Who’s not familiar with that: you are in any kind of school, supposed to quickly learn a lot about all kinds of subjects in order to be able to eventually pass the exam at the end of the semester or at some other point in the meantime. It’s not uncommon that we tend to get stressed out and sometimes even somewhat depressed because of it. One of the reasons for this phenomenon is that this approach of learning is simply working against our natural brain’s functionality. So why don’t we just explore other frameworks and processes that are instead aligned or at least more aligned with how our brain works.

Learning is the very basis for adaption

Formalized differently, one of the most important meta-skills we can learn and develop is how to actually learn properly. Our ability to quickly learn almost anything is one of the reasons why we are where we are now — learning is the very basis for adaption. And adaption is what enables an organism, a species to survive. Not being the strongest one as Darwin pointed out rightfully so. The acknowledgment of learning as being the crucial variable in the equation for survival might inspire you to think about how an individual and we as a society can become smarter in our approach to evolve our learning approaches and processes instead of working harder and longer hours. This comes with the realization that our time here is scarce and as a consequence, at least to most of us, precious which might be the reason why we don’t want to waste time on something which could be done more effectively and efficiently or will potentially be forgotten at some point anyway.

By the way, I found a fancy chart that visualizes the economic benefits of working smarter instead of solely harder. Just as a side note, the reason for the variation is of course not only because of “working smarter”. It’s much more complex than that. However, it’s one of many key drivers for the situation simplified in the chart below. And another thing worth mentioning here is that it’s not all about economics. But in the end, prosperity also comes with more freedom and opportunities. That’s why I think integrating this chart makes a whole lot of sense.

Average annual working hours vs. GDP per capita Source: Our World in Data

Let’s get back to learning again. Most of us had either pleasant or unpleasant experience of spending an incredible amount of hours on memorizing tons of facts, grammar rules for languages, mathematical functions, basic knowledge of natural science, key historical events and so on. Many, including myself at times, found themselves postponing exercising and learning in the course of it. Up to the moment, where only a day before the exam is left. Putting us at a sweet spot of desperate reading and rereading of our material and notes, in the hope that all the information would lodge in hour brains somehow. And how great the relief then was when we were done with the exam and we naturally offloaded all the “learned” things straight away. Sounds familiar? I bet so.

Learn smart, not solely hard

But hey, even outside of formal education, we are supposed to learn large amounts of new information on a regular basis. Such things might, for example, be new languages, profession-specific respectively technical terms, firm policies, sale pitches, speeches, the names of coworkers and customers, you name it. But learning through commonly practiced memorization is tedious and more importantly quite ineffective. If we aim to truly remember something, we need to collaborate with our brains and not work against them. Considering collaboration, we first need to understand our cognitive constraints and find functional workarounds or use the constraints in our a manner that they are working in our favor.

Luckily, learning is a hot field in science. Probably even more nowadays as we are in the process of trying to more effectively and efficiently teach machines how to learn particular tasks. One of the methodologies respectively its effect that is well-known among researchers and others and works well for humans are the so-called spaced repetition and spacing effect. The spacing effect refers to an effect triggered by the approach of learning in repetition cycles that are spread out in time. This way, people who make use of this approach can learn almost anything in slow but effective iteration processes.

There is evidence that this methodology works for all kinds of things such as foreign words, math, painting, and other skills. Moreover, it’s not necessarily an approach that works for one demographic better than for others, implying that anyone of any age, ranging from newborns to elderly folks, has the opportunity to deploy it. I actually think it is possibly the approach that any species in the world uses to learn new skills in order to survive over time and evolve in the process. It’s one that is not beneficial immediately given the cognitive constraints of our brains, but it’s one that can be extremely powerful when we are aware of it and use it consciously.

In their interesting book Mindhacker, Ron and Marty Hale-Evans write: Our memory is simultaneously magnificent and pathetic. It is capable of incredible feats, yet it never works quite like we wish it would. Ideally, we would be able to remember everything instantly, but we are not computers. We hack our memory with tools like memory palaces, but such techniques required effort and dedication. Most of us give up, and outsource our memory to smartphones, cloud enabled computers, or plain old pen and paper. There is a compromise…a learning technique called spaced repetition which efficiently organizes information or memorization and retention can be used to achieve near perfect recall.

Spaced repetition and the spacing effect

The first person to officially and scientifically identify the spacing effect of spaced repetition learning was Hermann Ebbinghaus (1850–1909), a German psychologist and pioneer in quantitative memory research. During his active years as a researcher, some of his most important findings were in the areas of forgetting and learning curves. His empirical results showed that the forgetting curve has its highest and fastest drop within the first 20 minutes after learning something. Afterward, the order of magnitude of the forgetting function decreases over time, resulting in about 20% recall capacity after only 31 days. When we stick to a regular repetition cycle, we can slow down the forgetting decay and achieve the inverse of it — the learning curve — over time as shown in the graphical representation below. So we see, frequency and iteration clearly matter, not hours spent.

Retention rate

Source: Data from Ebbinghaus (1885)

Another interesting observation made by Ebbinghaus which we most likely know intuitively anyway is that our emotions and intensity of attention when practicing learning have a great influence on our learning performance. He writes in Memory: A Contribution to Experimental Psychology:

Very great is the dependence of retention and reproduction upon the intensity of the attention and interest which were attached to the mental states the first time they were present. The burnt child shuns the fire, and the dog which has been beaten runs from the whip, after a single vivid experience. People in whom we are interested we may see daily and yet not be able to recall the color of their hair or of their eyes.

In other words, the meaning we assign to an encounter where learning occurs matters greatly for the behavior of the forgetting function. Things that have dramatical implications on ourselves is easier to be stored and remembered. This has great usability for us when thinking about the evolution process and allows us to procreate ourselves and survive.

How the spacing effect works

What we are yet trying to figure out is how the spacing effect actually works in our brains. There are only a few anecdotal aspects that are genuinely believed to support in the process: Besides the significant learning events, our brains also assign greater importance to things that are experienced repeatedly or repeated which is logical as regularly occurring things have a higher probability of being more important things we only come across once. Hence, we don’t have real troubles remembering and recalling information or things that we need in our daily lives, independent on our state of mood when being confronted with it.

The process of forgetting and learning are somewhat linked as already shown in the forgetting and learning curve graphic. This can be understood as reinforcement learning. When we review information close to the point of nearly forgetting it, our brains reinforce the memory as well as they add new details to gain more context. In a more practical sense, that’s why teaching others can be a powerful technique to further develop one’s skills and knowledge about a particular area.

What comes with the process of reinforcing memory is that retrieving memories changes the way they are encoded later on. So what might potentially be counterintuitive, the harder something is to remember at the moment, the better we will eventually recall it at some point in the future or the harder the strain, the greater the benefits. There is no learning without pain as we all know. Recall is more important than recognition.

Another interesting belief researchers have is that semantics are important in the context of learning. Hence, associations formed between words or things makes it easier for us to recall them. As an example, “a chef and the commis cooks are garnishing dishes in the kitchen” is easier to remember than “a chef and the commis cooks are garnishing dishes in the inner yard”. This is the case as the words “chef”, “commis cooks”, and “kitchen” are linked. Things behave the same when you are asked to remember more difficult sentences or coherences. Contrary to the belief that spaced repetition is what is effective, there is a theory that massed learning is just very inefficient. This comes with the realization that whenever we learn a (too) big chunk of new information, people tend to lose interest and attention which results in the disability to retain a higher percentage of the learned things. That’s why frequently spaced repetition cycles leverage our interest and maximized attention when processing information.

How to start with spaced repetition cycles

So how to go about creating an effective and efficient spaced repetition cycle? It’s quite easy: create your personal schedule for reviewing information you want to internalize (start with short intervals and then extend over time — an hour, a day, every other day, a week, a month, every six months, yearly — and experiment what works best for you), find a suitable tool for storing and organizing information, come up with an easy to use metric in order to track your progress and the possibility to reverse engineer your approach when progress is moderately low or inexistent, and don’t forget to set short review durations. Out full attention space is low. Start with something like 30 minutes and see how you’re doing. You may adjust (up or down) after making the first few sessions and reflections.

Enjoy the process!