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Intelligence, Assumptions, and the g Conundrum
Anne Corwin   Oct 21, 2007   Existence is Wonderful  

Cosma Shalizi at Three-Toed Sloth has written a long (but well worth the read) article entitled g, A Statistical Myth.

This article really elucidates a lot of the issues I have with the usual attempts to quantify “intelligence” and explain what causes it.

An excerpt:

the case for g rests on a statistical technique, factor analysis, which works solely on correlations between tests. Factor analysis is handy for summarizing data, but can’t tell us where the correlations came from; it always says that there is a general factor whenever there only positive correlations. The appearance of g is a trivial reflection of that correlation structure. A clear example, known since 1916, shows that factor analysis can give the appearance of a general factor when there are actually many thousands of completely independent and equally strong causes at work. Heritability doesn’t distinguish these alternatives either. Exploratory factor analysis being no good at discovering causal structure, it provides no support for the reality of g.

These purely methodological points don’t, themselves, give reason to doubt the reality and importance of g, but do show that a certain line of argument is invalid and some supposed evidence is irrelevant. Since that’s about the only case which anyone does advance for g, however, which accords very poorly with other evidence, from neuroscience and cognitive psychology, about the structure of the mind, it is very hard for me to find any reason to believe in the importance of g, and many to reject it. These are all pretty elementary points, and the persistence of the debates, and in particular the fossilized invocation of ancient statistical methods, is really pretty damn depressing.

I’ve avoided writing much about this particular subject so far, because I wanted to wait until I either wrote or found something that would make it clear that I am not basing my opinions on intelligence on mere “political correctness”, or on emotional appeals to some notion that every individual has the exact same set of abilities (which obviously isn’t true).

I’ve read a lot of literature on theories of intelligence, including a fair number of papers on g and on psychometrics.  I’ve also been professionally tested twice (on the Weschler Pre-School and Primary Scale of Intelligence at age 4, and on the Weschler Adult Intelligence Scale at age 20), so I have direct experience with at least one type of IQ test.

I don’t dispute the fact that people who score well on certain types of tests are statistically more likely to, say, graduate from college or hold down a particular kind of job, but I do dispute the utility of IQ testing in evaluating an individual’s “potential” or their ability to eventually process and understand intellectual and practical problems.  It just has always seemed to me as if much of the “intelligence” literature doesn’t tell the whole story, and is rife with implicit assumptions that are rarely ever examined. 

One thing that gives me some hope that this might not always be the case, though, is that some studies are approaching intelligence in a way that does demonstrate awareness of some of these assumptions. This article in Science Daily describes a study meant to (at least in part) bypass the language difficulties commonly observed in autistic persons:

Led by psychologist Laurent Mottron of the University of Montreal, the team gave both autistic kids and normal kids two of the most popular IQ tests used in schools. The two tests are both highly regarded, but they are very different. The so-called WISC relies heavily on language, which is why the psychologists were suspicious of it. The other, known as the Raven’s Progressive Matrices, is considered the preeminent test of what’s called “fluid intelligence,” that is, the ability to infer rules, to set and manage goals, to do high-level abstractions. Basically the test presents arrays of complicated patterns with one missing, and test takers are required to choose the one that would logically complete the series. The test demands a good memory, focused attention and other “executive skills,” but—unlike the WISC—it doesn’t require much language.

The idea was that the autistic kids’ true intelligence might shine through if they could bypass the language deficit. And that’s exactly what happened.

The difference between their scores on the WISC and the Raven’s test was striking: For example, not a single autistic child scored in the “high intelligence” range of the WISC, yet fully a third did on the Raven’s. Similarly, a third of the autistics had WISC scores in the mentally retarded range, whereas only one in 20 scored that low on the Raven’s test. The normal kids had basically the same results on both tests.

EDIT: Here’s a link to the paper describing the study referenced in the Science Daily article.  Recommended reading, since researcher Michelle Dawson has pointed out a few clarifications with regard to the paper and how it was described in the press release.

I’d be curious to know what some of you statistically-minded folk think of the idea of “g as a statistical myth”, as described in the first article I linked to.  I’ve noticed that a lot of discussions of intelligence and “g” I read around the Web are dominated by those who seem to have high confidence in factor analysis as far as its ability to support the notion of g, but I would like to know whether that confidence also translates to assuming that supposedly “g-loaded” tasks are probably accomplished as a function of the same “property”. 

It seems to me that to make such an assumption, a person would have to ignore all the evidence pointing to the fact that different kinds of brains may, in fact, operate and solve problems differently (and that while one skill might correlate with another in a typical person, this isn’t necessarily the case for a less typical person).

Anne Corwin was an IEET intern 2006-2007, and is an engineer and technoprogressive activist in California. She is a member of the Board of Directors of the Humanity Plus, and is active in the longevity movement through the Methuselah Foundation and in the neurodiversity movement addressing issues along the autism spectrum. Ms. Corwin writes the blog Existence is Wonderful and produces a related podcast.


That piece you linked by Cosma Shalizi ‘g is a statistical myth’ is incorrect.

The main thrusts of his argument is that test data do not statistically support a g-factor. Gould tried to discredit g but his argument argument was statistically incompetent (for a statistican’s critique see Measuring intelligence: facts and fallacies by David J. Bartholomew, 2004). Shalizi’s criticism is incredibly sophisticated, but likewise incorrect. In a nutshell, Shalizi is trying to argue around the positive correlations between test batteries. If those correlations didn’t exist, his argument would be meaningful. However, these intercorrelations are one of the best documented patterns in the social sciences.

Cosma Shalizi misrepresented Spearman and his two factor model. The author tried to present Spearman as ignorant of group factors (he should have called them out as such or noted that they are from the second stratum). The fact is that Spearman gave up on the two factor model and accepted group factors. The fact beyond that is that the predictive validity of group factors typically appears in the range from (and including) zero to about 4%. In other words, the two factor model is not rigorously correct, but it captures virtually all of the practical validity of any test.

For a discussion of neurological correlates with g see this discussion by Neurologist Paul Thompson:

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