Wednesday, September 29, 2021
  Up to a point

Humans want to solve problems. When a problem is important, we devote more attention to it, and try to solve it faster to minimize damage.

Trial and error is especially costly, so we've developed a variety of methods to avoid the trials. We learn from other people's experience via books and teachers. We remember our own experience via worklogs.

Bureaucrats work the opposite way because the cost function is reversed. Bureaucrats try hard to UNsolve every problem, and spend an eternity on "failed" efforts in the "wrong" direction, because SOLVING a problem is costly. A solved problem or a finished job means cuts in budget and power.

Here's an interesting neuroscience experiment that shows the desire to avoid error happens at the deepest brainstem level. It's not an overlaid function or 'social construct'.
Learning from error is often a slow process. In machine learning, the learning rate depends on a loss function that specifies a cost for error. Here, we hypothesized that during motor learning, error carries an implicit cost for the brain because the act of correcting for error consumes time and energy. Thus, if this implicit cost could be increased, it may robustly alter how the brain learns from error.

To vary the implicit cost of error, we designed a task that combined saccade adaptation with motion discrimination: movement errors resulted in corrective saccades, but those corrections took time away from acquiring information in the discrimination task.

We then modulated error cost using coherence of the discrimination task and found that when error cost was large, pupil diameter increased and the brain learned more from error. However, when error cost was small, the pupil constricted and the brain learned less from the same error. Thus, during sensorimotor adaptation, the act of correcting for error carries an implicit cost for the brain. Modulating this cost affects how much the brain learns from error.
Learning to follow dots is an unfamiliar task, and the cost was also unfamiliar. No chance of contamination from the conceptual level.

In this particular experiment the distractor was a face of a pretty girl. Not a Woke image of an Individual Experiencing Non-binary Colour And Gendour. A pretty white girl. Researchers might know how to talk Wokish to get the grant, but they also know what real people really want.

A more familiar task would be driving down the street and noticing a pretty girl on the sidewalk. Steering involves a complex interplay of visual and kinesthetic feedback loops. When traffic is clear, the cost of inattention is low, so we don't improve our precision. When the road is icy, the cost of inattention is high, so we focus much harder on the road and ignore the girl.

Trainers have been using this technique for centuries. Learn the basics in a simple situation, then stretch and improve the learning by occasional forced failures and distractions.

BUT: When high-cost and brand-new distractors of all types come at you fast and hard, there's no time for learning and no way to use textbooks or worklogs as a guide. You try to focus and try to get your job done in a minimal way. At some point you simply burn out and give up. This is how psychopaths destroy humans.

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