Dr. Kimberly G. Noble has studied the Visual Word Form Area of children in New York City schools (source). The VWFA seems more hardwired in higher SES children.
The Visual Word Form Area (VWFA) is a brain region that specifically recognizes written words. It seems to be composed of neurons that were originally used to recognize human faces. Though not essential for reading, it greatly speeds up this mental task (Gaillard et al, 2006; see earlier post).
Is the VWFA a product of gene-culture co-evolution? Did natural selection favor the reproductive success of individuals who were better able to recognize words? Or does this brain area develop independently in each individual through experience with reading?
The second explanation is the one now favored. Dehaene and Cohen (2011) argue that the VWFA is where the brain can most easily recruit neurons for the task of recognizing words. One problem with this developmental explanation is that the VWFA responds preferentially to images of letter strings even in kindergarten children who haven’t learned to read yet (Brem et al., 2010).
We could answer this question one way or the other by comparing populations that have a long history of reading with those that were illiterate until relatively recent times. Does the second type of population exhibit a less developed and less specialized VWFA even in individuals who learned to read at an early age?
Only two VWFA studies have dealt with the second type of population. In these two cases, some of the subjects were of sub-Saharan African descent. We should nonetheless remember that some sub-Saharan African societies, notably those of the Sahel, have a history of reading and writing that goes back over seven hundred years.
In the first of the two studies, Noble et al. (2006) examined brain responses to reading tasks that were performed by native English-speakers in New York City elementary schools. The students differed by socioeconomic status (SES) and by ethnicity. Eleven were African-American, five Latino, one Asian, fourteen White, and seven mixed or other. Brain activation varied as a function of SES. Among lower SES children, VWFA activation was much stronger in those who were “phonologically aware”, i.e., who explicitly knew how to represent and manipulate the sounds of language. But this pattern was absent in higher SES children:
In contrast, as the SES of the population increases, children demonstrating a similar range of phonological skill show an attenuated brain–behavior relationship in this region. This suggests that, among children who are likely to have adequate access to literacy resources, the relationship between reading precursor skills and left fusiform activity to reading may, to an extent, be reduced, marking an atypical relationship between cognitive skill and brain activity. A marginally significant PA × SES interaction was also observed in the left superior temporal region, demonstrating a similar trend. (Noble et al., 2006)
The authors attributed this non-correlation to “adequate access to literacy resources.” An alternate explanation would be that VWFA activation was more hardwired in the higher SES children. Strangely enough, the authors did not break down their data by race, so it is impossible to say whether SES was simply a proxy for ethnic background.
The second study was by Dehaene et al. (2010) on literate, illiterate, and ex-illiterate adults from Brazil and Portugal. No information is given on ethnicity, although many of the illiterate or ex-illiterate Brazilians were probably of African or part-African ancestry. The authors found that VWFA activation was much less apparent in adult illiterates than in adult literates, even when the data were controlled for SES and schooling.
Again, there is no breakdown of the data by ethnicity, although one might assume that SES and schooling were proxies for ethnicity. This is a flawed assumption, however, at least in Brazil:
Still, the patterns of racial in Brazilian education have remained and have transcended social class barriers. Nelson do Valle Silva and Carlos Hasenbalg have demonstrated that patterns of educated attainment remain unequal even when social class is eliminated as a factor: whites of the same social class have higher literacy rates and remain more likely to attend school, to stay in school longer, to be advanced through school more rapidly, and to secure better-paying jobs given the same educational qualifications. Silva and Hasenbalg conclude that “white children’s rates of school advancement are significantly more rapid than those of pardo [mixed] and preto [black] children. These differences result in profound educational inequalities that separate whites and nonwhites in Brazilian society.” (Davila, 2003, p. 8)
On the basis of these two studies, it is impossible to say whether the VWFA is hardwired or softwired. This brain area may result solely from developmental processes within the lifetime of each individual. Or it may be due to longer-term evolutionary processes.
A common problem is that both studies use SES to the exclusion of ethnicity. Yes, ethnic differences may simply reflect SES differences, but that arrow of causality should be proven and not assumed. In any case, SES varies imperfectly with ethnicity. With respect to the Dehaene etal. (2010) study, black Brazilians tend to be more illiterate than white Brazilians even among people of similar SES. With respect to the Noble et al. (2006) study, differences in phonological skill might likewise reflect ethnic differences, even if we consider only the lower SES children.
Why did both research teams ignore ethnicity? One reason, at least in the case of Dehaene’s team, is a belief that mental traits take eons to evolve. This might be true if the trait is radically new and different, but here the transition from face recognition to letter recognition is relatively simple. This is the kind of evolution that could happen over a few centuries, if the selection pressure were strong enough.
The other reason is a belief that ethnicity is genetically irrelevant, since genes vary much more within than between human populations. This fact is well known and beyond dispute. What is less well known is that the same pattern often appears when we examine the way genes vary within and between sibling species—even when such species are morphologically and behaviorally distinct. We should understand that we’re comparing apples with oranges when genetic variation within populations is compared with genetic variation between populations. Different populations typically occupy different environments with different selection pressures. Variation across a population boundary is thus more likely to involve genes that have real adaptive value. In contrast, variation within a population tends to involve genes of low adaptive value that are insensitive to the homogenizing action of similar selection pressures (Frost, 2011).
Brem, S., S. Bach, K. Kucian, T.K. Guttorm, E. Martin, H. Lyytinen, D. Brandeis, & U. Richardson. (2010). Brain sensitivity to print emerges when children learn letter-speech sound correspondences, Proceedings of the National Academy of Sciences U.S.A., 107, 7939–7944.
Davila, J. (2003). Diploma of Whiteness. Race and Social Policy in Brazil, 1917-1945, Duke University Press.
Dehaene, S. & L. Cohen. (2011). The unique role of the visual word form area in reading, Trends in Cognitive Sciences, 15, 254-262.
Dehaene, S., F. Pegado, L.W. Braga, P. Ventura, G.N. Filho, A. Jobert, G. Dehaene-Lambertz, R. Kolinsky, J. Morais, & L. Cohen. (2010). How Learning to Read Changes the Cortical Networks for Vision and Language, Science, 330, 1359-1364 https://hpc.hamilton.edu/~lablab/Dehaene_2010.pdf
Frost, P. (2011). Human nature or human natures? Futures, 43, 740–748.
Gaillard, R., Naccache, L., P. Pinel, S. Clémenceau, E. Volle, D. Hasboun, S. Dupont, M. Baulac, S. Dehaene, C. Adam, & L. Cohen. (2006). Direct intracranial, fMRI, and lesion evidence for the causal role of left inferotemporal cortex in reading, Neuron, 50, 191-204.
Hasenbalg, C.A., & N.V. Silva. (1990). Raça e oportunidades educacionais no Brasil, Cadernos de Pesquisa (Sao Paulo), 73, 5-12
Noble, K.G., M.E. Wolmetz, L.G. Ochs, M.J. Farah, & B.D. McCandliss. (2006). Brain–behavior relationships in reading acquisition are modulated by socioeconomic factors, Developmental Science, 9, 642–654.http://www.cumc.columbia.edu/dept/sergievsky/fs/publications/Noble-et-al-2006-2.pdf