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)
Conclusion
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).
References
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
