This is Part 4 of 4 of my summary of “How We Learn” by Benedict Carey. Other parts:
Interleaving
We’ve all heard the advice of using repetition when practicing a skill. Without a doubt, the repetition of a single skill works. As it turns out, practicing mixed skills works much better. “Varied practice produces a slower apparent rate of improvement in each single practice session but a greater accumulation of skill and learning over time.” For example, badminton players who practiced three different types of serves in random order did better in a slightly different setting (serving to the other side of the court) than players who practiced the same serves in blocks, one type of serve per training session. Another example comes from learning about art. “Counterintuitive as it may be to art history teachers … interleaving paintings by different artists was more effective than massing all of an artist’s paintings together.”
The discussion of mixed practice also touches on a phenomenon that I also observed in the past, when “kids who do great on unit tests — the weekly, or biweekly reviews — often do terribly on cumulative exams on the same material.” The same happens in sports when someone who performs very well during practice seems to lose it during an actual game. The reason is thought to be the inability of choosing a strategy for solving a problem on a test. In unit tests, we typically practice a single approach that we just learned. On a cumulative test (and in real life!), one needs first to decide which strategy is appropriate, and then apply it. Interleaving different types of problems during learning helps the skills to be more applicable under varying conditions.
Interleaving increases our ability to generalize and apply learnings in different situations. “The science suggests that interleaving is, essentially, about preparing the brain for the unexpected.” In practical terms, the advice is to mix learning new material with a dose of “stuff you already know but haven’t revisited in a while.”
Perceptual learning
What is a “good eye”? How can a chess grandmaster understand the position on the board in a few seconds, a professional baseball player decide to hit the ball, and an experienced airplane pilot to quickly make sense of the navigation panel with so many dials? Experience is critical here, but apparently, there is a type of learning that happens automatically without thinking and can help us develop a “good eye” for specific situations. Moreover, we can do it “cheap, quick and dirty.”
Perceptual learning happens automatically, i.e., without our conscious participation, when we are repeatedly exposed to whatever we want to learn to distinguish from one another – painting styles, airplane control readings, similar squiggles, sounds, pictures of birds – and given correct answers. “We have to pay attention, of course, but we don’t need to turn it on or tune it in.” During this process, “the brain doesn’t solely learn to perceive by picking up on tiny differences in what it sees, hears, smells, or feels. … it also perceives to learn. It takes the differences it has detected between similar – looking notes or letters or figures, and uses those to help decipher new, previously unseen material.”
An example of a practical application of perceptual learning is a “perceptual learning module” (PLM) – a computer program that trains pilots to read the airplane instrument panels. It displays the instrument panels with a choice of 7 possible answers describing the state of the plane, such as “Straight & Level” or “Descending Turn.” When the trainee gives a wrong answer, it flashes and provides the right one. Initially, novices are merely guessing. However, “after one hour [of training] they could read the panels as well as pilots with an average of one thousand flying hours.” Note that “it’s a supplement to experience, not a substitute.” The pilots still need to fly the plane.
In another example, the author describes a training system that he devised for himself to learn 12 painting styles by using a PLM loaded with images of 120 paintings, 10 per style, with interleaving of different styles. After one hour of practicing, he was able to identify the styles of previously unseen paintings with an 80% accuracy.
The implications of the success of PLMs are enormous. One can imagine a whole plethora of learning apps that build the “good eye” from learning Chinese characters and math equations to radiology and chemistry. It follows that in situations when an experienced professional would say that something is not right and then investigate, we can train ourselves to recognize such circumstances, given labeled data, without having years and years of experience.
Being a data scientist, I find perceptual learning to be fascinating since on the surface the process resembles so much how AI algorithms “learn” patterns in the data by “looking” at examples with correct labels. In deep neural networks, just like in our brains, we don’t necessarily understand exactly how the connections are made and why the system can recognize pictures of cats or human faces.
Conclusion
In this series of posts, I attempted to summarize the book’s findings and suggestions that I found interesting. There’s a lot more in the book itself, so I encourage you to read the original.
All quotes above are from the book “How We Learn“.
Photos by Tadas Mikuckis and Arie Wubben on Unsplash.