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. 2023 Apr 17;44(9):3717–3729. doi: 10.1002/hbm.26307

Dynamic brain responses to Russian word acquisition among Chinese adult learners: An event‐related potential study

Jiahui Zhang 1,2, Yan Huang 3,4, Caihong Jiang 1,4, Yong Xu 1,4, Hengyi Rao 1,4,5,✉, Hong Xu 1,2,✉
PMCID: PMC10203786  PMID: 37067101

Abstract

Human learners are capable to acquire foreign language vocabulary at an impressive speed even in adulthood. Previous studies have examined the neural mechanisms underlying rapid acquisition of Latin‐alphabet vocabulary and revealed dynamic changes in several event‐related potential (ERP) components during novel word learning. However, scant attention has been paid to the acquisition of Russian words. The present study used ERP and examined dynamic brain responses to rapid Russian word acquisition in 53 native Chinese speakers with no prior knowledge of Russian language. Behavioral data showed robust individual differences in Russian word acquisition, with most participants being able to rapidly learn a subset of novel Russian words in a few exposures. ERP results revealed significant learning effects in the P200, N400, and P600 amplitudes. Moreover, P600 amplitude changes predicted participants' word acquisition after learning. These findings demonstrated dynamic brain responses to rapid Russian word learning and suggested that the P600 component may serve as a bio‐marker for individual learning ability in Russian word acquisition.

Keywords: event‐related potentials, memory, neural mechanisms, word learning

The present study has for the first time investigated the dynamic brain changes during Russian word learning in adult learners with no prior knowledge of the Russian language. By adopting event‐related potentials technique, we found that the brain's response to novel Russian word was similar to but different from that of Latin‐alphabet words. Overall, our study suggested that the P600 component may serve as a bio‐marker for individual learning ability in Russian word acquisition.

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1. INTRODUCTION

Human brain possesses an impressive capability to learn novel words rapidly and efficiently, not only during childhood when the language system is developing, but also in adulthood when learning a foreign language. The majority of previous research on foreign vocabulary acquisition has focused on Latin‐alphabet languages, such as English, Spanish, and German (Balass et al., 2010; Bermúdez‐Margaretto et al., 2020; Bice & Kroll, 2019; Perfetti et al., 2005; Stein et al., 2006), while scant attention has been paid to the Cyrillic languages.

Russian is the most widely used Cyrillic language in the world, and it is also the official language of Russia, Belarus, Kazakhstan, Kyrgyzstan, and Uzbekistan. Currently, there are 235 million Russian speakers in the world and 38.2 million foreign learners, according to a report by the Social Studies Center of Russia in 2019 (ФГАНУ «Социоцентр», 2019). Despite its importance, however, few studies have examined the acquisition of Russian word in the adult learners' brain, and it remains unknown whether the neural mechanisms of Russian word learning would be similar or distinct to those of Latin‐alphabet word learning.

According to a revised model of lexical architecture proposed by Marslen‐Wilsonet al. (1997), lexical knowledge (semantics, syntax, and phonology) has mediated access to text via orthographic representations. According to Rosenthal and Ehri (2008), orthographic access improved students' memory of the pronunciation and meaning of new words, suggesting that orthographic knowledge benefited vocabulary learning, not only when languages are represented by the same alphabetic script and share cognates and morphological rules (e.g., French V.S. English, English V.S. Spanish, etc.) (Jouravlev et al., 2014), but also when languages are represented by different orthographic scripts, such as Russian V.S. English (Mathieu, 2016; Showalter, 2018). In Showalter's study (2018), native English speakers learned Russian‐like words via auditory presentations and pictured meanings in either the condition with orthographic input (OI) or the condition without OI and passed a picture‐orthography matching test. All participants performed near ceiling except participants in the condition with OI on words containing incongruent grapheme‐phoneme correspondences. Results showed that the orthographic input effects were robust enough to interfere with second language phono‐lexical acquisition even when the input does not contain novel phones. Although orthographic learning had positive transfer to both Latin‐alphabet and Cyrillic‐alphabet language learning, it was based on the letter‐forms which were similar or known to the participants previously. It was unclear how orthographic input correlated with word learning when the participants did not know the alphabet previously. Therefore, we attempted to uncover the individual learning mechanism of novel Russian words without any previous knowledge of the Cyrillic alphabet.

The event‐related potentials (ERP) technique has been recognized as a sensitive measure for tracking the acquisition of new linguistic knowledge with relatively high‐temporal resolution of milliseconds (Soskey et al., 2016). Therefore, in recent years, a growing number of researchers have used ERP to examine dynamic brain responses to rapid word acquisition (e.g., Bakker et al., 2015; Balass et al., 2010; Bermúdez‐Margaretto et al., 2018, 2020; Vasilyeva et al., 2019). Most previous research on novel word learning has used written word stimuli as learning materials in ERP studies (Bermudez‐Margaretto et al., 2018; Borovsky et al., 2010; García‐Gámez & Macizo, 2022; Mkrtychian et al., 2021) and these studies have established several ERP components related to novel word learning. The first one is the frontal‐centrally distributed positive‐going P200, which is relevant to orthographic word‐form processing and involvement of the attention system (Bermúdez‐Margaretto et al., 2020; Mkrtychian et al., 2021). The very few studies related to P200 in the pseudoword learning domain indicated that P200 might be larger (i.e., more positive) when a novel word form is repeated across training, which appears to reflect the general ease of orthographic processing at the sublexical level (Bermudez‐Margaretto et al., 2018). Furthermore, Mkrtychian et al. (2021) trained native Russian speakers with Latinized Russian pseudowords and found that high‐ability learners tended to exhibit lower P200 amplitudes than low‐ability ones, which was related to a higher attention‐system involvement in their study.

The second ERP component related to word learning is N400, the central‐distributed negative‐going peak with a latency of around 400 ms after the presentation of the stimulus. N400 has been found to exhibit less negativity across the visual repetition of written word form devoid of semantic content (Bermúdez‐Margaretto et al., 2020). Bakker et al. (2015) investigated the neural responses to novel and remote‐trained Dutch words and found that newly‐learned words initially elicited larger N400 negative deflections than existing words. These findings permit an observation of word learning using the central‐parietal negativity through the lens of semantic repetition.

The third component is P600, a central‐parietal positivity elicited typically in repetition and an old‐new paradigm within 500–800 ms after the onset of a word stimulus (Balass et al., 2010; Rugg & Curran, 2007). Recognition of previously exhibited stimuli is thought to induce an enhancement of the component, which reflects the encoding and strengthening of episodic memory traces (Bermúdez‐Margaretto et al., 2020; Rugg & Curran, 2007). In Perfetti et al.'s (2005) study, they trained adults to learn the meanings of rare words and make meaning judgments on pairs of real words. They found that the skilled comprehenders learned more words than less skilled comprehenders and exhibited a stronger episodic memory effect (P600). Such effect was interpreted as an episodic memory indicator that the participants recognized the trained words as recently experienced words during training. Moreover, in terms of semantic priming effect, P600 can also be interpreted as a marker of strategic but not fully automatic priming in recently trained pseudowords. Bakker et al. (2015) found that pseudowords preceded by semantically related primes elicited a more positive P600 response, suggesting that the controlled retrieval processes appear to enable semantic priming even when words are not fully lexicalized.

Notably, most of the studies in the visual word learning domain were investigating real word learning in Latin scripts (English, French, etc.) or hieroglyphic scripts (Chinese or Kanji in Japanese), but the neural mechanisms of Russian novel word learning remain poorly understood. The only research on Russian word learning using the ERP technique is performed on fast mapping (FM) in adults who learned acoustic word forms in a single picture judgment task (M. J. Vasilyeva et al., 2019). In Vasilyeva et al.'s study, researchers adopted an audio‐visual fast mapping paradigm that included familiar and novel acoustic Russian words and pseudowords in conjunction with novel and familiar images among 12 monolingual native Russian speakers. They found a significant enhancement in ERP amplitudes elicited by a native novel word form. This enhancement was maximal over 200–400 ms after the word onset and was claimed to be an index of fast mapping process where newly‐learned words were integrated into the brain's mental lexicon. Although Vasilyeva et al. performed a Russian word‐picture fast mapping task in their research, they focused on acoustic word‐form FM instead of the written word learning. As shown in previous studies, brain responses to acoustic and visual stimuli are different (Balass et al., 2010; Bermúdez‐Margaretto et al., 2020; Rugg & Curran, 2007; Vasilyeva et al., 2019) and language is not confined to the auditory modality and the neural mechanisms of learning novel vocabulary through reading are no less important (Bermúdez‐Margaretto et al., 2019). In this sense, it is essential to investigate the underlying neural mechanisms of Russian word learning among adults by using the ERP technique.

Previous research has suggested that word learning in Latin scripts and hieroglyphic scripts may be associated with different neural mechanisms as measured by ERP components. For example, Gao et al. (2022) found that Chinese orthographic processing was linked to N200, while P600 indicated a re‐analysis of word meaning or grammatical operation. However, studies on Latin scripts have revealed that word learning elicits changes in various ERP components, such as P200 (Bermúdez‐Margaretto et al., 2020), N250 (Morris et al., 2007) and N400 (Bermúdez‐Margaretto et al., 2020; Lavric et al., 2007; Morris et al., 2007). These findings suggest that the neural mechanisms of word processing and learning may be distinct for different alphabets in the human brain. As the Russian alphabet differs significantly from both the Latin and hieroglyphic alphabets, we hypothesized that the neural mechanism of Russian word learning may also be different from that of Latin‐alphabet words.

According to the research discussed above, the goal of the current study was to analyze the neural mechanisms involved in real Russian novel word acquisition in a paired‐associate paradigm. We chose Russian as the target language for study because it belongs to the Slavic language family written in the Cyrillic alphabet, which is in sharp contrast to Chinese or English with minimal overlap in both written and spoken forms. We recruited participants with no prior exposure to the Russian language or its alphabet. Following Bermúdez‐Margaretto et al. (2020), participants in our study received six training rounds of real Russian words in a paired‐associate learning paradigm. The use of a natural orthography ensures a greater ecological validity (Brennan & Kiskin, 2020) and the paired‐associate learning paradigm served as one of the analogs of the word learning condition in real life. Following previous evidence on word learning, we predicted that there would be significant neural modifications following several training rounds, such as the P200 effect indexing the orthographical processing, N400 effect indexing novel word semantic retrieval, and the P600 effect as evidence of the strengthening of episodic memory traces. Additionally, we hypothesized that there would be significant correlations between the mean amplitudes of N400/P600 components evoked during word learning and word learning performance.

2. MATERIALS AND METHODS

2.1. Participants

The 53 Chinese college students who had learned English as their second language were enrolled in this study (Mage = 20.46 years, SD = 2.58; 26 males, 27 females) and were paid for their participation. Participants were all Chinese‐English bilinguals and had no history of learning a third language. Prior to the EEG experiment, participants were required to complete the online Language History Questionnaire 3.0 (LHQ；Li et al., 2020), which assesses self‐rated L2 proficiency in listening, speaking, reading, and writing. Our results revealed that the participants' average self‐rated proficiency in L2 was 0.59 out of 1 (SD = 0.10), indicating that their English proficiency was at an intermediate level. Three participants were excluded due to hardware malfunction (n = 1) or excessive recording artifacts (n = 2). As a result, the sample size was 50. All of them were right‐handed native Chinese speakers with normal or corrected‐to‐normal vision and did not have learning experience of the Russian language, including its letters, grammar, phonetics, and so forth. None of them suffered from any psychiatric or neurological disorders. The current experiment was approved by the Ethics Committee of our university.

2.2. Stimuli

Stimuli for the current experiment consisted of 60 real Russian words, with an average word length of 5.13 letters (range: 3–7 letters). All of them were selected from the Dictionary of Russian Topical Classification of Ten Thousand Words (Jakovleva et al., 2012) and belonged to the following categories: universe, animals, fruits, trees, articles for daily use, and office supplies. Each Russian word (e.g., утюг) was paired with a Chinese meaning (See Supplementary 1).

2.3. Procedure

Participants were first instructed to read and sign the Experimental Informed Consent and then filled in a questionnaire concerning their basic information, such as gender, age, major, etc.

In the EEG experiment, participants were tested individually in a soundproof and electrically shielded room built specially for the EEG experiment. During the formal experiment, participants were seated comfortably with a viewing distance of 1 meter from computer screen and were instructed to move their head or body as little as possible with their eyes fixated on the center of the screen. Each trial started with a 500 ms 2 × 2 cm cross sign, which was followed by a blank screen with a duration of 500 ms. Then, novel words were presented pair‐by‐pair with a time duration of 2000 ms (Liu & Hell, 2020) (See Figure 1). Specifically, novel words were presented in black against a white background, subtending 0.2–11 of visual angle horizontally and 0.21 vertically (Jiang et al., 2013). Such time duration of stimuli presentation was comfortable for Chinese reading (Jiang et al., 2013; Jiang & Zhou, 2009; Ye et al., 2007) and Russian reading based on individual experiences.

FIGURE 1.

FIGURE 1

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(a) Experiment procedure. (b) A standard electrode map based on the international 10–20 system.

There were five novel‐word practices (stimuli repeated 6 rounds resembling the formal test, and there was a total of 30 trials) before the formal task. During the formal task, all 60 stimuli were presented in 6 rounds and appeared right at the center of the screen randomly at each round of training. Participants were instructed to remember the Chinese meaning of the novel Russian word as much as possible with highly concentrated attention drawn to each round of training. There was a break between each round of word learning training, and the time duration of the break depended on participants' mental state. If participants were prepared to begin a new training round after recovering from their tiredness, they could begin the next round by pressing the “ENTER” key. Each break was not longer than 5 min, and the whole experiment lasted for about 45 min for each participant. After 6 rounds of training, participants were given 20 min for to take a paper‐and‐pencil post‐learning test, which consisted of 60 novel Russian words. They were asked to recall what they had learned and write down the Chinese meaning for as many Russian words as they could. The number of Chinese words (total words = 60) correctly recalled would be computed and regarded as individual Russian word learning scores.

2.4. Data acquisition and preprocessing

EEG data were continuously recorded using a 32‐channel BrainVision ActiCap active electrode system (BrainProducts GmbH, Germany) (See Figure 1b). Electrodes were referenced online to a vertex reference (electrode FCz) and re‐referenced offline to an average of the left and right mastoids (electrodes TP9 and TP10). Impedance for the ground and reference electrodes were 3kΩ and below 10kΩ for the remaining electrodes. The EEG signals were digitized online with a sampling frequency of 500 Hz and amplified with a bandpass from 0.1 to 100 Hz.

Offline EEG data preprocessing were performed using EEGLAB (Delorme & Makeig, 2004) and ERPLAB toolbox (Lopez‐Calderon & Luck, 2014). After manually eliminating bad electrodes, excluding segments containing obvious artifacts (eye or muscle movements), and re‐referencing, we filtered the continuous EEG data by using a bandpass of 0.5–40 Hz. Then the data were segmented into epochs from −200 to 800 ms around the stimulus, with a prestimulus baseline of 200 ms. The signal was averaged for each round and each participant before grand averages were computed across all participants. An independent component analysis (ICA) was then applied to correct ocular artifacts. After that, remaining artifacts (≥ ±100 μV) were marked automatically and dropped from the following data analysis.

2.5. Data analysis

2.5.1. Repeated‐measures ANOVA

Based on previous studies (Bermúdez‐Margaretto et al., 2020; Borovsky et al., 2010; Ding et al., 2017; Jiang et al., 2013; Palmer et al., 2013), three time windows were selected for P200 (100–300 milliseconds), N400 (typically 300–500 milliseconds), and P600 (500–800 milliseconds), respectively. The repeated‐measures analyses of variance (ANOVA) (with a Bonferroni correction) were conducted on the ERP amplitudes in the selected time windows for the experimental conditions (round of word learning) and two regions of interest (ROI), each of which had three representative electrodes: frontal (F3, Fz, F4) for P200 (Bermúdez‐Margaretto et al., 2020) and central (C3, Cz, C4) for N400 and P600 (Bakker et al., 2015; Rugg & Curran, 2007). The Greenhouse–Geisser correction was applied for all repeated measures ANOVAs whose evaluated effects had more than one degree of freedom in the numerator (Greenhouse & Geisser, 1959).

2.5.2. Correlation analyses

Correlation analyses between the ERP amplitudes and word learning scores (0–60) were performed across three time windows at two ROIs. The mean amplitude values of each ROI were calculated first and then correlated with the learning scores using a non‐parametric Spearman's correlation analysis.

2.5.3. Multiple linear regression and mediation analysis

Multiple linear regression and mediation analysis were also performed to further reveal the interrelationship between different ERP components and behavioral scores.

3. RESULTS

3.1. Behavioral results

On average, participants could recall almost 30% ‐ 40% of the Russian novel words after six rounds of training (M = 22.77 ± 10.057, range 5–51), suggesting that participants were able to learn novel Russian words rapidly in a few exposures. As was shown in Figure 2, the scores were nearly normally distributed according to the Kolmogorov–Smirnov test (p > .05).

FIGURE 2.

FIGURE 2

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Distribution of the participants' scores.

3.2. Learning effect

3.2.1. ERP data

As can be seen from Figure 3, novel word pair stimuli elicited a broad but front‐maximized difference of P200 in the 100–300 milliseconds temporal window from first to sixth round of word learning, whereas Figure 4 shows that the word learning process induced an obvious and central‐maximized difference of N400 in the 300–500 milliseconds temporal window from first to sixth round of training and a larger late positivity in the 500–800 milliseconds time window, and a central‐maximized difference of P600 of 6th round of word training as compared with that of first round.

FIGURE 3.

FIGURE 3

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Averaged ERP waveforms at the frontal scalp area with three representative electrodes (F3, Fz, F4). Temporal windows are highlighted in grey shaded areas for P200 intervals. Topographic maps below ERP waveforms show the scalp distribution of the differences between the last round of training and the first round.

FIGURE 4.

FIGURE 4

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Averaged ERP waveforms at the central scalp area with three representative electrodes (C3, Cz, C4) for novel word learning exposures across the six training rounds. Temporal windows are highlighted in grey shaded areas from left to right for N400 and P600 intervals respectively. Topographic maps below waveforms show the scalp distribution of the differences between the last round of training and the first round.

3.2.2. P200 effect in the 100–300 milliseconds temporal window

The repeated measures ANOVA conducted for training rounds of novel word learning (from first to sixth) revealed a significant within‐subject main effect of learning rounds for P200 in the 100–300 milliseconds time window at frontal ROI, which contained F3, Fz, and F4 as the representative channels (F = 3.705, p = .006, η2 p = .070). Further, Bonferroni's multiple comparison test between each round showed that there were generally increased P200 responses from the first round of training to the fourth and a slight reduction of the component after the fourth training round. There were increases by a significant 1.025 μV from the first to the third round of learning (p = .043), a significant 1.147 μV from the first to the fourth (p = .008), a significant 1.055 μV from the second to the fourth (p = .013), and a marginally significant .933 μV from the second to the third (p = .073) (as it can be seen in Figure 5). Such changes suggested that the size of the P200 component increased gradually during the initial rounds of training and had a learning effect across all the six rounds of training.

FIGURE 5.

FIGURE 5

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(a) Line graphs depicting the mean amplitudes of each ERP component evoked during Russian word learning across six training rounds. The Bonferroni's multiple comparisons test of Repeated measures ANOVAs performed across training rounds revealed that changes at P200 time window showed significant fast increase within the first four exposures and then remained stable without significant changes, whereas changes at N400 and P600 temporal windows were significantly fast at the beginning (already at the second and third exposure for N400 components and at the second exposure for the P600 modulation) and remained generally stable over the rest of the training. (b) Scatter diagrams depicting the correlations between the mean amplitudes of each ERP component across six training rounds and individual word recall scores. Only the mean amplitudes of P600 was significant correlated with individual word learning scores.

3.2.3. N400 effect in the 300–500 milliseconds temporal window

For the N400 temporal window, a repeated‐measures ANOVA revealed a significant within‐subject main effect of training rounds for the central scalp area with three representative electrodes C3, Cz, C4, (F = 8.261, p = .000, η2 p = .144). The repeated exposures to Russian‐Chinese semantic‐paired stimuli were observed to modulate the N400 component specifically in the beginning period of the training. As can be seen from the middle line graph in Figure 5, further Bonferroni's multiple comparisons tests showed that the strongest reductions in the N400 amplitude were found from the first to the second (by −1.129 μV) and from the second to the third (by −1.031 μV) exposures (p = .046 and p = .038 respectively), whereas no significant differences of modulations were observed between the rest of the subsequent training rounds. Consequently, differences revealed between first and third (by −2.161 μV, p = .000), between first and fourth (by −1.983 μV, p = .001), between first and fifth (by −1.840 μV, p = .000) and between first and sixth (by −1.879 μV, p = .003) exhibited significant effects. Such pattern of results indicated that the modulation in N400 temporal window was significantly fast (starting already at the first and second exposures) and was maintained across the subsequent training rounds, which shows a learning effect elicited by the reductions of N400 across six rounds of word training.

3.2.4. P600 effect in the 500–800 milliseconds temporal window

In the P600 temporal window, the repeated‐measures ANOVA exhibited a significant training effect (F = 7.105, p = .000, η2 p = .127) of word learning rounds. Further, Bonferroni's multiple comparisons test between the mean of each round showed that there were significant differences between first and second training rounds (by 1.261 μV, p = .043), first and third (by 1.925 μV, p = .001), first and fourth (by 2.198 μV, p = .001), first and fifth (by 2.090 μV, p = .001), and first and sixth (by 2.457 μV, p = .002), whereas there were no significant changes in the comparisons between the rest training rounds and their subsequent rounds. This pattern of results exhibits that the modulation in P600 temporal window was very fast, starting already from the participants’ first exposure to word stimuli. Moreover, such pattern of results denoted a significant increase in P600 from the beginning of training rounds to the end of the training session.

3.3. Correlation between behavioral results and ERP data

The non‐parametric Spearman's correlation analyses between ERP components and scores showed differences in the ERP components in predictability, as can be seen from Graphs B in Figure 5. The interrelationship between P200 amplitudes and word learning performance was not significant (r = −.066, p = .651) in the frontal scalp area (representative electrodes are F3, Fz and F4). N400 interacted with word learning performance at a marginally significant level (r = .261, p = .067), while P600 correlated with word learning scores significantly (r = .419, p = .002).

To further reveal the interrelationship between N400, P600, and behavioral scores, we first performed a correlation analysis, which revealed a highly significant positive correlation between the change in N400 and the change in P600 (r = .769, p < .0001). Subsequently, we conducted a multiple linear regression model to examine the relationship between N400, P600, and word learning performance. The best‐fitting Model 2 (F = 5.825, p = .020) excluded N400 as an independent variable, indicating that P600, rather than N400, was the predictor of word learning performance (please see Table 1). Finally, we conducted a mediation analysis to further explore the relationships among these variables. Results showed that N400 significantly predicted learning performance (β = .2717, t = 2.0473, p = .0461) and P600 (β = .7984, t = 9.1854, p = .0000). However, when both N400 and P600 were included into the model as independent variables, the predicting effect of N400 on learning performance become insignificant (β = −.1104, t = −.5219, p = .6042). The bootstrap analysis found that the 95% CI for the indirect effect was [.0817, .6941]. These results suggested that the predictive effect of N400 on learning performance was fully mediated by P600. Therefore, P600, instead of N400, was identified the marker for individual Russian word learning performance.

TABLE 1.

Results of multiple linear regression analysis.

Predictor B R2 Adjusted R2 F p
Model 1 (All variables) .171 .135 4.836 .012
N400 −.110
P600 .479 a
Model 2 (Only P600) .166 .148 9.544 .003
P600 .391 b
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a

p ≤ .05.

b

p ≤ .01.

4. DISCUSSION

This study aimed to investigate the neural mechanisms underlying Russian word acquisition and to find out the ERP correlates of word acquisition performance. We hypothesized that there would be significant learning effects in the mean amplitudes of P200, N400, and P600 components across six training rounds. Additionally, based on previous literature, we hypothesized correlations between the neurophysiological modifications of N400, P600, and word learning performance.

In our study, we reported ultra‐rapid changes in electrophysiological signals elicited by novel words in Russian. Specifically, the repeated measures ANOVA revealed significant learning effects in all three ERP components (P200, N400, and P600). Furthermore, Bonferroni's multiple comparisons test revealed that the amplitude differences of three ERP components induced by the first two or three rounds of word learning were the largest. Thirdly, correlation analyses performed between the ERP modifications and the word learning scores revealed that: (1) no correlation was found between P200/N400 amplitudes and the learning outcome; (2) P600 amplitudes were significantly correlated with the learning scores. These results suggested that different ERP components elicited during word learning may represent different domains of Russian word acquisition.

4.1. The frontal P200: Orthographic access and attention modification

The brief training phase with the Russian word induced a significant enhancement of P200 amplitudes. P200 had been related to the orthographic word‐recognition processes (Barnea & Breznitz, 1998) and coding processes for graphic forms that require attention or temporary memory (Liu et al., 2003). Specifically, a smaller P200 indicated more sub‐lexical processing, and a larger P200 was related to more holistic lexical processing (Bermúdez‐Margaretto et al., 2020). Therefore, such enhancement of P200 across six training rounds in our study probably reflected the orthographic detection and recognition, modifying from sub‐lexical level recognition to more holistic lexical processing. Generally, this finding was in line with previous research on pseudoword form processing (Bermúdez‐Margaretto et al., 2020; Liu et al., 2003). They had reported similar P200 enhancement effects at frontal and central electrodes for real word recognition, indicating orthographic processing. The frontal and prefrontal cortex were thought to play a crucial role in the assimilation of new word forms (Mkrtychian et al., 2021), so the increase of brain responses in these regions was likely to indicate stronger orthographic discrimination for the novel written words. In conclusion, these results may indicate changes in the acquisition strategy for novel written words.

It is worth noting that the early P200 effect found across six exposures of Russian word learning may not only be an indicator of sub‐lexical processing, but also reflect a modification of the top‐down control over attention. Specifically, an increase of P200 amplitude could be associated with a decrease in the level of attention (Cnudde, 2021). In Cnudde's study, the P200 was also found in bilateral frontocentral electrodes, and the increased amplitude of the component was consistent with the account that attentional requirement decreases over time as the language decision task performance became more efficient. With the increase of the number of word learning rounds, the individual's familiarity with novel words increased, and the attention resources required decrease accordingly. Therefore, in our study, the significant increase of P200 amplitude may indicate the change of individual's attention during the word learning phase.

Interestingly, the P200 observed here demonstrated both similarities and differences compared with the previous studies. Being in sync with the previous studies (Bermúdez‐Margaretto et al., 2020; Cnudde, 2021; Mkrtychian et al., 2021), we observed a P200 increase in the frontal scalp area as the training rounds repeated, which was related to the orthographic learning and top‐down attention control. However, our results differ from previous studies in certain aspects (Bermúdez‐Margaretto et al., 2020; M. J. Vasilyeva et al., 2019). For example, Vasilyeva et al. (2019) adopted an audio‐visual fast mapping paradigm to train adults with Russian novel words in a word‐picture mapping task, and found a significant enhancement in neural activation expressed at 200–400 ms after the word onset, that is, shortly after the words could be identified in their experiment. Passive auditory ERP are known to be a bio‐index of automatic memory trace activation and build‐up (Partanen et al., 2017). Their results pointed toward semantic context advantage in the novel memory trace build‐up. Notably, such ERP component was not defined as P200 or N400 in their experiment. Different from their study, we used written word stimuli instead of acoustic words to train adult learners. Written word stimuli required orthographic processing, especially in paired associate paradigm. Consequently, we found an earlier positivity at 100–300 ms temporal window, a typical P200 component indexing orthographic processing. Second, as reported in a recent study of word forms devoid of semantic content (Bermúdez‐Margaretto et al., 2020), the P200 amplitude exhibited a significant increase right from the first to second rounds of training, while our P200 amplitude increased significantly only at the third training round. Such differences may result from the linguistic ecologic effect of real word stimuli, or the Cyrillic alphabet used in our experiment, which is unpronounceable and unfamiliar to our participants.

To conclude, our results of the learning effect in P200 principally went in line with previous Latin‐alphabet word learning studies, in which P200 may indicate the sublexical‐lexical processing during visual word learning, accompanied by attention modification.

4.2. The central N400: Lexico‐semantic access

A similar learning effect of Russian words was reflected in the modification of the amplitude of N400 responses. As reported in previous researches (Bakker et al., 2015; Barnea & Breznitz, 1998; Bermúdez‐Margaretto et al., 2020; Mkrtychian et al., 2021; Vavatzanidis et al., 2018), the N400 had traditionally been related to lexico‐semantic processing, suggesting that the N400 in our case may be elicited by processing the meaning of the Russian words. A reduction in the N400 amplitude could reflect the ease of lexical‐semantic processing (Vavatzanidis et al., 2018) and the facilitation of the lexico‐semantic access for novel stimuli (Bermúdez‐Margaretto et al., 2020). Specifically, the N400 amplitude became larger when a “(pronounceable) (pseudo)word is unexpected/incongruent, or its meaning is unknown” and smaller when a word was easier to understand (Borovsky et al., 2013). In our study, the unpronounceable real word stimuli were repeated across the training rounds, making it easier to access the word stimuli. Consequently, we observed an attenuated N400 in our study (Figure 4), which may result from the repetition of the lexical information (including orthographic and semantic information). Such N400 modification suggested increased ease of Russian word‐form and semantic processing caused by the pre‐activation of previously repeated lexical information. Notably, such ease of lexico‐semantic processing only increased significantly within the first three exposures. We suggested that the Russian word stimuli required a certain effort of processing, which could not be eased by repetition. Therefore, the N400 amplitude remained stable after the third training round.

On the other hand, no correlation was found between N400 amplitudes and the word meaning training scores even when there was a learning effect, which was different from the previous study on Latin‐alphabet word learning where the N400 was found to predict the learning performance (Batterink & Neville, 2014; Elgort et al., 2015; Junge et al., 2012). In Batterink & Neville's research, amplitudes of the N400 were found to correlate with learners’ L2 word performance at the midline central electrode region. Larger N400 were claimed to reflect learners’ effort to map the lexical form onto conceptual representations, and learners who engaged in this process to a larger extent would show better learning performance. This finding had also been supported by research on infants (Junge et al., 2012), children (Abel et al., 2020), and adults (Elgort et al., 2015). The divergence between our study and previous findings may result from the Cyrillic alphabet we used in our study, which was previously not known to the participants. As it was discussed above, the attenuated N400 observed here possibly resulted from the repetition of the word stimuli. There was not enough evidence of such ERP modification relating to the establishment of orthography‐meaning association, while the learning evidence in our study was the successfully established association between word forms and their meanings. Therefore, we did not find a correlation between N400 modification and the word meaning training scores.

4.3. The central P600: Episodic memory strengthening

The P600 in our study exhibited a remarkable enhancement (more positive) across six training rounds of Russian word learning, especially from the first to the second exposure, whose difference was statistically significant. P600 is a late (500–800 ms) central positivity typically associated with episodic memory retrieval (Rugg & Curran, 2007), and it was interpreted as an episodic memory indicator (Perfetti et al., 2005). Specifically, larger P600 has been associated with the establishment and enhancement of episodic memory traces for recognition of previous stimuli representation (Bermúdez‐Margaretto et al., 2020; Rugg & Curran, 2007). Russian word stimuli, in our case, demonstrated a statistically significant enhancement across six training rounds of word learning. This may indicate the encoding of the episodic memory traces of the lexical information, for example, links between novel word meaning and its word form. Larger P600 may represent the strengthening of such memory traces, which enabled controlled recognition and integration of lexical information.

On the other hand, it had been claimed that the smaller positivity of the component reflected more effortful meaning access or less meaning congruence (Fang & Perfetti, 2017). Therefore, the increase (i.e., larger positivity) of the P600 should indicate less effort utilized during meaning processing or more meaning congruence, which may be caused by, for example, the repetition of stimuli. In our study, we did find the P600 modified positively across repeated training rounds. Therefore, the larger P600 observed here may reflect less effortful meaning processing due to the pre‐activation of the lexical representations (Farshad et al., 2021). Notably, the repetition of word stimuli in our study provided more chances for stimuli encounters and, consequently, more chances for episodic memory establishment. The repetition account of the late positive component should go in line with the account of episodic memory strengthening.

Significant positive correlations were found in our study between the mean amplitude of P600 and the post word learning scores, which went in line with the previous studies on word learning (Bauer & Jackson, 2015; Kim et al., 2018; Nakano et al., 2010). As mentioned above, the P600 amplitude observed here reflected the strengthening of the episodic memory trace. According to the Levels of Processing Theory proposed by Craik and Lockhart (1972), deeper information processing results in more persistent memory trace. To ensure long‐term store of information, the learned Russian words need to be processed elaborately, from pronunciation/orthography to semantics. In other words, more elaborate processing of word information leads to better memorization and word learning performance. Indeed, previous research has found that the P600 reflects verbal memory processes (Bauer & Jackson, 2015; Kim et al., 2018; Nakano et al., 2010; Taylor & Olichney, 2007) and is strongly associated with individual memory capacities (Olichney, 2000; Taylor & Olichney, 2007). For instance, Olichney (2000) found that the amplitudes of P600 evoked in word learning were positively correlated with healthy individuals' word recall performance, which reflects individual's episodic memory ability, while N400 was correlated with amnesic patients’ word learning performance, which may reflect a short‐term memory process that serves language comprehension in real‐time. Consistently, in our study, we found that the mean amplitudes of P600 were strongly correlated with word recall performance, which was also associated with individual's episodic memory establishment and persistence in long‐term memory. Therefore, our findings provide additional evidence on the predictive effect of P600 on individual episodic memory in word learning.

4.4. Similarities and differences between Latin‐alphabet and Cyrillic‐alphabet word learning

Compared with the Latin‐alphabet word learning studies, our findings demonstrated similar brain responses to rapid Russian word acquisition. First, for most of the previous studies, the learning effect was found in the neural indicators such as P200 (Bermúdez‐Margaretto et al., 2020; Cnudde, 2021; Liu et al., 2003; Mkrtychian et al., 2021), N400 (Bakker et al., 2015; Bermudez‐Margaretto et al., 2018; Liu et al., 2007; Yum et al., 2014) or P600 (Bakker et al., 2015; Fang & Perfetti, 2017; Farshad et al., 2021; Vandenberghe et al., 2019). Our study was consistent with these studies and found learning effects on P200, N400, and P600. Second, similar to the previous studies (Finnigan, 2002; Rugg & Curran, 2007; Stein et al., 2006), the P600 component found in our study may be a potential marker for individual ability in Russian word learning, as it significantly correlated with participants' learning performance. A larger P600 amplitude potentially indicated a more fruitful learning outcome. However, whether the P600 found in our study is Russian‐specific remained elusive, for we did not investigate the neural mechanisms of learning non‐linguistic materials.

On the other hand, our findings also demonstrated different brain responses to Russian word learning compared with other Latin‐alphabet word learning studies. First, we found that P200 significantly increased only at the third exposure of the training session, which was different from a previous study on pseudowords (Bermúdez‐Margaretto et al., 2020). The P200 observed in Bermúdez‐Margaretto et al.'s study exhibited a robust increase as early as the second exposure. Second, the N400 in our Russian word training did not demonstrate any correlation with learning outcome even when there was a learning effect, whereas most of the studies found a correlation between the N400 component and the learning outcome (Abel et al., 2020; Barnea & Breznitz, 1998; Batterink & Neville, 2014; De Diego Balaguer et al., 2007; Elgort et al., 2015; Junge et al., 2012). Such divergence in results may be explained by the Cyrillic alphabet we used, which was not previously known to the participants, and the Cyrillic alphabet was also not used before in paired‐associate Russian word learning paradigm. In conclusion, these findings have implications for the comprehensive study of Russian word acquisition, especially for predicting Russian word learning outcomes using a potential marker of P600.

4.5. Limitations and future directions

In our study, we adopted paired‐associate learning paradigms which have ecological validity and are one of the analogs to word learning in real life. However, there are other pedagogical approaches that incorporate more focus on context and learning in naturalistic contexts, including auditorily‐based word learning, intentional word learning, incidental study, and so forth. Second, we only examined the Russian word learning neural mechanisms of participants aged about 20. However, there were studies reported differences in word learning among different age groups, such as children (M. Vasilyeva, 2021; Vavatzanidis et al., 2018) and adults (Perfetti et al., 2005; Soskey et al., 2016; Yum et al., 2014). Third, if possible, it would be better to include a control condition to investigate whether the ERP changes in this study were specific to the Russian learning effect, rather than adaptation or habituation effect. However, in the present study, we included a post‐learning test to measure participants' word learning performance after training section, and we have performed the correlation analysis between ERP data and behavioral results, which ensured that the ERP changes were most likely specific to the Russian learning effect. Besides, the adaptation or habituation effect is typically expressed as suppression of ERP responses (Bermúdez‐Margaretto et al., 2020; Henson et al., 2003; Rugg et al., 1994), which is not we observe here. Furthermore, other studies that employed a similar paired‐associate learning paradigm have observed learning‐related changes in ERP components including N400 and P600 (Bergström et al., 2007; Cosper et al., 2022; Gui et al., 2017), suggesting that the ERP changes observed in our study were likely related to learning. Nonetheless, we acknowledge that a control experiment would have been valuable in distinguishing the effects of learning and habituation. Last, the measuring technique in our experiment was limited. Though the ERP technique has high time resolution, it cannot provide the exact sources of the neural activities, while the functional Magnetic Resonance Imaging technique (fMRI) can perfectly manage it. In the future, it would be especially appealing to examine Russian word learning mechanisms in combination with one or more of the learning paradigms or among different age groups using different measuring techniques.

5. CONCLUSION

To our knowledge, this study has for the first time investigated the dynamic brain changes during Russian word learning in adult learners with no prior knowledge of the Russian language. We observed significant learning effects across the novel word training session in P200, N400, and P600 amplitudes. Moreover, P600 amplitude changes predicted participants' word learning performance. These findings suggest that Russian word meaning integration with lexical information is a fast and continuous process and that the P600 may serve as a potential marker for individual learning ability in Russian word acquisition.

AUTHOR CONTRIBUTIONS

Hong Xu and Hengyi Rao conceived the overall project. Jiahui Zhang and Yan Huang collected data, analyzed data, and drafted the article. All other authors reviewed and edited the manuscript for important scientific content and final approval.

FUNDING INFORMATION

The research was supported by the open project Cognitive Neural Mechanism of Russian Vocabulary Learning (2021KFKT001) of Shanghai Key Laboratory of Brain‐Machine Intelligence for Information Behavior, the Shanghai International Studies University major research projects (2021114002, 2021114003, 20171140020) and the National Natural Science Foundation of China (71942003).

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supporting information

Data S1: Supporting Information

Click here for additional data file. (117.7KB, pdf)

ACKNOWLEDGMENTS

The authors would like to thank all the participants in this study.

Zhang, J. , Huang, Y. , Jiang, C. , Xu, Y. , Rao, H. , & Xu, H. (2023). Dynamic brain responses to Russian word acquisition among Chinese adult learners: An event‐related potential study. Human Brain Mapping, 44(9), 3717–3729. 10.1002/hbm.26307

Jiahui Zhang and Yan Huang have contributed equally to this work and share first authorship.

Contributor Information

Hengyi Rao, Email: hengyi@gmail.com.

Hong Xu, Email: katiaxu@shisu.edu.cn.

DATA AVAILABILITY STATEMENT

The data and code used in the present study are available from the corresponding author upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting Information

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Data Availability Statement

The data and code used in the present study are available from the corresponding author upon request.

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