Peter B. Gray Ph.D.

The Evolving Father

Technology, Turing and Child Development

Parenting faces a new tech world.

Posted Aug 06, 2015

Peter Gray
Source: Peter Gray

While recently in the Bay Area visiting family, my daughters, niece, nephew and I spent a day at The Tech Museum of Innovation in San Jose. The kids were shaken up—in an earthquake simulator and watching the ups and downs of a roller coaster they designed. They also wandered through spaces allowing them to create an app, assess their heart rate, and morph their faces in creative ways. All this got me thinking of some of the intersections of technology, learning and child development.

A paper last year by Chris Kuzawa and colleagues in the “Proceedings of the National Academy of Sciences” reexamined patterns of human brain growth and development. In a creative synthesis, they showed that a driving reason why humans (compared to other great apes, for example) have such slow body growth is because of the massive amounts of energy required to develop and maintain our energetically expensive and massive brains. In other words, human children grow at a slower pace than chimpanzees, in part because human brains are drawing upon an energy budget at expense to overall somatic growth. They didn’t address why selection may have favored large human brains in the first place. Yet leading arguments focus on the need to bulk up the human brain in order to facilitate social learning capacities. Changes in hominin social life—in family organization, coalitions and scale—may have required more cognitive support; our social lives made our brains bigger.

So we need big brains to learn to successfully navigate a complex social world. What about designing a machine to learn? What would that machine look like, and would it have a huge “brain”? This gets us toying around with the idea of the Turing Test. In a 1950 paper, Alan Turing, also the focus of the Hollywood success “The Imitation Game,” provided a playful and operational test: if a person couldn’t tell the difference between answers given by a learning machine vs. another person, that machine had demonstrated artificial intelligence. Turing’s paper also anticipated various counter-arguments to his proposed test of artificial intelligence. Most relevant for our current purpose, Turing speculates about the basis of machine learning:

“In the process of trying to imitate an adult human mind we are bound to think a good deal about the process which has brought it to the state that it is in. We may notice three components.

(a) The initial state of the mind, say at birth,

(b) The education to which it has been subjected,

(c) Other experience, not to be described as education, to which it has been subjected.

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain…. There is an obvious connection between this process and evolution…”

Funny enough, Sir Charles Darwin (the famous naturalist’s grandson), who directed a national U.K. lab in the 1940s, lamented Turing’s obsession with learning machines. 

Most standard manifestations of thinking machines (awkwardly moving robots or computers without any human-like form) don’t resemble human children. For example, in the film “Ex Machina,” the story centers on designing a learning machine that faces the equivalent of the Turing Test. If you’ve seen the movie, you know whether or not that effort was successful. The machines in “Ex Machina” take the form of adult females, partly to learn by interacting with and serving the male designer. They were not built to look or act like human children, nor did they change appearance over time as a learning human would. Why not? Maybe that would defeat the purposes of designing learning machines: if they took years or decades to learn, they couldn’t fill the niches we want them to do now or in the more immediate future. They might not learn about the things we want them to learn. But learning machines with child-like characteristics (high voices, relatively larger heads, sense of innocence, etc.) could be optimized to attract attention from human “parents” in ways that allow the machines to learn in ways more closely resembling how humans learn.

Another aspect of technology and child development many of us parents may now face is the fact that our children are more tech savvy than us. My 11-year-old can design much more visually appealing presentations that I can, and she can answer some of the computer or phone questions on which I get stuck. This goes back to why and how children learn in the first place. We have evolved to be highly adept social learners, particularly during childhood. A natural curiosity propels our offspring to attune to the world in often highly adaptive ways (see a different Peter Gray’s blog on this kind of topic: https://www.psychologytoday.com/blog/freedom-learn). The video games kids enjoy (or other interactive games at the Tech Museum) activate these very capacities.

When I learn from my daughters, this is a kind of vertical cultural transmission, but from younger to older generation. This turns more evolutionary-typical processes of cultural transmission on their head. A body of research has attended to human cultural transmission among hunter-gatherers and in other smaller-scale societies. Much of children’s learning occurs through behavioral observation; among young children, this is especially oriented toward parents, though older children observe and learn much from other children. As Barry Hewlett and colleagues (2011) note, “Vertical transmission should be important in hunter-gatherers given our great ape phylogenetic heritage of mother-to-offspring transmission and parents’ potential inclusive fitness benefits from taking the time to transmit knowledge or skills. Theoretically, one can make the case, but this is also what hunter-gatherers say when asked how they learned a wide range of skills and knowledge.” (p. 1173) Formal teaching is fairly rare among foragers, although Hewlett and colleagues note that storytelling (from parents to youth) serves as an effective vehicle for transmitting cultural values and beliefs among Aka and Bofi foragers.  

In today’s rapidly changing world, including quickly-outmoded smart phones and computers, parents’ technological understanding is also often outmoded. Our kids keep us on our mental toes, as their wonder and tech understanding shape our own.

References:

Barrett, H. C. (2015). The Shape of Thought: How Mental Adaptations Evolve. New York: Oxford University Press.

Hewlett, B.S., Fouts, H.N., Boyette, A.H., Hewlett, B.L. (2011). Social learning among Congo Basin hunter-gatherers. Philosophical Transactions of the Royal Society B, 366, 1168-1178.

Isaacson, W. (2014). The Innovators: How a Group of Hackers, Geniuses, and Geeks Created the Digital Revolution. New York: Simon and Schuster.

Kuzawa, C.W., Chugani, H.T., Grossman, L.I., Lipovich, L. et al. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111, 13010-13015.

Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433-460.