Memorization and Learning

It’s funny how after so many years of being in school, there’s always something new to learn — especially about learning.  You would think that after more than 20 years of schooling somehow I would have learned every tip and trick, but nope, my ignorance seems to know no bounds.  Recently I rediscovered a technique that has greatly improved my learning: memorization.

Memorization and I have a very complicated history.  When I was but a wee lad, I was frequently forced to learn things using memorization such as: Chinese, piano and of course, the usual memorization-laden subjects in school: history, geography, biology, etc. Even though I was not particularly adept at memorizing things, I will say that I got through these formative years with relatively high grades by having a lot of assiduity (as Charlie Munger likes to say “sitting on your ass until you do it”) but these experiences left a sour taste in my mouth for memorization.  The subjects I much greatly preferred required less rote memorization and more thinking (just my kind of problems!).  Subjects like math, computers, English 1, physics and chemistry2 were really my cup of tea.  You just have to understand a single concept or technique and all of a sudden you can answer hundreds of questions, talk about efficiency!  Of course, it still requires a lot of practice but it was a huge departure from trying to memorize a list of seemingly random facts.

As with many things in life, I feel like I’ve come full circle.  Recently, I’ve started taking Chinese classes again as well as music lessons (mostly guitar, a bit of vocal).  One thing that is crystal clear is that memorization is hugely beneficial… in certain contexts.  For example, my Chinese speaking has always been lacking.  I was a shy kid and didn’t really make much of an effort to speak in Chinese.  However by memorizing and reciting the textbook lessons, character by character, I’ve been told my Chinese has improved quite a bit.  There’s something about actually moving your mouth, activating your vocal chords, and having sound come out that gets your neurons connecting properly.  Another more obvious explanation: if I wanted to get better at speaking, I should practice speaking more!  However, I will point out that the opportunities to memorize a piece of text and say it out loud are much more abundant than speaking to an actual human.  In any case, a very useful application of memorization.

The other domain where I’ve found memorization very useful is in music.  When I was young, I thought music was about memorizing the correct order of notes to play.  In my case, it was keys on a piano.  I just had to figure out the right order and timing and I was done!  Unfortunately, piano was not conducive to my learning style at the time and I quit after only a few months.  However, while learning guitar as an adult, I realized that playing an instrument was so much more than just hitting the right notes.  There are so many more abstract, higher level concepts like the subtleties of rhythm or the attack on the guitar.  If you’re still thinking about which note to play, you’re definitely not thinking about these higher level concepts.  Similarly, when trying to do two things at once, like singing and playing, there are so many things going on that you can’t be thinking about all of them at once.  Enter memorization.  By memorizing the “easy” parts, like the notes and lyrics, you can start focusing on the important parts of playing.  In the case of a B.B. King solo, getting every little lick to sound just like the king, signature vibrato and all.  Or when singing and playing, knowing the lyrics down pat, and playing the notes without thinking so you can concentrate on actually getting them to work together.  Again, another important application of memorization.

My conclusion (which in hindsight is quite obvious) is that memorization has (at least) two important purposes.  First, use memorization to physically enable you to learn something.  That is, getting your muscle memory working and all the related neurons in your brain.  For example, speaking a language, or playing a musical instrument.  There’s really no other way around learning these things except by rote practice — that’s how our bodies work.  Second, use memorization to be able to ignore the “easy” parts so you can concentrate on higher level parts.  For example, memorizing the lyrics to a song so you can focus on the actual singing, or memorizing the multiplication table so you can focus on algebra.  In this application, memorization is a means to end to learn some higher level concept that is too difficult if you don’t have the basics down.

I’m quite excited by my new (obvious) discovery on the usefulness of memorization.  Although I probably should have come to this conclusion sooner, I’m quite happy to be a little bit less ignorant.  One of my goals in life is to be less ignorant, an ignorance removing machine if you will3.  After all, you just have to be a bit better at ignorance removal and you won’t get eaten by a bear.


  1. Essay writing requires coming up with a structured argument/flow. []
  2. We really didn’t do a lot of organic chemistry in high school so most of the subjects was explaining concepts or doing a bit of math. []
  3. That’s just a cute way of saying that I like to learn. []

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Consistency and Commitment

If you’ve read my last post, a good model for human behavior is that of a moist robot.  We’re nothing more than mere automatons (or automata) except that we’re wet and squishy.  This post is about another common behavior among us squishy automatons: consistency and commitment.

Influence by Cialdini describes this tendency in detail as one of the big six principles for how others manipulate our squishy brains into doing things that we otherwise wouldn’t want to do.  The funny thing about these psychological tendencies is that we’re mostly blind to them — psychology researchers included!  A recent blog post by Andrew Gelman discusses how scientific research in (certain parts of) the psychology community is quite lacking (particularly from a statistical point of view):

The short story is that Cuddy, Norton, and Fiske made a bunch of data errors—which is too bad, but such things happen—and then when the errors were pointed out to them, they refused to reconsider anything. Their substantive theory is so open-ended that it can explain just about any result, any interaction in any direction.

The interesting thing is that one of the main authors, Fiske, is a well-respected researcher in her field.  Gelman is commenting about an open letter she sent criticizing social media and all the negative comments her research has received.  She asserts that the comments should be done in a “moderated” (read: research journal) where an editor can filter inappropriate comments.  While in theory it sounds good, practically, these psychology researchers are falling for consistency and commitment fallacy.  They believe that (implicitly) just because their research is published and they are well-respected then it has to be good (or at least not bad) even in the face of obvious errors, from Gelman’s post (emphasis mine):

She’s implicitly following what I’ve sometimes called the research incumbency rule: that, once an article is published in some approved venue, it should be taken as truth. I’ve written elsewhere on my problems with this attitude—in short, (a) many published papers are clearly in error, which can often be seen just by internal examination of the claims and which becomes even clearer following unsuccessful replication, and (b) publication itself is such a crapshoot that it’s a statistical error to draw a bright line between published and unpublished work.

Ironically from a psychological point of view, it all makes sense.  If your worldview is different from reality, what’s easier to believe: the nice rose-tinted glasses of your existing worldview or the unpleasant scent of cognitive dissonance?  From Gelman’s post:

If you’d been deeply invested in the old system, it must be pretty upsetting to think about change. Fiske is in the position of someone who owns stock in a failing enterprise, so no wonder she wants to talk it up. The analogy’s not perfect, though, because there’s no one for her to sell her shares to. What Fiske should really do is cut her losses, admit that she and her colleagues were making a lot of mistakes, and move on. She’s got tenure and she’s got the keys to PPNAS, so she could do it. Short term, though, I guess it’s a lot more comfortable for her to rant about replication terrorists and all that.

Funny enough, this is not the only ironic situation that I’ve come across.  If you’ve hung around enough programmers and data scientists, I won’t have to tell you that they’re (at least they say they are) all about “logical reasoning” and “rational thought”.   But from what I’ve seen, they’re some of the most wet and squishy amongst us all.  Just ask them what’s wrong with X, where X is any of the most popular programming languages, just be ready to sit through a half an hour rant that is heavily rooted in opinion over facts.

There are worse things than highly opinionated people though.  The worst case scenario is where consistency and commitment tendencies block the person from seeing their own mistakes, from Gelman’s post:

We learn from our mistakes, but only if we recognize that they are mistakes. Debugging is a collaborative process. If you approve some code and I find a bug in it, I’m not an adversary, I’m a collaborator. If you try to paint me as an “adversary” in order to avoid having to correct the bug, that’s your problem.

Being open to collaborators who point out our flaws (hopefully in a congenial way) is really the first step in learning.  Think about all the teachers you’ve had, probably half of the learning comes from them pointing out mistakes (the other half from them teaching you a better way of solving the problem).  Whether you’re a toddler or a famous researcher, everyone needs help finding their mistakes, and more importantly everyone can learn from them.  It’s easier said than done though because, after all, we’re just moist robots.

Moist Robots

Scott Adams has this really memorable term for how to think about people: moist robots.  Moist because — well, we’re wet and squishy.  Robots because there are certain predictable behaviors that we repeat.  In situations like A, most people do f(A).  In situation; in situations like B, most people will do f(B).  Sounds very robot-like to me1.

Now I’m sure you can remember a time where this rings true, everyone has that friend who thinks their going to win big at the casino despite what hundreds of years of math suggest.  This is great example of irrational behavior that has been programmed (in one way or another) into many of us moist robots.  Most of the time our squishy brains are great at detecting these problems but only when it’s not about us.  It’s easy to see your friend has no idea about probability, but much harder to see why you’re such a sucker for Instagram.

As you read this sentence, you can imagine how this story can play out in any number of ways.  Here’s a very common story, you see someone who is super successful, perhaps someone famous you saw on the interweb.  Naturally, you wonder how you can become super successful.  Your parents will tell you that all you need is hard work.  You’re not so sure since they also told you that the Easter Bunny was real, so you also read Ms. Super Successful’s biography.  The book tells you it was her passion that drove her to eventually become CEO of company X.  You’re mostly convinced but to be sure you listen to her commencement speech and any interviews you can get a hold of, and they tell you the same thing: hard work and passion got Ms. Super Successful to where she is, and so can you2!

But let’s not forget about one very important thing: we’re all still moist robots.  A very common pattern (or cognitive bias as the psychologists call it) with moist robots is that of self-serving bias: we have the tendency to perceive oneself in an overly favorable manner.  So when Mrs. Super Successful is attributing her success to something, she overemphasizes the aspects that she has control over, namely: hard work and passion.  Never mind the fact that her parents were in the 99th percentile of wealth, had connections with the trustees at an Ivy League school, and had an abundance of opportunities to learn the skills she needed to be successful — all she really needed was hard work and passion.  Which leads to another pattern called survivorship bias where us moist robots mistakenly draw conclusions about what worked based on who survived (e.g. successful people) instead of the entire population (e.g. all people, successes and failures).  If you looked at the people who worked hard and have passion, you would see a surprising number of moist robots who weren’t successful3.

And so a successful moist robot tells another young moist robot, how to be successful and we see a predictable pattern of behavior again (you’re a young moist robot in this situation).  You have multiple authority figures (parents, teachers, successful people) telling you to one thing (authority bias), these people are generally very likable (liking bias), and you see that all successful people have worked hard and followed their passion (suvivorship bias and bias from mere association), it’s no wonder we’re programmed to think that hard work and passion alone are enough to be successful.  We’re just moist robots after all.

Having said all of that, I’m still a big fan of working hard and following your passion.  It’s a pretty good program as far as us moist robots go.  I would add another crucial subroutine though: luck (or more aptly randomness).  Everything that happens is so dependent on our surrounding environment, which we rarely have control over, hence luck.  If you’re not factoring in luck then you’re not factoring in reality.  The trick is that your program should seek out and maximize the situations where luck is on your side and correspondingly move out of positions where it isn’t.  After all, what good is a (moist) robot if you can’t program it?


  1. If you want to learn more about moist robots, then you might be interested in getting Scott Adams’ book: How to Fail at Almost Everything and Still Win Big: Kind of the Story of My Life []
  2. Phrase intended, check out Stephen Colbert’s book []
  3. Although, you rarely see a successful person who doesn’t work hard and have passion.  It’s kind of a necessary but not sufficient condition []

On Vaccines, Guns and Good Science

Atul Gawande, the surgeon, writer, and public health expert (and author of a couple of my favorite books: The Checklist Manifesto and Better), delivered a commencement speech to CalTech recently, here’s an excerpt:

“The scientific orientation has proved immensely powerful… But even where the knowledge provided by science is overwhelming, people often resist it—sometimes outright deny it. Many people continue to believe, for instance, despite massive evidence to the contrary, that childhood vaccines cause autism (they do not); that people are safer owning a gun (they are not); that genetically modified crops are harmful (on balance, they have been beneficial); that climate change is not happening (it is).”

He goes on to describe how pseudoscience “experts” propagate these types of common sense misinformation while simultaneously dismissing scientifically established facts.  If you haven’t read the speech, I highly recommend it.

Beyond what the speech has to say, I want to emphasize two important points about good science and good explanations.  Science does not equal truth; it is incomplete, sometimes out right wrong, but that’s a feature not a bug.  The big reason science works is because you can show that these hypotheses are wrong.  Once you do, you can learn from it, and form a new hypothesis or model that describes the world a bit more accurately.  The established scientific knowledge that we take as “facts” have withstood every attempt we throw at it to prove it wrong; the ones that show cracks have either been discarded or corrected, what could be more rational?  On the whole, science moves forward by explaining the world with increasingly more accurate approximations — never complete but always self-correcting.  Contrast this to common sense and pseudoscience which never seem to move until contradictory scientific knowledge becomes so pervasive that it is forced to change.

The complementary point to this is about good explanations.  It’s natural for people to cling to their seemingly plausible pseudo-scientific beliefs even when faced with overwhelming evidence to the contrary (e.g. vaccines).  Despite this overwhelming evidence that science provides, it is hard for someone to change their world view and part with their intuitive beliefs — it’s not natural.  The scientific community has recently been doing a poor job of explaining science to the masses.  This is probably because many “intellectuals” find it necessary to attack the bad science instead of focusing on the good science.  What these “intellectuals” forget is that attacking bad science is the same thing as attacking a person’s world view — a very personal thing!  It’s no wonder that people don’t respond well to it.  Instead of rebutting bad science, a better approach is to explain the good science.  For example, instead of berating someone for thinking vaccines cause autism, explain how they can save their child’s life and the lives of many other children (who doesn’t want to save children?).  It’s not enough to tell them their wrong (besides being ineffective), instead it’s important to help them understand how the evidence is right.  This is how things change, not with scientific discoveries but with people.

It’s important for all of us to realize that rational thinking and cold hard facts aren’t the end of an argument but rather its beginning.  What comes after is what we scientists and intellectuals often forget: the human component.  Ironically, it’s all too human to think like this.  So next time you’re trying to educate someone on good science, don’t forget that the most important part: good (human) explanations.

You can’t outrun a bear

Here’s another old joke:

Two friends, John and Mark, are camping when a bear pops out of the bushes.
John starts to put on his tennis shoes.
Mark says, “What are you doing?  You can’t outrun a bear!”
John says, “I don’t have to outrun a bear — I just have to outrun you!”

This joke has two important lessons: (1) you really can’t outrun a bear, and (2) skill is relative.  The latter point is something Charlie Munger often emphasizes in a different way:

“You don’t have to be brilliant, only a little bit wiser than the other guys, on average, for a long, long time.”

I like this sentiment.  Just like John outrunning Mark, you just need to be a bit — even a tiny bit — better than the other guys with the big caveat that it’s for a long time (on average).  So less of a sprint and more of a marathon kind of idea.  It just takes a little discipline and an hour a day.

In any case, if there’s one thing I’ve learned in my short time here it’s this: always wear running shoes when camping1.


  1. Or is it only go camping with people slower than you?  I always forget. []