When I was just ten my first nephew was born. I think that had a lot to do with my interest in child development. For a few years when I was still a boy I had lots of time with babies and toddlers. I don’t have children of my own, and in many ways haven’t had a lot of connections with kids through my life. But I’ve never lost a sense of awe while watching children negotiate through the world.
I’m not proud that I messed up my first attempt at college in the mid-seventies and never got admitted to the child development program. But looking back I learned so much from my attempt and that’s mostly a result of the thousands of questions I was asking in a rich intellectual milieu. All that questioning nearly drove me over the brink.
As I’ve mentioned before discovering Gregory Bateson’s Steps to an Ecology of Mind and Christopher Alexander’s The Oregon Experiment were mileposts that gave me a sense there was a path somewhere through the thicket of questions. Both authors led me to more reading. Bateson introduced me to Cybernetics, and to a certain extent to anthropology. Alexander made me read more of his writing, but also I discovered early on that he’d studied with Jerome Bruner.
A couple of days ago I noticed a statement by Douglas Hofstatder in an interview in Popular Mechanicsabout the field of Artificial Intelligence:
I might say though, that 30 to 40 years ago, when the field was really young, artificial intelligence wasn’t about making money, and the people in the field weren’t driven by developing products. It was about understanding how the mind works and trying to get computers to do things that the mind can do. The mind is very fluid and flexible, so how do you get a rigid machine to do very fluid things? That’s a beautiful paradox and very exciting, philosophically.
I thought when I read that: Yeah that’s true, I should post some links. But it’s been harder than I thought to find some links to post. One difference in the early days of AI was the impetus to build a machine was to better understand how the mind works; the machine as a model to test. But the machines were so amazing the impetus became more about the machines. So much of the interesting and exciting work that was done got orphaned.
Still, many interesting connections got made, for example computer programmers picked up on Alexander’s book A Pattern Language. But there was also a change in sensibility or style that’s more broadly applicable. People think of the sixties as a sort of romantic era, and some of the ideas get dismissed as romanticism. One of the hallmarks of the time was a quest for meaning.
At the time my courses in psychology were very much bent towards academic psychology and behaviorism. Just as general guide, qualitative evidence has to do with meaning, whereas quantitative evidence is has to do with cause and effect and behavior. So it’s perhaps easy to over-emphasise the importance of meaning. But it does seem to me when it comes to evidence-based education the quantitative is dominant almost to the exclusion of the qualitative.
The search for meaning appears neglected. For me it’s difficult to imagine good evidenced-based education without a focus on meaning. Hofstadter I gather from other reading feels as if he’s somewhat on his own with his studies. But as he suggests there’s a body of work about understanding how the mind works that’s ripe for rediscovery. Jerome Bruner is still alive and his work is still relevant, and probably hasn’t been completely vanquished from the halls of education as an academic discipline.