Participants of this study initially included 58 normally developing Japanese junior high school students in Tokyo. All participants completed a questionnaire before commencing this study. Each of the participants and their parents gave written informed consent before his or her participation in this study. All of the procedures in this study were approved by the Human Subject Ethics Committee of Tokyo Metropolitan University. The Edinburgh Handedness Inventory (Oldfield, 1971) was used to determine hand dominance. Participants who participated in this study are all right-handed, no participants had psychiatric disorders, and the L1 of all participants and their parents was Japanese. As English had not been introduced as a mandatory academic subject at the elementary school level in Japan at the time the data were collected, participants’ age of exposure to L2 was 12–13 years old. Although participants’ L1 proficiency was not examined, it can be assumed that our participants had relatively equal L1 proficiency for the following reasons. The present study did not include individuals with one or more parents whose native language was other than Japanese (including native English speakers), so that participants’ daily language use was limited to Japanese. All the academic subjects taught in the schools attended by the participants were taught in L1, and their common, everyday language at their schools was Japanese. Also, Japan requires 9 years of compulsory education, 6 at elementary school, and 3 at junior high school. Since that education is compulsory and relatively uniform throughout the country, a relative equality of educational outcomes (including L1 proficiency in daily use) is seen in Japan. Thus, no participants were excluded at this stage. Also, no participants in this study had experience living abroad or in any English-speaking environment, or of attending international school or bilingual/immersion school. However, five participants were excluded from the analyses because of poor data quality caused by insufficient contact between the optodes and scalp (three participants) or data corruption due to deficient event triggers (two participants) in the fNIRS measurements. Consequently, 53 participants (31 boys and 22 girls, aged 12–15 years, mean age = 13.88, standard deviation of age = 0.93) were used for the current analyses. A t-test confirmed that there was no significant age difference between sexes [t(51) = -1.079, P = 0.279, n.s.].

Note that in the field of L2 acquisition/learning, there are generally two types of acquisition/learning environments. One is an environment where the target language is not typically spoken in everyday life and is generally acquired through formal instruction in a classroom setting after a native language has been acquired. In this case, the term “foreign language (FL)” is often used. Another is a more natural environment similar to that of native language acquisition. In this case, the term “L2” is usually used. However, L2 is often used more broadly to refer to the acquisition/learning of a language other than the native language. Therefore, in the present study, the broader definition of L2 is used although our participants learned English as an L2 in a classroom environment through formal instruction.

We had participants of the same age with different levels of English proficiency, thus in order to assess their overall English language proficiency, the Cambridge basic level English language exam, called the Key English Test (KET), was administered. KET is an elementary level exam focusing on basic everyday communication in written, and spoken English. It is the easiest of the Cambridge English exams, requiring students to have a basic knowledge of English. This qualification demonstrates that students are able to understand very basic instructions, both written and spoken, and the use of simple expressions and phrases. The KET exam consists of reading, writing, listening, and speaking sections; however, the speaking section was not used in the present study.

After the simultaneous fNIRS–ERP measurements, the students also took grammar tests, which used the same sentences as those presented aurally during the measurements. The grammar tests consisted of both listening and reading versions in order to assess the students’ grammatical knowledge of the English language, regardless of sensory modality (auditory or visual). Details of the grammar tests are provided in the Section “Experimental Conditions.” In all, four English language tests were administered to the junior high school students: KET listening test (KET_L), KET reading and writing test (KET_RW), grammar listening test (Grammar_L), and grammar reading test (Grammar_R).

We also examined whether auditory L2 performance correlated with WM capacity using a reading span test (RST), which was designed to measure the combined processing and storage capacity of WM during reading (Daneman and Carpenter, 1980). We used a Japanese version of the RST (Osaka and Osaka, 1992). Empirical evidence shows that WM capacity is an excellent predictor of performance on a variety of complex cognitive tasks, including tasks that measure language comprehension ability (Daneman and Merikle, 1996). The procedure and materials of the RST used in this study are briefly described below. Participants were presented with sentences typed on a card, and were instructed to read them aloud from individual cards while remembering target words underlined in red (one target word per sentence). The sentences presented were at the lower elementary school level so that they were easy enough for the adolescent participants to read. At the end of each set, they were presented with a blank cue card, at which time they were asked to orally recall the red underlined words from each sentence. There were four span levels varying from two sentences per set to five sentences per set; at each span level, five sets were prepared. The test was carried out in order of difficulty, beginning with two sentences per set and progressing to five sentences per set. Participants were given 5 s per word to orally recall the words (e.g., 10 s for two sentences per set, and 25 s for five sentences per set). The RST score included the total number of correct words recalled, the maximum being 70 (2 × 5 + 3 × 5 + 4 × 5 + 5 × 5).

Using simultaneous fNIRS–ERP, we measured the participants’ cortical hemodynamic changes and electrophysiological responses as they listened to sentences presented aurally in their L2 (English). While listening, participants viewed muted films to avoid movement artifacts and/or falling asleep while auditory stimuli were presented. The sentences presented include syntactically and semantically correct sentences (correct sentences: NP VP NP PP—NP, noun phrase; VP, verb phrase; and PP, prepositional phrase), ungrammatical sentences with changes in verb–object order (incorrect sentences: NP NP VP PP), and ungrammatical sentences with no VP (filler sentences: NP NP NP PP). Examples of the three types of sentences are given in Table 1. There were 48 sentences in each condition, so that a total of 144 sentences (48 sentences × 3 conditions) were presented. In the present study, syntactically correct- and incorrect-sentence conditions were used for data analyses.

During brain measurements, participants sat on a chair in a shielded room. A chin rest was used to maintain the head position. We used an event-related design. The software program Optseq¹ was used to optimally randomize the order of and spacing between stimuli, which prevents participants from anticipating each stimulus so as to detect brain response to auditory sentence stimuli. In addition to the 144 sentences (48 sentences × 3 conditions), 147 no-sound and 5 pure-tone events were included with the same durations as the sentences presented, which yielded a total of 296 events. One trial was 4000 ms in length and the total time of brain measurements was 1184 s (approximately 19.7 min). Average time for each of the four phrases contained within a sentence was 607, 497, 625, and 1149 ms for the first, second, third, and fourth phrases, respectively.) To ensure that participants were awake and heard the auditory stimuli throughout the brain measurements, participants were required to make simple keypress responses to the pure tone stimuli presented five times, and it was determined in advance that participants who responded less than four out of five times would be excluded from the analyses. Fortunately, as all participants responded at least four times, no participants were excluded.

We conducted simultaneous fNIRS–ERP measurements to assess brain responses during L2 syntactic sentence processing. The fNIRS system enables us to measure cortical hemodynamic changes and the ERP system enables us to measure electrophysiological responses.

Because the maximum scores differed across tests (KET_L 25, KET_RW 60, Grammar_L 48, and Grammar_R 15), raw scores were converted into percentages and are presented as such for the sake of uniformity. Mean accuracy (%) and standard deviations (mean ± SD) of the four language tests are given as descriptive statistics: KET_L 43.25 ± 22.16, KET_RW 42.89 ± 19.02, Grammar_L 54.44 ± 13.31, and Grammar_R 75.60 ± 21.04. Correlation analyses showed significant correlations between participants’ scores on all four language tests. Specifically, Grammar_L score was significantly correlated with scores of Grammar_R (correlation coefficient: r = 0.451, P < 0.001), KET_L (r = 0.605, P < 0.001), and KET_RW (r = 0.551, P < 0.001). This suggests that the Grammar_L score, which used the same correct and incorrect sentences as presented auditorily during brain activity measurements, predicts overall L2 (English) performance.

In our previous study of elementary school children aged 6–10 years, we found sex differences in L2 word processing (Sugiura et al., 2015). Although the ages (elementary school children vs. junior high school students) and the experimental conditions (word vs. sentence) examined in the previous and present studies are different, we focused on the sex effect in the present study.

To that end, we first examined whether junior high school boys and girls exhibited different performance on the auditory L2 tests. The results of statistical analyses indicated that girls obtained significantly higher scores for Grammar_L (P = 0.004, d = 0.98, Figure 2A) as well as for KET_L (P = 0.02, d = 0.76). We also investigated whether the RST scores differed between sexes. The mean RST score of all participants was 51.89 ± 7.85 (mean ± SD, where the maximum score is 70). The statistical analysis indicated that girls outperformed boys on the RST (P = 0.005, d = 0.82, Figure 2B). In sum, significant sex differences were identified for performance on the Grammar_L (assessing grammatical knowledge using the same sentences as presented for brain measurements), KET_L (assessing comprehensive listening ability), and RST (as an index of WM capacity) tests, on all of which the girls obtained higher scores than the boys.

Then, we examined whether the RST scores correlated with those of Grammar_L. Separate analyses were done for boys and girls, as prior analyses indicated significant sex differences for these test scores. As shown in Figures 2C,D, the results of single regression analyses indicated that there were significantly positive correlations between scores for RST and Grammar_L (r = 0.672, adjusted coefficient of determination: adjusted r² = 0.424, P < 0.001, Figure 2D) for girls, whereas no significant correlations appeared for boys [r = 0.042, adjusted r² = -0.033, P = 0.822 (n.s.), Figure 2C]. These results indicate that girls with a higher WM capacity attained significantly higher L2 test scores than did boys and girls with a lower WM capacity.

Figure 3 shows the grand average ERPs for correct and incorrect sentences, comparing boys and girls. There is a negative shift in incorrect sentences with an early timing in boys. In Figure 4, the mean amplitudes of the five exploring electrodes in each phase are plotted for boys and girls, respectively. We first performed ANOVAs for each phase (sex × grammaticality × electrodes). In phase 1, there was an interaction between sex and grammaticality [F(1,51) = 5.238, P = 0.026, ηp2 = 0.09]. The mean amplitude of incorrect sentences was more negative than that of correct sentences only in boys (P = 0.002, d = 0.67). Phase 2-a did not show any grammaticality effect, but in phase 2-b, a main effect of grammaticality was observed [F(1,51) = 13.467, P = 0.001, ηp2 = 0.21]. A main effect of grammaticality was also observed in phase 3 [F(1,51) = 7.278, P = 0.009, ηp2 = 0.16]. We further applied ANOVAs (grammaticality × electrodes) to each phase for boys and girls, respectively. For boys, main effect was significant for grammaticality in every phase [phase 1: F(1,30) = 9.964, P = 0.004, ηp2 = 0.25, phase 2-a: F(1,30) = 4.612, P = 0.04, ηp2 = 0.13, phase 2-b: F(1,30) = 9.556, P = 0.004, ηp2 = 0.24, and phase 3: F(1,30) = 8.193, P = 0.008, ηp2 = 0.22]. On the other hand, girls showed a main effect of grammaticality only in phase 2-b [F(1,21) = 4.982, P = 0.037, ηp2 = 0.19]. These ERP results suggest that boys were sensitive to the syntactic violations in L2.

We examine correlations between L2 proficiency and cortical responses using separate analyses for boys and girls. First, the correlations between Grammar_L score and cortical activations during correct- and incorrect-sentence processing were examined. The results are shown in Figure 5, in which magnitudes of Pearson’s correlation coefficients are rendered on a standard brain surface. As for correct-sentence processing (Figures 5A,B), both boys and girls had an increased degree of activation in the frontal region, including Broca’s area, and posterior language regions as proficiency increased. However, while activation in boys predominantly increased in anterior cortical regions with proficiency (Figure 5A), activation in girls increased relatively in posterior cortical regions, including the superior and middle temporal gyri (STG and MTG; Wernicke’s area), angular gyrus (AG), and supramarginal gyrus (SMG) (Figure 5B), as proficiency increased. Note that increased activation in the left hemisphere relative to the right hemisphere was observed with proficiency for both sexes, suggesting that left-lateralized activation with L2 development is common to both sexes.

Next, correlations between Grammar_L score and cortical activations during incorrect-sentence processing were examined and compared with those during correct-sentence processing. The results are described in Figures 5C,D. Intriguingly, boys and girls had totally different changes in response to incorrect sentences with proficiency. Specifically, girls exhibited a positive correlation between the test score and cortical activation in the broad region, and significant correlations were observed mainly in the posterior language regions, including STG, MTG, AG, and SMG (Figure 5D), which was the same pattern observed for correct-sentence processing. In contrast, boys exhibited a negative correlation between test score and cortical activation for all the cortical regions examined (Figure 5C).

Finally, since significant correlations between RST and L2 test scores were identified only in girls (Figures 2C,D), we further attempted to derive the pure characteristics of language processing and compare those characteristics between boys and girls by considering the effects of WM capacity. Thus, we conducted partial correlation analyses using RST score (the index of WM capacity) as a control variable to derive more language-specific cortical activation from the fNIRS data. The results of partial correlation analyses between Grammar_L score and cortical activation during sentence processing are shown in Figure 6. With regard to correct-sentence processing (Figures 6A,B), the overall results were the same as those of correlation analyses described in Figures 5A,B, indicating more left-lateralized activation with proficiency in both sexes. However, the results of the partial correlation analyses revealed further significant differences between boys and girls: as proficiency increased, while boys had significantly increased activation in the anterior compared to the posterior cortical region (Figure 6A), girls had significantly increased activation in a broad posterior cortical region (Figure 6B). Regarding incorrect-sentence processing (Figures 6C,D), the statistical results for boys (Figure 6C) were almost identical to those of the correlation analyses shown in Figure 5C, and for girls, there were no significant channels after adjustment for multiplicity (Figure 6D).

First of all, the behavioral results revealed that girls significantly outperformed boys in the L2 tests as well as in the RST (Figures 2A,B). Second, while L2 test scores significantly correlated with RST scores in girls, no correlations were found in boys (Figures 2C,D), suggesting that girls with higher WM capacity are more likely than boys and girls with lower WM capacity to rely on WM function to process L2 auditory sentences. This intriguing sex difference may imply that boys and girls have different strategies for L2 sentence processing.

The ERP results revealed significant differences in amplitude between correct- and incorrect-sentence processing in every phase [phase 1 (100–300 ms), phase 2-a (300–450 ms), phase 2-b (450–600 ms), and phase 3 (600–800 ms)] in boys, whereas significant difference was observed only in phase 2-b in girls (Figure 4). Importantly, significant differences in the amplitude between correct- and incorrect-sentence processing in phases 1 and 2-a were observed in boys, but not in girls. Given previous evidence that these time windows are indices of syntactic processing, it would appear that boys, relative to girls, were primarily responsive to rule-based syntactic processing. More specifically, the ERP component observed during phase 1 in boys is likely a consequence of automatic, initial syntactic phrase structure processing (ELAN), which is consistent with the current fNIRS results showing that boys have significantly increased activation in the left frontal operculum (BA44) with proficiency. The amplitude difference observed in the subsequent time window (phase 2-a) also supports the notion of an “innate” syntactic processing in boys because the LAN component reflects structural processing, including verb argument structure, which is related to thematic role assignment (Friederici, 2002). This means that the boys were sensitive to the structure of simple sentences in English and might have strongly expected a verb to follow an initial animate noun which could be the subject of the sentence.

Previous ERP studies using phrase structure violation sentences often observed a biphasic ELAN (LAN)—P600 pattern (e.g., Hahne and Friederici, 1999, 2002; Rossi et al., 2006). It is, however, interesting to note that, in the present study, neither group showed any positive effect, including during the P600 time window. The P600 component, which involves controlled processing unlike ELAN, reflects syntactic reanalysis or repair processes. Alternatively, boys showed a sustained negativity until phase 3. It is difficult to clearly distinguish the end of ELAN/LAN components within the present sustained negativity, but this negative effect was mainly observed in fronto-central electrodes, suggesting that the present anterior negativity indicates an effect continuation of ELAN/LAN components and a failure to achieve the explicit controlled syntactic processing during, for example, the P600 component. Interestingly, similar sustained negativity was also reported in an ERP study of children’s language development (Hahne et al., 2004), which revealed a similar aspect of the neural basis of L1/L2 syntactic development.

As for girls, there were no significant differences in response to correct and incorrect sentences in phases 1, 2-a, and 3, suggesting that girls were less likely to fully engage in syntactic processing when they heard incorrect sentences. Instead, the ERP result suggests that the girls focus on semantic processing. The amplitude differences that were identified only in phase 2-b in girls, which were observed in anterior to posterior electrodes, are considered to be an N400 component. Some L2 ERP studies have shown that L2 learners elicit an N400, but not P600, component in syntactically anomalous sentences (e.g., Hahne and Friederici, 2001; Weber and Lavric, 2008). Further, it should be noted that although a standard N400 effect is often seen in the 300–550 ms latency range, it has been identified with reduced amplitudes and delayed latencies in L2 learners (Hahne, 2001; Mueller, 2005). This ERP result showing that girls tend to rely on semantic attributes relative to boys is also in line with our fNIRS results.

The present fNIRS data identified sex commonalities and differences in response to passive L2 sentence listening. Note that a significant increase in activation in the left hemisphere relative to the right hemisphere was observed with proficiency during correct-sentence processing for both sexes (Figures 5A,B, 6A,B), suggesting that left-lateralized activation with L2 development is common to both sexes. Also, both sexes, but especially boys, exhibited cortical activation in the prefrontal cortex encompassing Broca’s area during the passive sentence listening. Given these results, the prefrontal region can be identified as being involved not only in language production, but also in comprehension in the early stage of L2 learning, and the current results demonstrate that fundamental aspects of L2 comprehension are processed by a shared neural network that also supports L1 processing. Thus, the present study clarified common aspects of language processing in L2 irrespective of sex. However, at the same time, sex differences manifested: as proficiency increased, boys had significantly increased activation in the prefrontal region, while girls predominantly had increased activation in the posterior language region, including the STG/MTG (Wernicke’s area), AG, and SMG, during correct-sentence processing. This trend was more significant especially after removing the effect of WM function (Figures 6A,B).

A number of previous lesion studies suggested that the left anterior brain regions are involved in syntactic processing, whereas the posterior brain regions, especially left temporal regions, are thought to process lexical semantics (Caplan, 1992; Goodglass, 1993; Grodzinsky, 2000); thus, two functionally distinctive regions, syntactic knowledge (rule-based grammatical knowledge) and lexical knowledge (word forms and meanings), had long been postulated. However, modern functional neuroimaging techniques have allowed remarkable advances in our understanding of brain–language relationships. A large-scale meta-analysis utilizing the results from 129 scientific reports, defined the composition of phonological, semantic, and sentence processing networks in the frontal, temporal, and inferior parietal regions of the left cerebral hemisphere, and updated the view of brain–language relationships (Vigneau et al., 2006). The results revealed distinct (although partially overlapping) networks for phonology, semantics, and sentence processing, and, importantly, all three language processes are supported by fronto-temporal networks with distinct, but partially overlapping, areas.

According to their results, the posterior temporal and parietal regions are related to all three language processes, with phonological clusters located in the STG and SMG, semantic clusters located in the STG, MTG, and AG, and sentence clusters located in the posterior portion of the STG as well as in the posterior part of the MTG. This proposal is consistent with previous work: the AG and SMG in the parietal region are known to be involved in semantic and phonological processing, respectively, and the SMG has also been shown to play a role in the retrieval and association of semantic knowledge (Damasio, 1990; Vandenberghe et al., 1996; Murtha et al., 1999; Wiggs et al., 1999). In contrast, the frontal region also supports all three language processes with phonological clusters located in a more caudal position in the frontal lobe, semantic clusters located in the anterior part of the inferior frontal gyrus (IFG), and sentence clusters located in the posterior part of the middle frontal gyrus and in the dorsal and upper part of the pars opercularis.

In another study, Friederici et al. (2003) reported brain areas for the processing of sentence-level semantic and syntactic information using an event-related fMRI paradigm. They found that processing of semantic violations at the sentence level relied primarily on the superior temporal region bilaterally, whereas processing of syntactic violations in sentences specifically involves the left posterior frontal operculum adjacent to Broca’s area. Consistent results have been reported for semantic processing (Caplan et al., 1998; Kuperberg et al., 2000; Ni et al., 2000) and for syntactic processing (Just et al., 1996; Stromswold et al., 1996; Caplan et al., 1998, 1999; Dapretto and Bookheimer, 1999; Embick et al., 2000; Friederici et al., 2000). They also mentioned that the left frontal operculum in the IFG (BA44) is responsible for on-line syntactic phrase structure building processes during auditory comprehension. In our study, boys had significantly increased activation with proficiency mainly in the left frontal operculum. Given the previous reports, boys are likely to engage in on-line syntactic phrase structure building processing during L2 sentence listening. With regard to the posterior STG, Friederici et al. (2003) identified that both sentence-level semantically and syntactically anomalous conditions generated greatly increased activation in comparison to correct sentences. Our results in girls are in line with their results in that girls had increased activation in the posterior STG with proficiency in sentence processing irrespective of sentence type (Figures 5B,D), and, importantly, their activation was significantly more increased in the incorrect-sentence condition (Figure 5D) than in the correct-sentence condition (Figure 5B). Since girls had significantly increased activation not only in the STG, but also in the MTG, AG, and SMG, it is highly possible that they process sentences through a consolidation of phonological, semantic, and sentential information.

With regard to incorrect-sentence processing, interesting sex differences were revealed in the fNIRS data. Girls had significantly increased activation with proficiency in the left posterior temporal and parietal regions in the incorrect-sentence condition (Figure 5D), similar to but more obvious than increased activation in the correct-sentence condition (Figure 5B), with accompanying increased activation in the right posterior temporal region (Figure 5D). Conversely, boys had decreased activation with proficiency in almost all brain regions examined, especially in the right homolog of Wernicke’s area (Figure 5C). The observed diametrical responses during passive incorrect-sentence listening were beyond our expectations, but are very interesting. As proficiency increases, boys may tend to goof off as they hear an incorrect sentence, and this response is completely different from that in the case of correct-sentence processing. Importantly, this distinction between the two conditions implies that boys are increasingly able to distinguish between correct and incorrect sentences with proficiency, irrespective of their poor performance in the Grammar_L compared to the girls. Intriguingly, the differences in the response to the correct- and incorrect-sentence conditions in boys (Figure 5A vs. Figure 5C) were more remarkable than those in girls (Figure 5B vs. Figure 5D). More importantly, ERP data demonstrated that boys had a faster response than girls in distinguishing between correct and incorrect sentences, showing ELAN/LAN reflecting syntactic processing. Given the evidence available, we can postulate that boys may preferentially engage in rule-based syntactic processing while listening to a mixture of correct and incorrect sentences, and that their brains may respond to the grammatical differences in an implicit manner during sentence listening. Thus, at the beginning of L2 learning as a mandatory academic subject in junior high school, boys are behaviorally less likely to explicitly distinguish between grammatically (syntactically) correct and incorrect sentences compared to girls, but they may develop syntactic competence before being aware of it.

Previous studies have indicated that rule-based syntactic processing depends on procedural memory supported by a basal ganglia-frontal lobe system, while lexical memory depends on declarative memory supported by a temporal/temporo-parietal system (Ullman et al., 1997; Ullman, 2001). The procedural memory system is known to support a variety of well-established motor, perceptual and cognitive skills, and through this system, we implicitly acquire, store, and use knowledge. Thus, it is plausible that rule-based syntactic processing is also supported by this system. Furthermore, as mentioned in the paper by Lum et al. (2012), who examined multiple memory systems and their interactions in relation to language functions, learning in procedural memory is slower than in declarative memory: it proceeds gradually, as stimuli are repeated and skills practiced. It is quite conceivable that boys are slower at learning syntactic metaknowledge than girls if they rely on procedural memory to acquire rule-based grammatical knowledge, and that their brains may implicitly respond to differences between correct and incorrect sentences at the beginning of learning. This view is further supported by the ERP data. Previous ERP work suggested that early ERP components are best explained by a model with feedforward connections only and that backward connections become essential only after 220 ms (Garrido et al., 2007) because there is not enough time for return activity to pass from higher-level to low-level brain areas (e.g., ELAN). Given that significant differences in ERP responses were observed between the correct- and incorrect-sentence conditions during phase 1 before 220 ms in boys (Figure 3, left side) combined with all the previous information available, it seems that boys are likely to implicitly detect differences in phrase structure between correct and incorrect sentences, or syntactic phrase structure violations, in an automatic manner even though their implicit awareness is not reflected in their behavioral performance (L2 tests scores).

In contrast to boys, girls with higher L2 proficiency seem to be earnest in comprehending both incorrect and correct sentences. They imposed an even greater activation load with proficiency in the incorrect-sentence condition (Figure 5D) than in the correct-sentence condition (Figure 5B), with increased activation in the bilateral temporal and the left parietal regions. Note that not only the prefrontal region, but also the posterior language region have been reported to exhibit increased activation with WM loads (Vigneau et al., 2006). A previous study revealed a dissociation of activation in two cortical regions in the WM network: a major role of the anterior region is monitoring information, whereas a crucial role of the posterior region is manipulating information in WM (Champod and Petrides, 2010). The results of our partial correlation analyses provided valuable additional information. After deriving more language-specific cortical activation by removing the WM function effect, the increased activation in the posterior STG with proficiency observed in incorrect-sentence processing in girls (Figure 5D) was less prominent (Figure 6D). This suggests that the significant increase in posterior STG activation for incorrect-sentence processing (Figure 5D) was a consequence of increased WM load. Incorrect-sentence processing may be more difficult than correct-sentence processing, when sentences are presented auditorily, leading to greater activation for incorrect- than for correct-sentence conditions. Given previous findings, girls are very likely to process sentences by drawing on all available functions: phonological, semantic, sentential processing, and the respective WM subsystems, and this is probably why their RST and L2 test scores were closely correlated (Figure 2D). Since girls significantly outperformed boys in the L2 grammar test, and they tend to preferentially focus on sentence comprehension by considering semantic aspects, they seem to explicitly distinguish between correct and incorrect sentences.

Sex differences in language performance are debated in both behavioral and neuroscience studies, which often provide results indicating female superiority; however, it is a controversial topic. Most previous studies regarding sex differences have found such differences, but do not or cannot provide a comprehensive understanding of the details of and mechanisms underlying the pertinent brain functions. The present study regarding young adolescents provides a comprehensive understanding of this issue. By using fNIRS and ERP simultaneously, the present study has produced consistent results: ERP results show higher sensitivity to syntactic violations among boys and higher sensitivity to semantics among girls, results which are validated with fNIRS findings of activations in the IFG BA44 and the STG/MTG/SMG/AG, respectively. Girls overall had explicitly better scores than boys on the L2 grammar test, which is in line with previous studies showing female superiority. However, if we had looked only at their behavior, we would not have been able to elucidate the details of the underlying strategies and brain activity of both sexes, such as the point that boys are implicitly aware of syntactic structure irrespective of their inferior performance compared to girls on the L2 grammar test.

Combining both behavioral and neuroimaging data, we demonstrated sex differences in L2 sentence processing; this finding is not a judgment about which is better or worse, but rather an opportunity to deepen our understanding of the individual differences in strategies for language learning. It is quite understandable that on average, there are differences in the strategies preferred by boys and girls: the strategies they employ are respectively more likely to allow them success in L2 learning. Boys seem to engage early on during L2 learning in implicit, rule-based syntactic processing; conversely, girls seem to rely on a myriad of cognitive functions during L2 processing, allowing better overall explicit language performance despite less automaticity in detecting syntactic errors. While it may be reasonable to draw on all available knowledge and functions (phonological, semantic, and syntactic) simultaneously during sentence processing for better understanding if one has sufficient WM capacity, it may make more sense for some individuals to focus only on singular points (e.g., violation points) or rule-based syntactic processing in order to reduce the load imposed on their WM. Alternatively, boys may simply prefer efficient strategies regardless of WM capacity.

The present findings provide insight into the mechanisms behind how junior-high-school aged boys and girls master an L2. They may also contribute to future L2 education by providing a foundation upon which to base thorough and meticulous approaches that will open the way for effective teaching methods that take sex and/or individual differences into consideration in school education, for example, an approach that bolsters intuitive structural processing for girls and the ability to retain the accumulative information in sentences for boys; and these and subsequent findings will allow the development of English learning methods based on cognitive neuroscience evidence.

Lastly, with regard to sex differences in WM capacity and a further possibility of its influence upon L2 performance, it is presumable that catechol-O-methyltransferase (COMT) Val¹⁵⁸Met genotype may be relevant. Previous studies have identified that the COMT genotype influences WM function (e.g., Bruder et al., 2005; Tan et al., 2007; Vijayraghavan et al., 2007; Diaz-Asper et al., 2008; Sambataro et al., 2009; Cools and D’Esposito, 2011; Stokes et al., 2011), which is implicated in dopamine functioning. Importantly, behavioral studies have indicated an association between WM performance and the COMT polymorphism in children and adolescents in a normal population (Diamond et al., 2004; Wahlstrom et al., 2007; Barnett et al., 2008; Dumontheil et al., 2011). In fact, in a former study, we found significant COMT genotype effects on language functions in children (Sugiura et al., 2017), and in the present study we have demonstrated some effects of WM function, reinforcing the idea of an association between the two. While we did not find sex differences in the genotype effects in our former study dealing with children 6–10 years of age, the participants in the present study were adolescents aged 12–15 years. Indeed, Barnett et al. (2007) found a significant genotype effect on executive function and verbal IQ, and subsequent analyses including sex as a factor found significant genotype effects only in boys. Importantly, these effects were significantly greater in pubertal than in prepubertal boys. Furthermore, another study of the COMT gene in children (Gaysina et al., 2013) assessed verbal and non-verbal cognition at ages 8–15 years using a longitudinal design. In that study, COMT was associated with reading comprehension, verbal ability, and global cognition at age 15 years in pubescent boys, but not at age 8. These findings suggest that the sex difference in WM capacity and a further possibility of its influence upon L2 performance observed in the present study may be due to sex differences in the COMT genotype effects, although future studies are needed to confirm this assumption.

One limitation of this study is that we could only employ a passive listening task. In a future study, looking at cortical activation during an active listening task (i.e., by asking the participants to detect incorrect sentences) would have the potential to show cortical regions related not only to syntactic processing but also to memory and attention functions for performing the active task (Vannest et al., 2009) and to reveal how actual performance during a task is linked to activations.

In the present study, we used the RST to measure WM capacity, as we thought that it was more closely related to language performance compared to a non-linguistic WM test. However, in addition to the RST, it may be interesting to use a non-linguistic WM test and compare the results.

Despite having more boys than girls among our participants, there were few boys with a high WM capacity comparable to that of girls in this age range. If there had been some boys with a WM capacity comparable to that of girls in the current study, we may have been able to separate the sex factor from the WM factor more precisely. It would be interesting if one could separate these two factors entirely in a future study by including a large number of boys and girls with high WM capacity. It would also be interesting to examine whether the sex differences observed in this study with later L2 learners can be replicated in boys and girls that are early bilinguals.