The Well-Designed Child

John McCarthy (who, in 1960, wrote the first paper on Lisp), has written a provocative piece called "The Well-Designed Child". It's been published in Artificial Intelligence recently (see below), but seems to have been mostly complete by 1997. The point of the piece is to advocate for a kind of common-sense nativism in understanding human and animal capabilities. McCarthy points out that the world has a number of properties that the well-designed child would not learn ab initio. These include object persistence, the tendency for objects to continue exist even when unseen, and the consequences of gravity, which make it reasonable, for many purposes, to conceptualize the world as mostly two-dimensional.  In the same way, he says that the human bias to perceive objects in terms of natural kinds is such a powerful structuring device that evolution ought to build it in as an assumption rather than requiring each child to learn its effectiveness anew.

For linguists, this article could be seen as a contribution to the debate about linguistic nativism: whether the human child has an inbuilt language faculty. But McCarthy touches on that only in passing, being much more concerned to demolish the tabula rasa hypothesis as a basis for the design of child-like robots, and to rehearse some claims about what the language of thought might be like.

The thing I really like about this article is the way it emphasizes the continuity between human capabilities and those of other animals. According to McCarthy, many of the things that make us human are similar to or even identical with the things that make dogs canine, cats feline, mice murine and octopuses octopoidal (although we would expect sea creatures, who live in a genuine 3D environment,  to be out of line with us on the stuff about gravity). And those things are largely about the styles of conceptualization which allow organisms to be effective in the particular world in which we find ourselves.

References

John McCarthy, The well-designed child, Artificial Intelligence, Volume 172, Issue 18, Special Review Issue, December 2008, Pages 2003-2014, ISSN 0004-3702, DOI: 10.1016/j.artint.2008.10.001.
(http://www.sciencedirect.com/science/article/B6TYF-4TMJ41M-2/2/6076a1f21080a46b5bb52900faf763c7)