This early study in the field

This early study in the field of the cognitive neuroscience of SES has important implications. First, it GSK126 suggests that lower-SES children with poor reading skills − compared to performance-matched higher-SES children − are not underperforming because of underlying neurological impairment. Rather, their brains are activating as we would hypothesize they should be. Therefore, their underperformance must be explained by other factors. Second, given the use of SES as a proxy for the environment, Noble et al.’s (2006) findings suggest that the environments in which children grow up can moderate their patterns of neural activation in ways that do not necessarily present behaviorally. As a related point, differential patterns of activation can produce the same level of phonological skill. Finally, these findings indicate that even with evidence of true neurological dysfunction, children may learn to compensate by recruiting other regions of the brain. Noble and colleagues suggest that the higher-SES children have been exposed to more books in their homes, allowing them to develop such compensatory strategies. Because they did not directly measure book-reading in the home, however, it is not clear whether this − or a completely different factor that differentiates higher- from lower-SES children − underlies such a finding. For example, the potential impact of different amounts of school resources (Carter and Welner, 2013), different levels of comfort in educational settings (Walton and Cohen, 2007), and different attitudes toward the importance of tests (Steele, 1997), all might contribute to these findings. Future studies should examine more explicitly and systematically what specific aspects of SES may be driving such a result. Regardless of potential environmental mediation, Noble et al. (2006) demonstrated that SES is related to differences in neural recruitment in the absence of language differences. Critically, they were able to do this by studying children at the lowest end of language proficiency − those with severe reading delays. This sampling, however, limits the extent to which their findings can be used to understand the neurological basis of observed SES-based language deficits in children within the normal range of abilities. Raizada et al. (2008) attempted to address this question by examining patterns of neural activation in 5-year-old children as tundra biome completed a rhyming task in the MRI scanner. In addition to collecting information about family SES, they asked children to complete a number of behavioral tests that measure intelligence and language skill. Next, they examined correlations among measures of language and cognition, task performance, functional activation, and SES. The only significant correlation was that between SES and left-minus-right recruitment of the IFG during the task. Whereas higher-SES children recruited the LIFG more than they did the RIFG, lower-SES children showed less hemispheric specialization. This difference in specialization, however, was not related either to any behavioral measures of language or to the children’s performance on the rhyming task. To examine whether differential neural activation underlies language differences, Raizada et al. (2008) tested the potential mediation of language skill on the association between SES and functional activation. Yet when behavioral measures of language were controlled − together or individually − the relation remained statistically significant. Raizada and colleagues suggest that their finding points to early SES-related neural substrates of language delay that behavioral measures cannot yet identify. In fact, there is evidence that hemispheric specialization in adulthood is related to language outcomes (Josse and Tzourio-Mazoyer, 2004). Raizada et al. (2008), however, assessed children who were 5 years old, an age when hemispheric specialization is just beginning to emerge (Amunts et al., 2003). There is little evidence to suggest that early development of hemispheric specialization − as opposed to protracted development which, as Noble et al. (2012) and others have suggested, might be beneficial − leads to, or predicts the development of, superior language abilities. Similarly, relations between SES and any behavioral measures of language in Raizada et al.’s (2008) sample did not reach significance. Thus, even functional findings that are unrelated to any measures other than SES are difficult to interpret. This difficulty is compounded by the fact that scholars too often interpret any SES-related differences in neural function as indicative of an underlying linguistic delay, even when there is no evidence in their study to support such an assumption.