I recommend this well written refutation of Cosma Shalizi's much loved (in certain quarters) g, a Statistical Myth, an attack on the general factor of intelligence. Over the years I have not encountered a single endorser of Shalizi's article who actually understands the relevant subject matter. His article is loved for its reassuring conclusions, not the strength of its arguments. I am sure many "thinkers" resisted Darwinism, the abandonment of geocentrism, and even the notion that the Earth is a sphere, for similar psychological reasons. Some pessimists (speaking, for example, of the quantum revolution in the early 20th century) remarked that science advances one funeral at a time, as the older generation passes away in favor of the next, more open-minded, one. In the case of g it appears we have regressed significantly under relentless attack; social science papers from 50 years ago often seem more clear headed and precise than ones I read today. All battles must be fought and refought again a decade or two later.
As I write here:
We can (crudely) measure cognitive ability using simple tests. (It is amazing to me that this is a controversial statement.) Randomly sampled eminent scientists have (very) high IQs, and given the observed stability of adult IQ the causality is clear ...Optimistically, we are only a decade away from genomic prediction of g scores (see Eric, why so gloomy?). The existence of such a predictor may allow us to finally push the boulder to the top, and keep it there.
As I mention in talks on this subject, the fact that cognitive abilities reliably have positive correlation is highly nontrivial. Add to this the well-established validity and stability of g and you have a construct that must be taken seriously. See also IQ, Compression and Simple Models.
Is Psychometric g a Myth?
Shalizi’s first error is his assertion that cognitive tests correlate with each other because IQ test makers exclude tests that do not fit the positive manifold. In fact, more or less the opposite is true. Some of the greatest psychometricians have devoted their careers to disproving the positive manifold only to end up with nothing to show for it. Cognitive tests correlate because all of them truly share one or more sources of variance. This is a fact that any theory of intelligence must grapple with.
Shalizi’s second error is to disregard the large body of evidence that has been presented in support of g as a unidimensional scale of human psychological differences. The g factor is not just about the positive manifold. A broad network of findings related to both social and biological variables indicates that people do in fact vary, both phenotypically and genetically, along this continuum that can be revealed by psychometric tests of intelligence and that has has widespread significance in human affairs.
Shalizi’s third error is to think that were it shown that g is not a unitary variable neurobiologically, it would refute the concept of g. However, for most purposes, brain physiology is not the most relevant level of analysis of human intelligence. What matters is that g is a remarkably powerful and robust variable that has great explanatory force in understanding human behavior. Thus g exists at the behavioral level regardless of what its neurobiological underpinnings are like.
In many ways, criticisms of g like Shalizi’s amount to “sure, it works in practice, but I don’t think it works in theory”. Shalizi faults g for being a “black box theory” that does not provide a mechanistic explanation of the workings of intelligence, disparaging psychometric measurement of intelligence as a mere “stop-gap” rather than a genuine scientific breakthrough. However, the fact that psychometricians have traditionally been primarily interested in validity and reliability is a feature, not a bug. Intelligence testing, unlike most fields of psychology and social science, is highly practical, being widely applied to diagnose learning problems and medical conditions and to select students and employees. What is important is that IQ tests reliably measure an important human characteristic, not the particular underlying neurobiological mechanisms. Nevertheless, research on general mental ability extends naturally into the life sciences, and continuous progress is being made in understanding g in terms of neurobiology (e.g., Lee et al. 2012, Penke et al. 2012, Kievit et al. 2012) and molecular genetics (e.g., Plomin et al., in press, Benyamin et al., in press).