The 4 pillars of learning
Cognitive science has identified four key factors for successful learning: attention, active engagement, feedback, and finally, consolidation of what has been learned. Stanislas Dehaene, a graduate of the École Normale Supérieure, cognitive psychologist, and neuroscientist, presents these four pillars and explains why they are essential to consider in order to help children learn better from an early age.
1. Attention is a filter that must be captivated and channeled.
Attention is the filtering mechanism that allows us to select information and modulate how we process it. And by understanding that in doing so, it eliminates in order to concentrate, we suddenly realize the profound accuracy of the term "concentration." The attention system breaks down into three attentional systems: alertness, orientation, and executive control. Attention massively modulates brain activity: therefore, the key challenge for knowledge transferers, whether they are parents, teachers, or trainers, is to draw attention to the "right level. " Learners must be alert.
However, there are limits to attention.
Firstly, filtering implies that performing two tasks simultaneously is very difficult —in fact, a bottleneck phenomenon has been observed in the prefrontal cortex. In reality, when we "multitask," we are not doing two things at the same time; we are simply switching from one task to another, temporarily omitting the first, and at the expense of signal acquisition.
Then, when we are focused, stimuli that are irrelevant to the task at hand simply become... invisible! This is perfectly illustrated in a famous video, where the challenge is to count the exact number of passes made by the players dressed in white. In light of such an experiment, it is clear that attention, which is selective by nature, leads to overconfidence— we are prepared to argue that what has gone "under the radar" of our perception simply never existed. This is because the filter is, in a sense, one-way. This is a lesson that can be extrapolated to many areas of life. The challenge, therefore, is to focus attention properly, and in this regard, what has been called the "teacher effect" is crucial: one teacher will succeed in capturing attention where another will get bogged down, or even draw attention to irrelevant aspects – and it should be noted that this is a pitfall into which many school and training textbooks fall, with an overload of illustrations and colors placed in an attractive but chaotic manner. Far from this overdose of information, the aim is, on the contrary, to channel attention.
Finally, executive control, which acts as a lever for attention, is essential: it involves inhibiting undesirable behavior that would cause "double tasking": for example, not getting distracted by leaving the place of activity to do something else, starting to talk to someone else, etc. In this regard, progress is particularly noticeable in children from families where certain behaviors are not emphasized—for example, staying at the table to eat. This teaching of cognitive science sheds new light on the issue of discipline, but also on inequalities between social backgrounds. It also provides tools to combat these inequalities.
2. Active engagement.
The guiding principle could not be clearer: a passive organism does not learn. We must therefore seek active engagement. Teachers can only motivate children or learners if the latter are motivated themselves. However, without testing the reliability of knowledge, we remain under the illusion of knowing – and it is highly likely that this applies to everyone in one area or another. Children and learners must be able to test themselves. Paradoxically, making learning conditions (reasonably) more difficult will lead to greater engagement and cognitive effort, which are synonymous with better attention.
3. Feedback.
Error is human but also essential. While active engagement rather than passive listening is crucial, it is not enough. Current thinking suggests that the cortex functions as a kind of prediction generating machine that integrates prediction errors: it makes a prediction, receives sensory information in return, and compares the two. The difference creates an error signal that propagates through the brain, allowing it to adjust and improve the next prediction. Feedback is therefore essential.
The brain operates through iterations, in cycles that can be broken down into four successive stages: prediction, feedback, correction, new prediction. This is referred to as the Bayesian brain, after the inference of the same name, or the statistical brain. It organically internalizes statistics. In simple terms, it continuously adjusts its aim through experience, which is another way of saying that error is fundamental. Indeed, if error signals allow us to refine our predictions, learning can only occur when there is an error signal; otherwise, nothing changes.
Transposed to pedagogy, this implies that mistakes are normal, inevitable, and... fertile. Provided, of course, that they are actively noticed by the learner, who, far from ignoring them, must overcome them. On the other hand, to be fruitful, they must not be overly punished, as stress inhibits learning. Worse still, a feeling of helplessness would nip future efforts in the bud. So what is the best way to overcome mistakes and achieve success? We should focus on motivation through positive reinforcement and rewards— intangible ones . Of course, this does not mean "monetizing" success, or even paying children to get good grades. On the contrary, since humans are social animals, success should be reinforced through social reinforcement: approval, validation, and encouragement.
4. Consolidate what has been learned.
We only need to think back to our first steps toward getting a driver's license to realize that at the beginning of this learning process, there is a conscious effort, and faced with the multitude of signals to manage in real time, a feeling of not being able to do it, of being overwhelmed. It's terrifying! However, this is a typical example of what is known as explicit processing: a situation, or rather a stage, in which the prefrontal cortex is heavily engaged by executive attention. And, at the culmination of learning, the challenge will be to transfer from the explicit to the implicit. Indeed, gradually, by transferring to unconscious networks that are faster and more efficient, the brain achieves automation. This frees up the prefrontal cortex, which becomes available again—much like freeing up system resources in a computer, which, instead of being saturated and performing its tasks with great difficulty, once freed up, can "surf" smoothly, without unnecessary tasks running in the background. We also find the phenomenon of bottlenecks in our cortex, which again evokes a computer's RAM, a buffer memory that can only process a given volume of information at a time before moving on to the next. Let's return to the fundamental example of reading. At the beginning, children must consciously remember each of the correspondences between letters and sounds and apply them one by one, in the same way that adults had to learn an "alien" language. And, as we see with children in this early literacy phase and also with dyslexics, the more letters we have stored, the longer it takes. It's linear and serial! However, adults and children from the third grade onwards no longer have this effect: we read an eight-letter word as quickly as a three-letter word, because the processing is no longer serial, but massively parallel: all the letters are read at the same time! It is easy for an adult, a teacher, to forget this initial difficulty and not realize what is being asked of the child. And when all the "percentage of resources" of our "central processing unit" is used up in decoding, we cannot concentrate on the meaning of the text.
The phenomenon of automation is therefore crucial because it frees up high-level resources.
The role of sleep
Before concluding, a word about an unexpected factor in consolidating learning: sleep. It has been discovered that allowing a person to sleep, even just a nap, without relearning, improves performance. This is because the brain works during sleep: it "organizes" the new information it has recorded, probably by replaying it at an accelerated speed. This accelerated speed allows it to detect patterns, consolidate episodic memory (memories of past events), and, using algorithms, make generalizations and even arrive at discoveries. The scientific journal Nature devoted an article to this phenomenon: many mathematicians report having found the solution to a problem they had been struggling with the day before in the morning—and by repeating the experiment in the laboratory, this phenomenon has been verified.