Eureka, part 2
Eureka, part 3
Eureka, part 4
Eureka, part 5
So, MOOCs. Why don't they work (setting aside, for the moment, Anne D's experience)?
I had just gotten to the point where sitcoms gave me the answer I was looking for when I had to take a train to the city.
So, to reprise:
Ed brought up movies.
When you go to the movies, he said, the screen is huge, the sound is deafening, all the lights are turned off, you can't talk to your friends, and you have to stow your cell phone. Plus a movie lasts only a couple of hours, then you never have to see it again unless there's a sequel that you really want to see, and you don't have to see that for at least a year.
And even at the movies, even with all the ploys and devices filmmakers and theater designers have developed to hold your attention, if the plot sags, your mind wanders.
MOOCs don't have any of those things, so good luck. The wonder of it all is not that the drop-out rate for MOOCs is catastrophic, but that anyone thought they were a good idea in the first place.
TV, he said, had had to follow in the footsteps of movies. TVs are bigger, the sound is louder, the experience more immersive….
That's not really true, I said. It's definitely not true of sitcoms. Sitcoms are the exact same hokey, flat-lit, 3-camera affair they always were, with the laugh track telling you when to laugh, and they work. They always have.
That's when it hit me.
"Reward prediction error"
In graduate school (I have a Ph.D. in film studies) I was intensely interested in comedy; I wrote my dissertation on 1950s comedies. 1950s comedies are fabulous, but what I really wanted to know was: what is humor?
What makes things funny?
I've wondered about that for my entire adult life, and have probably, finally, found at least a partial answer, which has to do with -- hold your breath -- the basal ganglia. (For passersby, I have been slaving over a basal ganglia writing project for years now.)
Research on the basal ganglia is very new, so take what I'm about to describe as provisional.
The basal ganglia seem to be all about reward prediction error.
"Reward prediction error" means that learning happens when you predict a reward and you are wrong.
Here's how it works. (Or may work).
Dopamine spikes or drops in response to "prediction" errors, that is to mistakes we make predicting rewards.
- If you expect something good to happen & it doesn't, dopamine drops. That feels bad.
- If you expect something bad to happen and it doesn't, dopamine spikes. That feels good.
- If you're not expecting anything good to happen one way or another, and all of a sudden, out of the blue, something good does happen, that feels great. Because dopamine.
(I've learned just this week that "because" has become a preposition.)
So, if you're expecting a check to come in the mail and the check comes in the mail, no dopamine spike. You open the check, you're mildly happy (if that), you deposit the check. Life goes on.
If you're not expecting a check to come in the mail and a check in the exact same amount as the expected check-in-the-mail arrives without warning, that feels great because dopamine.
A surprise check in the mail feels great because dopamine fires in response to good surprises (or to "better-than-expected" rewards.)
Reward prediction error is the basis of reinforcement learning.
It's unfortunate that "reinforcement learning" is called "reinforcement learning," because "reinforcement," to me, sounds as if learning takes place when the same thing happens again.
Instead, reinforcement learning takes place when something new happens, something you didn't expect.
("Something new" includes something old but better -- or worse -- than you expected. I know the whole thing gets incredibly confusing right around this point, but just remember the surprise check in the mail: how different it feels from the fully anticipated check in the mail. The surprise check in the mail produces reinforcement learning; the expected check in the mail does not.)
For the record, I don't know how learning via distributed practice, via repetition, relates to reinforcement learning, so that question will have to remain a mystery for the time being.
Reinforcement learning in the sense of the incidental learning we do naturally throughout the day (what should I do again? what should I not do again?) depends on mistakes. "Reinforcement learning" happens when we are wrong, when we are wrong in a very specific way: reinforcement learning happens when we are wrong about the goodness or badness of what comes next.
Drug addiction is probably a phenomenon of reward prediction error, btw.
Normally we habituate to good things. We get used to them; we no longer feel ecstatic when they occur. But addictive drugs always spike dopamine -- that is their effect inside the brain -- and that is what makes them addictive.
Cocaine spikes dopamine every time you use it, so your brain is always getting a 'REMEMBER THIS AND DO IT AGAIN' message, and your interest in taking cocaine always increases. Addiction is a form of learning, a form of overlearning, more exactly.
At least, that is the way I understand the reward prediction error theory of drug addition, as a "disease of learning and memory."
(Interesting 2012 research here…dopamine and GABA…)
Surprise is good
The long and the short of it: surprise is good.
Good surprise is good.
Bad surprise is bad.
All surprise, however, appears to be informational. Our brains react strongly, and we learn.
Which brings me to sitcoms.
Why are funny things funny?
Funny things are funny, at least in part, because humor -- humor that works -- is surprising. If it's not surprising, it's not funny.
And that means humor tells your brain you've made a reward prediction error. The punchline of a good joke or gag is unexpected, so dopamine spikes up. Dopamine spikes feel good, so we come back for more.
A good sitcom doesn't need an immersive setting or loud music or arresting imagery to hold our attention.
A good sitcom sets the reward prediction errors coming one on the heels of another, and that is plenty.
Eureka, Part 4 t/k
Flipping the Classroom: Hot, Hot, Hot
MOOCs grow the gap
The New York Times is surprised
In the world of MOOCs, 2+2 is never 4
World's funniest joke: humor depends on surprise
Dick Van Dyke on comedy
Philip Keller on the flipped classroom
If students could talk
Who wants flipped classrooms? (Salman Khan on liberating teachers)
Are math & science lectures boring in a way humanities & social science lectures are not?