In a previous post, I mentioned three objections to the theory of failure-driven learning. In this post I want to talk about the third of these: “What’s the science behind it?”
The idea that you learn from failure has been around more or less forever–since ancient Greece, at a minimum–but scientific support for it can be traced back to Classical Conditioning theory. As you probably remember from psych 101 (or the lyrics to a Rolling Stones song), Ivan Pavlov performed experiments in which he observed that dogs who were repeatedly given food after hearing a bell ring would begin to salivate whenever they heard the bell. This and similar observations led Pavlov to postulate the theory that came to be known as Classical Condition. In common-sense terms (and very much not in the appropriate technical language) the theory says that when dogs and other intelligent creatures observe two events juxtaposed consistently, they come to expect the second when they experience the first.
Classical condition theory evolved in a variety of ways–including, notably, into operant conditioning, which the effects of “reward” and “punisment” into account. One of the most important evolutions was
Pavlov’s simple model is not a failure driven theory, except in the very weak sense that it stipulated that whenever the first of two previously juxtaposed events occurs without the second, the expectation that the second will follow the first in the future is diminished. Subsequent models of classical conditioning, however, made expectation failure much more central. The most influential of these, the Rescorla-Wagner model, says that the amount of learning triggered by the observation of an event depends on the how “surprising” the event it (yes, that’s the technical term). “Surprise” means the degree to which what happened different from what was expected. So this is a full-fledged failure-driven learning model.
The Rescorla-Wagner model does a much better job of accounting for the data in learning experiments than the baseline Classical Conditioning model. One of the most important differences that it accounts for a phenomena called “blocking.” Blocking happens where there is an existing association between two events A and B,
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