Ian Dunbar is not a heretic- he wants you to know that. Operant conditioning is real. It’s been proven. The experiments behind the theory were repeated thousands of times, and they were validated. It’s great science.
It’s just not useful for dog trainers. Or at least, not most of it. Ian believes that only about 10% of learning theory applies to us because learning theory was laboratory-generated. That is, the experiments were implemented, monitored and controlled by computers, carried out using Skinner boxes, and the animals used were “simple” and had “few interests.” In contrast, we are humans, and we train real dogs in the real world.
If we choose to use learning theory in training, then we must learn to train like a computer. That’s not necessarily bad- computers have some pretty good traits. They are tireless. They are completely consistent in both monitoring the trainee’s behavior and in providing feedback. This allows them to have very clear criteria. By contrast, we humans often have unrealistic or unclear criteria, and are inconsistent in our observations and feedback. So, why wouldn’t we want to train like a computer?
Well, computers as trainers have some drawbacks. They can not qualitatively assess an animal’s performance- they can’t see cute or flashy behaviors and train that into the final product. While they can give feedback, they cannot give instructive feedback. All a computer can do is say yes or no- provide a click and treat or a buzz and shock. They cannot explain why the animal was wrong or what he should do instead, and they cannot explain how important or urgent compliance is. Humans can.
Then there is the matter of reinforcement schedules. Ian identified seven reinforcement schedules: continuous, fixed interval, fixed ratio, variable interval, variable ratio, random, and differential. Ian explained that six of these seven schedules will maintain a behavior, but only one will improve behavior. The one that will improve behavior- differential reinforcement- is the one that computers cannot use. (Personally, I disagree. Computers may not be as good at it as we are, but there is no reason a computer couldn’t reward faster responses. In fact, they might be better at that than I am- I do not have a stopwatch in my head.)
Because we humans cannot be as consistent as computers, and because computers cannot provide instructive feedback the way we can, Ian sees no need for us to try to emulate computers. He finds this to be especially true because most dog owners don’t need nor want the precision that comes about from training like a computer. As a result, he really doesn’t have any use for the vast majority of learning theory.
So what does Ian like? Thorndike’s Law of Effect, which more or less says you should reward the good stuff and punish the bad stuff. Ian says this is simple, elegant, and pure. It doesn’t get into complicated and confusing types of rewards or punishment which cause endless arguments on the internet. Thorndike tells it like it is.
Again, Ian’s orientation as a trainer of pet dogs is obvious. The average dog owner doesn’t care about precision, and doesn’t have the consistency or patience needed to sort through the various quadrants and schedules, so I understand why Ian thinks we should avoid discussing learning theory with clients. We need to quit worrying about the science and terminology and just train. We should help them, not confuse them. It’s hard to argue with that.
Still, as a dog geek, I struggle with this idea. Personally, I enjoy understanding the science behind what I’m doing. Ian said it himself: learning theory is valid. I like thinking about what I’m going to do. I love planning my sessions. I also think it’s fun to take data and evaluate what I’ve done with the goal of doing better next time.
As a competitor, I want precision. I enjoy the challenge of being consistent enough to get amazing results. I strive to be as clear as possible in my criteria. In many ways, I do try to train like a computer, and I don’t think that limits me. I enjoy pairing clicks with not only treats but also heart-felt praise when my dog does something exceptional. I see no reason to have to choose between computer or human. That’s sort of the beauty of being human, after all: I can think outside of the box and combine the best of both approaches.
I know I’m not the normal dog owner. I spent Halloween weekend at Ian’s seminar, and I’m spending hours writing up my notes for this blog, after all. I would ask all of you if you’re normal dog owners, but I suspect I know the answer to that. You are reading this, after all.
Instead, go ahead and analyze what Ian said. Tell me how it makes sense, and then how it confuses you. Tell me how you use, or don’t use, learning theory with your dog. I know you’ll have lots to say!