Learning fundamentals: what helps us learn?

2024 Author: Seth Attwood | [email protected]. Last modified: 2024-01-07 16:23

The author of How We Learn, Stanislas Dean, outlined the four pillars of learning. These include attention, active engagement, feedback, and consolidation. We reread the book and went into more detail about these features and what helps to strengthen them.

Attention

Attention solves one common problem: information overload. The senses transmit millions of bits of information every second. At the first stage, these messages are processed by neurons, but a deeper analysis is impossible. The pyramid of attention mechanisms is forced to perform selective sorting. At each stage, the brain decides how important a particular message is, and allocates resources to process it. Correct selection is fundamental to successful learning.

The teacher's job is to continually guide and attract the attention of students. When you pay attention to a foreign word just uttered by the teacher, it becomes fixed in your memory. Unconscious words remain at the level of sensory systems.

American psychologist Michael Posner identifies three main systems of attention:

1. an alarm and activation system that determines when to pay attention;

2. an orientation system that tells you what to look for;
3. a control attention system that determines how to process the information received.

Attention management can be associated with "focus" (concentration) or "self-control." Executive control develops as the prefrontal cortex forms and matures during the first twenty years of our lives. Due to its plasticity, this system can be improved, for example, with the help of cognitive tasks, competitive techniques, games.

Involvement

The passive organism learns little or not at all. Effective learning involves engagement, curiosity, and active hypothesis generation and testing.

One of the foundations of active engagement is curiosity - that same thirst for knowledge. Curiosity is considered the fundamental drive of the body: the driving force that drives action, like hunger or the need for security.

Psychologists ranging from William James to Jean Piaget and Donald Hebb have pondered the algorithms of curiosity. In their opinion, curiosity is "a direct manifestation of a child's desire to learn about the world and build its model."

Curiosity arises as soon as our brain detects a discrepancy between what we already know and what we would like to know.

Through curiosity, a person seeks to choose actions that will fill this gap in knowledge. The opposite is boredom, which quickly loses interest and becomes passive.

At the same time, there is no direct connection between curiosity and novelty - we may not be attracted to new things, but we are attracted by those that can fill the gaps in knowledge. Concepts that are too complex can also be intimidating. The brain is constantly evaluating the speed of learning; if he finds that progress is slow, interest is lost. Curiosity pushes you to the most accessible areas, while the degree of their attractiveness changes as the educational process develops. The clearer one topic is, the greater the need to find another.

To trigger the mechanism of curiosity, you need to be aware of what you do not already know. This is a metacognitive ability. To be inquisitive means to want to know, if you want to know, then you know what you do not know yet.

Feedback

According to Stanislas Dean, how quickly we learn depends on the quality and accuracy of the feedback we receive. In this process, mistakes constantly occur - and this is absolutely natural.

The student tries, even if the attempt is doomed to failure, and then, based on the magnitude of the error, thinks about how to improve the result. And at this stage of error analysis, correct feedback is needed, which is often confused with punishment. Because of this, there is a rejection of learning and a reluctance to try something at all, because the student knows that he will be punished for any mistake.

Two American researchers, Robert Rescorla and Allan Wagner, put forward a hypothesis in the 70s of the last century: the brain learns only if it sees a gap between what it predicts and what it receives. And the error indicates exactly where expectations and reality did not coincide.

This idea is explained by the Rescorla-Wagner theory. In Pavlov's experiments, the dog hears the ringing of a bell, which is initially a neutral and ineffective stimulus. Then this bell triggers a conditioned reflex. The dog now knows that sound precedes food. Accordingly, profuse salivation begins. The Rescorla-Wagner Rule suggests that the brain uses sensory signals (sensations generated by a bell) to predict the likelihood of a subsequent stimulus (food). The system works as follows:

• The brain predicts by calculating the amount of incoming sensory signals.
• The brain detects the difference between the forecast and the actual stimulus; prediction error measures the degree of surprise associated with each stimulus.
• The brain uses the signal, the error, to correct its internal representation. The next prediction will be closer to reality.

This theory combines the pillars of learning: learning occurs when the brain picks up sensory signals (through attention), uses them to predict (active engagement), and assesses the accuracy of that prediction (feedback).

By providing clear feedback on mistakes, the teacher guides the student, and this has nothing to do with punishment.

Telling students that they should have done this and not otherwise is not the same as telling them, "You are wrong." If the student chooses the wrong answer A, then giving feedback in the form: "The correct answer is B" is like saying: "You were wrong." It should be explained in detail why option B is preferable to A, so the student himself will come to the conclusion that he was mistaken, but at the same time he will not have oppressive feelings and even more so fear.

Consolidation

Whether we are learning to type on a keyboard, play the piano, or drive a car, our movements are initially controlled by the prefrontal cortex. But through repetition, we put in less and less effort, and we can do these actions while thinking about something else. The consolidation process is understood as the transition from slow, conscious information processing to fast and unconscious automation. Even when a skill is mastered, it requires support and reinforcement until it becomes automatic. Through constant practice, control functions are transferred to the motor cortex, where automatic behavior is recorded.

Automation frees up brain resources

The prefrontal cortex is not capable of multitasking. As long as the central executive organ of our brain is focused on the task, all other processes are postponed. Until a certain operation is automated, it takes effort. Consolidation allows us to channel our precious brain resources into other things. Sleep helps here: every night our brain consolidates what it received during the day. Sleep is not a period of inactivity, but active work. It launches a special algorithm that reproduces the events of the past day and transfers them to the compartment of our memory.

When we sleep, we continue to learn. And after sleep, cognitive performance improves. In 1994, Israeli scientists conducted an experiment that confirmed this. “During the day, the volunteers learned to detect a streak at a specific point in the retina. Task performance slowly increased until it reached a plateau. However, as soon as the scientists sent the subjects to sleep, they were in for a surprise: when they woke up the next morning, their productivity increased dramatically and remained at this level for the next few days,”Stanislal Dean described. That said, when the researchers woke the participants during REM sleep, there was no improvement. It follows that deep sleep promotes consolidation, while REM sleep promotes perceptual and motor skills.

So, learning stands on four pillars:

• attention, providing reinforcement of the information to which it is directed;
• active involvement - an algorithm that prompts the brain to test new hypotheses;
• feedback, which makes it possible to compare forecasts with reality;
• consolidation to automate what we have learned.