Smarter than the Average Primate

In 1948, Alan Turing wrote: "An unwillingness to admit the possibility that mankind can have any rivals in intellectual power occurs as much amongst intellectual people as amongst others: they have more to lose." Accordingly, comprehensive comparisons between the intellectual powers of great apes and humans are rare - perhaps because we feel safe in assuming that the human intellect is in all ways superior to that of other primates. But recent work suggests this assumption may not be entirely sound.

For example, a recent New Scientist article (via NeuroEthics) contains a provocative statement by primatologist Tetsuro Matsuzawa, who argues that chimps may systematically outperform humans in certain tests of short-term memory. Given a task in which subjects must press stimuli in the order in which they appeared, all of the adult chimps tested have been equal or superior in performance to humans. This trend holds when the comparison is between adult chimps and adult humans, as well as between chimpanzee infants and human infants.

Perhaps just as surprisingly, children are frequently better at these games than adults, both in humans and chimpanzees. This finding meshes well with previous indications that, like the great apes, children may in specific cases be intellectually superior to human adults. For example, Elman has shown how limitations in working memory span - characteristic of children - can in some cases result in language learning benefits (although the applicability of this model to the real world has been hotly debated). Other work has shown that human children are in some cases better able to inhibit false memories than human adults are. Finally, Fisher & Sloutsky have shown that children in some cases have better recognition memory than adults.

Mindblog has recently pointed out this new Science article by Pennisi, in which the author argues that the higher cognitive capacities of animals have been only recently appreciated, based on the recent emergence of the view that intelligence arises from the demands of social living. According to this view, higher cognitive skills become an exaptation, in which they found use not only in social settings but also in more everday situations (e.g., finding ripe fruit and later remembering its location).

In the same article, Pennisi details how we managed for so long to overlook the startlingly advanced cognitive skills of higher primates. "Apes rarely did well on self-awareness, memory, gaze-following, gesture, spatial learning, and other tests at which even young children excel," writes Pennisi, but "6 years ago, Hare and his colleagues showed that under the right circumstances, chimps could pass some of these tests with flying colors. The secret was that chimps are exquisitely tuned in to their competition, particularly when food is involved, and will do everything they can to get a treat."

The implications for developmental approaches to intelligence are startling. We know that social interaction is a critical part of human development; to what extent might the current lack of "social interaction" among artificial intelligences influence their potential? Of course, there are a few notable exceptions to the idea that current AI is socially isolated, but in large part, this aspect of intelligence development remains relatively unexplored.

Related Posts:
Scientific Paradises
Intelligence Tradeoff


Anonymous Anonymous said...

fascinating, great post.

7/11/2006 11:08:00 AM  
Blogger Chris Chatham said...

Thanks!@ Glad you like it. I'd been collecting little links about this kind of thing for a while, under the general heading of "human-competitive to human-superior"

7/11/2006 06:42:00 PM  
Blogger georgeborrow said...

Very interesting post. In finance, different artificial intelligences and machine learning algorithms _are_ competing in a type of social environment: D. E. Shaw's and Jim Simon's machine learning algorithms fight over the same alpha in the electronic exchanges of the world. Soon theyre will be more artificial intelligences in the mix: the next generation will involve machine learning algorithms that make it harder for other machine learning algorithms to spot market participants. So maybe scientists who "sell out" and go to work for hedge funds will ultimately do more for science than those who stay in the academy.

7/14/2006 05:09:00 AM  
Blogger Chris Chatham said...

George - excellent point; I had forgotten about AI's loose in the stock market.

I would love to do a post about that stuff, but I'm afraid I'm not familiar with much that's public record, merely anecdotes. There's the prediction company, but there's not much detailed information on their site about the technology they use.

7/14/2006 07:58:00 AM  
Blogger georgeborrow said...

I blog on them now & then, when you get right down to it, it is probably a machine learning algorithm coupled with a Hidden Markov Model. With regard to robots doing science & trading, I'm particularly interested in Judea Pearl's work on causality, and finding ways where the iterative process of arriving at a conclusion helps determine the confidence interval of that conclusion.

7/18/2006 08:54:00 AM  

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