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Race, Brain Size, and IQ: the Case for Consilience

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Race, Brain Size, and IQ: the Case for Consilience Continuing Commentary MacDorman, K. F. (1997) Memory must also mesh affect. Behavioral and Brain Simpson, G. B. & Kang, H. (1994) Inhibitory processes in the recognition of Sciences 20:29. [EW] homograph meanings. In: Inhibitory mechanisms in attention, memory and Martin, A., Ungerleider, L. G. & Haxby, J. V. (2000) Category specificity and the language, ed. D. Dagenbach & T. Carr. Academic Press. [EN] brain: The sensory/motor model of semantic representation of objects. In: The Tipper, S. P. & Driver, J. (1988) Negative priming between pictures and words: new cognitive neurosciences, ed. M. S. Gazzaniga. MIT Press. [rAMG] Evidence for semantic analysis of ignored stimuli. Memory and Cognition Neumann, E. & DeSchepper, B. G. (1991) Costs and benefits of target activation 16:64–70. [EN] and distractor inhibition in selective attention. Journal of Experimental Tipper, S. P., Lortie, C. & Baylis, G. C. (1992) Selective reaching: Evidence for Psychology: Learning, memory, and Cognition 17:1136–45. [EN] action-centered attention. Journal of Experimental Psychology: Human (1992) An inhibition-based fan effect: Evidence for an active suppression Perception and Performance 18:891–905. [rAMG, EN] mechanism in selective attention. Canadian Journal of Psychology 46:1–40. Treisman, A. & DeSchepper, B. G. (1996) Object tokens, attention, and visual [EN] memory. In: Attention and performance, vol. XVI: Information integration in Neumann, E., McCloskey, M. S. & Felicio, A. C. (1999) Cross-language positive perception and communication, ed. T. Inui & J. McClelland. MIT press. priming disappears, negative priming does not: Evidence for two sources of [EN] selective inhibition. Memory and Cognition. 27:1051–63. [EN] Wright, E. L. (1985) A design for a human mind. Conceptus 47:21–37. [EW] Newell, A. (1980) Physical symbol systems. Cognitive Science 4:135–83. [MK] (1992) The entity fallacy in epistemology. Philosophy 67:33–50. [EW] Piaget, J. (1970) Genetic epistemology. Columbia University Press. [EW] (1993) The irony of perception. In: New representationalisms: Essays in the Schyns, P., Goldstone, R. L. & Thibaut, J.-P. (1998) The development of features in philosophy of perception, ed. E. L. Wright. Avebury. [EW] object concepts. Behavioral and Brain Sciences 21:1–54. [rAMG] Searle, J. R. (1980) Minds, brains and programs. Behavioral and Brain Sciences 3:417–24. [rAMG, MK] Commentary on Steven Rose (1999). Précis of Lifelines: Biology, freedom, determinism, by S. Rose; The Penguin Press, 1997. [Reprinted as Lifelines: Biology beyond determinism. Oxford University Press] BBS 22(5):871–921. Abstract of the original article: There are many ways of describing and explaining the properties of living systems; causal, functional, and reductive accounts are necessary but no one account has primacy. The history of biology as a discipline has given excessive au- thority to reductionism, which collapses higher level accounts, such as social or behavioural ones, into molecular ones. Such reduc- tionism becomes crudely ideological when applied to the human condition, with its claims for genes “for” everything from sexual ori- entation to compulsive shopping. The current enthusiasm for genetics and ultra-Darwinist accounts, with their selfish-gene metaphors for living processes, misunderstand both the phenomena of development and the interactive role that DNA and the fluid genome play in the cellular orchestra. DNA is not a blueprint, and the four dimensions of life (three of space, one of time) cannot be read off from its one-dimensional strand. Both developmental and evolutionary processes are more than merely instructive or selective; the organ- ism constructs itself, a process known as autopoiesis, through a lifeline trajectory. Because organisms are thermodynamically open sys- tems, living processes are homeodynamic, not homeostatic. The self-organising membrane-bound and energy-utilising metabolic web of the cell must have evolved prior to so-called naked replicators. Evolution is constrained by physics, chemistry, and structure; not all change is powered by natural selection, and not all phenotypes are adaptive. Finally, therefore, living processes are radically indeter- minate; like all other living organisms, but to an even greater degree, we make our own future, though in circumstances not of our own choosing. Race, brain size, and IQ: The case testable predictions the sine qua non of science. That is why I as- for consilience sociate myself with those commentators (like Alcock 1999) who argued that, based on its long track record of success, to assume J. Philippe Rushton some sensible degree of reductionistic determinism is the way of science. That is also the view of E. O. Wilson (1998, pp. 30–31), Department of Psychology, University of Western Ontario, London, Ontario, N6A 5C2, Canada. [email protected] in whose “sociobiological footsteps” I am proud to follow, and who http:/www.ssc.uwo.ca/psychology/faculty/rushton.html is one of those “ultra-Darwinists” that Rose dismisses. Still, I was surprised that only one of the commentators (Martindale 1999) brought up the relationship between brain size and IQ, and he Abstract: Data from magnetic resonance imaging (MRI), autopsy, en- docranial measurements, and other techniques show that: (1) brain size made mention of a review by Jensen and Sinha (1994) only in pass- correlates 0.40 with cognitive ability; (2) average brain size varies by race; ing. No one referred to the remarkable Magnetic Resonance and (3) average cognitive ability varies by race. These results are as replic- Imaging (MRI) studies showing a correlation of 0.40 existing be- able as one will find in the social and behavioral sciences. They pose seri- tween brain size and IQ among humans. There are now well over ous problems for Rose’s claim that reductionistic science is inadequate, in- a dozen MRI studies (e.g., Gur et al. 1999; Tan et al. 1999; see efficient, and/or unproductive. Rushton 1995 and Jensen 1998 for reviews). The MRI brain-size/ IQ correlation provides a challenge to Rose’s anti-reductionism. Rose (1999) clearly doesn’t like much of today’s behavioral and Brains have evolved via natural selection for behavioral complex- brain sciences, which he characterizes as filled with “reduction- ity (i.e., intelligence), they show substantial heritable variance ism,” “reification,” “arbitrary agglomeration,” “ultra- Darwinism,” and, worst of all from Rose’s perspective, they show racial varia- and “neurogenetic determinism.” However, his proposed alterna- tion at birth, 4 months, 1 year, 7 years, and adulthood (see Fig. 1; tives, autopoiesis and homeodynamic lifelines – inasmuch as they Rushton 1997). actually involve anything different – are unlikely to generate Rushton’s (1997) study, based on the enormous (N 5 35,000) 648 BEHAVIORAL AND BRAIN SCIENCES (2003) 26:5 Continuing Commentary 1391 1362 1378 1400 sample included both men and women. The regression equation relating the two measures is: number of cortical neurons (in bil- 1167 1200 African Americans 1134 1154 lions) 5 5.583 1 0.006 (cm3 brain volume). Thus, a person with a 3 European Americans brain size of 1,400 cm has, on average, 600 million fewer cortical 1000 s 3 r e 819 neurons than an individual with a brain size of 1,500 cm . The dif- t East Asian Americans 801 806 e m 800 i ference between the low end of the normal distribution for adult t n 3 3 e 557 578 586 brain size (1,000 cm ) and the high end (1,700 cm ) works out to C 600 | c i b be 4.2 billion neurons. That amounts to 27% more neurons for a u C 400 315 332 335 41% increase in brain size. The best estimate is that the human brain contains about 100 billion 1,011 neurons. Even storing in- 200 formation at the low average rate of one bit per synapse, which 0 would require two levels of synaptic activity (high/low; on/off), Birth 4 Months 1 Year 7 Years Adults the structure as a whole would contain 1,014 bits of information. U.S. Collaborative Perinatal Project U.S. Army Contemporary supercomputers, by comparison, typically have a memory of about 109 bits. Figure 1 (Rushton). Population Differences in Brain Size. Mean Increasing neurological complexity has increased in inverte- cranial capacity (cm3) for African Americans, European Ameri- brates and vertebrates alike over 700 million years of evolutionary cans, and Asian Americans at birth, 4 months, 1 year, 7 years, and history. This increase entailed metabolic and life-history costs, and in adulthood. From Rushton (1997, p. 15, Fig. 2). Copyright 1997 the tradeoffs would not have occurred without an adaptive ad- by Ablex Publishing Corporation. Reprinted with permission. vantage. In the competition to find and fill new niches, there has always been (and likely always will be) “room at the top.” Linear theorizing of the observed data lights the path to greater under- standing. Rose’s approach is a slide into obfuscation. Collaborative Perinatal Project, also found that at age 7, brain vol- ume estimated from external head size measures correlated 0.20 with IQ scores in the East Asian subsample, just as it had earlier been shown to do in the white and black subsamples (Broman et References al. 1987). Although the more accurate MRI measure of brain size yields correlations much higher than the 0.20 in other samples, the Alcock, J. (1999) The myth of genetic determinism again. Behavioral and Brain head circumference correlation r of 0.20 is still significant. More- Sciences 22:885–86. [JPR] over, the Asian subsample in the study averaged a higher IQ (110) Beals, K. L., Smith, C. L. & Dodd, S. M. (1984) Brain size, cranial morphology, at age 7 than did the white (102) or the black subsamples (90). climate, and time machines. Current Anthropology 25:301–30. [JPR] Broman, S. H., Nichols, P. L., Shaughnessy, P. & Kennedy, W. (1987) Retardation The pattern of increasing mean brain size from Africans to Eu- in young children.
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