Main »


...uplinks, including horizontal links to competitors or antipatterns

Practice makes permanent, not perfect. Training results in competence at the trained behavior, not the intended behavior.

Consider the following anecdotes:

...this list could go on and on. There are endless examples in our common cultural narrative of reinforcement-learning-gone-wrong; just think of the pianist who can only play scales, the neural net that was intended to identify images of tanks but instead only distinguished cloudy days from sunny ones, or the fourth grader who reflexively says “I love you” to his classmate over the phone before hanging up in embarrassed silence.

There is a common pattern to these and many other failures, and recognizing it can both prevent you from ingraining the wrong habits and “turbocharge” your efforts to train the right ones.

At its core, the turbocharging model is simple. It begins with a single claim: people tend to get better at the things they practice, and (usually) not at the things they don’t. More formally: behavior tends to be self-reinforcing—each repetition of a behavior makes another future repetition of that same behavior more likely.

What this means in practice is that (according to the model) intent has little or nothing to do with results. In the anecdotes above:

In each of these cases, the people involved did indeed gain proficiency with the specific skill they had actually practiced, but that skill was not quite the one they wanted.

There are caveats to this principle (more on them below), but taken as a given, it provides a powerful tool both for evaluating a given training scheme and for generating training schemes that will actually work. The world is full of things that are “supposed to” teach us some skill or another, despite the fact that many of them bear no close resemblance to the desired final competency.

Previous participants armed with this principle correctly predicted a number of ways in which traditionally trained aikido students might react given an actual unexpected attack (flinching, reflexively stepping back after blocking, defaulting to defenses other than the intended/ideal one because of the absence of the customary “trigger”); when given the failure mode described above for the French language student, they rapidly generated the concept of immersive learning from scratch.

The key is to attend to detail on the movement-by-movement or thought-by-thought level. Returning to our hypothetical math student—it’s not sufficient to ask ourselves whether they’re “listening to the instructor” or “thinking through the example.” Instead, we must ask unambiguous questions like:

...only with that level of detail can we understand what specific skills are actually being rehearsed (and thus ingrained and reinforced), and then make judgments of—and improvements to—a given training scheme. The Turbocharging algorithm is:

1. Select a skill you want to acquire or improve.

2. Select a practice method (either a preexisting one you wish to evaluate, or a preliminary one you wish to strengthen).

3. Evaluate the resemblance between the method and the desired skill.

a. How closely does the "practice trigger" resemble the real-world triggers that you hope will elicit the behavior?
b. Where the practice trigger and the real-world triggers differ, does the practice method vary the trigger, so as to make the behavior more likely to generalize?
c. How closely does the “practice action” resemble the real-world actions you’ll want to perform when you encounter the trigger?
d. Where the practice action and the real-world actions differ, does the practice method vary the action, so as to make the behavior more flexible and adaptable?

4. To the extent that the answers from (3) are cause for concern, adjust your practice method (or choose a new practice method altogether).

If you are training parkour and you would like to get good at climbing walls, then climb lots of different walls—don’t do squats or lift weights or train on trampolines. If you are halfway through and you discover that you need more raw strength, then you might do squats or lift weights, but you’ll be doing so in order to build strength, not “because” doing squats or lifting weights will make you better at the skill of climbing walls.

Similarly, if you are learning to code and you would like to get good at creating algorithmic solutions to problems, then find lots of different practice problems that have algorithmic solutions. If, instead, you want to build websites, then build websites. Always be wary of advice that you should do activity A “because it will make you good at activity B.” Sometimes this is actually true, but more often than not, it’s wasted motion.


Make your practice as close to the real situation as you can.