52 Aces

Learning, competition and capitalism

Memory

Photographic Memory

This is part of my 5 Minute Concepts series, which is designed to help you understand fundamental concepts about subjects like learning, memory and competition in the shortest time possible. Each episode is available in video format on my YouTube channel and audio via my podcast. If you prefer to read, the transcript is below.

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Transcript:

Let’s talk about photographic memory, a topic that I’ve been asked about more times than I can count.

First, we need to get one thing out of the way: photographic memory is a myth. That’s right, nobody has ever been able to prove that they have a photographic memory.

The reasons for this are related to what I talked about in Why Your Brain is Lazy, namely that your brain is forced into making trade-offs because it uses so much energy all the time.

When your brain encounters a stimulus in the world, it makes a decision to either keep it (what’s called “encoding”) or get rid of it. This is based on whether your brain sees the stimulus in question as salient.

If it is salient, then the encoding process will probably kick off, and if it’s just an everyday, run-of-the-mill stimulus, then it won’t.

Your brain will always make this trade-off, and no amount of training can circumvent such a fundamental biological principle. It’s an evolved survival strategy designed to reduce the amount of energy that gets wasted during the memory formation process, and there’s no way to escape it.

The question, then, is: why do so many people believe that photographic memory is real?

A simplified answer is that popular media loves to use it as a short-hand for high levels of intelligence and most people don’t question that stereotype.

Elon Musk is often used as an example of the hyper-genius who possesses a photographic memory, but as far as I know that claim about his memory has never been tested.

The more complete answer is that there are people who do exhibit extraordinary memory abilities, and those abilities get mis-classified as photographic memory.

There are some people who could be called savants who have exhibited world-class memory abilities. Kim Peek, the inspiration for Dustin Hoffman’s character in Rainman, could absorb incredible amounts of information in one sitting. Stephen Wiltshire is a savant who can recreate skylines in incredible detail after seeing them once.

These savants do have great memories, but there are two important qualifiers to consider: 1) their memories are never good enough to qualify as “photographic” (Stephen Wiltshire’s pictures contain many mistakes, for example), and 2) the memory abilities they possess appear to come at a huge cost, as they’re not able to take care of basic everyday tasks.

So their increased capacity for memory is a trade-off that doesn’t appear to be beneficial to their survival, which says a lot about how evolved the standard memory algorithm is.

Some people also exhibit what’s called hyperthymesia, or superior autobiographical memory. This group of people have an uncanny ability to remember the minute details of their day-to-day lives.

These individuals appear to have some kind of focused memory algorithm that doesn’t envelop their overall memory abilities. In other words, their brain prioritizes a specific type of information for encoding, but that benefit doesn’t overlap with any other facet of their memory. 

One last example is the group of people who compete at memory competitions. Memory competitors do things like memorize entire decks of cards within a few minutes.

This is all accomplished with the use of what are called mnemonics, which are memory tricks that can be used to memorize (for short periods of time and with lots of practice) specific bits of information.

Nobody with a claimed photographic memory has ever won a world memory championship, which is hilarious since you’d think that’s where they’d show up. If you had a photographic memory, why not cash in on it?

Anyway, the general idea to take out of all this is that photographic memory doesn’t exist. You can improve your memory in specific ways with specific techniques, but overall you can’t get away from the fact that your memory automatically tosses most of what it encounters.

Why Your Brain is Lazy

This is part of my 5 Minute Concepts series, which is designed to help you understand fundamental concepts about subjects like learning, memory and competition in the shortest time possible. Each episode is available in video format on my YouTube channel and audio via my podcast. If you prefer to read, the transcript is below.

Want to know when new content shows up? Sign up for my newsletter here.

Transcript:

Let’s talk about why your brain is lazy.

The short explanation is it’s lazy because it has to conserve energy as much as possible.

Energy in turn needs to be conserved because, even though your brain only represents about 2% of your total body mass, it burns through about 20% of your daily calories.

In other words, your brain’s an energy hog.

The reason it’s a hog is because it runs everything in your body, and keeping the human body running is, to put it lightly, a complex task.

Why is it so complex? Well, most of what you do is unconscious. Conscious thought only represents a small portion of the work your brain is doing.

For example, you don’t run your nervous system or maintain your internal organs with conscious thought. That’s all happening in the background, and all of it takes energy.

Your brain is never really turned “off” as a result, even when you think it is — it’s always busy managing something in your body.

This is why the whole “you only use 10% of your brain” myth is such a joke. Your brain is a constant hive of neuronal activity at all times, and it’s because the brain is busy running the whole system. This is true even when you’re asleep — so even if you think you’re idle and nothing is happening, your brain is busy running everything.

Anyway, because it’s so busy allocating resources all over your body, your brain has developed a long list of cognitive shortcuts as a means of saving energy.

Forgetting is the best example of a cognitive shortcut: your brain forgets most of what it encounters because it would be too energy-intensive to remember tons of useless data.

The brain is thus optimized to be, to borrow someone else’s terminology, a “change detector.”

Your brain’s lazy memory algorithm focuses on encoding new memories that are salient, and dropping everything that isn’t memorable.

For example, you’ll remember if, during your daily drive to work, a zebra steps in front of your car even though you live in a major metropolitan area. That’s such an unusual event that it’s guaranteed you’ll remember it — it’s so salient that your brain will encode it as a memory.

This is why repetition is important in learning: you need to tell your brain (through repeated exposures) that a given stimulus is worth the energy expenditures involved in remembering it.

Your brain does this in order to take note of environmental cues that could potentially influence your survival. 

We suck up anything that’s unusual because big changes in our environment can be dangerous. A zebra running around in the street could be indicative of a problem at the local zoo, which in turn could mean there are dangerous animals running around that could eat you.

With all of these lazy shortcuts, your brain is making a trade-off of some kind. The brain is asking itself: “Is it worth pouring resources into this?” And if the answer is “no” then the brain doesn’t prioritize itself around that stimulus.

People get frustrated with these shortcuts, but they’re an indicator of a healthy brain. There are exceptions, like Alzheimer’s or CTE, but in general shortcuts like forgetting mean your brain is acting in accordance with its lazy nature.

The key idea to pull from all of this is that your brain does what it does for a good reason, and you won’t be able to learn or use your memory well if you don’t understand these internal dynamics. 

Much of learning revolves around finding ways to use your brain’s built-in mechanisms for your benefit, but there will always be limitations. Don’t get frustrated, just accept that your brain will never be perfect.

To put this all in perspective: supercomputers that take up entire floors of industrial buildings can’t touch the capabilities of the human brain. 

How is it that such vast amounts of electrical and computing power come up short when trying to handle tasks, like language, that we find trivial? This is one of the enduring mysteries of the brain. But it should clue you in to the fact that your brain, despite its laziness, is not as flawed as you might think.

Double- and Single-Loop Learning

This is part of my 5 Minute Concepts series, which is designed to help you understand fundamental concepts about subjects like learning, memory and competition in the shortest time possible. Each episode is available in video format on my YouTube channel and audio via my podcast. If you prefer to read, the transcript is below.

Want to know when new content shows up? Sign up for my newsletter here.

Transcript:

Let’s talk about double- and single-loop learning.

These are ideas from cybernetics and they have to do with the way that a system optimizes its behavior.

With single-loop learning, you get a system that is more or less following predetermined rules. So you have an algorithm and you follow that algorithm, single-loop learning just means that you’re optimizing that particular algorithm — trying to get maximum results.

A more realistic example would be something along the lines of you are told what to do in a day and you just do your very best to do those things every day.

So the analogy would be a job, say you’re a janitor and you’re told “go sweep the floor,” and single-loop learning would just mean you do your very best to sweep the floor and every single day you’re coming up with new improvements to get better at sweeping the floor.

What’s more important is double-loop learning, and double-loop learning involves questioning and optimizing the system itself.

If you are the janitor and you’re sweeping every day, and you’re getting really good at it, double-loop learning would say what’s a better way to clean the floor? Maybe it’s not sweeping. Maybe it’s vacuuming. Maybe it’s mopping.

Maybe it’s something along those lines that just doesn’t exist within that particular algorithm. It means you’re stepping outside of the system.

And sometimes you’re even breaking the system altogether. This is an important concept in and of itself.

But in general double-loop learning is the hardest learning to engage in because it’s very easy for somebody to tell you what to do.

It’s very easy to follow the rules and optimize around those rules. What’s hard is to come up with new rules all together to step outside the system and ask yourself “is this even a system I want to be part of? Is this even a game that I want to be playing?”

That’s not easy to do. The world is filled with people who are excellent at single-loop learning, and in fact, we come with a lot of hardware and software, so to speak, that allows us to be just out of the box very good at single-loop learning.

Whereas double-loop learning requires real thought and effort and, more importantly, risk-taking. That involves trade-offs that mean saying “I’m going to try something that may fail.”

So if you want to take your learning to a new place, if you feel like you’re plateauing for whatever reason, then chances are good that you’ve just been engaging in way too much single-loop learning.

You need to start stepping into the double-loop paradigm and do something different. Ask yourself if you’re even trying to optimize the correct thing because, the fact is, if you’re optimizing around the wrong system, it doesn’t matter how good you are at optimizing.

And that’s really the conundrum that we all face on a day-to-day basis, we’re trying to figure out should I continue optimizing this system I’m in right now, or should I come up with a new system altogether?