So the brain is the most complicated computational device in the known universe.
The brain is indeed the most complex device in the known universe.
But it’s far from perfect, and the human brain, despite all its amazing features and
abilities, has many glitches and problems and brain bugs.
One ability that the brain has is to store memories, and we store memories of many different
shapes and forms.
But the human brain is also very fallible when it comes to memory.
And there’s some things that the brain is very ill-suited to remember.
And those things are like long lists of numbers or long lists of unrelated words—or names,
for that matter.
And one of the reasons why is: it goes a bit beyond this notion that we didn’t evolve
to remember numbers or we didn’t evolve to remember names, which is certainly true.
But it’s a bit deeper than that in terms of the architecture of the brain.
So one of the operational principles—to the extent that we understand how the brain
works, we can refer to one of its principles.
One of its sort of design principles, if you will, is what I’ll call an “associative
architecture”.
Much of what we understand about the brain is based on associations.
If somebody says, “What’s a zebra?”
you know what a zebra is in part because what that concept is associated with.
You might associate it with Africa, with black and white stripes, with “it looks like a
horse”.
So we understand to a certain degree the world around us based on associations.
Now when we’re memorizing long lists of numbers or random names, they don’t come
with any built-in associations.
So this results in something that sometimes we call the Baker Baker paradox.
And the Baker Baker paradox is that it’s easier to remember somebody’s profession—if
they tell you “I am a baker”—than it is to remember their name if they tell you
“My name is Mr. Baker.”
It’s the same word but the brain is better able to store that information in the context
of a profession.
So why is that?
Because when somebody says “I am a baker,” implicitly and unconsciously the brain has
a number of associations that are already built in with that concept.
So maybe you think of getting up early, maybe you think of funny hats, maybe you think of
bread.
Now when somebody says “I am Mr. Baker,” that name by itself doesn’t have any implicit
connections.
So it’s sort of standing alone, so you don’t tap into the associative architecture of the
brain, of your neural circuits, which have all these links and connections between concepts
and words and images and knowledge.
So the brain as a computational device is well-suited for certain types of information
storage and processing, and ill-suited for others.
And understanding what our natural strengths and weaknesses are certainly makes us capable
of making better decisions.
Many of the decisions we make end up being good decisions, but many of the decisions
we make are poor decisions, and sometimes we make decisions that are not in our own
best interests.
In order to understand how the brain makes decisions, of course, is a mystery—we don’t
fully understand how the brain works or where our decisions come from—but as a simplifying
rule we do have, we often simplify it into having two systems within our brain.
Sometimes we call those the automatic system and the reflective system.
The automatic system is sort of quick, and sometimes you can think of that as your intuition.
It’s associative in nature.
It’s emotional.
It makes quick sort of heuristic decisions.
Whereas the reflective system which is much more deliberative, knowledge-based, relies
on symbolic reasoning.
Now to get an idea of these two systems in operation, as a very loose analogy I can ask
you “What do cows drink?”
So the part of your brain that just thought of milk is your automatic system.
So that’s the system that didn’t reflect on the answer, just popped into your head,
“milk.”
Now hopefully your reflective system said, “Whoa, whoa, whoa.
Wait a minute, that’s not the correct answer.
Cows drink water.”
So your reflective system is a bit slower and it’s a bit fact-based.
It can analyze what’s just happening.
So in some situations we—in very complex situations in which we don’t have all the
facts and we have to make a quick decision, yes, the automatic system can help us survive.
If you recognize a dangerous situation in the middle of a jungle and you have to act
quickly maybe it’s a good thing to just emotionally react to that and either fight
or flight.
Now the reflective system in most situations comes with education and learning, and we
have to struggle a bit more to tap into the full potential of our reflective system.
And I think as individuals in a society we—much of the learning and education we go through
requires tapping into our reflective system.
Another example is if in certain math problems or with probability problems, those are things
where the brain’s intuitive system (or automatic system) don’t work very well.
So if I say well, I’m going to throw up four coins.
What’s the probability that I’ll get two heads and two tails?
So there a lot of people, your brain wants to say “half, the probability is 50 percent
that you’re going to get two tails and two heads.”
If there’s anything we know from probability is that you shouldn’t trust your automatic
system, because the brain didn’t evolve to do probabilities quickly or to estimate
complex mathematical questions automatically.
So you need the reflective system to come in and figure out the answer to that question,
and that it’s 6/16 if you go through the number of possible combinations there.
So you have this balance between your automatic system and your reflective system that’s
driving most of our decisions.
And part of our own self-knowledge requires that we come to understand when we should
rely on one and when we should rely on the other.