On autism and parenting.

On autism and parenting.

I was driving away from the elementary school when I got a call from my kid’s teacher.

“I just noticed, she doesn’t have her glasses. She says she doesn’t need them, but …”

“Oh, man,” I said, ever the bumbling parent. My kid totally needs her glasses. When we took her in for an eye exam, the optometrists were pretty sure she didn’t know her letters. She was reading 400-page chapter books by then. “I’ll run them right over.”

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Sometimes I wish that I was the sort of parent who’d notice whether his kid was wearing glasses. To be able to close my eyes and picture my children’s faces.

I’m not.

My kids have been research subjects for several studies conducted by Indiana University’s developmental psychology program. For one – conducted when my eldest was between nine months and two years old – my kid and I sat at opposite sides of a little table and played with some toys. We were wearing eye-tracking cameras. We were told, “Just play together the way you would at home.”

For two of the sessions, I brought my kid to the psychology lab. For one, my spouse brought her. The researchers said, “Yeah, no problem, data from both parents would be good.”

After the study was finished, they gave us a flash drive with the videos of us playing.

When I was playing with our kid, I only looked at the toys. There’s the little truck, front and center in my field of vision!

When my spouse was playing, she only looked at our child.

At least our kid was normal, looking back and forth as we played. Sometimes focusing on her parent, sometimes on the toy, while we said things like, “See the truck? The truck is driving toward the edge of the table, vroom vroom. Oh no, the truck is going to fall off the cliff! What a calamity!”

Actually, only one of her parents said things like this. The other parent asked whether she wanted to hold the blue truck.

We learned later that they had to throw out all our family’s data.

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My children are lucky that my spouse and I have such dissimilar brains.

“Assortative mating” – when animals raise children with partners who closely resemble themselves in some way – probably explains the recent rise in autism rates. Many traits that are beneficial in small doses – creativity, analytical thinking, malaria resistance – make life harder for people who have a larger dose – schizophrenia, autism, sickle cell anemia.

Compared to prior generations, humans travel more now, and we choose romantic partners from a wider selection of people. So it’s easier to find someone who resembles us. Someone who is easy to live with. Easy to love. “We have so many similar interests!”

But children benefit from having dissimilar parents. My kids are being raised by an exceptional empath … and by me. I give them, um, their love of monsters? Lego-building prowess?

And the parents benefit, too. Love is a journey – romance helps us grow because we learn how to love a partner. We become richer, deeper people by welcoming someone who is dissimilar from us into our lives. When everything is easy, we don’t become stronger.

Which is, perhaps, a downside of the artificial-intelligence-based dating programs. These typically match people who are similar. And if things feel hard, well … there’s always another match out there. Instead of putting in the effort to build a life that fits everyone, you could just spin the wheel again.

My spouse and I have a good relationship. We also had years that were not easy.

We’re better people for it now.

And hopefully our kids will benefit from that, too. Even if they sometimes go to school without their glasses.

On perception and learning.

On perception and learning.

Cuddly.

Fearful.

Monstrous.

Peering with the unwavering focus of a watchful overlord.

A cat could seem to be many different things, and Brendan Wenzel’s recent picture book They All Saw a Cat conveys these vagrancies of perception beautifully. Though we share the world, we all see and hear and taste it differently. Each creature’s mind filters a torrential influx of information into manageable experience; we all filter the world differently.

They All Saw a Cat ends with a composite image. We see the various components that were focused on by each of the other animals, amalgamated into something approaching “cat-ness.” A human child noticed the cat’s soft fur, a mouse noticed its sharp claws, a fox noticed its swift speed, a bird noticed that it can’t fly.

All these properties are essential descriptors, but so much is blurred away by our minds. When I look at a domesticated cat, I tend to forget about the sharp claws and teeth. I certainly don’t remark on its lack of flight – being landbound myself, this seems perfectly ordinary to me. To be ensnared by gravity only seems strange from the perspective of a bird.

theyallsawThere is another way of developing the concept of “cat-ness,” though. Instead of compiling many creatures’ perceptions of a single cat, we could consider a single perceptive entity’s response to many specimens. How, for instance, do our brains learn to recognize cats?

When a friend (who teaches upper-level philosophy) and I were talking about Ludwig Wittgenstein’s Philosophical Investigations, I mentioned that I felt many of the aims of that book could be accomplished with a description of principal component analysis paired with Gideon Lewis-Kraus’s lovely New York Times Magazine article on Google Translate.

My friend looked at me with a mix of puzzlement and pity and said, “No.” Then added, as regards Philosophical Investigations, “You read it too fast.”

wittgensteinOne of Wittgenstein’s aims is to show how humans can learn to use language… which is complicated by the fact that, in my friend’s words, “Any group of objects will share more than one commonality.” He posits that no matter how many red objects you point to, they’ll always share properties other than red-ness in common.

Or cats… when you’re teaching a child how to speak and point out many cats, will they have properties other than cat-ness in common?

In some ways, I agree. After all, I think the boundaries between species are porous. I don’t think there is a set of rules that could be used to determine whether a creature qualifies for personhood, so it’d be a bit silly if I also claimed that cat-ness could be clearly defined.

But when I point and say “That’s a cat!”, chances are that you’ll think so too. Even if no one had ever taught us what cats are, most people in the United States have seen enough of them to think “All those furry, four-legged, swivel-tailed, pointy-eared, pouncing things were probably the same type of creature!”

Even a computer can pick out these commonalities. When we learn about the world, we have a huge quantity of sensory data to draw upon – cats make those noises, they look like that when they find a sunny patch of grass to lie in, they look like that when they don’t want me to pet them – but a computer can learn to identify cat-ness using nothing more than grainy stills from Youtube.

Quoc Le et al. fed a few million images from Youtube videos to a computer algorithm that was searching for commonalities between the pictures. Even though the algorithm was given no hints as to the nature of the videos, it learned that many shared an emphasis on oblong shapes with triangles on top… cat faces. Indeed, when Le et al. made a visualization of the patterns that were causing their algorithm to cluster these particular videos together, we can recognize a cat in that blur of pixels.

The computer learns in a way vaguely analogous to the formation of social cliques in a middle school cafeteria. Each kid is a beautiful and unique snowflake, sure, but there are certain properties that cause them to cluster together: the sporty ones, the bookish ones, the D&D kids. For a neural network, each individual is only distinguished by voting “yes” or “no,” but you can cluster the individuals who tend to vote “yes” at the same time. For a small grid of black and white pixels, some individuals will be assigned to the pixels and vote “yes” only when their pixels are white… but others will watch the votes of those first responders and vote “yes” if they see a long line of “yes” votes in the top quadrants, perhaps… and others could watch those votes, allowing for layers upon layers of complexity in analysis.

three-body-problem-by-cixin-liu-616x975And I should mention that I feel indebted to Liu Cixin’s sci-fi novel The Three-Body Problem for thinking to humanize a computer algorithm this way. Liu includes a lovely description of a human motherboard, with triads of trained soldiers hoisting red or green flags forming each logic gate.

In the end, the algorithm developed by Le et al. clustered only 75% of the frames from Youtube cat videos together – it could recognize many of these as being somehow similar, but it was worse at identifying cat-ness than the average human child. But it’s pretty easy to realize why: after all, Le et al. titled their paper “Building high-level features using large scale unsupervised learning.”

Proceedings of the International Conference on Machine Learning 2010
You might have to squint, but there’s a cat here. Or so says their algorithm.

When Wittgenstein writes about someone watching builders – one person calls out “Slab!”, the other brings a large flat rock – he is also considering unsupervised learning. And so it is easy for Wittgenstein to imagine that the watcher, even after exclaiming “Now I’ve got it!”, could be stymied by a situation that went beyond the training.

Many human cultures have utilized unsupervised learning as a major component of childrearing – kids are expected to watch their elders and puzzle out on their own how to do everything in life – but this potential inflexibility that Wittgenstein alludes to underlies David Lancy’s advice in The Anthropology of Childhood that children will fair best in our modern world when they have someone guiding their education and development.

Unsupervised learning may be sufficient to prepare children for life in an agrarian village. Unsupervised learning is sufficient for chimpanzees learning how to crack nuts. And unsupervised learning is sufficient to for a computer to develop an idea about what cats are.

But the best human learning employs the scientific method – purposefully seeking out “no.”

I assume most children reflexively follow the scientific method – my daughter started shortly after her first birthday. I was teaching her about animals, and we started with dogs. At first, she pointed primarily to creatures that looked like her Uncle Max. Big, brown, four-legged, slobbery.

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Good dog.

Eventually she started pointing to creatures that looked slightly different: white dogs, black dogs, small dogs, quiet dogs. And then the scientific method kicked in.

She’d point to a non-dog, emphatically claiming it to be a dog as well. And then I’d explain why her choice wasn’t a dog. What features cause an object to be excluded from the set of correct answers?

Eventually she caught on.

Many adults, sadly, are worse at this style of thinking than children. As we grow, it becomes more pressing to seem competent. We adults want our guesses to be right – we want to hear yes all the time – which makes it harder to learn.

The New York Times recently presented a clever demonstration of this. They showed a series of numbers that follow a rule, let readers type in new numbers to see if their guesses also followed the rule, and asked for readers to describe what the rule was.

A scientist would approach this type of puzzle by guessing a rule and then plugging in numbers that don’t follow it – nothing is ever really proven in science, but we validate theories by designing experiments that should tell us “no” if our theory is wrong. Only theories that all “falsifiable” fall under the purvey of science. And the best fields of science devote considerable resources to seeking out opportunities to prove ourselves wrong.

But many adults, wanting to seem smart all the time, fear mistakes. When that New York Times puzzle was made public, 80% of readers proposed a rule without ever hearing that a set of numbers didn’t follow it.

Wittgenstein’s watcher can’t really learn what “Slab!” means until perversely hauling over some other type of rock and being told, “no.”

We adults can’t fix the world until we learn from children that it’s okay to look ignorant sometimes. It’s okay to be wrong – just say “sorry” and “I’ll try to do better next time.”

Otherwise we’re stuck digging in our heels and arguing for things we should know to be ridiculous.

It doesn’t hurt so bad. Watch: nope, that one’s not a cat.

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Photo by John Mason on Flickr.