Stephen Jay Gould, intelligence and he Mismeasure of Man (original) (raw)

Stephen Jay Gould on intelligence
by
Korb KB.
Department of Computer Science,
Monash University,
Clayton, Victoria, Australia.
Cognition. 1994 Aug;52(2):111-23

ABSTRACT

In The Mismeasure of Man (1981) Stephen Jay Gould provides a typically readable history of one of our most vexatious intellectual enterprises: the scientific study of intelligence. Gould is successful, as always, in rendering the relevant scientific debates accessible to general readers. What Gould does less well is to carry through his attack on prior attempts to understand natural intelligence scientifically: attempting to muster all possible arguments against such science, he conjures up a variety of impossible arguments as well. One such argument urges that Gould's predecessors are not to be taken seriously because they are racists and have let their racism influence their scientific practice. Gould has no difficulty in demonstrating the influence of racism; where he goes astray is in his dismissal of such prior work as simply unscientific because the racist conclusions preceded the collection of data. Advancing hypotheses prior to experimentation is how all of science proceeds, and is no mark of inferior work. And no science is immune to influences--racist or otherwise--from the culture in which it is embedded, as Gould elsewhere readily acknowledges. Another failed argument claims that all of the factor analysts studying intelligence have committed the intellectual sin of reifying the factors uncovered in IQ tests--concluding that the factors are real solely on the basis of how a factor analysis summarizes IQ data. Gould concludes that factor analysis is worthless for the study of intelligence. However: (1) contrary to what Gould suggests, the factor analysts themselves warned against concluding that the factors "discovered" are physiologically real merely on the basis of a factor analysis; and (2) factor analysis nevertheless remains a strong candidate technique for developing causal models worth investigating subsequently by other means.

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