Edited by: Asghar Iran-Nejad, University of Alabama, United States
Reviewed by: Ruomeng Zhao, MacPractice, Inc., United States; Laura Morett, University of Alabama, United States
*Correspondence: Baoguo Chen
This article was submitted to Educational Psychology, a section of the journal Frontiers in Psychology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Previous studies have found quantity of exposure, i.e., frequency of exposure (Horst et al.,
The accumulation of vocabulary is the foundation of language learning, particularly for one's second language (L2). The majority of vocabulary learning in L2 learners very often comes from explicit exposure and explicit teaching in the classroom (Ellis et al.,
One controversial issue is how many encounters L2 learners need to acquire the meaning of a novel word (Nagy et al.,
In sum, previous behavioral studies have shown that quantity of exposure, i.e., frequency of exposure, is important to L2 contextual word learning and that several repetitions are needed for learning to occur. However, what was left out in these studies was the sentence constraint effect. It has been found that learners learn fast in high constraint sentences. For example, in the study of Ma et al. (
L2 contextual word learning was also influenced by proficiency level. Pulido (
Compared with behavioral studies, the ERP technique has high temporal resolution and could reveal ongoing brain responses of language processing. The amplitude of the N400 component measured at centroparietal electrodes is an index of the difficulty in integrating semantic information into context (Kutas and Hillyard,
Borovsky et al. (
Borovsky et al. (
Mestres-Missé et al. (
However, there have been few ERP studies investigating L2 contextual word learning. Elgort and Warren (
It is still unknown how many exposures a learner needs for successful L2 contextual word learning. Few studies have manipulated the quality of sentence reading materials to study L2 contextual word learning. Variation of sentence constraint and/or comprehension difficulty of reading materials may lead to the unpredictability of L2 word learning times. Thus, we hypothesized that multiple times in L2 word learning are needed for low-constraint contexts. When reading materials were highly constrained (i.e., high quality), word learning can happen very rapidly. To verify this hypothesis, we used high-constraint sentences to explore the effect of number of exposure on L2 contextual word learning. Furthermore, L2 proficiency was also investigated in the current study.
We used the ERP technique, which has high temporal resolution, to explore the above questions. Following the design of Mestres-Missé et al. (
Learners read four high-constraint sentences containing the same target word, and then judged the semantic relatedness between the target word and a meaning probe. The accuracy rates and response times were recorded to reveal their comprehension and acquisition of the meaning of novel words. Brain potential activities were recorded during sentence reading to observe the change of brain responses as the novel words were being learned.
In accordance with previous studies, we chose 300–500 ms post-stimulus as the time window to observe the N400 effect, which indicates the process of meaning acquisition (Perfetti et al.,
Forty-four right-handed college students, who were all native Chinese speakers learning English as a second language, were recruited from Beijing Normal University. All participants had normal or corrected-to-normal vision. This study was approved by the ethics committee of the School of Psychology, Beijing Normal University. All participants gave their written informed consent before the experiment.
Real words were 108 high frequency concrete nouns. Word frequency (mean logFreq = 10.04,
One hundred and eight pronounceable pseudowords were constructed using Wuggy (Keuleers and Brysbaert,
Word length of pseudowords ranged from 5 to 7 (
For the semantic relatedness judgment task, semantically related or unrelated words were selected to pair with the 108 real words. Word length of semantically related words ranged from 3 to 11 letters (
Four sentences were constructed for each real word, and then the real word was replaced by a pseudoword to create the M+ condition, in which the meanings of novel words were consistent and could be abstracted. The M− condition, in which the meanings of novel words were inconsistent and could not be abstracted, was created by reorganizing four sentences of four words into one group and replaced the four words with one pseudoword. The length of sentences ranged from 7 to 17 words, with the key word (real word or pseudoword) always appearing at the end of the sentence. The constraint of sentences was rated by a separate group of 43 college students from the same school of the participants. They completed the sentences in a cloze test with the first noun that came to their mind. The cloze probability was calculated by counting the percentage of times the same word was provided for each sentence. The mean cloze probability of these sentences was 89% (
Examples of four sentences and test pairs of words from each condition.
arram | Farm | Agriculture | Related | He raised chickens, sheep, and cows on the |
He raised chickens, sheep, and cows on the |
He knows well about planting crops because he lives on the |
A good salesman should win the trust of the |
||||
Cotton, corn and vegetables were all grown on his |
The boy fell in love with a girl, and he wrote her a love |
||||
Kids of poor village families often help parents with the work on the |
They made fun of me by putting salt in my coffee instead of |
||||
banble | Shape | Hotel | Unrelated | Circles, triangles, and squares are different in |
Circles, triangles, and squares are different in |
Liquid flows freely without a fixed |
They played in the river, and caught several |
||||
Blind person use their fingers to feel the object's |
To guard the house against thieves, they raised a |
||||
The building looks like a ball, it's round in |
No one answered the door, when I rang the |
The College English Test (CET) (see the Procedure section) and Quick Placement Test (
Background information of participants by proficiency level: Mean (
Higher proficiency | 21.63 | 10.67 | 3.21 | 3.13 | 3.96 | 3.66 | 48.67 |
(1.69) | (1.09) | (0.58) | (0.34) | (0.55) | (0.64) | (3.19) | |
Lower proficiency | 22.15 | 11.30 | 2.50 | 2.40 | 3.30 | 3.05 | 40.90 |
(2.99) | (1.03) | (0.89) | (0.82) | (0.66) | (0.60) | (3.86) | |
−0.73 | −1.97 | 3.16 |
3.95 |
3.62 |
3.27 |
7.31 |
This study used a mixed experimental design: 4 (sentence presentation order: 1st, 2nd, 3rd, 4th) × 3 (word type: R, M+, M−) × 2 (proficiency level: higher, lower). Here, sentence presentation order and word type were within-subjects factors, and proficiency was a between-subjects factor. All the sentences were counter-balanced across participants according to word type (R, M+, M−), to make sure no words/pseudowords or sentences were repeatedly presented for each participant. The four sentences within each group were presented randomly.
Participants were divided into two groups based on their College English Test (CET) levels. The CET is a test designed by the Ministry of Education of China to estimate the English proficiency level of Chinese college students. It includes listening comprehension, reading comprehension, writing, translation, and cloze task (Zheng and Cheng,
E-prime software version 2.0 was used to present stimuli on a computer screen. Participants were seated in front of the computer and practiced several trials prior to the formal experiment.
The first part of each sentence was presented as a whole, with the last word of each sentence presented separately. The experiment began with the presentation of a fixation cross in the center of the screen for 500 ms. After the fixation, the first part of the sentence was presented and would not disappear until learners pressed the spacebar to continue, and then a blank screen lasted 1,000 ms, followed by the last word/pseudoword of the sentence which was presented for 500 ms, and then a blank screen lasted for 1,200 ms followed by the next trial started.
Each group included four sentences of one word/pseudoword and each block included six groups of sentences. When learners finished a block, a question mark appeared on the screen for 1,000 ms as a prompt for participants to do the semantic relatedness judgment. In this task, learners read six word pairs corresponding to the six groups of sentences just presented and judged whether the words were semantically related. Within each block, the six groups of sentences and the corresponding word pairs were presented in pseudo-random order. “Related” or “Unrelated” responses were made by pressing “F” or “J” on the keyboard. Half of the participants were asked to press “F” for “Related,” “J” for “Unrelated.” The other half press “J” for “Related,” “F” for “Unrelated.” The two words appeared on the screen simultaneously, and if no response was detected within 5,000 ms, the stimuli would disappear followed by a blank screen for 200 ms. The whole experiment lasted for 1.5–2 h.
Finally, all participants were given a checklist of all the sentences they had just read to confirm that they had no difficulty in reading these sentences. In this checklist, all the pseudowords were replaced with the corresponding real words. The participants were asked to mark the sentences or words which were difficult to them. Because no marks were made on any items, we presume the materials could be easily processed by the participants.
The behavioral data for all 44 participant learners were reviewed. Cases of no response or responding too early (less than 200 ms) were excluded (1.66%). A mixed-effects logistic model of accuracy and a mixed-effects model of response time were built to analyze their performance in the semantic relatedness judgment task (Baayen et al.,
Participants were seated comfortably in a chair, relaxing, and minimizing eye movements and blinks. They read the sentences quietly. The electroencephalogram (EEG) was recorded from 64 Ag/AgCl electrodes placed according to the extended 10–20 positioning NeuroScan 4.5 system (
Segments with electrical activity ±100 μV at any electrode sites were rejected. EEG segments of 800 ms with a pre-stimulus (the last word of the sentence) baseline time of 100 ms were selected and averaged offline to obtain the ERPs. Baseline correction was performed in relation to the pre-stimulus time. The signals were re-referenced using an average value of both right and left mastoid offline.
One lower proficiency learner was excluded due to too many artifacts (only 33.33% of trials were available), so the final dataset was 43 participants, including 24 higher proficiency learners and 19 lower proficiency learners. After artifact rejection, 5.3% of trials were rejected.
In accordance with previous studies, we analyzed the mean amplitude within the time window of 300–500 ms upon the presentation of the last word of the sentence. To increase the signal-to-noise ratio over the 64 channels, as done in the study by Batterink and Neville (
For each of the two proficiency groups, accuracy and response times for different conditions are shown in Table
Accuracy (%) and response time (ms) of semantic relatedness judgment task: Mean (
Accuracy | 90 (29) | 87 (34) | 69 (46) |
Response time | 1,462 (739) | 1,475 (721) | 1,855 (835) |
Accuracy | 85 (36) | 86 (35) | 63 (48) |
Response time | 1,539 (752) | 1,583 (761) | 1,879 (845) |
A mixed-effects logistic model of accuracy was built in which word type and proficiency were fixed factors, subject and item (i.e., combination of sentences and key words) were random factors, and word length was a covariant (Table
Mixed-effects logistic model of accuracy in the semantic relatedness judgment task.
(Intercept) | 0.4406 | 0.4874 | 0.90 | 0.366 |
Word Type M+ | 1.4099 | 0.1457 | 9.68 | 0.000 |
Word Type R | 1.6444 | 0.2051 | 8.02 | 0.000 |
Proficiency | 0.0561 | 0.2658 | 0.21 | 0.833 |
Word Length | 0.0638 | 0.0767 | 0.83 | 0.405 |
Word Type M+: lower proficiency | 0.6290 | 0.2164 | 2.91 | 0.004 |
Word Type R: lower proficiency | −0.6072 | 0.2227 | 2.73 | 0.006 |
Tukey
M+ - M− = 0 | 1.4099 | 0.1457 | 9.68 | 0.000 |
R - M− = 0 | 1.6444 | 0.2051 | 8.02 | 0.000 |
R - M+ = 0 | 0.2344 | 0.2184 | 1.07 | 0.655 |
Lower proficiency-Higher proficiency = 0 | 0.0561 | 0.2658 | 0.21 | 0.996 |
Higher proficiency (R - M+) = 0 | 0.2344 | 0.2184 | 1.07 | 0.869 |
Higher proficiency (R - M−) = 0 | 1.6444 | 0.2051 | 8.02 | 0.000 |
Higher proficiency (M+ - M−) = 0 | 1.4099 | 0.1457 | 9.68 | 0.000 |
Lower proficiency (R - M+) = 0 | 0.2126 | 0.2380 | 0.89 | 0.935 |
Lower proficiency (R - M−) = 0 | 2.2516 | 0.2189 | 10.29 | 0.000 |
Lower proficiency (M+ - M−) = 0 | 2.0390 | 0.1639 | 12.44 | 0.000 |
R(Lower proficiency-Higher proficiency) = 0 | 0.6633 | 0.3022 | 2.20 | 0.208 |
M+(Lower proficiency-Higher proficiency) = 0 | 0.6851 | 0.2956 | 2.32 | 0.161 |
M−(Lower proficiency-Higher proficiency) = 0 | 0.0561 | 0.2658 | 0.21 | 0.999 |
We also examined whether the accuracy of different conditions was significantly higher than chance level. For the R condition, accuracy was above chance level (higher proficiency learners:
For the response time data, a mixed-effects model was constructed in which word type and proficiency were fixed factors, subject and item (combination of sentences and key words) were random factors, and word length was a covariant. Results are summarized in Table
Mixed-effects model of response time in semantic relatedness judgment task.
(Intercept) | 1484.67 | 117.57 | 12.63 | 0.000 |
Word Type M+ | −384.49 | 30.11 | −12.77 | 0.000 |
Word Type R | −385.45 | 36.95 | −10.43 | 0.000 |
Proficiency | 621.24 | 80.14 | 7.75 | 0.000 |
Word Length | 19.50 | 12.65 | 1.54 | 0.124 |
Word Type M+: Lower Proficiency | 79.84 | 45.03 | 1.77 | 0.076 |
Word Type R: Lower Proficiency | 48.02 | 45.01 | 1.07 | 0.286 |
Tukey
M+ - M− = 0 | −384.49 | 30.11 | −12.77 | 0.000 |
R - M− = 0 | −385.45 | 36.95 | −10.43 | 0.000 |
R - M+ = 0 | −0.97 | 36.83 | −0.03 | 1.000 |
Lower proficiency-Higher proficiency = 0 | 621.24 | 80.14 | 7.75 | 0.000 |
The group-level average waveforms and scalp distribution elicited by different word types are shown in Figure
The group-level average waveforms and scalp distribution elicited by different word types of the first presented sentences for higher proficiency
The group-level average waveforms and scalp distribution elicited by real words of four sentences (R condition) for higher proficiency
The group-level average waveforms and scalp distribution elicited by pseudowords of four sentences (M+ condition) for higher proficiency
The group-level average waveforms and scalp distribution elicited by pseudowords of different sentences (M- condition) for higher proficiency
A repeated-measures ANOVA was conducted on the mean amplitude in the 300–500 ms time window, and the results showed a significant main effect of word type,
There was a significant main effect of brain region,
There was a significant interaction between sentence presentation order and brain region,
There was a significant three-way interaction among word type, brain region, and proficiency,
The main findings was that in the R condition, N400 amplitudes evoked in first presented sentences were significantly larger than in the second, third, and fourth presented sentences. In the M+ condition, N400 amplitudes evoked in the first presented sentences were significantly larger than in the third and the fourth presented sentences, suggesting that participants gradually acquired the meaning of the pseudoword throughout sentence reading. In the M− condition, no sentence presentation order effect was found.
The present study explored the effects of high quality sentence encounters and proficiency level on L2 contextual word learning. Behavioral results of accuracy showed no statistical difference between the M+ condition and the R condition for both groups of learners, but lower accuracy in the M− condition. These results suggests that the M+ condition—but not the M− condition—effectively facilitates novel word meaning acquisition. Besides, accuracy results showed that both groups of learners were equally familiar with the real words, and they both acquired the novel word meaning in the M+ condition. However, response time results showed that higher proficiency learners spent less time in the semantic relatedness judgment task, suggesting that higher proficiency learners were better at processing novel words as well as already known words.
For the ERP results, in 300–500 ms time window, significant negative components were found in the M+ and M− conditions compared to the R condition when learners read the first sentence. This negative component normally found between 300 and 500 ms in the frontal and parietal regions was evoked by semantic violation, and it is a typical N400 effect in sentence reading. As the sentence number increased, the N400 patterns for the three types of words started to differ. In the R condition, the N400 amplitude decreased rapidly; in the M+ condition, the N400 amplitude decreased slowly and this decrease became significant upon the third sentence; in the M− condition, the N400 amplitude showed no obvious changes across the four sentences. Consistent with our predictions, this divergence in N400 amplitude change among the three conditions directly reflected the course of word learning. Real words are words learners already know and don't need to be learned again. Novel words in the M+ condition are words for which learners could form consistent meanings, so they can be learned gradually as sentence number increases, and this learning process could be reflected directly by the decreasing of the N400 amplitude. Novel words in the M− condition are words for which learners could not form consistent meanings, so they cannot be learned and no changes would be observed in the EEG signal.
According to previous findings, L2 word learning through sentences needs multiple exposures. However, previous studies did not control sentence quality, and have not explored how sentence quality modulates the influence of exposure times. In this study, the EEG signals showed novel words were learned successfully in the M+ condition as sentence number increased, suggesting that the first two high quality sentences might play an important role in providing multiple exposures. We believe the key point between few encounters or multiple encounters needed to acquire L2 word meaning is the quality of language input. When the quality of language input is low, more exposures are needed. When the quality of language input is high, L2 learners could rapidly assign meaning to the novel word. This is to say, when the sentence contexts are highly constrained, the number of exposures of L2 novel words does not matter that much. It is the high quality that really matters.
The current findings are consistent with previous studies on L1 contextual word learning. Borovsky et al. (
Contrary to one prediction, we did not observe the effect of L2 proficiency in the semantic relatedness judgment accuracy and the N400 amplitude. Nevertheless, higher proficiency learners did respond faster than lower proficiency learners in the semantic judgment task. The possible reasons for this weak L2 proficiency effect might be, firstly, that the materials used in this study might be too easy for all the participants; second, the difference between the higher proficiency level and lower proficiency level might not be large enough. Nonetheless, considering previous findings about the effect of language proficiency in native speakers (Perfetti et al.,
Here it should be noted that in the present study, we only focused on the very initial phase of word meaning acquisition, namely, the process of building form-meaning mapping, but not on the succeeding consolidation phase. To reach the final goal of word acquisition, more exposures are needed for the consolidation of form-meaning mappings.
In sum, by creating four high quality sentences for each novel word, and recording the brain electrical activity during word learning through reading, we directly observed real-time L2 contextual word learning. The results provide direct evidence that L2 learners can rapidly acquire word meaning in high constraint sentences without multiple times of exposure, and L2 proficiency level affects learners' efficiency of using high quality language information.
TM and BC designed the experiment and wrote the manuscript; TM collected and performed data analysis; LL and HL edited and revised 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. The reviewer, LM, and handling Editor declared their shared affiliation.
This work was supported by funding from Beijing Education Science Planning of 13th Five-Year(CADA17077, The mechanism of second language word learning for Chinese-English bilinguals) for BC. We would like to thank Dr. Susan Dunlap for editing the manuscript.
1Due to the longer duration (1.5–2 h) of the experiment, we were not able to recruit an equal number of high- and low-proficiency English learners. Hopefully, the mixed-effect model analysis would guarantee the validity of the behavioral results despite uneven sample sizes (Lan et al.,