together. At the end of the day, the piece of music has also been “saved” in the orchestra’s newly acquired skill of playing it together. In order to retrieve it, the concrete dynamic of the musicians first must be activated, leading to the piece of music. Likewise, a piece of information in the brain is encoded in conjunction with the interaction between the neurons, and when the neurons “practice,” they also adjust their harmonization with each other, making it easier to trigger their interaction next time. In order for a neural network to learn, the neurons must also adjust their points of contact and thereby redesign the entire architecture of the network.
Because the brain does not have a conductor, the nerve cells must rely on tuning themselves to their neighboring cells. What happens next on a cellular biological level is well known. Simply put, the adjustments among the neural contact points that happen during learning follow a basic principle: contact points that are frequently used grow stronger while those seldom put to use dwindle away. Thus, when an important bit of information pops up in the brain (that is, when the neurons interact in a very characteristic way), the neurons somehow have to “make a note” of it. They do this by adjusting their contact points with one another so that the information (the state of activity) will be easier to retrieve in the future. If in specific cases, some of the synapses are quite strongly activated, measures are taken to restructure the cells to ensure that it will be easier to activate the specific synapse later on. Conversely, synapses that go unused because of a lack of structural support are dismantled over time. This saves energy, allowing a thinking brain to function on twenty watts of power. (As a comparison, an oven requires a hundred times as much energy to produce nothing but a couple of bread rolls. Ovens are apparently not all that clever.)
This is how the system learns. By altering its structure so that its state of interaction can more readily be triggered. In this way, the piece of information is actually saved in the neural network—namely, “between” the nerve cells, within their architecture and connection points. But this is only half of the story. In order for the piece of information to be retrieved, the nerve cells must first be reactivated. The more interconnected the points of contact are, the easier it is to do this, even though information cannot be derived from these contacts alone. If you cut open a brain, you will see how the cells are connected but not how they work. You won’t have any idea what has been “saved” in the brain, nor what kind of dynamic interaction it could potentially produce.
Under stress, learning is best—and worst
THIS NEURAL SYSTEM of information processing is extremely efficient. It is much more flexible than a static computer system, requires no supervision (such as a conductor) and, in addition, is able to adapt to a vast range of environmental conditions. However, this learning method also has its weaknesses. Because the process of building neurons is subject to regular biological fluctuations, we don’t always learn equally well. When we are under stress, for example, we tend to tighten up more readily. Anyone who has felt the pressure of studying for a test knows how hard it is to prepare with this kind of learning stress. It feels like an arduous task to try jamming the most important bits of information into your head. Or, if you do manage to squeeze them in, you then can’t seem to get them back out at the crucial moment (during the test). Why does stress affect our learning process so negatively?
First of all, let me give you the good news: stress is not something that blocks our learning. On the contrary, stress is actually a learning accelerator. Under acute stress conditions (for example, if we are scared or even positively surprised), the brain’s neurotransmitter noradrenaline first makes sure to activate precisely those regions of the brain that heighten our attention.1 About twenty minutes later, this action is further supported by the hormone cortisol, which silences the distracting background flurry of nerve cells.2 We are then able to become more focused and concentrate. The conclusion? Under acute stress, we are extremely capable of learning. For example, if we cross the street and almost get hit by a car, we take note of this for future street crossings. This is even the case when we are positively stressed. For instance, most of us will never forget our first kiss—even if we only experienced it once.
When our brain is under stress, our neural network is animated, enabling us to learn more rapidly. However, if the content that is being learned does not have anything to do with our stress, it’s a very different story. The main goal of a brain under stress is to concentrate only on the stress-related relevant information. Everything else becomes unimportant. And this is what makes learning under stress a two-edged sword. When test participants are placed under stress conditions by having their hands submerged in ice water for three minutes while simultaneously tasked with memorizing a list of words, a few days later, they are readily able to recall all of the words having to do with ice water (such as “water” and “cold”), but they cannot remember any of the other arbitrary words (“square,” “party”).3
If you are almost hit by a car, you are able to draw an immediate correlation between looking both ways before crossing the street and possible death. And you will never forget it. But if you are studying Latin vocabulary, you have to stretch your imagination three times as much to establish a connection between the phrase “alea iacta est” and the consequences of bad test scores.
A brief interim conclusion at this point: the brain learns quite well under stress if the main point of the learning has to do with the cause of stress itself. After touching a hot stove only once, we are quick to learn that it was not such a good idea. Stress hormones actively regulate the dynamic of the neurons in order to better retain emotional content (the pain from a hot oven is much more important than what brand of oven it is). This is all about emotions, by the way, not facts.4 Facts, facts, facts are boring. Which leads us to the next learning weakness of the brain.
The memorization weakness
DO YOU REMEMBER the list at the beginning of the chapter? Can you recall even half of it? If yes: I owe you congratulations and respect. How did you go about memorizing this list? If you used mnemonic devices, storytelling, or images to help you to relate the various words, did you realize that you actually increased the amount of information that had to be learned? You made yourself “learn” more than was necessary in order to retain the information. This is a paradox. You might ask an additional question: Why does any of it matter at all? The words on the list are mostly arbitrary and have no relevance or context to you. Why should you be bothered to learn them? Merely because an author demands it of you?
This is precisely the point. Our brain is good at adapting to many different situations, actively adjusting itself, and learning new things, but this doesn’t include raw information such as a few random words, bits of data, or facts. Research shows that the upper limit of objects that can be memorized (without using memory tricks such as mnemonic devices or storytelling) is around twenty. Which is not very much. The list at the beginning of this chapter only takes up 146 bytes on a computer hard drive, while a picture of a zebra could easily take up a million times more space. And yet we prefer to imagine, as in a dream, a zebra with a lollipop wandering through a labyrinth (words from the list) instead of learning each of these four words separately. But why is the brain so bad at saving a few simple pieces of information, such as a couple of words?
The reason once again has to do with the way the brain works. The brain doesn’t learn information by rote and then save it somewhere. Instead, it organizes knowledge. There’s a difference. Let me give you a simple example to illustrate. I could list off to you the exact sequence of goals (and who scored them) from the historic semifinal in the 2014 World Cup soccer game between Germany and Brazil that ended in a score of 7–1: 11th minute: 1–0, Müller; 23rd minute: 2–0, Kroos; 24th minute: 3–0, Kroos . . . Okay, I’ll spare any Brazilian readers the rest of the list and get to the point. Once you have assembled all of the data from this game, what do you know about the game itself? Not much, since you are not witnessing the shocked expressions of the Brazilian team or the joy of Philipp Lahm, the German team captain. The significance
