I read it once and have no idea what point they are trying to make. Are they saying, be it reinforcement or punishment, either way its an exercise in futility?
Let's say that you repeat something over and over again with the same individuals, with random Gaussian outcomes on each trial. What will happen is that an individual who is at the extreme on one trial (positive or negative) will be more likely to be in the middle on the next trial, because the extreme deviations are extremely unlikely, and the middle deviations are relatively likely. So the really good person on one trial is likely to look worse on a subsequent trial, and the really bad person is likely to look better.
If you praise the person who did well on the initial trial, then, you will see good outcome -> praise -> worse outcome. If you punish the person who did poorly, you will see poor outcome -> punish -> better outcome. But this is spurious, because the good -> worse transitions and bad -> better transitions would have happened regardless of whether you praised or punished.
I think the thought experiment is imperfect for various reasons but does illustrate an important potential source of bias pretty well.
It only tells you what to account for if you want to test it
I think the lecturer and the objecting instructor are talking about two different things.
The instructor's thesis is that positive reinforcement is more effective. I'm assuming that this was a measured, long term result, e.g. students that were positively reinforced did better on final tests or other summarizing measurements.
The objecting instructor is talking about single instances "in the seat," not measured over time. "More often than not, the next maneuver ..."
I'm assuming that a student's performance will be somewhat more random than an experienced practitioner's performance. So there would be a tendency for regression to the mean in isolated instances, and a small enough sample of instances would not be enough to predict final measured proficiency.
Also because of the way the objecting instructor phrased his objection, I'm assuming that he hadn't checked his observations against the final summarizing measurements.
This is how I made sense of the conflict between the two people.
Is doing well on one maneover mostly signal (the pilot skill level) or noise (random chance).
In the book Nate talks about his own career as a poker player, and how there's so much chance involved that he still didn't really know if he was a good player or not after doing it for a couple of years, that's how hard it is to tease the signal from the noise. Would beating himself up about every lost hand and celebrating every win be worthwile, or would it just distract him from the real task of slightly beating the odds and grinding out a profit at the margin.
The point is praise is better. Not futile. Easy to misread reversion to the mean as the consequence of praise / punishment.
I am sure that isn't what you are saying, but for the sake of argument, here's a counter.
After raising a few kids, and watching many other parents do the same, I have found that both praise and punishment is an absolute requirement for success. But praise is good for personal bonding, and punishment for learning responsibility.
As an adult we punish ourselves (if raised to self evaluate) and on the extreme side we justify our selves in error if only given praise. Self evaluation cannot be done without self punishment.
Before you go nuts on what "self punishment" means in this context, I will provide one. At the lowest level it means acknowledging that an error was "my fault". The most minimum amount of punishment possible is taking responsibility. It hurts, it's punishment. Blaming others is easy, and doesn't hurt at all, but will not help you avoid repeating a mistake.
Having to apologize for a mistake or hurting others is a form of self imposed (or parent imposed in some contexts) punishment. And it's extremely effective in resolving conflict.
Again, I am sure that I read into your point, but it seemed fitting to address this purely from an argumentative perspective.
As an example, 'regression to the mean' ensures that it seems like blaming works better than praising.
Because of regression to the mean, an exceptional result is more likely to be followed by a more average one, irrespective of the instructor’s reaction. What the instructor actually achieved was condition themselves to respond in a certain way to a student’s performance, rather than conditioning the student to respond to the instructor’s feedback as intended!