I love a good mash-up, so let me ask you this: What do you get when you mash-up the ideas of two prolific female academics, one in social work and the other in theoretical physics?

The answer is: my musings for this week’s post, which boils down to the phrase “the ugly truth.”

 

A Tale of Two Academics

 

So which two academics am I talking about and which two ideas?  Here’s a quick rundown (I’ve included links to their webpages at the bottom of this post in case you want to follow-up):

__________________________

Brené Brown – Ph.D. in social work

 

Currently based at the University of Houston, Brown studies topics like the intersection between courage, vulnerability, and leadership.  She’s an academic researcher, a public speaker, and runs a non-profit that disseminates much of her work in the form of research-based tools and workshops.

__________________________

Sabine Hossenfelder – Ph.D. in physics

 

Currently based at the Frankfurt Institute for Advanced Studies, Hossenfelder studies topics like the foundations of physics and the intersection between philosophy, sociology, and science.  She’s an academic researcher, a public speaker, and writes pieces communicating science to the public as well as maintaining a blog well-known in physics circles.

__________________________

In the midst of simultaneously reading the most recent popular books published by these two researchers (Dare to Lead by Brown and Lost in Math by Hossenfelder), I was struck by a link between the two.  That link had to do with the premise of Hossenfelder’s book and one of the leadership skills Brown promotes in her book.

Both of these involve the word “beauty.”

 

Sabine Hossenfelder’s Lost in Math

 

Hossenfelder argues that physicists (in her case, especially taken to mean theoretical particle physicists and cosmologists) have been led astray by using the concept of “beauty” to guide theoretical decision-making as well as lobbying for which experiments to carry out to test those theories.  By “using” I mean that she illustrates through one-on-one interview snippets how theorists rely on beauty to help them make choices about what to pursue and what to pass by.  She also illustrates, through a review of the literature, how theoretical physicists have tried to define beauty both with words (like “simplicity” and “symmetry”) and with numbers (through concepts like “naturalness”, the belief that dimensionless numbers should be close to the value 1).

According to Hossenfelder, this beauty principle does not drive the theoretical effort among just a small few, but among the working many.  And she thinks it’s a problem.  Her main  reason for pointing the finger is the belief that this strategy has not yet produced any new successful theoretical results in the last few decades.

The best quote to sum up Hossenfelder’s book in my reading so far is this:

 

“The modern faith in beauty’s guidance [in physics] is, therefore, built on its use in the development of the standard model and general relativity; it is commonly rationalized as an experience value: they noticed it works, and it seems only prudent to continue using it.” (page 26)

 

Funny that Hossenfelder should mention values.  Values are something Brown talks about at length.

 

Brené Brown’s Dare to Lead

 

The crux of Brown’s book Dare to Lead is about acknowledging and leveraging qualities that make us human (vulnerability, empathy, values, courage) in a forthright, honest, and authentic way in order to become better leaders.  Brown illustrates her concepts with numerous organizational and individual leader case studies peppered throughout the book, as well as copious academic research from her team on this specific topic.

According to Brown, the prime cause of a lack of daring leadership is cautious leadership, best expressed through the metaphor of entering an arena fully clothed in heavy duty armor.  The energy put in to developing and carrying the armor takes away from the energy left to masterfully explore the arena.

Here, I’m most interested in her thoughts on values and the role they should play in daring leadership.

In case you’re wondering, Brown defines leadership as “anyone who takes responsibility for recognizing the potential in people and processes, and who has the courage to develop that potential.” (page 4)

(It’s the idea of developing potential, which resonates with scientific discovery, that caught my eye when I read the back cover of the book on a lay-over in Amsterdam.)

Brown traces much of our motives to our values: they drive our behavior and determine our comfort level when we take actions that either align with (causing us to feel purposeful or content) or run counter to (causing us to feel squeamish or guilty) our values.

The best quote to sum up Brown’s discussion of values is this one:

 

“More often than not, our values are what lead us to the arena door – we’re willing to do something uncomfortable and daring because of our beliefs.  And when we get in there and stumble or fall, we need our values to remind us why we went in, especially when we are facedown, covered in dust and sweat and blood.” ( page 186)

 

One last detail from Brown’s book will prime you for my mash-up:  On page 188 of her book, Brown gives a lengthy list of 100 plus items (derived in her research) from which to identify your core values.

The ninth word down on the list of values?  Beauty.

 

Beauty is Just Another Motive

 

So here’s where the mash-up begins.  And let me throw in one more element, just to make it fun.  Let me put this all in a metaphor, like something from a cheesy crime procedural TV show.  Ready to put two-and-two together and solve a mystery?

So, according to Hossenfelder a crime against physics has been committed (the failure to come up with something new in a timely fashion, after spending a lot of money trying to come up with something new).

Physicists have taken advantage of the means (applying beauty as a guiding principle) and the opportunity (being employed as physicists, exclusively at academic institutions in her examples) to commit this crime.

If you watch enough crime shows, you’ll know the overused phrase that TV detectives rely on.  Find the “means, motive, and opportunity” and you’ll find your criminal.

Hossenfelder has already singled out physicists as the perps.  But as a detective she would be at a loss for motive (other than maybe, “everybody else was doing it and I wanted to keep my job”).

Here, I imagine Brown chiming in as her spunky detective partner.  Hossenfelder has laid out her analytic,  but impersonal accounting, and now Brown swoops in to add the humane touch.  “No, no, Sabine,” Brown says.  “Beauty was not the means; it was the motive. The means was getting the research funding, the students, the equipment.  But the motive, well that’s just people being people: it was the pursuit of beauty they could call their own.”

Okay, maybe melodrama and mash-ups don’t go together so great, but this is an interesting line of thought:

Brown’s work suggests that the pursuit of beauty as a methodological choice may not just be about expediency or experience, but also about personal fulfillment.  That’s deep stuff.  And if it’s true, then it throws the idea of changing tactics into a different category.

Then it means your changing the motive, not the means.  Beauty isn’t just about a guiding principle that might work, it’s about what you believe gives your work meaning when it does succeed.  And convincing someone to change their motive is a much taller order than convincing them to change their means.  Especially if their motives are values-driven (whether they realize it or not).

 

If You Can’t Be the Change You Want to See in the World then Bring the Change

 

Trying to constrain what motives are most likely to bring about scientific discovery seems to me like it might be a fool’s errand.

Odds are it’s about the right time, right place, and right motive, to put you in a position to recognize the undiscovered.  In Hossenfelder’s defense, I think she is unwilling to accept human motives (in an appendix she advises that you try to remove human bias completely) because she’s afraid it will undermine the ability to understand the truth (understanding and truth are numbers 109 and 108 on Brown’s values list).  But there’s more than one way to reach an outcome.  If our motives are driven by values and run deep, then instead of asking scientists to change motives, we could also just bring in more people with different motives and give them a seat at the table.  In that way you bring these alternatives by bringing in people who value those approaches and use them by default.

And Brown’s values list includes a lot of words that easily might be interesting alternative motives (or guiding model-building principles), like adaptability, balance, curiosity, efficiency, harmony, independence, knowledge, learning, legacy, nature, order, and simplicity (just to name a few).

In the spirit of a seat at the table of debate, Hossenfelder’s book offers a counter-value to the beauty principle in model-building (understanding and truth).  And Brown’s book offers a counter-value to the stoicism principle in leadership (courage and vulnerability, # 24 and # 113 on her values list).  These two researchers bring their own motives and values, serving as the bearers of not only alternative perspectives, but more importantly alternative actions that might help make progress.

[In case you’re wondering, my three core values, in priority order, are hope (# 52 on the list), respect (# 87), and affection (not on Brown’s list).  That may help clarify my motives for everything on The Insightful Scientist website.]

 

The Ugly Truth

 

You might wonder why I suggest giving more people with different values a seat at the table, your discussion round table.

Why not just try one set of values and if it doesn’t work then replace them with a new set of people with different values?  Or why not just try and change your values until you achieve success?

The tricky thing about values is that it’s hard to change them once their set, usually sometime in middle childhood.  The useful thing about values is that it’s also hard to put yourself in someone else’s shoes.  That lack of imagination, empathy, and sympathy usually turns into skepticism.  And skepticism done right can be tremendously helpful to science, especially when it comes to verifying possible discoveries.

If we can’t understand, or don’t agree with, someone else’s motives then we automatically want and need more data and evidence to agree with their conclusions.  We set the bar of proof higher when it’s an ugly truth (to us) than when it’s a beautiful explanation (to us).

For example, suppose one scientist believes that embracing complexity captures the wonder of nature by valuing diversity, while another scientist believes that simplicity captures the wonder of nature by valuing connection.  We may find that while one of these people thinks needing many models for specific cases has a greater feel of “truthiness” the other person believes that having as few models as possible means you’re on the right track.  The gap between these two approaches must be bridged because at the end of the day scientific discovery is about consensus converging to a base set of truths through observation and evidence.  Filling the gaps between scientific findings and their associated motives ensures that science has a more solid foundation.  And, in our example, we may find that while at one time resources make simplicity the better strategy, at another time complexity may be just the thing for a breakthrough.

Conflicting values and the guiding principles they generate in scientific work are like unfamiliar or misshapen vegetables usually hidden from view.  It’s going to take more convincing to put money into it by buying it, to invest effort in it by cooking it, and to be willing to internalize it by swallowing it.  You’d maybe rather just ignore it or toss it in the garbage.  But you never know, one person’s ugly truth may turn out to be another person’s satisfying ending.  If we don’t all sit down and share a meal together, how will we find out?

 

Interesting Stuff Related to This Post

 

1. Website – Brené Brown’s homepage
2. Website – Sabine Hossenfelder’s blog Backreaction
3. Elisabeth Braw, “Misshapen fruit and vegetables: what is the business case?”, The Guardian (online), September 3, 2013, https://www.theguardian.com/sustainable-business/misshapen-fruit-vegetables-business-case.

 

Related Content on The Insightful Scientist

 

Blog Posts:

• Insight Exchange

 

 

How to cite this post in a reference list:

 

Bernadette K. Cogswell, “The Ugly Truth”, The Insightful Scientist Blog, August 9, 2019, https://insightfulscientist.com/blog/2019/the-ugly-truth.

 

[Page feature photo:  A pretty, pert bunch of Laotian purple-striped eggplants, roughly the size of ping pong balls. Photo by Peter Hershey on Unsplash.]

When someone asks you what you do for a living, how do you answer?

Do you give your job title?  Do you say what kinds of project(s) you are working on?  Do you give your company name or name of the topic you work on?

From researchers of all stripes, working in non-profits, volunteer and hobby groups, schools, universities, industry, and government, you hear many answers.  But when scientists get together I’ve noticed people tend to label themselves as one of four “flavors” of scientist: as an experimentalist, a theorist, a computationalist, or a citizen scientist (sometimes called a “hobbyist” or “amateur scientist”).

Often times, scientists will use these labels when they get nervous about having to answer questions.  If you listen to or watch videos of a lot of science talks for scientists, you might have noticed this too.

I’ll give you a few examples from physics:

If someone is asked an intensely mathematical question they might say “I’m just an experimentalist, so that’s above my paygrade.”  If someone is asked to defend the possibility of building a real prototype they might say, “Oh I’m just a theorist, so I don’t know about building things, I can just tell you the physics is there.”  If an audience member asks a question that gets a dismissive response from a speaker, they might say “I was just curious.  I follow the topic as a hobby, but I don’t really keep up with the details.”

Lately, as I’ve started studying connections between researching fundamental physics and the science of scientific discovery, I’ve been asked many times, “What would you call yourself?”, “How should I introduce you to people?”, or “What would you say you do?”

Which got me thinking about how we see ourselves as scientists.  And I’ve started to wonder if using labels as personal identities might be hurting our attempts to actually discover things.

 

Finding the Third Way

 

So, “experimentalist”, “theorist”, “computationalist”, and “citizen scientist”.  First off, I should define what I mean by these words:

“Experimentalists” conduct laboratory experiments to gather new data and generate equations to describe data they’ve collected.

“Theorists” look through old, new, and especially anomalous data to invent new descriptions and equations to explain the misunderstood and to predict the unobserved.

“Computationalists” run large-scale precision calculations on computers to simulate meaningful phenomena and generate equations to capture the real world in a form they can put on computer.

“Citizen scientists” conduct projects to satisfy their curiosity and support their community and generate equations for joyful distraction or to improve the quality of life of a group they care about.

I think these labels apply to any scientific field—agriculture, psychology, geology, chemistry, physics, computer science, engineering, economics, you name it.  And I emphasize equations because I think that’s what distinguishes the fine arts (literature, music, art, dance, etc.) from the sciences.  The sciences try to represent Nature using numbers, language, and symbolic math, while the fine arts try to represent Nature using sound, light, movement, color, texture, and shape.

Like I said in the opening of this post, I certainly see people use these words to navigate tricky audience questions.  But I also think they get used in two other ways, depending on what kinds of scientific discoveries people are pursuing: longstanding problems in mature fields, or unrecognized opportunities in emerging fields.

 

Work Identity

 

In mature fields, the kinds with lots of funding and famous teams that people can name off the top of their head, I think three of these four labels (experimentalist, theorist, computationalist) are used by scientists and that they mean them as a sort of personal identity.  That’s because mature fields tend to have larger networks of people working in them.  With larger networks comes more specialization (to help manage the large volume of people and ideas).  People get assigned to roles and they develop expertise in that particular role over the course of their work career.

In mature fields even training tends to start labeling people early.  For example, at my current institution undergraduates in their first year are already assigned to “Physics Theory” track (which requires fewer lab hours and more math) versus “Physics” track (which requires more lab hours and less math).  And in the United States at the Ph.D. level students are divided into either experimental or theoretical tracks.  Computational folks usually fall into one or the other track as a sub-category, depending on whether or not they mainly work on simulations for large experimental collaborations, or simulations for a small (maybe five people or less) theoretical group.

Meanwhile, the pursuit of scientific discovery in mature fields tends to take the form of trying to answer longstanding open questions.  The kind that make headlines in popular science journals.  In physics these are things like the nature of the early universe or why the universe has more matter than antimatter.

When individual scientists choose to see labels like experimentalist, theorist, or computationalist as work identities, they engage with discovery in more limited ways.  They do so only to the extent that the field at large has decided they should have a role in it.

So, for example, if anomalous data is generated by an experimental group, but the field decides that it’s most likely an experimental error causing the blip, then computationalists and theorists will be discouraged from contributing to the discussion, or will suffer a hit to their credibility if they join the debate.

 

Stay in your lane.

 

Work identities are kind of like a rule that says, “Stay in your lane.”  But if the key finding is to be found by taking an off-ramp, then progress will be slow or non-existent because there’s not enough freedom of intellectual movement.

Also, I mentioned at the beginning that only three of the four labels appear in mature fields.  There’s rarely any place given to the voices of citizen scientists or hobbyists at all.

 

Work Ethic

 

On the flip side, there are emerging fields and topics.  These areas are so new that very few people are actually studying them, no rules have been established yet, and even the kinds of discoveries being pursued are hard to define.  Emerging fields are uncharted territory so anything is possible.

With so few people working on them, emerging topics don’t need hierarchies, they just need bodies willing to do the work.

So an experimentalist will be someone who values running a huge amount of tireless trial and error.  A theorist is someone who values digging around to think up reasons, and ideas, and questions.  In emerging fields you are more likely to be dismissed by co-workers until the value of the project proves itself and gains more acceptance in the mainstream. So taking on a hobbyist work ethic becomes more important as you have to value things like “passion” and “obsession” to keep people motivated through the tough times.  And a computationalist is someone who values grinding through data on computer until all those numbers start to look like a pattern.

 

Mindset over matter

 

So in science, I think that means the labels we usually think of as identities in mature fields become a kind of work ethic in emerging fields; a style of taking on each and every task to bootstrap your way to a successful breakthrough.  They are not so much you, as they are the mindset you approach them with.

This mindset over matter approach is what allows researchers in emerging fields to pursue high-risk opportunities that may lead to scientific discoveries, or may prove to be dead ends.

But this still puts the brakes on the speed with which discoveries could be made, because I think researchers still feel like they have to find people who either innately have that mindset, were raised with that mindset, or have acquired that mindset by experience or training.

In other words, in both mature and emerging fields these labels are seen as compartmentalized rather than fused—you can own one, but not the others.

 

Troubleshooting Approach

 

That brings me back to the cryptic header I started this post with, “Finding the Third Way”.  I think of this as “finding the middle way”.  To me that means using these labels as skillsets and thinking of the whole pursuit of scientific discovery as a troubleshooting exercise.

The trouble might be that you’re bored and you want something interesting to do with your weekends, so you’re going to volunteer as a citizen scientist to contribute to research on soil health in your local area…just because you love veggies.

Or the trouble might be that you’re tired of having patients die on your watch from a preventable condition, so you’re going to raise money to run experiments on cheap lifestyle interventions to reduce the number of deaths.

Or the trouble might be that you think nuclear weapons are dangerous, but there’s all this plutonium sitting around in stockpiles with no safe, permanent way to get rid of it, so you’re going to dig into all the theories on how to dispose of anything that might give you a breakthrough idea to help solve the problem.

My point is that we solve problems that matter to us.  Personal problems, social problems, global problems.  But the problems are what matter most, not the fields.  Scientific discoveries are often made because their discoverers saw a problem that they couldn’t let go of and so they worked until they found a way to solve it.

These aren’t abstract, philosophical things.  They are practical, specific challenges that we tackle one troubleshooting step at a time.  And over the course of solving that problem, every one on of the roles I’ve mentioned will probably come into play.

So instead of always looking, or waiting, or hoping that we can involve someone willing to take on “the experimentalist”, or “the theorist”, or “the computationalist”, or “the citizen scientist” responsibilities, we should consider building up a reserve of each of those things within ourselves.

 

Moving Beyond Our Training

 

If we want to give ourselves the best chance of solving a problem that matters to us and discovering something along the way, then maybe we shouldn’t be just one of those things (experimentalist, theorist, computationalist, hobbyist) in our lifetime.

Maybe we should be all of those things at one time or another.

They’re just skills.  Not destiny.

Like the logo for my website says, “Discovery awaits the mind that pursues it.”  And I chose “The Insightful Scientist” as my websites name for reason.  Because I wanted to remind myself every day that I pull open the homepage that science is about discovery and that science is bigger than just physics or just the ways I was trained to pursue discovery as a theoretical physicist.

We use our training as far as it will take us. But if the science is bigger than our training then we don’t give up, or say it’s a job for someone else.  We just stretch our minds a little wider open, learn a new skill, and jump once more into the fray.

 

Mantra of the Week

 

Here is this week’s one-liner; what I memorize to use as a mantra when I start to get off-track during a task that’s supposed to help me innovate, invent, and discover:

Be a person of many hats.

So when people ask me what I am or what I do I think I’ll start saying:

“I’m a Bernadette.”

“And it just so happens that the problem I’m trying to solve right now is how to put the science of scientific discovery into practice in neutrino particle physics.”

I won’t label myself as a theorist, or a neutrino physicist, or an academic.  Because the titles don’t matter.  The problems we’re trying to solve do.

There’s an English expression that says taking on different roles at work is like wearing different hats.  Well, I’m willing to wear whatever hat gets the problem solved, even if I don’t look good in fedoras.

 

Final Thoughts

 

So let’s recap the ideas and examples I’ve talked about in this post:

• I narrowed down the labels we use for scientists to four: experimentalist, theorist, computationalist, and citizen scientist.
• I classified scientific discovery into two types: trying to answer longstanding questions in old fields and recognizing new opportunities in young fields.
• I argued that we use the four labels as identities or work ethics; but that a more agile approach is to think of them as skillsets.

Have your own thoughts on how we label ourselves as researchers and whether or not this helps or hinders the pursuit of scientific discovery?  You can share your thoughts by posting a comment below.

 

Interesting Stuff Related to This Post

 

1. Website – Chandra Clarke’s Citizen Science Center, sharing open science projects.
2. Web article – Angus Harrison, “Self-taught rocket scientist Steve Bennett is on a mission to make space travel safe and affordable for all – from an industrial estate in Greater Manchester,” interview in The Guardian online, April 4, 2019, https://www.theguardian.com/science/2019/apr/04/building-rockets-all-over-house-space-travel-safe-affordable-for-all.

 

How to cite this post in a reference list:

 

Bernadette K. Cogswell, “Experimentalist, Theorist, Computationalist, Citizen Scientist: Work Identity or Work Ethic?”, The Insightful Scientist Blog, March 29, 2019, https://insightfulscientist.com/blog/2019/be-a-person-of-many-hats.

 

[Page Feature Photo:  Fedoras fill a costume rack at the Warner Brothers movie studio in Burbank, California.  Photo by JOSHUA COLEMAN on Unsplash.]

Tell me if this sounds familiar to you:

You have a lightbulb moment.

A great idea you’ve never seen or heard before.  It seems like it could really move things in an amazing new direction.  You’re excited.  No, SUPER excited.  You deluge your friends and family with all the amazing, awesome outcomes your idea could have.

Once that first flush of excitement passes and the adrenaline from having had a genius moment settles you maybe start to look around for useful info on parts of your idea outside your knowledge base.  And that’s when it happens.  You come across a paper, a talk, a website, a colleague in conversation, where they discuss something painfully close to your supposedly “novel” idea.

The idea’s already been done.

To add salt in the wound, as you dig more you find out that “the idea”, what you thought was “your idea”, was tested out by some genius years ago.  And they’ve already written about it, or tried it out, and moved on.

*sound of your ego and hope deflating here*

In my case, the “offending paper” was written before I was even born.  That’s four decades old!  I never even stood a chance of getting the first idea on that table.

So what does any of this have to do with old papers that have low citation rates?  In other words, ideas that have been out there for a while, but nobody seems to care or talk about?

 

Deciding if the Old Paper in Your Reading Pile Should Still Be There

 

Well, as a matter of fact, the paper in my example was exactly that kind of paper—it had vanished into history like an unliked and unshared tweet or Facebook post.

But if you read my Research Spotlight summary (link at the end of this post) on a Nature paper about “Team Size and ‘Disruptive’ Science” you would have learned that researchers recently discovered a link between teams that publish more “disruptive” scientific papers, patents, or computer code and the research papers they cite:  Teams proposing new ideas more often cited old unpopular papers.  By unpopular I mean those old papers weren’t cited very often, ever.

It turns out that the paper that proposed the same idea I had was an old paper (well, older than I am) and nobody seemed to cite it.  I had a good handle on just how unpopular it was because it was written by a European physicist in my own exact research field, it was published in a respectable journal, the physicist gave talks about it…  And yet I’d literally never heard of him, his work, or his contribution to this idea.

Before I read the Nature paper I mentioned before on teams and disruptive science, I assumed that this paper I found and its lack of fanfare was a bad omen:  “That means his/my idea must be a bad one.”  I had a little pity party for myself and then I tucked the PDF and my notes into a file on my laptop only to review on rare and sentimental occasions.

But in light of reading the Nature paper, I’ve completely re-evaluated my attitude and thoughts toward both the idea and my predecessor’s paper.  Instead of setting it aside, I need to re-evaluate what low citations means in this case.

And as I thought about it more and included my own experiences in publishing papers, I realized that low citation rates could have at least three meanings for a paper.  I nickname these “the niche”, “the bad”, and “the visionary.”

 

The Niche Paper

 

For niche papers the low citation rate reflects the fact that no one really cares about the paper’s content.

 

There might be a few reasons for this.  One reason reflects the content itself.  It could just be an overly specific topic (like the singing habits of mice…don’t look shocked, mice do actually “sing”), or a topic that it’s nearly impossible to research because the tools and situations don’t exist yet (like extra-dimensional theories of how neutrino particles get their mass).

The other reason reflects a failure of communication.  Maybe the authors used completely different technical jargon or math notation than anybody else has in published work.  So even if we try hard, the rest of us just might not know what the heck they’re talking about.

But there’s a third possible reason suggested by reading a paper in Technological Forecasting & Social Change, which is the focus of this week’s Research Spotlight summary (link at the end of this post).  Maybe it’s an emerging field, working right at the edge of known knowledge.  As a result, it’s living in a sweet, but difficult, spot: at discovery’s edge.  At this point in history, it falls into a niche because both of the two above reasons will trip up the paper: (1) no one will care about it because it’s not “a thing” or “trending” yet; and (2) no one will understand what it’s talking about because the focus of study is so new or under-researched that many ideas, concepts and words will have to be invented to talk about it.

And by the way, don’t assume that “emerging” just applies to stuff in the last 5 years.  Sometimes emerging science takes decades to incubate, with just a few researchers keeping the embers alive, before it really takes off and becomes a new field of study in its own right.

Of course only the first kind of niche paper (the too specific) and the third kind (the emerging field) are potentially useful for breakthrough science, innovations, or inventions.  The second kind (the Greek-speak) just needs a good re-write.

 

The Bad Paper

 

For bad papers the low citation rate reflects the fact that the work it describes just wasn’t that good.

 

There are lots and lots of reasons, big and small, why a paper might be bad.  You could write volumes about this topic and, unfortunately, find lots of real examples to illustrate what you mean.  In fact, right now I bet you can picture an example you thought was junk work and that you still wonder to yourself, “How did that get (published/funded/awarded/bought/greenlit)?”

I have no desire to make this post a laundry list of complaints against certain papers I’ve seen (I have no patience with pessimism or destructive criticism).  The point here at The Insightful Scientist is to make progress toward scientific discovery and insight by finding fresh, valuable ways to move forward.  Not wallow and howl at the bad stuff people sometimes produce.

So let me stick to what you need to do here: recognize when a paper is “bad” so you can move on from it quickly.

Right now, I’ll just point out two reasons that are big red flags that you should avoid using a  paper at all, even to inform your own thinking, let alone to cite in one of your own writings.

First, if a paper uses inconsistent logic to either (1) justify its own findings or (2) compare itself to the works of others then you should consider it a “bad” paper and avoid it.  You don’t want that bad mental habit to rub off on you or to have your credibility tainted by association (you’ll need that credibility later on when you want to encourage a broader community to engage with your ideas).

Second, if a paper does not give sufficient information to evaluate its methods or conclusions then you should consider it a “bad paper” and leave it out of your information pile.  Again, it’s a bad habit, not laying out fully and clearly in writing what makes your work tick.  So do yourself a favor and find a better paper.  [The exception here is in sharing information about a patent or potentially patentable invention, where sharing too much detail could lead to problems in market competition.  But the answer is simple: if you publish you have an obligation to share.  The purpose of making something public by writing about it is to expand the public knowledge domain.  If you don’t want to share, don’t publish.]

What I like about using these two red flags, to seek and ignore bad papers that have wandered into your information orbit, is that you can check for them even if the paper is well outside your area of expertise.

And if a radical breakthrough is your goal, you should be reading outside your expertise.

I’ve been reading in sociology, biochemistry, and library sciences to try and answer a neutrino physics question (those other fields help improve my skill set ,which makes me more adept at tackling my own field).  Research suggests that this kind of intentional, broad information gathering can trigger radical insight.

Do what it takes to get the job done.  Read widely, and filter out bad papers as you find them.

 

The Visionary Paper

 

For visionary papers the low citation rate reflects the fact that the ideas presented are too far ahead of their time for others to recognize or act on yet.

 

I know, I know.  All you futurists, innovators, scientists, inventors, and entrepreneurs out there (myself included) are drooling over this category.

Visionary.

The word just smells of greatness, and we all want to make a contribution that will make it into this category.  So it’s only natural to get a little over-excited and want to label a paper related to your own “big dream” science or innovation as “visionary”.  It gives us a feel-good moment and a sense of fate, an image of what our own future might look like.

But if you remember my story from the beginning of this post, that kind of warm-and-fuzzy meets adrenaline-pumping moment is what got us into this awkward mess, sorting papers into categories, in the first place.  So here we are trying to be mature about this low citation paper and figure out what it means that someone else already came up with it, but no one paid attention.

On The Insightful Scientist I have made it my mission to learn how to be a pro at scientific discovery and share that with others.  So let’s get objective.  How can we tell if the ideas are ahead of their time?

I’ll assume that the paper has avoided any of the red flags that would make it a bad paper to rely on. (If you’re avoiding that evaluation because you’re afraid to see that paper not make the cut, have courage and be decisive.  If the paper is “bad,” it’s bad for your long-term discovery goals.)

As you evaluate the paper, remember that you’re at an advantage because you’re a “future human” 5, 10, 20, 40, even 100 years after the paper was written.  You know how some aspects of the “story” (i.e., the science) actually turned out and you can use that to help you evaluate.

Did this old paper have the right mindset—is it logically consistent, does it emphasize objectivity and evidence, and does it share information willingly?  Did other ideas presented in the paper turn out to be true or stand the test of time?  Did the paper get those ideas right, even though they were based on some false assumptions?  Are those false assumptions of the “past humans” who wrote the paper mostly a result of not having access to the data, technology, populations, or even big pots of money like we future humans have now?

What you’re really trying to figure out is if the authors had good research instincts (due to experience, mindset, or both), even in the face of limited resources.  If they did, then it’s possible they had honed their visionary skills about the topic and you might be looking at a visionary paper.  It may have provided a past blueprint for a good idea that the future can now act on.  If you want some examples of papers in this category, check out the link toward the end of this post.

And if your final decision is that the low citation paper you’ve got is visionary…build on it!

 

Learning to Sort Papers Like a Pro

 

If you remember, at the beginning of this post, I said this whole stream of thought came about because I had a low citation paper sitting in a neglected folder.  I’d originally, purely based on citation rate, dismissed it as “bad”.  But upon re-evaluating it I’ve decided it is  somewhere between niche and visionary.  I’m still working out which category I think it fits in best.

But the important point is that I’ve re-engaged with the paper and I’m wrestling with the science, ideas, and methods it presents in a much more thoughtful way.  I’m not falling in love with it (like a novice might) and I’m not dismissing it out of hand either (like an old-hand might).  I’m handling it like a pro who knows that when it comes to pursuing scientific discovery with deliberate skill, learning to distinguish between the niche, the bad, and the visionary is part of your job description.

 

Mantra of the Week

 

On a final note, before I sum this post up in a short bullet list, let me say this:

If you’ve read some of my past posts from 2018, especially the old versions, then you know I sometimes like to end with an artsy, one sentence tagline, and I use the post feature photo to illustrate it.

These one-liners are what I memorize to use as mantras when I start to get off-track during a task that’s supposed to help me innovate, invent, or discover.

This week’s one-liner is:

Awaken sleeping giants.

If you want to change the knowledge landscape then sometimes you have to dig into the past to find ideas that are sleeping giants.  Once awakened, the rumble and weight of their presence will cause heaven and earth to stand-up and take notice.  And as physicist Isaac Newton once supposedly said, “If I have seen further, it is by standing on the shoulders of giants.”

 

Final Thoughts

 

So let’s recap the ideas and examples I’ve talked about in this post:

• I suggested a way to sort old unpopular papers in your information pile into three categories: the niche, the bad, and the visionary.
• I pointed out why you should throw out papers falling into the bad category and consider building on papers in the niche and visionary categories.
• I talked about how each of these categories of papers fit into the big picture of the pursuit of scientific discovery.

Do you have your own sorting and sifting criteria for papers?  You can share your thoughts by posting a comment below.

 

Interesting Stuff Related to This Post

 

1. Web Article – Carl Zimmer, “These Mice Sing to One Another — Politely,” The New York Times, February 28, 2019, https://www.nytimes.com/2019/02/28/science/mice-singing-language-brain.html.
2. Web Article – “Like Sleeping Beauty, some research lies dormant for decades, study finds”, Phys.org website, May 25, 2015, https://phys.org/news/2015-05-beauty-lies-dormant-decades.html.

 

Related Content on The Insightful Scientist

 

Blog Posts:

• At Discovery’s Edge

 

Research Spotlight Summaries:

• Team Size and “Disruptive” Science
• Making Discoveries Using Published Articles

 

How-To Articles:

• —

 

How to cite this post in a reference list:

 

Bernadette K. Cogswell, “Low Citation Papers: The Niche, the Bad, and the Visionary”, The Insightful Scientist Blog, March 15, 2019, https://insightfulscientist.com/blog/2019/awaken-sleeping-giants.

 

[Page Feature Photo:  Standing figure and reclining Buddha at the Gal Vihara site in Sri Lanka.  Photo by Eddy Billard on Unsplash.]

It’s that time of year when you suddenly realize that any goals, plans, or New Year’s Resolutions you have for 2019 already seem like a bad idea.  I certainly have.

I’ve been sick, I’m in the midst of a significant professional transition, and I still can’t even find the notebook where in December 2018 I wrote down all the wonderful things I hoped to make happen in 2019.   I only remember off the top of my head two goals: (1) eat more nutritious food daily and (2) practice my scientific discovery skills daily.  The food goal has been easier to do.  In fact, at least I’ve started on it!  But the discovery goal has only seen me spend two dedicated hours since 2019 started practicing my ability to invent new theoretical equations.

Why are some goals and habits so much easier to follow through on?  In particular for The Insightful Scientist why are some habits, like making progress on a big discovery goal, so hard to practice?  I think that some of the same basic things that hold us back from finishing fitness, diet, hobby, money or other personal goals also plague our ability to act on discovery goals.  So let’s talk about ways to fix our discovery habits and make 2019 a better discovery year.

 

3 Keys to Good Discovery Resolutions

 

You’ve probably heard these suggestions before, but let me remind you of three things that you should have in place to increase the chances that you’ll follow through on a goal.

In this case, I’m going to use my own attempts over the last three months to come up with a 30-day discovery skills mini-workout for myself as an example.  I always remind myself of best practices to develop new habits (and make room for them in my mind and life) by reading a post from Leo Babauta’s wonderful Zen Habits site (I’ve been a fan since 2010).  I’ll condense lots of Leo’s advice into a list of three keys for successful goals and habits:

 

• Have a well-defined goal, so you know when you’ve succeeded.
• Have a clear picture of concrete actions to take to achieve the goal, so you’ll act.
• Have a way to monitor your progress towards your goal, so you’ll adapt and stick with it.

 

Let me break this general advice down and translate it into my specific example to give you one idea of how you might make these keys work for you and your scientific discovery goals.

 

1. Define Your Goal

 

Original Poorly Defined Goal:

Spend 30 days of daily practice focused on improving my scientific discovery skill set.

That was the original goal statement I had in mind in January.  Is it “well-defined”?  No.

You can tell if your goal definition is well-defined by how long it takes you to try doing your first day on a program, once you’ve fully committed to starting it as soon as possible.  For me it took me 19 days (I use a Bullet Journal to loosely track events, so that’s how I know) before I sat down and tried it.  That’s a nineteen-day delay after I said, “I’ll absolutely, whole-heartedly start this tomorrow”.

As a general guideline, I have since set seven days as the maximum time from firmly committing to start now and actually starting.  If it takes me longer than that, odds are good I don’t have a clear enough goal in mind, so I procrastinate.

For this example, I was looking for a discovery skill set goal, not a discovery project goal.  At the end of this post, I’ll come back and briefly talk about applying this idea to a topic-specific scientific discovery research project.  But for now, we’re talking about skills.

To come up with a better goal and hit the refresh button on starting my program, I did a few things.  I freed up space.  Literally.  I did a Getting Things Done mind sweep and emptied one room in my house of major distractions (pictures, books, papers, decorative objects).  Then I spent time in my de-cluttered space and asked myself the same question over and over again: what exactly do I hope to be able to do at the end of my 30-day discovery skills project that I cannot do right now?

For me, as a theoretical physicist, a key skill is to be able to generate an equation that represents a new physical idea.  In fact, generating equations is a key part of scientific discovery for many scientists.  So, that’s the skill I wanted to focus on first.  After two weeks of concentrated thinking, I came up with a working solution:  A daily practice I call “creative math”.  I can hear mathematicians groaning already, but physicists are notoriously more irreverent toward math—we’ll happily build the Lego equivalent of a Bugatti so long as it mostly gets the job done.

So, let me re-define my goal now using creative math.

 

Final Well-Defined Goal:

Over a 30-day period engage in at least 30 creative math sessions total, lasting no less than 20 minutes and no more than 1 hour each, with a minimum of 1 practice session a day, excluding Sundays.

 

Now we’re clearer.  I could make a simple tracker (maybe something Bullet Journal style, or using a phone app like Loop Habit Tracker or Habit Bull, or even use a simple mark on a calendar) and just check off as I succeed at completing each session.  And I can (and have) put two timers on my phone, one labelled “CMath 20” the other “CMath 60” to keep me on track during sessions (kitchen timers, Time timers, and Pomodoro apps have also worked well for me).

That’s one key in place to jump start my discovery skills program.  Two to go.  So what do I mean by “creative math” anyway?

 

2.  Define Concrete Actions

 

Original Poorly Planned Actions:

Spend at least 1 hour of butt-in-chair time practicing a discovery skill.

First, I should explain my quirky phrase “butt-in-chair time”.

I use this to specify what I mean by having actually tried on intellectual tasks.  For fitness goals, defining “try” and “effort” is easier: do so many reps, walk or run so far, lift a certain amount of weight, etc.  But how do we define a good level of try for intellectual tasks?  I define butt-in-chair time as the hours or minutes spent actively hand writing or typing up material directly relevant to producing the task outcome.  If you do more work standing up (whiteboard, or machine shop bench anyone?) then you might think of another phrase (Hand-on-board time? Powered-up-tool time?).

These sessions don’t have to be continuous, but the minutes have to add up to the target total.  If I just sit there thinking, having an internal conversation, checking email, WhatsApp or whatever, that time doesn’t count.  But if I’m writing in a notebook at a coffee shop (like right now), at my desk, on the tram, seated at a bus stop, on an airplane…you get the idea.  All of that time counts.  The total estimate won’t be perfect, but it does make me more honest about “Did I actually try?  Or did I just pretend to try?”

From the newly defined goal, I’ve set the activity for this butt-in-chair time as “creative math.”  The goal of the session is to generate an equation that represents a physical situation.  Over the course of the 30 days I should be able to see improvement (or lack thereof) in my ability to invent these equations.

First, I needed to devise a layout for this creative math.  I knew the final session “outputs” (the physical artifacts demonstrating that I had actually completed a session) needed to be pen-and-paper pages (these are easiest to buy everywhere and use everywhere; so no excuses for not completing a session).

Also, not doing it in a digital format (i.e., in an app or using software) helped with another aspect: I wanted to practice using internal conceptual resources, not pulling from external sources.  So, no textbooks, guides, internet searches, or even my own research notes, allowed during the session.  If I could learn to be competent without those tools, I could become more masterful with those tools.

This just left the overall format for my pen-and-paper pages.  At the time I was learning about Mike Rohde’s “sketchnotes” system as part of my on-going research.  So, I adapted his sketchnote task to my idea.  A sketchnote is traditionally a one-page sheet of handwritten and drawn notes, taken down during a talk or lecture, and designed to capture just the essential points, using descriptive doodles and hand drawn fonts.

I adapted this to come up with a creative math template using a one-page style with a central box, which emphasizes that I am looking for an equation, and a doodle and fancy typeface statement outlining the physical situation I want to describe with my equation.  Then I spend 20 minutes to 1 hour filling up the front side of the sheet of paper with keywords, questions, and phrases affecting the physical situation, which I then immediately put into a math form.  Toward the end of my session I combine all the math forms I’ve got into one final equation which counts as my “answer”.

My only other rule is that I avoid using common notation for any of my math.  I do that to avoid (1) biasing myself toward what I think the final answer “should” look like (this also slows me down and makes me more mindful of what I’m doing) and (2) cheating by using equations I already know from memory.

 

You might wonder why I go to the effort of avoiding using things I’ve already learned when working a creative math practice session.  The reason will become clear when I discuss the third key to developing a solid scientific discovery skills practice program in the next section.

Before I close out this section, let me pull it all together and write down a new and improved concrete actions statement:

 

Final Well-Planned Actions:

Spend at least 20 minutes a day minimum, 1 hour maximum, of focused butt-in-chair time producing one page of creative math, at least six days a week.  A completed creative math page includes a statement of the specific physical situation being modeled, a doodle of that situation, and a final guess at one equation that describes an aspect of the situation.

 

Do you see how I keep moving from a generic desire to a specific intent of when and how to act and what specifically to do?  That mental transition is what you’re after before you start your own scientific discovery program.

Now we just need one more piece to have a solid plan we can start and finish:  we need some way to monitor our progress.

 

3.  Monitor Your Progress

 

Original Poor Tracking Idea:

Make daily practice pen-and-paper handwritten sheets and put them in a binder to get a portfolio of practice pieces.

 

Following on the sketchnoting theme and sticking to pen-and-paper, I initially planned to monitor my progress in a very visual and physically tangible way: I was going to make a pile of “stuff”.  The bigger the pile, the more practice I had under my belt!  That pile was going to be handwritten pages representing multiple attempts.  Like art students who have hundreds of practice sketches tucked into a portfolio, I would have creative math pages tucked into a binder.

This was a pretty solid first thought, but it did not get at the heart of my discovery practice goal.  Monitoring pages evaluates my level of consistency and the accumulation of practice hours.  Good information, but not the most important thing.

The most important thing to monitor is: are the invented equations I’m coming up with getting better over time?

To answer this, I had to come up with a simple but more sophisticated way to think about the equations I was creating.

First, I broke the equations into two elements: ingredients and connections.  Ingredients are math variables like mass, density, temperature, etc.  Connections are math operations like subtraction, powers, derivatives, etc.  I then developed a new template to go on the back side of a creative math page.  On it I list and count the number of ingredients and connections in my answer.  Then I look up the actual answer (in papers, sites, textbooks, etc.) and list and count its ingredients and connections.  Then I check to see how much I got right!  The goal is to get all ingredients listed with connections in the proper order.  Only pieces I get right count in my percent correct.

 

This brings me back to why I use unusual notation.  I did two practice sessions in January, full of enthusiasm, and in my Bullet Journal started a collection called “Creative Math Ideas”, so I would have a stockpile of physical situations to use each day during practice.

 

It turns out my enthusiasm was a case of running before I could walk.  They were all good questions, but many of the topics I initially picked did not have easy-to-find answers (the papers were too niche, or science didn’t have a clear answer yet).

To get around this I realized I needed to start out with simpler examples where I had already seen an answer, so I knew one existed.  But I didn’t want to cheat and use memory.  After all, at some point, even the simplest problems were all scientific discoveries.  Two hundred years ago, the vast majority of science known today hadn’t been discovered yet.

So even simple problems are good practice for discovering so long as you actually try to discover them for yourself.

By using non-standard notation and relying on personal experience rather than textbook knowledge, you can treat these problems as creative math candidates.

 

Final Good Tracking Idea:

Estimate at the end of every creative math session how good my invented equation is, by writing down the percentage of ingredients and connections I got right, checked against a known correct math equation for that physical situation.

 

And that’s the final piece in place for a solid 30-day program to improve one of my scientific discovery skills.  I started it eight days ago and so far so good!

 

Trying the 3 Keys with a Discovery Research Project Instead of a Discovery Skills Project

 

You may like this idea of a discovery skills mini “boot camp” that you could do as a yearly goal or refresher.  But what if you wanted to adapt it to a project rather than a skill set?

I’ve dropped some hints along the way as to how this might change.  The three keys must still be met.  But for the first key your goal would be a physical output rather than an ability improvement.  For the second key you would tailor your butt-in-chair time to whatever output you need.  If it’s code it would be coding sessions, a physical prototype then building and modifying parts or refining a  schematic, and so on.

Unlike in my example, which had different problems each session, in each session you would now work on the same problem with one new variation.  If you’re doing equations then session 1 would have version 1 of that equation, session 2 would have version 2 of that same equation, and so on.  You might come up with variations by emphasizing an aspect that’s a strong physical limitation or by emphasizing a failed aspect of the previous version.

Finally, for the third key you would monitor progress by evaluating at the end of every session how well that version fits the solution criteria you need.  Is it cheap enough?  The right size, shape, or speed?  Does it explain the unexplained part?  Does it create the graph features you want to match?

If you spend a little time in the beginning thinking it through, you can come up with a 30-day kickstart to get you putting in meaningful time trying to discover what matters to you.

 

Final Thoughts

 

That’s my creative math practice in a nutshell and how I used three keys of good goal and habit setting to come up with it.  This creative math practice is how I got back on track with my New Year’s Resolution to practice my scientific discovery skills and become a better discoverer daily.  I’m running my 30-day creative math practice program right now and it’s already helped me notice some new avenues to explore in my physics research.

So let’s recap the ideas and examples I’ve talked about in this post:

• I covered an example of how to define a 30-day practice program to improve your skills at inventing equations describing physical situations off the top of your head.
• I discussed the three keys to creating a good practice program: (1) define a clear and specific program goal; (2) define concrete steps to take on a regular schedule; and (3) define a way to monitor your progress toward your goal.
• I pointed out ways you might adapt my example scientific discovery skills program into a discovery research program by using the “butt-in-chair time” idea to produce new ideas on a regular schedule.

If you’ve got your own practice techniques I’d love to hear about them.  Or if you try out the program I’ve shared here I’d like to know how the experience goes.  And if it helps inspire you to a breakthrough let me know!  You can share your thoughts by posting a comment below.

 

Interesting Stuff Related to This Post

 

1. Blog Post – Leo Babauta, “Set Powerful Deadlines,” April 26, 2016, https://zenhabits.net/deadlines/.
2. Web Article – Andrew Krok, “Life-size Lego Bugatti actually works, has over 1 million pieces: It gets its power from 2,304 Lego electric motors”, Roadshow Reviews by CNET, August 30, 2018, https://www.cnet.com/roadshow/news/lego-bugatti-chiron-life-size/.
3. Blog Post – Mike Rohde, “Ideas not Art – Students learn how to use sketchnotes to improve their notetaking in lectures”, December 31, 2018, https://sketchnotearmy.com/blog/2018/12/31/ideas-not-art-students-learn-how-to-use-sketchnotes-to-improve-their-note-taking-in-lectures-1.
4. YouTube Video – Dr. Ellie Mackin Roberts, “Research #BulletJournaling”, December 7, 2016, http://www.elliemackin.net/blog/category/bullet-journal.

 

How to cite this post:

 

Bernadette K. Cogswell, “Three Keys to Creating a Discovery Skills Practice Program”, The Insightful Scientist Blog, March 8, 2019, https://insightfulscientist.com/blog/2019/three-keys.

 

[Page Feature Photo:  Keys in an equipment room in China.  Photo by Chunlea Ju on Unsplash.]

Putting things into categories is helpful.  Sometimes it lets you recognize shared commonalities between things that you didn’t notice before.  Other times it gives you a mental shortcut to know how to interact with something—once you know its category, you’re more likely to know what it’s for and what to do with it.

In the first Insight Exchange I recently hosted at my home institution I structured the process of scientific discovery into a five-phase cycle and I also structured the types of scientific discovery into four categories.  The purpose of this “typology” of scientific discovery was to help guide the conversation in the group.  I also had two hunches: (1) that scientists pursuing similar types of discoveries, even if they are from different fields, will share similar challenges and setbacks; and (2) that each category of scientific discovery has a set of associated strategies uniquely suited to making progress on that kind of discovery.

This idea of discovery categories and associated strategies is a keystone of my goal to build software that helps promotes scientific discovery.  As I work on finalizing a first evolution of a territory map of scientific discovery and strategies (to be released under Spark Points sometime later this year) I keep mulling over the questions: What distinguishes the types of scientific discoveries? And what strategies are most useful for what types of scientific discoveries?

As always, I’m looking for ways to answer these questions that work across a broad range of fields, not just physics.  For the Insight Exchange I used a four-category breakdown of types of scientific discoveries: object, attribute, mechanism, technique.  Each of these are labelled by the primary type of knowledge being sought, as described in the little list below.

 

CATEGORIES OF DISCOVERY

• OBJECT –
  • new object
• ATTRIBUTE –
  • new property of a known object
• MECHANISM –
  • new behavior or phenomenon, or explanation of a known behavior or phenomenon
• TECHNIQUE –
  • new tool or method to generate a known object, attribute, or mechanism

 

For example, in my own field of neutrino physics open questions related to each category would be:

 

EXAMPLES OF CATEGORIES OF DISCOVERY IN NEUTRINO PHYSICS

• OBJECT –
  • do additional neutrinos exist beyond the three known standard model (SM) ones?
• ATTRIBUTE –
  • does the neutrino have a non-zero magnetic moment?
• MECHANISM –
  • what is the origin of neutrino mass?
• TECHNIQUE –
  • how can you develop a detector capable of observing beyond the standard model (BSM) physics using coherent elastic neutrino nucleus scattering (CEvNS)?

 

So, in this classification of scientific discovery, it’s a little like playing a professional version of the childhood question game “Animal, Vegetable, or Mineral?”.

The group in the first Insight Exchange did not seem to take issue with my category labels too much, but many people felt their personal scientific discovery goal did not cleanly fit into one category and listed it as belonging to multiple categories.  So as a workshop strategy, this typology didn’t work out too well (since I had planned to put people into small teams grouped by their category, with the thought that they might share more of the same challenges and, therefore, be better positioned to offer each other feedback).  Best laid plans.  Instead, I ended up assigning teams completely differently (in such a way as to ensure a good diversity of scientific fields and career stages within each team).

So, I’ve gone back to the drawing board a little to keep thinking about this idea of types of scientific discovery.  So far, I’ve been struggling to find the words to yield the right material when doing a literature review search:  Is it “categories” of scientific discovery?  Is it “types”? A “typology of scientific discovery”?  Or maybe “the classification of scientific discoveries”?

My search remains unsuccessful to some degree, but I did find two very short writings that attempt to do the same thing.  The first is an editorial in Science magazine by a former editor of that publication, Daniel E. Koshland Jr. a professor of biochemistry and molecular and cell biology, entitled “The Cha-Cha-Cha Theory of Scientific Discovery”.  Koshland’s theory is that historical patterns of scientific discovery (and non-scientific discoveries) suggest that discovery can be divided into three categories: charge, challenge, and chance.

In Koshland’s theory charge discoveries are about finding a solution to a well-known problem.  In the charge type of discovery, the discoverer’s primary role is to view the same data and context already well-known to all, but to come to some novel conclusion by perceiving that collection of facts in a way no other researcher has.  In the challenge type of discovery, the discoverer’s primary role is to bring cohesion and consistency to a body of well-known facts and/or anomalies that are in tension or lack a unifying conceptual framework.  Lastly, in the chance type of discovery, the discoverer’s role is to perceive and explain the central importance of a known or recently observed fact obtained by accident.  In his editorial, Koshland gives numerous examples of each type of discovery from fields as diverse as chemistry, physics, and biology.

More importantly, he extends his category theory to note an additional pattern:

“…the original contribution of the discoverer can be applied at different points in the solution of a problem.  In the Charge category, originality lies in the devising of a solution, not in the perception of the problem.  In the Challenge category, the originality is in perceiving the anomalies and their importance and devising a new concept that explains them.  In the Chance category, the original contribution is the perception of the importance of the accident and articulating the phenomenon on which it throws light.”

[D. E. Koshland, Science, vol. 137, p. 761 (2007)]

Before I do a little comparison of these different ways to categorize scientific discovery, let me also throw another article into the mix.  Keiichi Noe, a professor of philosophy at Tohoku University, wrote a contribution, entitled “The Structure of Scientific Discovery: From a Philosophical Point of View”, to a book on discovery science.  The actual focus of Noe’s paper is on the mental process by which discovery is achieved and how this might be translated into a computational algorithm.  But to elucidate such strategies, Noe first defines two types of discovery.

For Noe, one type of scientific discovery is “factual discovery”, the “discovery of a new fact guided by an established theory” (p.33).  In contrast, the second type of discovery is a “conceptual discovery”, “which [proposes] systematic explanations of…phenomena by reinterpreting pre-existing facts and laws from a new point of view” (p.33).  In Noe’s framework, the significance of these distinctions is that the scientist must bring a different kind of thought process, in particular a different implementation of the imagination, to the pursuit of each kind of discovery.  For factual discovery what Noe calls a “metonymical imagination” is needed; whereas, for conceptual discovery a “metaphorical imagination” is needed.

In the case of the discovery of new facts, the metonymical imagination refers to a way of thinking in which newly discovered items that are closely related, as seen through the lens of existing theory, are grouped together.  As Noe puts it these “discoveries…complete an unfinished theory” (p. 37).  In contrast, in the case of the discovery of essentially new theory, the metaphorical imagination refers to a way of thinking in which hidden or implied links are created between unrelated items that share common characteristics.  In these discoveries “[a change of] viewpoint from explicit facts to implicit unknown relations [occurs]” (p.37).

If we use the five-phase discovery cycle (question-ideation-articulation-evaluation-verification) as a common grounding point, then these three different typologies of scientific discovery—my quartet, Koshland’s trio, and Noe’s duo—each represent a different way of thinking about the discovery cycle.  For me, the discovery classifications emphasize the type of output desired by the discoverer at the end of the discovery cycle (i.e., after successful verification)—is it an object, a description, an explanation, or a method.  For Koshland the emphasis is on the point at which the discoverer must innovate within the discovery cycle in order to discover something new—either ideation (charge and challenge discoveries) or articulation (chance discoveries).  For Noe, the emphasis is on the overarching viewpoint and mindset that the discoverer applies in moving through the entire cycle—do you use the prevailing view or replace it.

It’s also easy to see that depending on which typology of scientific discovery you use, you will also perceive different strategies and techniques as more useful.  Within my typology, mathematical and logical strategies are better suited to mechanism discoveries, building and prototyping strategies to techniques, and a mix of both to object and attribute discoveries.  For Koshland, strategies that boost ideation or streamline articulation will simultaneously advance discovery.  Noe has explicitly defined useful tactics, the metonymical imagination for factual discovery and metaphorical imagination for conceptual discovery.

Which brings me to the end of my musings for this week.  Some weeks, coming up with an image that sums up my new perspective on scientific discovery is incredibly challenging.  But this week Koshland has made it easy for me:

If scientific discovery is a kind of dance, wherein the dancers become more skilled and graceful with time, producing ever more intricate choreographies of knowledge, then typologies of scientific discovery are merely styles of dance that one can practice.  For me it’s a kind of folksy American square dance or mannered English quadrille, for Koshland a vibrant Cuban cha-cha, and for Noe a delicate French pas de deux from ballet.  But whatever your style for dancing with discovery, knowing the kind of dance you’re in just might help you improve your moves.

In December I plan to post a series of log entries dedicated to the use of analogies.  These posts will be true “log” items, in the sense that they will journal my progress as I try to create a “recipe” for applying analogical thinking in a research physics context, in order to generate insight and foster scientific discovery.

In the mean time, I picked up a copy of psychologist Margaret Boden’s book, The Creative Mind:  Myths and Mechanisms, for this week’s reading, thinking that I would be covering a separate intellectual arena.  But it turns out there was an intriguing section on analogies!  Needless to say, I wasn’t about to set it aside for a month and a half.  So, unofficially, this has become the first log entry in the analogy series.  If you’ve been following the Physicist’s Log and Boden’s name sounds familiar, that’s because it is; I used her in my earlier log entry discussing the definition of scientific discovery “In the Name of Discovery”.

Boden’s book explores the mechanisms, from a cognitive psychology standpoint, that underpin our ability to think new thoughts.  She draws on a wide range of research, most prominently computational psychology (mimicking processes in the mind using computer algorithms).  The advent of computational psychology and its studies into creativity, problem solving, and discovery are fortuitous because a recipe for practice and a computational algorithm are much the same.  Both provide (1) content necessary to obtain a given outcome and (2) a sequence of implementation guiding the use of that content to obtain the outcome.  (This attention to both content and action, I mentioned earlier in the log entry “The Physicist’s Repertoire”.)

Now, discovering why analogies play a role in scientific discovery is an attempt at scientific discovery itself.  So it should follow a scientific discovery cycle, which I’ve formulated as the process of question → ideation → articulation → evaluation → verification.  We can trace this flow in Boden’s discussion, as well as in a resource she cites in her book, essayist Arthur Koestler’s The Act of Creation, which also tackles questions about human creativity.

In the spirit of a recipe, or what Boden might prefer to call a “conceptual space…a structured [style] of thought…that is familiar to (and valued by) a certain group” (p.4), I will use the discovery cycle as an outline to frame my own discussion.  [Physicists respond to the idea of recipes, hence the reason that one of the most heavily referenced books in physics computation is called Numerical Recipes.]

BEGIN DISCOVERY PROCESS…

Question

I’ve already stated the starting question, the frame that highlights the desire to know or do more with something, which ignites a discovery chase:  why do analogies play a role in scientific discovery?  Or the more hypothesis friendly: by what mechanisms does analogy play a role in creativity?  Here we assume that scientific discovery, at the level of an individual thinking up a new idea, represents a sub-type of creativity.  Boden further crafts this overarching question into two sub-questions to frame the main discussion of analogies in her book (pps. 186 – 198):  How are existing analogies evaluated for relevance?  How are new relevant analogies generated?

Ideation

Ideation is about coming up with answers to the questions posed in the first phase of scientific discovery.  For the question of “How are existing analogies evaluated for relevance?” the overall idea presented by Boden as an answer (not necessarily her idea per se, but more a synthesis of the existing research) is that the mind contains a storehouse of possible analogs against which it can compare the present example, and it determines (or selects) a good match based on a set of criteria to be considered (called constraints).  The exact nature of this set of criteria, and the relative importance given to a certain criterion in the evaluation process remains an open question, but three areas are cited: structural match (correspondence between elements or relations), semantic match (similarity between meaning), and “pragmatic centrality” (likelihood that the match is important to the originator of the analogy).  For the second question of “How are relevant new analogies generated?” the overall idea elicited by Boden is that the mind contains a base set of knowledge and a set of descriptive identifiers which classify that knowledge.  When given a source item, the mind tries to generate a target item, drawing from its knowledge base and relying on its descriptive identifiers to tell it which features of the source item are the important ones to be re-created in the new target item.

Articulation

Boden cites studies of computational algorithms designed to either identify the best analog for a target item from among a set of pre-loaded items, or to generate an analog to a target item based on a set of creation rules.  These analogical mapping programs (ACME, ARCS, COPYCAT, SME) represent the articulation of the ideas from the previous phase of the scientific discovery cycle.  They translate an internalized mental conception into externalized physical artifacts, with well-defined content and relations that can be tested.  They are, of course, highly idealized and very simplified, but that’s what makes the clearest science: the ability to tinker with just one feature and see how the world responds, in order to better understand that feature’s role in “how things work.”

Evaluation

In the case of a bit of computer code, evaluating the overall utility of the initial idea is easy enough.  Run the code, interpret the output, i.e., asses the analogy returned and see whether or not it matches what a human being would have provided as the answer.  The more times it does, the more it suggests the processes coded may represent actual processes in the mind.  Some of the codes cited above do match human outputs, so it seems there is something useful to the ideas behind analogies that they encode.

Verification

Which brings us to the last step in the scientific discovery cycle.  I will take an associative mental leap at this point and jump to a discussion of Koestler’s work since, as an exemplar, it fits better into the phase of verification.  Also, much of Boden’s discussion around analogies, throughout her book, is driven by a passage in Koestler’s book (which somewhat echoes physicist Richard Feynman’s comments on discovery, previously covered in “Echoes of History”):

“Thus the real achievement in [scientific] discoveries…is ‘seeing an analogy where no one saw one before’.  The scientist who sets out to solve a problem…in the jargon of the present theory…experiments with various matrices, hoping that one will fit.  If it is a routine problem of a familiar type, he will soon discover some aspect of it which is similar in some respect to other problems encountered in the past, and thus allows him to come to grips with it…But in original discoveries, no single pre-fabricated matrix is adequate to bridge the gap…Here the only salvation lies in hitting on an auxiliary matrix in a previously unrelated field…”

[A. Koestler, The Act of Creation, p. 201]

Koestler defends his conclusion through a study of the role of discovering hidden analogies in two scientific discoveries: Benjamin Franklin’s discovery of lightning conducting rods and Nobel Prize winner Otto Loewi’s discovery of chemical transmission of nerve impulses.

In Franklin’s case, Koestler traces the final insight, or “Eureka moment” as Koestler prefers to call it, to Franklin’s recognizing an analogy between directing a pointed object toward a storm cloud to increase the likelihood of conduction and floating on his back as a boy with a kite tied to his toe being pulled along by the wind.  As Boden suggests, pragmatic centrality is key to Franklin’s analogy playing a role–he only considers a kite a valued analogy to a rod getting closer to a thundercloud because of his relationship with kites as a child as a way to get closer to the wind.

In Loewi’s case Koestler traces the discovery to a hidden analogy between recognizing that medications sometimes had the same effect on organs as observations of stimulation by electric impulse, and yet the drug case relied on a ‘soup theory’ (the correct analogy of a mechanism diffusing in a liquid), whereas, the impulse model relied on a ‘spark theory’ (the incorrect analogy of electricity jumping a gap or being conducted along a wire).  Here it’s a combination of pragmatic centrality-recognizing the importance of medication effects- and the weighting of value criteria that help select the preferred analogy.

“Verification” may be in the eye of the beholder here, but nonetheless Koestler’s approach to seeking case studies shows the idea behind verification, to take your articulated ideas into the real world and see if they hold up.

…END DISCOVERY PROCESS.

Like all of the readings that appear in these logs, there’s a lot to process and it can be difficult to translate it into your own habits and work.  Over the next few months it will be a key goal here at The Insightful Scientist to help shoulder some of that processing burden by trying to distill this wealth of research into everyday actions instead of leaving them as one-time theories.  But as for the logs, I’ve found it helps to come up with short “one-liners” that capture the heart of what I should be trying in my own work.  These one-liners are what appear as the titles to many log entries.  If I remember nothing else of what I read (or wrote!) then at least I always carry with me those titular reminders to “Feed the White Wolf”, that “What You Fire is What You Forge”, that “Representation (Not Rightness) Rules”, and so on.

For this week, I was struck by the analogy Koestler uses to summarize his thinking that the basis of discovery is finding new analogies:

“The essence of discovery is that unlikely marriage of cabbages and kings [a reference to a Lewis Carroll poem]—of previously unrelated frames of reference or universes of discourse—whose union will solve the previously unsoluble problem.  The search for the improbable partner involves long and arduous striving—but the ultimate matchmaker is the unconscious…the greater fluency and freedom of unconscious ideation; its ‘intellectual libertinage’…[its] indifference towards logical niceties and mental prejudices consecrated by tradition; its non-verbal, ‘visionary’ powers.”

[A. Koestler, The Act of Creation, p. 201]

Then my job as a discoverer is to seek the improbable partner, the previously unconnected and seemingly unrelated universes, whose union will make a more expansive whole.  Who knows: maybe pineapples and pools, the theme of this week’s log entry image, is a visual union that contains within it an intellectual union worthy of discovery.