Verbal repetition refers to articulating a word after hearing it (Moritz-Gasser and Duffau, 2013). This process is important in language acquisition, both developmentally and in learning a second language (L2), and has applications in language rehabilitation.

Repetition and imitation is one of the oldest methods used to teach an L2. Since 1631, repetition has been seen as the most effective way to learn an L2 for functional use; ever since then, the most successful methodologies in L2 teaching and learning have included repetition and imitation of words and sentences to a greater or lesser extent (Celce-Murcia, 2001). However, repetition and drilling have also been heavily criticized, particularly after the introduction of cognitive approaches, when drilling by repetition was considered mechanical and meaningless (Haycraft, 1978; Richards and Nunan, 1990; Cross, 1995; Larsen-Freeman and Anderson, 2013).

The use of repetition as a learning tool stems from behaviorism (Watson, 2013; Skinner, 2014), which argues that the environment affects human behavior, and advances the idea that learning occurs as a result of repeated exposure to a given stimulus. This view has been criticized for attributing a passive role to the learner, while ignoring active internal factors in the learning process (Watson, 2013). Behaviorists have claimed that many scholars misunderstand behaviorism and its concepts (Skinner, 1974).

In recent years, repetition has received a lot of attention and several studies have focused on its role in different types of learning (e.g., Bygate et al., 2013; Horst, 2013). Repetition has been used and proven to be beneficial in language treatment for children with developmental language disorders and adults suffering from aphasia after stroke or head trauma (Kempler and Goral, 2011). In particular, repetition, imitation and drilling are popular among L2 teachers and learners, at least for word learning and accurate pronunciation, since they are helpful and can support language learners in achieving functional communication in daily life (Berthier and Lambon Ralph, 2014).

The use of repetition to enhance neuroplasticity has been vindicated by recent studies on the recovery of function following brain damage (Crosson et al., 2007). Among the methodological approaches used to examine the links between repetition and functional neuroplasticity, Hope et al. (2014) used a multifactorial approach and identified both linguistic and non-linguistic processing areas involved in word repetition. They identified which linguistic and non-linguistic processing areas are involved in word repetition and categorized them in eight different processing groups. The authors showed that areas underlying word repetition include both language processing areas, similar to the model proposed by Price (2012), and general processing areas. Further, the results of many studies of healthy participants and clinical rehabilitation studies addressing the effect of repetition on the learning of novel stimuli suggest that related neuroplastic changes are associated with cognitive control (e.g., Carey et al., 2005; Kimberley et al., 2010; Berthier and Lambon Ralph, 2014).

In addition, it has been demonstrated that complexity is a factor that can impact repetition-induced neuroplasticity, particularly neuroplasticity relevant to cognitive control components (Sadato et al., 1996; Wulf and Shea, 2002; Hlustík et al., 2004; Carey et al., 2005). Evidence for the complexity of repetition processes comes from anatomical and functional neuroimaging studies and computational modeling and shows that repetition is sustained by auditory-motor integration processes, with a supporting hub located at the Sylvian fissure at the parietal–temporal boundary (SPT). Damage to the SPT results in poor performance on word repetition, even in the absence of white matter damage (Hickok et al., 2003; Rogalsky et al., 2015). Together, the evidence shows that repetition entails complex integrated processes, involving language, executive and motor cognitive and neural systems.

Among the neuroimaging tools used to study repetition processes, tractography has been quite popular and has proven informative. In their study, Duffau et al. (2005) showed the importance of the dorsal pathway involving the arcuate fasciculus and the superior longitudinal fasciculus in allowing the conversion of auditory input stored in working memory into phonological-articulatory representations sustaining repetition. This evidence is in line with Hickok and Poeppel’s (2007) model, which included a bilateral ventral stream involved in speech comprehension, and a left-hemisphere-dominant dorsal stream that maps acoustic speech signals to frontal lobe articulatory networks. Along the same lines, Saur et al. (2008) described a dorsal tract involved in mapping of auditory input to motor plans during repetition, and Berthier et al. (2012) showed the importance of the ventral pathway and the arcuate fasciculus (Berthier and Lambon Ralph, 2014) in repetition. Moreover, in their study, Moritz-Gasser and Duffau (2013) strongly supported an interaction between the ventral and dorsal pathways; they claimed that, while auditory stimuli are stored in working memory and sent to the articulation areas through the dorsal pathway, the ventral pathway contributes to semantic processing.

The role of the dorsal pathway in repetition was already hypothesized by Wernicke (1874). Lichtheim (1885) translated Wernicke’s idea into a diagram. The anatomical correlates of these perspectives (the arcuate fasciculus) were identified years later by Monakow, and accepted by Wernicke in 1908 (Catani and Mesulam, 2008; Glasser and Rilling, 2008). Recent tractography studies have revealed that the temporal and frontal areas are connected through a direct Broca-Wernicke and an indirect Broca-inferior parietal lobule-Wernicke pathway (Catani and Mesulam, 2008; Glasser and Rilling, 2008).

While white matter tractography is extremely informative for understanding the neural correlates of language tasks such as repetition, the functional dimension of these connections remains to be understood. For example, it is well accepted that the brain regions sustaining performance on a given task are not necessarily linked by fiber tracts. Thus, brain regions may be anatomically segregated but nevertheless activated in the context of a given task. Moreover, while activation maps provide information on the average level of involvement of different brain regions involved in the task, information about how these regions constitute networks whose configuration may vary depending on the task is another matter, to be addressed by functional connectivity methodologies (Rogers et al., 2007). Network functional connectivity methods allow one to describe how a given set of relevant areas may cooperate to perform a given task at a given moment in time. Moreover, changes in network configurations as a function of different factors can be addressed by looking at network integration. Thus, changes in integration levels within and between networks cooperating on a given task are estimations of the ease of communication or information flow among these networks and their respective components (Rubinov and Sporns, 2010).

Several studies have looked at between-network integration to explain language processing. For example, Makuuchi and Friederici (2013) used dynamic causal models to examine levels of integration between core language and general domain (working memory) networks for syntactic processing, and reported increased levels of integration between the two target networks with increased processing load.

With regard to word repetition, functional connectivity studies are limited. However a few studies have looked at changes to structural and functional connectivity in the process of word learning. Specifically, functional connectivity and DTI studies suggest that learning new words requires fast and efficient interaction between the frontal and the temporal lobes (López-Barroso et al., 2013). Also, both functional (Yang et al., 2015) and structural differences in connectivity across individuals have been related to variability in word learning success (Catani et al., 2007). With regards to functional connectivity studies, the evidence suggests that four dorsal and ventral networks including motor, frontal, temporal and parietal areas are associated with word learning, with variable degree of engagement through learning phases (López-Barroso et al., 2015). In particular, the dorsal auditory-premotor network has been reported to show more strength and association with individual performance immediately after word learning (López-Barroso et al., 2015). Finally, functional connectivity studies on word learning in an informal learning context, and comparing learners and non-learners, report stronger connectivity between the left and right supramarginal gyri in learners than for non-learners (Veroude et al., 2010). Also, learners seem to rely more on language network, which is better integrated with other networks to process tonal and lexical information of target L2 words (Yang et al., 2015).

The relationship between frontal and temporal cortical areas has been measured by regional cerebral metabolic rates of glucose using positron emission tomography (PET) in word repetition (Karbe et al., 1998). Significant correlations connected frontal and temporal areas with the left planum temporale as a hub, bilaterally.

PET scanning was used in another functional connectivity study to compare network interactions for word and non-word repetition in illiterate participants and compare network interactions for non-words between illiterate and literate participants (Petersson et al., 2000). Differences between the two comparisons included interaction differences in the general domain processing areas and control network. Literate subjects also showed differences in the attentional network and connections between Broca’s area and the inferior parietal region. Thus, word and non-word repetition engages both language and control networks and the level of engagement of the two networks differ in literate and illiterate participants. These results may suggest that the level of engagement of the control network depends on how complex the task is for each group, based on the amount of previous repetitive exposure to similar stimuli (i.e., literate people have more repetitive exposure to words and non-words than illiterate people).

Exposure to similar stimuli, among other factors, depends on L1-L2 linguistic distance. Unlike linguistically distant languages, languages that belong to the same linguistic family and have the same root (e.g., French and Spanish) share many structural similarities (Aitchison, 1999; Finch, 2005). Cross-linguistic similarity, refers to what the learner perceives to be similar (Kellerman, 1977; Odlin, 1989; Ringbom, 2007), and is an important modulating factor in L2 learning (Ringbom, 2007) by affecting the magnitude of learning (Corder, 1979). Since learners “already have a potential vocabulary” (Ringbom, 2007, p.11), L2 words that are similar to L1 equivalents do not require much effort for being stored in the mental lexicon and may be easily activated in L2 production (Ringbom, 2007). Neuroimaging evidence suggests that learning L1-L2 similar words require less cognitive resources including less cognitive control (Ghazi-Saidi and Ansaldo, 2016).

The results of functional connectivity studies suggest that verbal repetition is sustained by both language processing and general domain cognitive control abilities (Berthier et al., 2017). In our previous work (Ghazi-Saidi et al., 2013), we examined network integration changes within and between the language and cognitive control networks in a group of Persian native speakers (L1) learning French words (linguistically distant L2). This work showed that network integration levels within and between language and cognitive control networks decreased with increased L2 proficiency.

The present study goes a step further in this regard, by looking at the regulatory effects of language distance as a complexity factor that modulates repetition effects in network configuration. To do so, by comparing the results of the present study and the previous study (Ghazi-Saidi et al., 2013), and adding further analyses in both groups, we examined changes in integration level between the cognitive control and language networks as a function of language distance between L1 and L2. Language distance is one of the least investigated aspects of L2 neuroplasticity effects (Ghazi-Saidi et al., 2012; Li et al., 2014).

This study examined the influence of L2 word learning through verbal repetition on integration values within and between the language and cognitive control networks in a close and a distant language pair. Two groups of adults learned French (L2) through repetition and imitation by means of a computerized vocabulary-learning program. One group’s L1 was close to L2 (i.e., Spanish), whereas the other group’s L1 was distant from L2 (i.e., Persian). For each group, behavioral responses and functional integration values were collected at two points in time: after a week of training and after 4 weeks of training. Both groups received equivalent training of 15 min a day.

The results show a significant and equivalent improvement in behavioral responses (AR and RT for L2 naming) in both learning groups. Thus, both groups learned L2 words equally well in terms of accuracy and speed attained after training. With regard to network integration values, similarities and differences were observed across groups. Specifically, for Persian native speakers, integration values decreased between the language and control networks in L2, whereas these values remained stable for the L1 (Ghazi-Saidi et al., 2013). As for the Spanish speakers, there were no changes in integration values over time, neither with L1 nor with L2.

The behavioral results provide evidence of the efficacy of verbal repetition in L2 vocabulary learning, as shown by the significantly faster and more accurate performances obtained by both groups at the second learning phase. These observations are in line with previous work (e.g., Snedden, 1931; Bartels et al., 2010) on the efficacy of verbal repetition in novel vocabulary learning. Faster responses and better accuracy reflect increased automaticity following repetition (Forster and Chambers, 1973; Brown and Watson, 1987; Segalowitz and Hulstijn, 2005). Previous work shows that verbal repetition favors automaticity (Haier et al., 1992a,b; Raichle et al., 1994; Fischler, 1998; Segalowitz and Hulstijn, 2005), considered to be a sign of successful learning (Segalowitz and Frenkiel-Fishman, 2005). Thus, learning through repetition occurs by increased exposure to the stimulus, which favors automatic—thus faster and more accurate—processing (Logan, 1988). Furthermore, the results of this study show that—at least for vocabulary learning—the repetition strategy is equally efficient with close and distant language pairs. This result is consistent with past work on the efficacy of repetition in L2 learning, with both linguistically close and distant languages (e.g., Horst et al., 1998; Rott, 1999; Waring and Takaki, 2003; de la Fuente, 2006; Webb, 2007; Schmitt, 2008).

Efficacy of repetition in L2 vocabulary learning is reflected by functional connectivity patterns. Specifically in Persian native speakers, the decrease in functional integration values between the language and control networks at phase 2 shows that repetition decreases cognitive load (Abutalebi et al., 2005; Altarriba and Heredia, 2008; Leonard et al., 2011; Parker-Jones et al., 2011). The integration value reflects the information flow in the network. In other words, drilling through repetition induces neuroplastic changes reflected by the disengagement of the cognitive control network in sustaining L2 word retrieval in phase 2, in line with evidence from previous functional connectivity studies (Dodel et al., 2005; Prat et al., 2007; Majerus et al., 2008; Veroude et al., 2010).

Previous functional connectivity work by Dodel et al. (2005), Prat et al. (2007), Majerus et al. (2008) and Veroude et al. (2010), on different tasks in bilingual speakers report a decreased functional connectivity when processing demands increase. Specifically, functional connectivity decrease associated with increasing demands has been reported in reading tasks (Prat et al., 2007); syntactic processing load (Dodel et al., 2005); short-term memory demands (Majerus et al., 2008), and finally phonological processing (Veroude et al., 2010).

Previous studies in other cognitive domains, such as learning finger movement sequences, have shown that cognitive demand increases with task complexity (Robinson, 2001, 2011; Horberry et al., 2006; Coynel et al., 2009; Reimer, 2009; Linck et al., 2014), and that a reasonable cognitive load ensures engagement in the task, whereas an extremely high load can result in disengagement from the task (Wulf and Shea, 2002). In this study, the results with the Persian native group show that the complexity of the task was reasonable enough to require recruitment of the control system and keep it engaged throughout the learning process.

Another way of interpreting the results of this study invokes the notion of stimulus novelty, or how familiar the word is. Previous works (Petersson et al., 2000; Baddeley, 2003; Berthier et al., 2012; Berthier and Lambon Ralph, 2014) showed that verbal repetition may involve cognitive control abilities. Specifically, Berthier and Lambon Ralph (2014) argued that—in the context of aphasia therapy—neuroplastic changes related to the repetition of novel stimuli reflect cognitive control demands, an argument in line with previous studies on rehabilitation from traumatic brain injury (Kimberley et al., 2010), and motor learning experiments in healthy participants (Carey et al., 2005). Furthermore, the amount of cognitive control deployed in verbal repetition has been hypothesized to depend on word novelty (Chein and Schneider, 2005), as novel words consume more cognitive control resources than salient or familiar words. Converging evidence on the role of stimulus novelty comes from motor skill learning studies (Pascual-Leone et al., 1995; Sadato et al., 1996; Wulf and Shea, 2002; Hlustík et al., 2004; Carey et al., 2005; Kimberley et al., 2010) and language learning studies (Bialystok, 1999; Robinson, 2005; Ghazi-Saidi et al., 2013; Calvo and Bialystok, 2014; Ghazi-Saidi and Ansaldo, 2016).

In the present study, the Persian group showed changes in integration values between the language and control networks over time and with L2 only, whereas in the Spanish native group, no changes over time were observed in either language. We argue that the degree of overlap between L1 and L2 items—which is a function of L1-L2 distance—determines how novel L2 items are. More specifically, although both groups were exposed to an equal amount of repetition and achieved equal behavioral performance, in the group with a close pair of languages, stimuli were more salient—or less novel—and thus did not require the recruitment of the cognitive control system. Conversely, in the Persian native group, the novelty of L2 items was higher due to the lesser degree of L1-L2 overlap, and thus the load on the cognitive control system was higher. Hence, these results show that the absence of behavioral effects of repetition does not preclude differences at the network level. In the present case, repetition resulted in equivalent performance across experimental groups, but the persistent engagement of the cognitive control network in Persian speakers reflects effortful processing due to language distance effects. Hence, these results show that the impact of repetition on functional connectivity patterns is modulated by changes at the network level, even in the absence of behavioral differences across the experimental groups. These results have implications for L2 teaching and learning, as well as for interventions in cases of bilingual aphasia. Language learners, teachers and therapists should therefore consider the impact of language distance when setting a minimum number of repetitions for learning new words. In the Communicative Language Teaching approach, practice in the form of drilling and repetition is usually banned, as this is considered mechanical and unnatural. The present study brings neuroimaging evidence that in the case of distant L2 words drilling can be beneficial to improve proficiency. However, with distant languages, the amount of repetition or the amount of practice should increase to achieve optimal performance.

The results of the present study provide neurofunctional evidence of the effects of repetition as a neuroplasticity agent in L2 learning. While L2 integration values between language and control networks decreased over time in the group with a distant language pair (Persian and French), no changes in integration values within or across languages or networks were observed in the group with a close language pair (Spanish and French). Novelty is higher for L2 words of distant language pairs than in close language pairs (Ringbom, 2007); hence, processing a close L2 vocabulary consumes less cognitive control resources than processing a distant L2, from the very beginning of the learning process; conversely, cognitive demands with a distant L2 remain strong, even when the L2 vocabulary has been consolidated.

These results show that repetition effects in L2 learning are modulated by word novelty, a factor that depends upon L1-L2 distance. Implications for L2 teaching and intervention plans with bilingual patients concern the amount of repetition required to reduce cognitive load as a function of L1-L2 distance. Further studies of the modulatory effect of language distance on repetition effects on L2 learning are required.

LG-S: Recruitment and data acquisition, data analysis, interpretation, drafting, revision and edition of the manuscript. AIA: supervision, revision and edition of the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.