Category Archives: Psychology

Knowing the Fundamentals

Ok, I've been sitting on this topic for awhile, but I was recently inspired to revive this post after being asked some very general questions by a tour group that came through the lab. Next time we'll be back to doing some data collection and analysis. Maybe gravity tide measurements? Anyhow, on with the topic of the day: knowing the fundamentals.

On a (now old) episode of the podcast Technical Difficulties, Gabe Weatherhead (@macdrifter) was chatting with Brett Terpstra (@ttscoff) and Rob Trew (@complexpoint). The show was mostly about how everyone got started writing computer code and some tool suggestions. One line made me stop on my walk home to type it into my reminders to write this post.

Rob quoted a line from the Windows 95 API Manual (a programming interface manual for the non-programmers out there). It said "The nature of an expert is not someone who knows all the details, it's someone who understands the fundamentals really well." Rob points out that therein lies the key to problem solving. This statement really resonated with me when I looked back on problems that I've encountered in the past, both scientific and technological.

We often think of an expert as someone that is in the top few percent of the knowledge leaders in their field. Experts should know all of the details of their subject, including the latest "bleeding edge" research right? While many experts do stay up to date, I began re-examining the people that I considered to be experts.

The professor may be the ideal example of this. While academics often get the connotation of the aloof and socially insulated genius, it's really not true. (In fact, our academic heroes are just people too, listen to the latest Nerds on Draft for that side story.) Professors have to teach the same material over and over again during their career. Sure, they should be pushing the frontiers of their field within their research group, but that's not what should be done in the education potion of the career. When you teach something, you end up deeply learning it yourself. In fact, that is part of the value in teaching! Be continually re-iterating the fundamentals to ourselves, we can stay primed to approach a new problem with a honed set of tools.

What could these fundamentals be? Well, that depends on your work. Maybe it is knowing the basics of programming or how to do basic chemical balance/thermodynamics calculations. Maybe it is knowing the fundamental operation of the product that you sell, or knowing the backstory to a concept you are helping someone with (such as the history of a topic).

I can't count the number of times that I've been trying to figure out a solution to a problem or how to build something when, after hours of no progress, something will make me start again. This time I look from a fundamentals viewpoint and can generally see a way to a solution or at least enough of the way to be able to ask an intelligent question.

Ideally, we are prepared for this way of problem solving by getting the basics of many fields during our undergraduate careers. Unfortunately that doesn't always happen. We have all sat in a math class, economics class, etc when the professor goes deep into a subject that they adore and leaves us in the dust. Another common occurrence is that the application of the fundamentals is not shown or sometimes not even implied. Not that students should be guided by the hand to the solution, but sometimes a firm nudge is necessary. I didn't necessarily appreciate this early in my undergraduate career, but later became a mass consumer of basic knowledge.

Next time you are on Amazon or in the library, browse over to a section with a topic of interest and pick up an introductory book. Read some sections, try some problems, and you'll be amazed at the other angles you can suddenly see as avenues of attack to a problem. You can even pickup some of your old text books and remind yourself of the fundamentals that all too often slip from our minds with time.

Literature Inertia - Maintaining Stability or Discouraging Exploration?



Recently I've been thinking a lot about literature inertia and the best ways to accommodate and deal with it.  What is literature inertia? It is a phrase that a professor I had at Penn State used to describe the common theme in fields of research where things are done a certain way because that's the way they have always been done.  Everyone bases their analysis or technique on one "seminal" paper at some point in the past.  The methods in that paper are likely the first methods tried that succeeded, and everyone has used them ever since.

I can see some benefits to literature inertia.  For one, it provides a consistent way things are done or a "standard" analysis program that all scientists in the field use.  This kind of stability allows long term comparison and inter research group comparability.  That's fantastic! Maybe the method isn't exactly ideal, but it is the same everywhere and eliminates some of the variables that would otherwise be present.   Inertia of a field also means that the wheel isn't re-invented all of the time, which saves the researcher time and lets them pursue the research, not the methods.  But is that best for the advancement of science?

The downsides of literature inertia are just as significant as its advantages though.  The original methods or code that become the "standard" is likely one of the first that worked well when the research was in the discovery phase.  It is also, by necessity, a bit old.  There are likely better methods developed that could produce better results.  I also believe that the pressure to use a standard procedure is discouraging exploration.  Funding isn't commonly given to explore and test new ways of solving a solved problem! Literature inertia can also bias a field against an idea for decades.  There are some sub-disciplines that are considered to be very delicate research areas.  Working on these new and poorly understood areas runs the risk of having your career marked early as being a borderline crank.  Many reasonable ideas have been floated in these fields, but quickly shot down by those following the inertia.  Often these ideas are thrown out with little work done to legitimately check their validity.  Likewise, one true crank can make an entire area taboo for all researchers.

So what's the answer to this problem? Well, like so many things in science, it probably lies in the gray area in between.  While some stability is needed so that each researcher isn't approaching a problem from completely different directions, there should be less discouragement of exploration.  Standards are also temporary.  Nothing in research is truly permanent.  Standards will become out-dated and need replaced.  This process isn't easy, painless, or fun, but necessary if science is to remain current and relevant.

Computer data formats are one example I can think of to illustrate inertia. There are many great formats that will stick around for some time such as JSON, HDF5, NetCDF, etc.  Some labs still insist on having their own data format though! This is puzzling because the computer scientists have done a very good job of making a flexible data format that is supported by most major programming and scripting languages.  The labs using in-house formats must distribute readers (normally only in one or two languages) or share bulky text files to collaborate with others.  Why do these labs insist on their format? Because it is what they have been using for years and they don't want to invest the time and effort to change to a more open format.  Inertia, for those groups, is crippling their ability to use more recent tools.  That matters because if more tools are available to analyze data and they are easy to use, researchers will find it easier to explore their data.

Another example is inversion techniques commonly used to solve for things like earthquake location problems.  Some fields are using inversion techniques that came about in the 1950's.  These techniques work, in fact, they have been tuned over the years to work very well.  For operations on a day to day basis, that stability is important.  It is the job of researchers to try new techniques though and explore/improve.  Every technique has a weakness, and trying many is important!

I do think that many standard techniques will be challenged with a new group of researchers coming into the job market, but I am concerned about how going against literature inertia could damage long-term job prospects.  I've heard well respected traditional faculty say things like "This computer data management problem isn't a decision for you early career people or something you should be involved with."  Like wise I've also seen some excellent ideas get pushed out because it isn't the same way things have always been approached.  This attitude is likely propagated by the pressure to publish and the damping that puts on free exploration.  What do you think?


Rule #32 - Enjoy the Little Things (and why it's scientifically sound)

I'm going to deviate from my normal format of the physical sciences and take a short look into the human brain.  Anyone who has seen the recent comedy 'Zombieland' probably remembers the rules that the main character (Columbus) had.  If fact, Columbus had many rules including: cardio, double tap, beware of bathrooms, don't be a hero, etc that he used to survive Zombieland.

Today I came across an article about happiness and it made me recall the rule he learns from a redneck type called Tallahassee.  The rule (#32 in his book) is 'Enjoy the Little Things'.

The Psychology Today article (found here) examines the negative bias of our brains.  Scientists have shown time and again that negative events have more influence on us that positive events.  While this is likely a remnant of what helped us survive early on, it can sometimes get in the way of happiness today.  Just think about the last time one bad event ruined a whole day, week, month, or in extreme cases, ruined your year.

A group of researchers even examined married couples and found that those who were long-term and happy had a ratio of happy to bad events of about 5:1.  Couples that had less, especially much less, were more likely to get separated in the near future.  For years we've heard that marriage isn't 50:50, but who would have guessed it's approximately 83.3:16.7?

After the study, scientists have decided that we are better of to enjoy more small happy events than just a few great big happy events.  While doing something big like buying a new car may make you happy, it can be negated by just a few simple bad events.  If you had many small happy events in life, you would likely be a happier person.  To sum it up: enjoy the little things!