do timed assignments in class, written in our notebook. I took class notes, in my notebook. Then I started scribbling physics ideas in my notebook too. (I had started out college in physics, got discouraged, ran out of money for college, and finally switched to psychology to graduate early. But physics still echoed in my heart.)
In the early years, and through my writing degree, I called my notebooks “No. #” and numbered them in a continuous chain.
I still keep notebooks to this day. Though they’ve gone through name changes (for a while in my Ph.D. I called them “Research Notebook #” to fit in) they are still filled up using the same basic skill set I was taught in my microfiction writing class.
When I graduated my Ph.D. program and moved on to a postdoc, I felt a little more constrained by the academic paradigm and took to calling my notebooks “Spark #”.
Then I found out about Bullet Journaling (I use a loose hybrid version of it now) and took to calling them “BuJo #”. But I always kept the same continuous numbering. I’m on Bujo-38 right now.
This art of writing an assorted collection of things in a notebook is a skill, which I learned that day in my first microfiction lecture. And it was probably the wisest investment I made of anything I spent my hard earned part-time job money on during that decade.
Keeping a notebook is skill, not just an activity. And it illustrates my point for this last part of my series on necessary components of a scientist’s repertoire (especially when you passionately want to make a discovery):
Adding skills to your repertoire gives the best return on investment of any single element of your scientist’s repertoire.
From a first principles perspective:
Skills are invaluable in your scientist’s repertoire because they can be put to many uses.
In principle, my reasoning is simple: skills are generalizable elements of one’s scientific repertoire, plus they are often low tech, easy to acquire, and they have a broad application.
I may have learned the skill of keeping an “ideas notebook” in a writing class, but I went on to use it when working in physics and in nonproliferation policy.
(I’ve also used the skill of keeping a notebook for personal projects like moving, changing jobs, and adapting my lifestyle routines.)
It’s important to distinguish between activities and skills.
Activities are things like writing certain types of content in your research notebook (maybe free writing, keeping a log of experimental trials, or keeping a list of concepts or sources to look up).
But a skill is knowing how to keep a research notebook so that it achieves a certain purpose (such as filled with mindmaps, free writes, and sketchnotes to engender creative fusion of received ideas or filled with dates, steps, prototype sketches, and outcomes for patent applications).
Skills are a nice hybrid between activities and mindset: they are where thinking meets doing, but at a higher level than just activities. As such, skills are invaluable in a scientist’s repertoire and skills ensure that the activities you undertake are focused and well-executed.
Practice one skill (like keeping a notebook), combined with the proper resources (e.g., paper, pen or pencil), and you can eventually put it to a hundred uses.
From an applied perspective:
Adding skills to your scientist’s repertoire requires practice and feedback.
So, being able to acquire skills, which can then be applied to broad range of problems, is a tremendous value added to your repertoire.
I think the best way to acquire skills is through a combination of received instruction (i.e., training and feedback from a more skilled individual) and practice to gain mastery over the small elements of a skill.
Of course, that’s not always possible. Just look at scientific discovery. Nearly all science training programs train you in how to conduct experiments, utilize math, and run analyses. But none actually teaches a module specifically on how to combine all those ingredients (and some other things) to actually make a scientific discovery!
In cases where you know a skill is needed, but you can’t receive instruction then I think you have to do the best you can to instruct yourself. The most important part of being self-taught is providing yourself ways and set times to give yourself feedback. And you always, ALWAYS, need practice or you won’t gain a skill.
For example, let’s go back to the case of the research, or ideas, notebook I’ve been talking about. If you don’t have someone to instruct you in how to create or use one, then you might try teaching yourself.
Set up a notebook (i.e., put in a spot for a table of contents or an index to track the content you add to the notebook, add page numbers, and add a notebook title), think about a few ways to use it, pick one way to try, and then test it out (for a minimum of 7 days and a maximum of 30 days, assuming daily use).
Most importantly, provide yourself regular feedback sessions on whether or not your notebook is generating the kinds of results you want. Ask yourself questions and assess outcomes:
Are you coming up with ideas more frequently (e.g., daily instead of monthly or weekly)?
Are more of your ideas getting tested (e.g., are you generating and running quick tables of numbers, small experiments, or bits of code to try the ideas out)?
Are you able to remember to perform certain tasks more regularly and more diligently after tracking them in a notebook (e.g., do you systematically track changes you make to every iteration of an experiment, piece of code, website, etc. when you are fine-tuning or checking the response)?
Check in with yourself to see if things are working or if they need to be adapted or practiced more. I think the key part is to be aware of the process and to consciously monitor your process as you practice the skill.
Don’t just focus on the outcome of a skill, process is crucial too. Process is the piece that is generalizable, even more so than the outcome. You want to learn how to add numbers, not just learn how to add 2+3 and 6+8.
Skills can be hard work to acquire. As I said, skills are gained through practice—practice, practice, practice, and more practice eventually adds them to your repertoire.
Practice takes skills from “things you have to think about” to “things you do automatically” and ensures that you perform consistently, and with precision, every time you execute the skill.
But skills, once the basics are learned, can be fine-tuned, and used frequently to improve them. Plus, you can always gain more finesse in a skill by seeking feedback from a more practiced expert.
Lastly, skills are not a one-off that you acquire when you are young, or in school, or a training program, and then don’t have to worry about anymore. We’re striving to be insightful scientists, not lazy scientists. Whether we are early-, mid-, or late-career, we should be adding skills to our repertoire. Discoveries won’t come looking for you, you go looking for them. And you better care enough to have the skills to do it at any age and stage.
By analogy:
Skills are like language; you can do more with it than you can without it.
The analogy I think of for the role of skills in the scientist’s repertoire is the following:
Language skills let you say, orchestrate, or participate in events, beyond just grammar and vocabulary.
Learning vocabulary and grammar are language activities. But speaking, reading, and writing are language skills that pay you back a thousand fold. They can be put to almost any use.
Skills in the scientist’s repertoire are just the same. The value of skills in your scientist’s repertoire cannot be overstated, only underestimated.
From a holistic perspective:
You can synchronize or learn from others with companion skills.
I’ve talked a lot in the previous three posts about making use of teams or communities, a body of people with diverse repertoires working in harmony, when I mention the holistic perspective relative to the scientist’s repertoire.
Again, it’s all true here, but with a nice added dimension. I call them “companion skills.”
Maybe you are excellent at generating and manipulating mental ideas quickly. But you struggle to articulate them into testable form. Someone else might have the perfect companion skills: they are able to build mock-ups, generate quick-and-dirty code, or do back-of-the-envelope calculations in their sleep.
In fact, some discoveries have relied on this kind of companion skills team (Einstein’s work with his mathematician friend Marcel Grossman to produce general relativity is a famous example).
Working with someone who has a companion skill is a chance to move discovery along at a faster pace.
But it is also a chance to watch the skill in action and to learn by observation.
Interestingly enough, we learn better from watching the mistakes of others than from making our own mistakes. So even if your skills companion gets it wrong, it will be a valuable opportunity to become a more skilled discoverer yourself.
Usually when we think about “skills” in science we think about math. Or maybe statistics, or writing code, or using special high-tech hardware.
But more subtle skills are key too. The ability to use conceptual or structural analogies is a good example. And the ability to generate and manipulate mental models is another subtle skill.
We should take every opportunity we get to strengthen our repertoires, across all four themes (activities, mindset, knowledge, and skills).
But if there “aren’t enough hours in the day” then I think skills are the most fruitful place to start.
The more skilled you become, the more conversant you will be in the art of discovery.
Bernadette K. Cogswell, “Elements of the Scientist’s Repertoire, Part 4 of 4: Skills – On why skills give the most value of all the elements in a scientist’s repertoire”, The Insightful Scientist Blog, December 27, 2019, https://insightfulscientist.com/blog/2019/elements-of-the-scientists-repertoire-part-4-of-4-skills.
[Page feature photo: A student artfully displays books to photograph during her roadtrip. Photo by Clarissa Watson on Unsplash.]
On why knowledge is the easiest ingredient to add to a scientist’s repertoire.
This is the third post in a four-part series. In an older post I defined “knowledge” as “recognizing what you don’t know.” You can view that post here.
How can a black woman write from the perspective of a white man who would have lived 250 years before she was born?
When I was writing my Master’s in English (with a concentration in creative writing) that question put me in a quandary.
I had switched from physics to psychology and then ended up in creative writing (before ending up back in physics). When I had to decide the kind of fiction writing I wanted to specialize in I went for what I thought would be the obvious choice: something I love to read and something my science background would make me good at.
You’re probably guessing I went for science fiction. You’d be wrong.
I chose historical fiction.
I figured historical fiction also required excellent research skills. So, as a middle-class, 30-something African American woman living in the 20th century, I had to figure out how to write from the perspective of my chosen main character, who was a 20-something Caucasian man from a wealthy background living in the 18th century.
To do that I needed information (aka knowledge) about that time and place. Lots of information.
In general, trying to pick up a perspective to use is always like this. It often requires knowledge. Last week I talked about how mindset is such a crucial part of the scientist’s repertoire. Knowledge is a key part of putting that mindset (or perspective) to good use.
But today I’m going to argue that knowledge is actually the easiest ingredient to add to your scientist’s repertoire.
From a first principles perspective:
Internet access lets you easily add other people’s knowledge to your science repertoire.
I know many of my neutrino phenomenology colleagues would argue with my claim right away. Knowledge can be incredibly hard, and expensive, to come by in the current era of big data, big networks, and big experiments.
For example, many neutrino phenomenology studies (scientific investigations of what experiments can tell us about the properties of fundamental particles called neutrinos) can take literally years before the highly sensitive detectors can detect even a handful (maybe five to twenty) of the desired particle interactions we are trying to observe.
Another good example of hard to come by knowledge is longitudinal studies in health:
It’s hard to get a large group of participants. When you get them, it’s hard to keep them: they flake out, they stop adhering to the study requirements, or worst case scenario they die before the study ends. And it’s hard to track all those participants over long enough time scales, like decades, to be able to draw correlations about how certain life choices or environmental exposures affect health outcomes.
I don’t disagree that some knowledge is not easy to get.
But relative to mindset, which can be invisible and is strongly ingrained, or relative to skills or activities, which take tremendous training and practice to implement, knowledge is relatively easy to pick up and change.
That’s because my thinking is focused on an individual’s knowledge, not the sum total of all knowledge. (On InSci I focus on how the individual can improve their scientific discovery practices.)
For an individual, read a line of text here or there and… Boom!
Now you know that male otters use communal toilet spaces. The knowledge base in your scientist’s repertoire just increased.
Partly this has to do with the vast knowledge reserve we have today, namely the internet. It’s easier than ever to call up resources like public databases, personal recollections, peer-review pieces, and conversations or talks on just about any subject. All of this is fodder for scientific investigation, if handled properly.
Additionally, knowledge is easier to add to your repertoire because when you tell someone, like a funder or investor, that you need to get more knowledge they usually agree with you (assuming the topic is important to them).
If you tell them you want funding to change your mindset, or to get trained in certain skills, or to have a chance to participate in certain activities, it can be a harder sell. Just look at the amount of money put into knowledge-making machines, like the LHC at CERN, versus the amount put into skill-building modules, like learning to conduct literature reviews, at your average university. It’s a fortune versus a pittance.
(As a sad side note: I was required to take an entire one semester course just on how to conduct a decent literature review for my Master’s in English. But it was never discussed in my coursework for my degrees in Psychology or Physics. No wonder my Physics Ph.D. advisor always marveled that I was able to find obscure or hard to find items. After going through four months of rigorous teacher feedback on how to find every known copy of an American slave account published between 1600 and 1800, anywhere in the world, finding an English translation of an old Russian neutrino paper seemed manageable.)
Some might say that knowledge may be acquired relatively easy, but disseminating it is still hard, especially in today’s world of proprietary mechanisms like patents and trade secrets. And then there are people who are just miserly with giving out information.
Perhaps. But if the knowledge exists, and it’s in written or other recorded form, then history suggests that the knowledge always eventually enters the public domain.
Sometimes that may be in 5 years, other times in 100 years.
But either way, the knowledge belonging to the one becomes the knowledge belonging to the many, in the long term.
Compare that to skills or activities. It’s hard to find someone who can teach you the earliest ways of the cave painters, because that craftsmanship has died out. But our knowledge that cave paintings exist and of some of their properties lives on.
Arguably, the fact that knowledge eventually “goes public” doesn’t guarantee that you personally can add it to your repertoire, but this depends on what knowledge and what time frame you’re interested in.
Right now you have access to knowledge that would have been closely guarded two hundred years ago within a wealthy elite. But today you are barred from knowledge that is closely guarded within the corporate and government spheres.
By analogy:
Knowledge is to science what ingredients are to cooking.
Still, overall, especially when it comes to science, everyone shares the same agenda. The acquisition of knowledge is paramount. Science is the business of learning new knowledge about our universe.
It’s sort of like cooking:
Everyone agrees that ingredients (~ knowledge) like herbs, spices, vegetables, etc., are crucial to succeeding at cooking (~ science). You may not have access to the same cooking ingredients as somebody else. But everyone sees the necessity of having at least some ingredients if you want to cook. And most everybody, everywhere, has access to some cooking ingredients, at least some of the time.
From a holistic perspective:
Most groups agree that knowledge is powerful so they value its pursuit.
In this way we share a common understanding that knowledge is a crucial science ingredient and, hence, many people are working to expand the collective knowledge domain. That’s why the internet is so full of facts (bits of knowledge). And that web trove of data grants you a way to add it to your own repertoire stash.
It’s easy to find experts in many domains, to find resources of every type and description, and to find attempts to capture, share, interpret, or analyze knowledge. It’s a common theme that knowledge is a powerful tool. You don’t have to do much convincing for people to be in synergy on this point.
And why do groups value science and knowledge so much?
Because you can put it to various uses. Every group has its own use case in mind. And having the knowledge makes implementing those use cases possible.
From an applied perspective:
Cultivate credible and thoughtful sources to add knowledge to your science repertoire.
This brings me to my last point. Because we are in such an information-rich environment in the current era, from a practical standpoint, knowledge is very easy to add to your repertoire.
You just need to develop a mental, or written, collection of credible and valuable sources and refer to them often.
This can be people with field expertise or databases with written, visual, or auditory records.
It might be experiments or conferences where you can be exposed to fresh knowledge.
It might be a timeless book or essay, or a person who is a tremendous conversationalist.
You just need to be mindful that your knowledge base continues to grow over time. It can’t stagnate.
As a discoverer your job is to find new causal links and new meaning. Often times old causes and a lack of meaning are due to insufficient evidence to suggest the right explanation. So getting more evidence in the form of new knowledge is key.
Also, knowing about available activities and mindsets is a form of knowledge. Again, it’s important to have broad experience with new ones from credible and valuable sources.
So while some might argue that gaining new knowledge is the most time-consuming, costly, and resource-intensive part of expanding a scientist’s repertoire, despite all these hardships, I still think it’s the easiest to accumulate, relative to the other three themes.
There are many cooks in the science kitchen, willing to throw in a dash of this and a dash of that to see what new explanation they can cook up.
With so much enthusiasm to contribute to science going around, it’s easy to find people capable of bringing new ingredients to the table.
After all, scientific discovery is partly about spicing things up and bringing a new flavor of thought to a much chewed over idea.
Gorman, James. “A River Otter’s Hot Spot? The Latrine.” The New York Times, September 19, 2016, sec. Science. https://www.nytimes.com/2016/09/20/science/river-otters-socialize-at-the-latrine.html.
Bernadette K. Cogswell, “Elements of the Scientist’s Repertoire, Part 3 of 4: Knowledge – On why knowledge is the easiest ingredient to add to a scientist’s repertoire”, The Insightful Scientist Blog, December 20, 2019, https://insightfulscientist.com/blog/2019/elements-of-the-scientists-repertoire-part-3-of-4-knowledge.
[Page feature photo: Dried beans and spices for sale at a market in Vietnam. Photo by v2osk on Unsplash.]
On why mindset is the most effortful part of a scientist’s repertoire.
This is the second post in a four-part series. In an older post I defined “mindset” as “caring enough to find out what you don’t know”. You can view that post here.
I was reminded of mindset while cleaning up my old archived files (a.k.a., my digital junk drawers) when I found an article in the Guardian that I read earlier this year (link below).
In the article, writer and professor of English and Environmental Humanities, Vybarr Cregan-Reid, argued that social challenges supported by inactivity, like obesity and joint problems, won’t be solved by gym exercise alone. We need to make our days more labor intensive.
However, as Cregan-Reid pointed out, as a society we have moved toward a mindset of making things less labor intensive. As a result we have engineered activity out of our day.
By draining the labor out of our day, think of the following examples:
Turning on your TV with voice control or remote instead of getting up to walk over to the TV set and turn it on and change the channels.
Using pre-programmed electronics that turn on by themselves at a set time (coffee pots, house lights, automatic sprinkler systems) and don’t require that we get up or physically go to the location to initiate the sequence ourselves.
Think talking to Alexa, Siri, Cortana, or Google instead of a walk to a car or bus and walking around a library, eventually handling the weight of an encyclopedia.
Now, I’m not advocating a return to the dark ages or old school methods. But Cregan-Reid has a marvelous point.
Total up the number of things we’ve made less labor intensive and you’ve got a fundamental deficit of motion that cannot be compensated by 30 mins to 1 hour a day in the gym. You’ve automated yourself into a sedentary corner.
The article caught me at a good time as I was just then going through a health and wellness reboot and this seemed like a radical mindset shift to me.
What if, in my routines, programs, and products, I tried to add a little labor intensiveness back in, rather than trying to strip, streamline, and fast track labor out?
Instead of auto-magical, what if I went labor-loaded?
If you’re reading this you’re probably thinking, “No way. I don’t have time, energy, money, enough working hours, enough quiet time, etc. to make that work.”
It strikes you as so radical (like it did me when I read the Guardian article) because it’s a mindset shift.
From a first principles perspective:
It’s hard to add mindsets to your repertoire because mindset can be invisible.
The idea of automating things and making them more efficient, faster, and less effortful is becoming so ubiquitous that it’s a good example of why, in principle, I think mindset is the hardest part of our repertoire to manipulate.
Mindset, and all the attitudes, values, beliefs, and narratives that go with it, tends to be invisible.
Mindset is ingrained, indoctrinated, assumed, and shared.
Without broad experience and exposure to many mindsets, you often don’t even realize you hold a particular mindset, or I should say that you subscribe to a particular mindset. It’s like a monthly mental cost you don’t even realize you’re paying. It just comes out, dare I say it, auto-magically.
And because often times everyone else in the groups we associate with (colleagues, citizens, family, friends, etc.) share the same mindset, everybody pays the cost and so nobody perceives it as costly. It’s just the done thing.
For example, in her book The Culture Map, Erin Meyer, who studies the intersection of culture and business, describes three different modes of persuasion that most countries in the world can be divided up into:
Cultural Persuasion Mode 1 — “Principles-first: Individuals have been trained to first develop the theory or complex concept before presenting a fact, statement, or opinion.” [Meyer, Culture Map, p. 96, Fig. 3.1]
Cultural Persuasion Mode 2 — “Applications-first: Individuals are trained to begin with a fact, statement, or opinion and later add concepts to back up or explain the conclusion as necessary.” [Meyer, Culture Map, p. 96, Fig. 3.1]
Cultural Persuasion Mode 3 — Holistic: Individuals have been trained to “give more attention to [the background] and to the links between [the background] and the central figures” [p. 109] and consider the interdependencies, interconnectedness [p. 110], and surrounding impact [p. 112]. [Meyer, Culture Map]
Can you imagine the difference this will produce in a group of scientists debating whether or not a particular finding verifies a scientific discovery? Or even how it will affect convincing them to pursue a certain scientific problem? Or cause an over-reliance on one methodology, like theory, case studies, or confirmatory rather than exploratory research?
However, in order to make discoveries, you often have to shift your perspective to see new connections, perceive a missing assumption, adapt an assumption, or discard an idea.
These assumptions and perspectives lurk in the realm of mindset, and like all mindset pieces, they are, therefore, hard to notice. And as a result even harder to shift as necessary.
Depending on the culture you were raised in, now live in, or admire, it can be even harder to recognize that your mindset was partly acquired through acculturation (socialization via exposure to a given culture) and not evaluation (consciously and actively selecting a fruitful alternative).
From an applied perspective:
There is a mental burden when you vary the repertoire mindset applied to a problem.
So how would you put in the necessary effort, in practice, to expand the range of available mindsets in your scientist’s repertoire?
Well, mindset is basically about the fundamental perspective, point of view, or thinking framework you apply to a particular science problem or exploration you are engaged in.
To expand your repertoire you have to get better at looking at the same thing many different ways.
So as far as I can tell, all the research and my experience seem to point to three steps: (1) become aware of the mindset you have, (2) become aware of alternative mindsets you could have, (3) practice shifting your mindset.
Repetition and practice are key. It helps if you have someone to give you external feedback. It also helps if you can use cues and triggers to remind you when you are slipping into or out of a particular mindset.
And expect it to feel hard (e.g., fatiguing) and uncomfortable (e.g., hard to remember). Changing out of your default mindset will take more attention and focus than you usually need.
The goal is not to shift your mindset permanently from one focus to another. The goal is to acquire the ability to shift your mindset repeatedly, as the need arises.
The need will often strike when you are confronted with novel data, anomalous or insufficient evidence, and/or are working in a team that holds very different beliefs about what constitute valid arguments, reasons, and evidence, than you do. You encounter the last situation when you work in a group where everyone is from the same topical field, but different countries, or on a team where people from different fields work together.
From a holistic perspective:
Finding a balanced mindset repertoire for yourself or a group takes time.
This brings me to a last point about why mindset is so tough to manage in the scientist’s repertoire.
It’s called working in groups.
Homogeneous groups tend to share the same mindset. So, it can be difficult to perceive alternate mindsets when everyone is telling you the same thing.
Also, if you do choose to leverage a different mindset in order to achieve some specific discovery goal, you are likely to get more pushback. It’s as if groups tend toward physical equilibrium just like physical systems do. If there is a high degree of difference across the system, it wants to smear itself out until there’s little difference here or there.
On the flip side, if you are working in a heterogeneous group, responding to all the different mindsets can be hard to manage!
The trick is to understand and respect the role that each mindset plays in moving your project forward. And then have a strong core vision that keeps it all together toward a shared purpose (and here on InSci that purpose is always scientific discovery).
As a bonus, seeing a new mindset gives you an opportunity to add it to your repertoire, and having a team member with that mindset gives you an automatic source for feedback.
By analogy:
Acquiring and using a mindset is like an online subscription.
Now back to the analogy I used earlier, about mindset being like a monthly fee.
When you select a mindset to follow, let’s say in looking at a science problem, it incurs a cost as well as confers some value. You’ll be better able to see some things, but you’ll lose sight of others.
A particular discovery mindset should never be subscribed to as all or nothing. It should be assessed at regular intervals, just like any subscription service. When the cost it incurs outweighs the benefit or service it brings to you or your community, then it’s time to set it aside and pick up something better suited.
Knowing the range of available mindsets is part of the challenge. And deftly switching between mindsets is even tougher.
But the advantage gained from shutting off the autopilot and building your “embrace the effort” mindset is worth every coin you put into subscribing to it.
Why wait for history to provide the right person, with the right mindset, at the right time to make a discovery, when you can jump in and seek out the mindset that will get the job done, by you, right now?
Bernadette K. Cogswell, “Elements of a Scientist’s Repertoire, Part 2 of 4: Mindset – On why mindset is the most effortful part of a scientist’s repertoire”, The Insightful Scientist Blog, December 13, 2019, https://insightfulscientist.com/blog/2019/elements-of-the-scientists-repertoire-part-2-of-4-mindset.
[Page feature photo: A small thinking figure sits with all its coins on the table. Photo by 𝓴𝓘𝓡𝓚 𝕝𝔸𝕀 𝕞𝔸ℕ 𝕟𝕌ℕ𝔾 on Unsplash.]
Elements of the Scientist’s Repertoire: Activities
On why activities should be the largest part of a scientist’s repertoire.
This is the first post in a four-part series. In an older post I defined “activities” as “tasks you complete to finish skilled procedures”. You can view that post here.
One day when I was in high school an English teacher gave our entire class an odd assignment:
She told us to go outside, lay down on the grass around the school, and stare at the sky.
After about 15 minutes of this impromptu freedom she rounded us up (or woke some up from a nap) and took us back inside.
Once we were back at our desks she asked us to “free write” for 25 minutes. We had to write as fast as we could, without stopping, thinking, or stopping writing. We wrote whatever came into our minds.
That was the first time I ever heard about free writing, and it created a lifelong habit.
Writers use free writing all the time, as part of their writer’s repertoire. It’s a way to unstick lose thoughts, capture fleeting impressions, and allow new associations to spring to mind.
It’s also a regular practice that keeps writers’ craftsmanship fresh, nimble, responsive, and consistent.
Do a little free writing every day and writing that novel or screenplay won’t seem so intimidating.
So what about scientists? Do scientists need these kinds of activities in their repertoire?
The answer is yes.
And the most important thing is not what activities you have, but how many activities you have in your repertoire.
From a first principles perspective:
Reacting to new data requires a broad spectrum of activities in your repertoire.
To see why that might be true stop and think about this for a moment:
Making a scientific discovery is an unpredictable venture.
It may require millions of dollars, years to construct, design, and approve, and big data (like the DUNE neutrino experiment under construction, see link below).
Or it may be as simple as accidentally letting something sit around somewhere unusual (like discovering the microwave by melting a candy bar in your pocket, see true story in link below).
What is most difficult to control, in these varied moments of either precision engineered insight or haphazard serendipity, is the resources needed to bring everything together.
Life happens. Accidents happen. Windfalls happen.
You never know what you are going to encounter.
Against the backdrop of life scientists always contend with a built-in handicap—we know we don’t have all the evidence for a better model.
If we did, we’d already recognize and know the model. But instead, we don’t have enough information, so we have to discover the model.
Discovering the missing evidence, or ideas, or logic to get to this better model requires lots of activities, because activities are like tools in a toolbox when you’re making repairs.
We know our picture of the world is naturally broken, because we don’t have enough information.
Therefore, we need to go in and repair it.
But, like a plumber visiting someone’s house, you can’t possibly know precisely what’s broken, or what you’ll need to fix it, until you see it in person.
This seeing it in person is what we call new observations.
Sometimes, you have correctly guessed the problem with your existing model and diagnosed the solution beforehand (like the discovery of general relativity and the bending of light around stars).
But other times you’re caught off guard, an anomaly strikes, and you have to scramble to find the right tools to fix your model (like the discovery that neutrino particles can change a particle property called flavor as they travel).
Those “fixes” are the activities in your repertoire. Have too few, and you’ll be poorly equipped for the job.
From a holistic perspective:
More activities in your repertoire makes better use of resources.
It’s also best to have a lot of activities in your repertoire because it makes the most of the community of fellow scientists and supporters you have around you.
“Many hands make light work,” is a popular expression.
By having many activities, you can divide up the load and split it among many people, making something that might take one scientist a career or lifetime to do, possible within ten years or less.
Having a collection of experts with varied talents at your disposal also allows you to divvy up the activities in efficient (by expertise) or interesting (by non-expertise) ways.
This can bring fresh ideas to stuck agendas, and greater speed to stagnant ones. A different person performing the same activity in a slightly different way has a way of infusing momentum into a situation. It’s like a little symphony of activity and industry being carried out one scientific project at a time.
Also, perhaps you don’t have the resources at all to carry out one activity.
Having alternative activities in your repertoire, which serve the same function but use the resources you’ve got, can prevent you from being blocked.
From an applied perspective:
Activities can be added to your repertoire through daily experience.
So in practice, what are these activities and how do we acquire more of them?
Well, my little story at the beginning is an example of an activity that applies just as well to scientists as to writers.
Free writing is a good way for anyone to get ideas out of their head. This makes your ideas less abstract and more concrete.
Seeing your ideas in writing also lets you evaluate them, adapt them, expand them, reject them, or confirm them.
So activities, like free writing, are the mundane, everyday tasks that fill the time and make the business of discovery happen.
Activities are what you do to fill the hours and minutes that you sit at a desk, work in a lab, lie on the grass and stare at the sky, or any other moment of your discovery path.
Other activities that appear in a standard scientist’s repertoire include tasks like
performing literature reviews,
visualizing new models,
honing working definitions of things to test in the lab,
sifting through analogies to find the one that best highlights an important feature of a phenomena,
managing a reference bibliography,
creating a project time management tool,
brainstorming, and so many more.
Activities affect every outcome and trajectory of your discovery path.
The more activities you have in your repertoire the better. Because you never know what you will need. And some activities are quite specific to only certain use cases.
How do we add activities to our repertoire?
Good old-fashioned experience. Try out new activities every day.
See someone else do it and you can learn it.
Realize you need it and you can invent it.
Read about it and you can adapt it.
Luckily, you don’t need lots of memory capacity for activities. You just need awareness. So the more activities in your repertoire the merrier.
By analogy:
A scientist’s repertoire activities are like events on a travel itinerary.
That activities in a scientist’s repertoire are like activities in a travel itinerary is an even better analogy than the one I used earlier with the plumber.
Activities will vary depending on what territory you are going to explore and why you are going (i.e., the purpose of your “trip”).
If you wander into a new or familiar topic just to be inspired for an hour or so then perhaps activities like pondering, walking, reading, conversing, or sketching will be on your agenda.
But if you are journeying deep into the heart of undiscovered territory, a place with no roads to travel, no lights to guide your way, and no maps to tell you where you are, then your “itinerary” will look very different.
You will need to engage in careful activities, like logging your movements, photographing and observing the territory as you go, and scrutinizing what your observations mean about where you are headed.
And there will be plenty of practical activities to get done too, like making sure your tools are maintained and respected so they are ready for heavy use. (I’m looking at all those poor messy archived folders, overflowing email inboxes, and derelict scraps of “ideas to get back to” tumbling out of nooks, crannies, boxes, and folders you have lying around. Respect your tools.)
I have begun collecting a list of activities that apply to scientific discovery. I hope to be able to synthesize them into a cohesive framework I can share.
As we each follow where the data lead, like a compass aligned to true north, activities empower each and every step we take.
By building up a better picture of all the activities filling a scientist’s repertoire and knowing which ones are most useful for a discovery trip, you and I can better fill our days with meaningful industry instead of misplaced busywork.
Bernadette K. Cogswell, “Elements of a Scientist’s Repertoire, Part 1 of 4: Activities – On why activities should be the largest part of a scientist’s repertoire”, The Insightful Scientist Blog, December 6, 2019, https://insightfulscientist.com/blog/2019/elements-of-the-scientists-repertoire-part-1-of-4-activities.
[Page feature photo:Beautiful shallow focus photography of a compass on a wooden table. Photo by Jordan Madrid 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.]
Many of us believe we struggle because we can’t come up with ideas.
My new opinion is that having “breakthroughs” is not the reason why we struggle with scientific discovery. Knowing what to do after you’ve had a breakthrough is where you’ll have challenges.
I have come across people who self-identify with one of two camps when it comes to “coming up with ideas”:
One camp believes it is “creative” and is good at coming up with ideas.
This creativity may be perceived as labor intensive (“I need a lot of time to think”), as idiosyncratic (“I only do my best work when I work after midnight while listening to songs from the musical “South Pacific” and writing while standing at the kitchen counter”), or as mystical (“Things just come to me when I dream, or they pop into my head in the shower”).
The other camp believes it is “not creative” and will not be able to come up with ideas.
This lack of creativity may be perceived as a biological trait (“I just wasn’t born with the creativity gene”), as practical (“I just stick to the facts and don’t let my imagination get carried away”), or as un-learnable (“I’ve just never gotten the hang of thinking up stuff”).
We all want to engineer Eureka! moments into our workflow.
“Coming up with ideas” is just another phrase for a “breakthrough”. Or in the case of science, we call these ideas or breakthroughs “scientific hypotheses” (and when they are proved right they become “scientific discoveries”).
Most people I’ve met believe that what holds them back is the inability to engineer a breakthrough moment. They think that scientific discovery eludes them because of their inability to come up with a good idea. So they believe they struggle with generating magical Eureka! or Aha! moments, where things come together and new understanding suddenly appears.
In the pilot Insight Exchange event, where I brought together academics from different science fields and at different career stages to talk in small groups about what was holding them back from scientific discoveries in their own work, the most consistent piece of feedback I got afterward was that people wanted me to give them more strategies to engineer breakthroughs.
But we already have breakthroughs daily because we’re hard-wired to see meaning and patterns.
I recently learned about the work of neuroscientist Robert Burton on the cognitive and emotional basis for feelings of “certainty” (the belief that our understanding of something is accurate). According to Burton, we are cognitively hard-wired to come up with ideas, i.e., breakthroughs.
More importantly, we are built to experience feel-good sensations when we believe we have achieved a breakthrough, i.e., when a spontaneous and unconscious understanding rises to consciousness.
That feel good sensation arrives in the form of dopamine, a chemical released in the brain that triggers the brain’s reward and pleasure centers.
There are a couple of important aspects to this finding.
First, being rewarded for achieving a feeling of certainty about our knowledge encourages us to do it again. Like any pleasurable event, we seek to repeat or renew those pleasant feelings.
So Eureka! once, and you’ll want to Eureka! again and again.
As an aspiring discoverer, this probably all sounds pretty good. It might appear like we are biologically designed to experience pleasure when we discover things, which would encourage us to discover more things. It seems like a progress-promoting positive feedback loop, right?
Maybe. But the seduction of Eureka! is a double-edged sword.
Why? Because we experience the pleasant sensations and dopamine hit when we believe that we have understood something, even if our understanding is wrong, such as when it’s based on incomplete information.
Basically, we search for meaning and patterns and our brain rewards us when we find meaning and patterns, no matter what (you can read more on this in one of Burton’s articles published in Nautilus, which I’ve linked to below).
Unfortunately, our brain’s reward system doesn’t depend on whether we’ve got the right pattern or meaning.
Our internal reward centers are indiscriminate. Come up with a wrong explanation that your brain at least perceives as a reasonable possible pattern and you can still feel the exact experience of an Aha! or Eureka! moment. Even if you’re dead wrong.
A second important aspect is that we have intentionally evolved to recognize patterns and assign meaning to information we receive.
Burton uses the classic example of our ancestors recognizing lions (a pattern) and knowing what seeing a lion means to a very tasty looking pre-historic ancestor (the meaning). We need to be able to put together growling, fur, four legs, claws, teeth, maybe a jungle or savannah plains, that the sun is high in the sky means feeding time, that lions eat smaller animals like us, etc. in order to be able to say “Aha! I’d better run before I get eaten!”
We need to be able to combine many types of sensory information (visual, auditory, smell, tactile perceptions of temperature and time of day) and experiences (seeing lions eat other animals or even other people) together in order to be able to recognize one pattern (a hungry lion) and its meaning (I’m in danger).
What I am trying to drive home is the point that the two pieces that combine to make a breakthrough–pattern recognition and meaning-making–are processes each and every one of us engage in every second of every day.
We are creating hypotheses about how people interact with us, what world events mean for our lives and livelihoods, how the weather will affect our health and plans for the day, and what the ending to the TV show we are watching or book we are reading will be.
Many of the ideas that we have about these things will be right, but many of our ideas will be wrong.
It is the same process as scientific discovery—we acquire data, we search for patterns, we perceive patterns, and we make meaning from those patterns.
I don’t need to give you strategies to experience breakthroughs. You’re doing it all the time.
But as Burton’s work highlights, the problem is that many of our breakthrough ideas are just wrong, even when we feel sure they must be right.
The real trick is to sift through all the wrong-headed Eureka’s to find the one Eureka! that’s actually accurate.
If I could go back and give my Insight Exchange participants a new take home message I would point out to them how many breakthrough ideas they had already had. They had probably already thought up and dismissed ideas about new methodologies, new sources of funding, and reasons why certain pieces of data might fit together. But they had also already discarded many of those ideas as too silly, too hard, to unlikely, to flaky, or to unfounded.
That they had discarded ideas was not the problem.
The problem was, when they dismissed those earlier ideas, they had also subconsciously and simultaneously dismissed their skill in thinking up new things.
It was this failure of self-awareness that was harmful to their forward progress.
Many of them had put themselves in the “I’m not creative” camp and so they had fixated on finding new ways to become capable of coming up with ideas.
They were focused on fixing an imaginary problem.
You have had many ideas, you are having ideas right now, and you will continue to have ideas. That’s the take home idea I wish I’d given my pilot Insight Exchange group.
So, the discovery part comes in what you do with any ideas you have.
In Burton’s Nautilus piece, he hints at the fact that that we are more likely to latch on to false meaning and patterns (which, remember, our brain finds just as rewarding as accurate meaning and patterns) when we have limited or inconclusive data.
Hence, the activities and skills we need are not just how to evaluate ideas, but also how to evaluate and gather data when what we have is inconclusive or limited.
And the mindset we need is just to be aware that no matter how much information we have, we are always, on some level, operating in a world of limited and inconclusive data.
The above two sentences might sound familiar. They are called the scientific method.
It is well-designed to help us react wisely to our internal hunger for Eureka! so that we can find the accurate, and not just the available, the explanation.
Formulating a cohesive understanding is still very much a work in progress for me and I do much of that thinking “out loud” here in the pages of The Scientist’s Log.
As Burton cautions, searching for certainty in our understanding can be a dangerous game of giving ourselves what we want, instead of giving ourselves the truth.
But Burton also proposes that the best remedy is to give up certainty in favor of “open-mindedness, mental flexibility and willingness to contemplate alternative ideas” (Scientific American, 2008).
Thus, it turns out that fighting for the alluring Eureka!, those lightbulb moments from cartoons, isn’t the struggle we discoverers have to overcome. It’s the siren song of Eureka! and its pleasurable aftermath that we need to learn not to pursue at all costs.
The word “Eureka” derives from the Greek meaning for “I found it.”
The ideas we find lurking in our minds are sometimes new sources of illumination rising from the depths of the sea of knowledge. But other times they are just jetsam and flotsam washed up on the beach of bad ideas.
The discoverer’s way is to learn to tell the good lightbulbs from the duds and to treat the pull of Eureka! like a pleasant pastime and not an alluring addiction.
Interesting Stuff Related to This Post
Robert Burton, “Where Science and Story Meet”, Nautilus (April 22, 2013), http://nautil.us/issue/0/the-story-of-nautilus/where-science-and-story-meet.
Bernadette K. Cogswell, “The Seduction of ‘Eureka!’”, The Insightful Scientist Blog, November 15, 2019, https://insightfulscientist.com/blog/2019/the-seduction-of-eureka.
[Page feature photo:Unusual junk, a lightbulb, washed up on a beach in South Africa. Photo by Glen Carrie on Unsplash.]
