Flow Doesn’t Lead to Mastery

The concept of flow, first introduced by psychologist Mihalyi Csikszentmihalyi, is the enjoyable feeling that happens when you are totally immersed in an activity. You stop feeling self conscious, with your attention being completely absorbed by the task at hand.

Chances are you’ve felt flow many times before. Maybe during a game, sports or even your work. Often your performance is best during a flow state—you may have felt your best games or work come from the flow of effortless focus. But what about learning?

Anders Ericsson, the psychologist behind deliberate practice, argues that flow doesn’t lead to mastery:

“[T]he characteristics of flow are inconsistent with the demands of deliberate practice for monitoring explicit goals and feedback and opportunities for error correction. Hence, skilled performers may enjoy and seek out flow experiences as part of their domain-related activities, but such experiences would not occur during deliberate practice.”

Ericsson believes deliberate practice—a specific type of practice characterized by immediate feedback, focused improvement and mental strain—is the activity that produces mastery. Yet, he also argues that this is “inconsistent” with flow.

Graph comparing optimal enjoyment vs optimal learning

Effective Learning is Effortful

This idea, that learning is most effective when it as done at a level of mental strain above what would typically constitute enjoyment, is a big part of the logic motivating ultralearning.

It’s no surprise that people tend not to like mental strain. Contrary to the assumption that elite performers and experts are intrinsically driven to improve, Ericsson finds evidence that, “deliberate practice is not inherently enjoyable, but individuals engage in it as an instrumental means to improve their performance to attain the highest levels.”

Comparing accomplished musicians with those of lesser achievement, Ericsson finds:

“[T]he best musicians spent very little time on playing music for fun and less time on leisure than other less accomplished expert musicians and nonmusicians of the same age.”

This drive to practice did not come from the best musicians simply enjoying deliberate practice more. Rather it came from their desire to get better.

With ultralearning, the idea is very similar: engage at a level of strain and mental difficulty that exceeds your comfort threshold. No, it’s not always the most fun, but that’s also why it works. Most people don’t see the same results from ultralearning in their self-motivated learning projects because, without strongly structured external motivation, most people don’t reach a high enough level of intensity.

Consider that most people, when looking for online classes, seek out good lectures and videos, rather than good problem sets and homework. Watching lectures is easy. It’s not deliberate practice. Actual mental strain working through the problems in question is hard, but it works.

Learning for Fun

This pursuit of uncomfortable intensity isn’t entirely without rewards. Long-distance runners also experience painful intensity in their sport, but this can come with a “runner’s high” that can be addictive. While I don’t know of any neurochemical basis for an “ultralearner’s high”, learning with such intensity often has a similar joy.

This doesn’t mean learning intensely is always a grim chore. Instead, I’m arguing that learning (like running) shouldn’t be expected to yield results at a level of intensity that feels like flow.

Setting ambitious learning goals and tight constraints, as I’ve tried to do in my own projects, is one way of regulating the environment to push the intensity higher than your comfort level. Another method, as Ericsson advises, is to get a coach to work with who can push you.

For some, the object of learning itself may be so compelling as to push the learner into a state of intensity, even if that spirit was never formalized as a goal. I think it’s likely that Albert Einstein’s quest to understand the universe or Bobby Fischer’s obsession to become the best chess player of all time, could have generated the required intensity.

However, I’m also agree with Ericsson that learning simply for fun, without any such constraints, is unlikely to lead to mastery.

How the Brain Changes with Expertise

How does the brain rewire itself as you learn things? Recently, I came across some interesting research that used fMRI to visualize how the brain changes as you learn something new.

The first interesting tidbit is that brain activation goes down as you learn. The better a subject got at a skill, the less of their brain was being used actively.

At first this might sound surprising. Think of the old myth is that human beings are only using 10% of our brains. Even if you didn’t believe that, you might still be inclined to think that using more brain is better. Instead the opposite seems to be true—those better at a skill are actually using less!

Thinking a bit more, however, and this decrease in brain activity should make sense. As you learn something, your brain wires more specialized circuitry for solving the problem with less effort. Using less of your brain is an advantage, since that is more efficient.

This also explains why focus is so important in learning. When the subjects started learning the frontal and parietal cortices, believed to be involved in the control of attention and planning, are considerably more active. After reaching proficiency in the skill, however, this additional activation goes down considerably.

Learning Changes Mental Strategies

The second piece of information from this research I found interesting was that learning often involves changing mental strategies to solve the problem at hand. In other words, what you’re actually doing when performing a skill as a novice and as an expert are often radically different.

The example studied in the research involved teaching subjects mirror reading:

“early practice involves mental rotation of the letters, and late performance involves recognition of the rotated word and recall of the meaning without slow algorithmic rotation”

Novice mirror readers’ visual cortices would light up as they mentally rotated the image of the word they were reading in order to recognize it with their normal reading pattern recognition. As they got better, pattern recognition learned to just recognize the mirrored words without flipping them in the mind’s eye first.

Language learners often experience a similar phenomenon. When they start learning a language they “think in translation” coming up with the word in their native language first, and only after searching for the correct translation of the word to use.

Now we have confirmation that these types of strategy shifts actually do occur in the brain.

Early and Late-Stage Learning Strategies

Researchers call this shift in neural activation due to changes in mental strategy functional reorganization.

Functional reorganization is a useful concept for making sense of many learning situations.

Consider language learning again. When learning new words, in the beginning, I often found it valuable to use specialized tools such as spaced repetition systems or the keyword mnemonic. These make sense as being beginner mental strategies to the problem of new vocabulary.

I hypothesize that my visual cortex would also light up as I used the keyword mnemonic to decode the translation of a word because of more-easily-remembered visual images. However, as I became more fluent using that word, I’d stop using the mnemonic, the association would just be between the English word and it’s translation. As I used it even more, that association would also be unnecessary, I would simply retrieve the foreign word directly when I need to use it.

This suggests that the mental strategies to speak the language changed at least twice in my learning of the word. First using the mnemonic. Then using a remembered translation pair. Finally using the word directly. Each, probably involving less brain activation as more specialized circuits developed.

Is Effective Learning Finding the Right Cascade of Strategies?

Could it be that effective learning is mostly about finding the right cascade of these mental strategies? Short-term, effortful strategies that can be employed quickly, slowly being replaced by less effortful but more slowly developed long-term strategies.

Efficient learning would then try to find out where a gap persists—i.e. when there isn’t a good novice or intermediate mental strategy to get the right answer before the expert strategy can develop?

This is all speculative, but I’m sure thinking about learning strategies in terms of novice-to-expert functional reorganization will have a lot of influence on looking at ways to improve my learning strategies for future projects.