Recent evidence has shown that convergence of print and speech processing across a network of primarily left-hemisphere regions of the brain is a predictor of future reading skills in children, and a marker of fluent reading ability in adults. The present study extends these findings into the domain of second-language (L2) literacy, through brain imaging data of English and Hebrew L2 learners. Participants received an fMRI brain scan, while performing a semantic judgement task on spoken and written words and pseudowords in both their L1 and L2, alongside a battery of L1 and L2 behavioural measures. Imaging results show, overall, show a similar network of activation for reading across the two languages, alongside significant convergence of print and speech processing across a network of left-hemisphere regions in both L1 and L2 and in both cohorts. Importantly, convergence is greater for L1 in occipito-temporal regions tied to automatic skilled reading processes including the visual word-form area, but greater for L2 in frontal regions of the reading network, tied to more effortful, active processing. The main groupwise brain effects tell a similar story, with greater L2 than L1 activation across frontal, temporal and parietal regions, but greater L1 than L2 activation in parieto-occipital regions tied to automatic mapping processes in skilled reading. These results provide evidence for the shifting of the reading networks towards more automatic processing as reading proficiency rises and the mappings and statistics of the new orthography are learned and incorporated into the reading system.

Statistical learning (SL) approaches to reading maintain that proficient reading requires assimilation of rich statistical regularities in the writing system. Reading skills in developing first-language readers are predicted by individual differences in sensitivity to regularities in mappings from orthography to phonology (O-P) and semantics (O-S), where good readers rely more on O-P consistency, and less on O-S associations. However, how these regularities are leveraged by second-language (L2) learners remains an open question. We utilize an individual-differences approach, measuring L2 English learners’ sensitivity to O-P, O-S, and frequency during word-naming, across two years of immersion. We show that reliance on O-P is leveraged by better readers, while reliance on O-S is slower to develop, characterizing less proficient readers. All factors explain substantial individual variance in L2 reading skills. These findings show how SL plays a key role in L2 reading development through its role in assimilating sublexical regularities between print and speech.

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Brice et al. (2019) presented data from the first epoch of a longitudinal study of the neurobiological underpinnings of first-language (L1) and second-language (L2) processing.Results showed a similar network of activation for reading across L1 and L2, as well as significant convergence of print and speech processing across a network of left-hemisphere regions in both L1 and L2 with greater activation and convergence for L2 in anterior regions, and greater activation and convergence for L1 in posterior regions of the reading network.Here, we present the first look at longitudinal changes in these effects. L2 showed relatively few changes in activation, with some shifts in the weighting between ventral and dorsal processing. L1, however, showed more widespread differences in processing, suggesting that the neurobiological footprint of reading is dynamic, with both L1 and L2 impacting each other.Print/speech convergence showed very little longitudinal change, suggesting that it is a stable marker of the differences in L1 and L2 processing.

Risks associated with school dropout have been studied in West Africa, yet more research is needed to understand what protective factors can be associated with academic resilience (i.e., remaining in school despite facing adversity). At the beginning of our longitudinal study in rural Côte d’Ivoire, 1195 students (Mage=10.75, SDage=1.42) were enrolled in fifth grade. Two years later, 7% of students had dropped out. Characteristics related to the child (e.g., child labour), family (e.g., socioeconomic status), and school (e.g., teacher quality) were first examined distinctly. We then applied a cumulative risk (CR) framework to examine child-, family-, and school-level CR and their interactions. To understand academic resilience, we used findings from our risk analysis to identify a “high-risk enrolled” subset of children and compared them to the children who dropped out. Children who dropped out were older, involved in more child labour, had poorer literacy, owned fewer books, and attended schools with poorer learning environments. Child-level CR most strongly predicted dropout (b=-.860, OR=.424); however, children with low child-level CR were more likely to drop out when family-level CR was high (b=.227, OR=1.250). School characteristics (better infrastructure and teachers) were protective for children who were at high risk of dropout yet remained enrolled. Child-, family-, and school-level factors all contributed to dropout and these factors interact to affect dropout. Although child- and family-level factors contribute significantly to dropout, certain school factors may mitigate these risks and promote academic resilience.