How to define “scientific insight” so that you can become an insightful scientist.

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 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,

1. how to define it in a science context,
2. what role it plays in advancing scientific discovery,
3. how to recognize it in science examples, and
4. 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

1. 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.
2. “Gary Klein’s Triple Path Model of Insight,” Farnam Street Blog, September 9, 2013, https://fs.blog/2013/09/the-remarkable-ways-we-gain-insights/.
3. See Allan Franklin’s contribution on the History of the Neutrino (held September 2018) conference page, “Prehistory and Birth of the Neutrino,” https://neutrino-history.in2p3.fr/prehistory-and-birth-of-the-neutrino/.

Related Content on The Insightful Scientist

The Scientist’s Log (Blog Posts)

• More in this series:
  • Starting post in “The Architecture of Discovery” series (1 of 23)—Overview.
  • Previous post in “The Architecture of Discovery” series (1 of 23)—Overview.
  • Next post in “The Architecture of Discovery” series (2 of 23)—Invention (coming soon).
• Misfits Matter

The Scientist’s Repertoire (Tutorials)

• —

Research Spotlight (Summaries)

• —

The Illustrated Scientist (Printables)

• —

How to cite this post in a reference list:

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.]

I once told a fellow graduate student at a nuclear physics summer school that, “I don’t speak math.”  He found this very funny, and me very funny.  But I absolutely meant it.  In fact, I was angry about it.  By that time, I had already met the sleep-depriving scientific discovery question I’ve dreamed of answering for the last decade.  I had been trying to solve it.  It’s why I attended the nuclear physics summer school at all.  It was considered “outside my area”, since my Ph.D. advisor and I had agreed I would declare my concentration as particle physics.  I thought gaining more knowledge would help me make progress.  But then I discovered that I don’t speak math.  I read math.  I calculate math.  I derive math.  But I don’t speak math.

In my current conception of the scientific discovery cycle the flow goes like this: question → ideation → articulation → evaluation → verification, with constant feedback between phases, and the ability to reset to an earlier phase as needed.  At the time of the nuclear physics summer school, I had the question in mind and I’d come up with three possible ideas for answers.  But my efforts completely died at “articulation”, re-phrasing my mental conceptualization of each answer as mathematical equations, because I didn’t speak math.

What do I mean by “speak” math?  And how is this different from reading and calculating?

Put it in another context.  As someone who idly studied five languages besides my native English (no, I can’t speak them all now) and who has a parent who raised me as semi-bilingual and does her professional work in at least two languages, I’ve experienced the feeling of “reading without speaking” many times before.

“Read” means I can identify things on signs that I’ve memorized or seen before.  “Read” means that I can sometimes derive related things, like word signs for the women’s toilet in a restaurant versus the signs I saw at the airport.  “Read” means I can muddle through restaurant menus, especially if there are pictures.

“Speak”, on the other hand, means I can mention to a restaurant server that the ladies’ room is out of toilet paper.  “Speak” means I can make a special meal request that’s not on the menu at all.  “Speak” means I can compose a Physicist’s Log entry about scientific discovery, even when I’m not sure how to define it, how to describe it, or how to achieve it.

“Read” means recognition, “speak” means “creation”.  While I can read math just fine, I can’t create new mathematical expressions with meaning off the top of my head, the way I can churn out sentences in a log entry.  Because I “can’t speak math”, there’s a bottleneck in my discovery cycle, right at the phase of articulation.

I’ve spent years since that summer school digging around looking for practices to help relieve the bottleneck:  Do more math! (Funny how more reading doesn’t equal better speaking.)  Try Fermi questions! (Back of the envelope calculations to answer odd questions about everyday life; but mostly just add and multiply things.)  Just practice modeling!  (Writing down just the starting equation, given any kind of physics word problem.  But this assumes you already know the physics and just need to recognize it in the problem.  What happens when nobody knows the physics yet?)

It wasn’t until I started studying cognitive psychology and scientific discovery that I came across a new option in a book called Where Mathematics Come From:  How the Embodied Mind Brings Mathematics Into Being, written by George Lakoff and Rafael Nunez, a linguist and a psychologist team who study the mind and mathematics.  Their theory is simple: all mathematics comes from lived sensory-motor experience that we then translate into the domain of mathematics via conceptual metaphor.  ALL mathematics; addition, subtraction, the concept of numbers, imaginary numbers, algebra, trigonometry, and on and on.  The final case study they do of the famous Euler equation and all the conceptual metaphors it requires is fascinating.  Most interesting in their theory is the sense that mathematics is not just derived (recognized, manipulated, objectively discovered), but that it can also be contrived (built, constructed, subjectively created).

In Lakoff and Nunez’s scheme, one could learn to speak math.  One could learn to construct mathematical expressions in the same way we construct sentences by consciously, explicitly building math expressions based on careful selection and combination of the underlying embodied metaphors (and still strictly adhering to the operational ground rules of math).  That this is based on conceptual metaphor (closely aligned to analogy and, hence, scientific discovery), and that the metaphors are based on physical experience (suited to a physics focus on the natural world), was music to my ears.

So, I may not speak math yet.  What’s more, taking Lakoff and Nunez’s approach may require a little re-education when it comes to how I think about math.  But now I know speaking math is possible.  And in the pursuit of scientific discovery, the re-education of an educated mind is a small price to pay to keep the discovery cycle alive.

Every good story needs an arc: a master strategy that drives all the action from start to finish.  Something that, for the writer, guides every word written with the knowledge that an outcome must be obtained: the arc must begin and end.  It cannot run off to infinity.  Are not research undertakings much the same way?  You must achieve some end, be it innovation, new knowledge, new technique, or discovery.  And, as the lead researcher, you must keep that in mind at all times, allowing it to guide your actions.

Of course, this is just an analogy.

But I am struck by how frequently analogies and analogical thinking appear in the literature on discovery.  In cognitive psychology some consider analogy one of the key problem solving skills in working at the boundary of knowledge. It appears again in research on problem solving where written, drawn, and even animated analogies have been studied.  It’s even factored into relevant concepts like design innovation.

So I find it a great irony that, despite general agreement that analogical thinking plays a role (and possibly a crucial one) in scientific discovery; despite the fact that analogy appears almost universally as a reasoning skill across cultures; despite the fact that analogy can be applied to any human knowledge domain and that analogy can use as source material any human knowledge domain; analogy is called in the technical jargon a “weak problem solving method” (in reference to its general domain use; versus “strong” methods, which are highly domain specific). If ever a bit of technical jargon did a disservice to its meaning, I think it’s here.

In physics we tend to marginalize analogical thinking as something handy for pedagogy, or public engagement, or just private understanding.  [Here I refer to conceptual analogy; not mathematical analogy, which is used heavily in physics.]  But we rarely envisage analogical thinking as a systematic, efficient, front-line professional research strategy for even “low hanging fruit” questions, let alone for the serious and risky business of discovery.  But the research suggests to me that it can be.

Which brings me back to the analogy of an arc.

Initially, I struggled to develop a clear strategy in pursuing scientific discovery.  In other words, I struggled to find my story arc.  But it now seems to me that the main task of scientific discovery and scientific inquiry is to explicate new analogies–to draw links between the known world and still unknown aspects of Nature.

If I define analogy as a way of identifying similarities between seemingly dissimilar things, isn’t that the very foundation of what we do in physics?  To say that an apple falling from a tree and a planet orbiting the sun are both acted upon and behave that way through the same concept of gravity is the mental act of finding similarity in the apparently disparate.  In fact, to say that any set of behaviors or characteristics, for any observable, physical system, can be explained by a common law, function, or model seems a pretty radical act of analogy to me.  So, each and every scientific research question is in some nuanced way an analogical research question…or a scientific ARQ, you might say.

There is still much more to this story of course.  There is a great deal of deeply thoughtful research about analogies—qualitative, quantitative, and operational.  Ideas about how to use it to communicate, to educate, to investigate.  Frameworks for how to define the relationship between the source domain of the analogy and the target domain where it is applied.  And on and on.  I believe this research contains some of the first scientific discovery strategies to formalize and try.  But this will not be an easy task.  Still, at least now I have the first step in pursuit of discovery…

The first step toward discovery is to find your ARQ.