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/lp/springer-journals/processing-of-numeral-noun-structures-by-l1-persian-learners-of-l2-Lkt5wo2qVT
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- Springer Journals
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- Copyright © The Author(s) 2023
- eISSN
- 2363-5169
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- 10.1186/s40862-022-00177-y
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Abstract
mtsh_85@yahoo.com; masum.tsh@gmail.com Within the last two decades, researchers have begun to investigate how L2 learners Department of English, Faculty process syntactic, morpho-syntactic, and lexical information during the comprehen- of Languages and Literature, sion of L2 sentences. The present study aimed to add to research by investigating how Yazd University, Yazd, Iran L1 influences L2 processing of sentences indicating plurality in constructions involv- ing numerals. More specifically, the research investigated whether Persian speaking low-proficient L2 learners of English showed L1 transfer effects performing a self-paced online reading task. To address this issue, employing the IBEX software, reaction times on critical regions and accuracy rates of learners’ performance were measured on four types of structures (i.e., Numeral + Count Noun, Numeral + Classifier + Mass Noun, Numeral + Classifier + Count Noun, and Numeral + Non-referential Noun + Noun) and two sentence types (ill-formed vs. well-formed). Statistical analysis indicated the effect of both structure and sentence type on reaction times on the critical regions studied. Results also indicated traces of L1 effect in processing Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun structures. Concerning the Numeral + Count Noun and Numeral + Classifier + Non-referential Noun + Noun, no clear evidence for L1 transfer effect was observed. Further studies employing a larger sample size, investigat - ing the issue at higher proficiency levels, and having native English speakers as control group are suggested. Keywords: Reaction time, Accuracy rate, L1 transfer effect, Plurality Introduction Clahsen and Felser (2006) have discussed four major potential factors for differences between L1 and L2 processing, i.e., lack of related grammatical knowledge, influence of L1, cognitive resource shortage, and changes due to maturational puberty. The focus of the present study is on the second proposed factor; that is, L1 influences during L2 pro - cessing. There has been an abundance of research on the effect of L1 transfer on syn - tactic, phonological, and lexical domains; however, the available experimental findings in the area of morpho-syntax are far from convincing. There have been some studies related to online sentence processing which have found evidence for the effect of L1 transfer on parsing (Dussias & Sagarra, 2007; Frenck-Mestre, 2002; Juffs, 2005) while © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate- rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 2 of 21 there are other studies which did not reveal any effect of L1 on learners’ L2 process - ing (e.g., Felser, et al., 2003; Papadopoulou & Clahsen, 2003). To shed more light on this issue, the present study aimed to investigate how first language (Persian) knowledge of plurality constrains or facilitates the course of L2 sentence processing. The author lim - ited the investigation to the processing of plural morphemes in the structures involving numerals. Review of the related literature Some researchers have disapproved L1 transfer as a significant factor in L2 perfor - mance. Ellis (1994) suggested that L2 learners with different mother tongues go through the same process of L2 development; hence, she emphasized the universal process and considered no role for L1 transfer. Sabourin (2003) attempted to investigate L1 trans- fer effects on subject verb agreement violations in Dutch language, using the event- related potential (ERP) technique to explore second language learner brain responses. The results found the same ERP pattern for all the participants, hence no transfer of the properties of L1 while processing L2. Likewise, Felser et al. (2003) and Papadopoulou and Clahsen (2003) disconfirmed the L1 transfer effects. A study by Juffs (2005) sug - gested that the existence of wh-movement in L1 benefited the learners’ processing of wh-movement in L2, which means that L1 does exert an effect. Dussias and Sagarra (2007) indicated evidence for learners’ transfer of parsing strategies into L2. Some studies have found proficiency as a significant factor, which means the higher the level of L2 learners, the less the interference of L1 (e.g., De La Colina & Garcia Mayo, 2007; Di Camilla & Anton, 2012; Elston-Güttler et al., 2005; Frenck-Mestre, 2002; Storch & Wigglesworth, 2003; Su, 2001; Swain & Lapkin, 2000; Tian & Jiang, 2021). For exam- ple, Frenck-Mestre (2002) showed that L1 transfer of relative clause attachment pref- erences was more evident in low-proficiency L2 English learners while proficient L2 learners could do native-like processing and decrease the amount of L1 transfer. Su (2001) also found that L1 transfer decreased with increasing proficiency. Transfer of morphology/morpho‑syntax L1 effects in morphology/morpho-syntax have been investigated less adequately than in L2 syntactic processing. Similarly, investigations in this area have revealed that the pres- ence or absence of certain morpho-syntactic features in L1 influences the L2 acquisi - tion and processing. Research has generally indicated that if a morphological marking is absent in L1, L2 speakers rarely show native-like processing of this feature in L2. On the other hand, in cases the feature is present in L1, L2 speakers can show native-like processing. For instance, Jiang (2004) suggested that Chinese learners were insensitive to number disagreement, probably as Chinese lacks morphological number marking. Simi- lar result on the importance of L1 and L2 similarity in the acquisition of L2 morphology has been obtained by Jiang (2007). Barto-Sisamout, et al. (2009) employed a self-paced reading task to test the influence of the presence or absence of morpho-syntactic rules in L1, employing a group of Spanish learners of English, Chinese learners of English, and a native control group. The findings suggested lack of clear interference effects. In Persian, an abundance of research has addressed the effects of Persian, as the first language, on the acquisition of a second or third language (e.g. Heidari Darani, 2012, T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 3 of 21 Jabbari, 2018; Khany & Bazyar, 2013; Molaie et al., 2016). However, studies on morpho- syntactic features are rare. Ghilzai (2017), examining the effect of L1 (Urdu, Japanese, and Persian) on L2 English case and agreement processing, concluded that learners indicated similar patterns in terms of reaction time and accuracy, refuting the possible effect of L1. Mobaraki and Mohammadpour (2011) investigated ten children’s L2 Eng - lish acquisition of functional categories and the role and degree of L1 influence in this regard. Findings revealed no presence of the features at the initial stages or the learners’ reliance on their L1. Nevertheless, there has been no empirical study investigating the acquisition of English plurality markers by Persian learners. Plurality in English and Persian In English, plurality is marked by adding the morpheme "s" to the singular word form. In Persian, the morpheme "ha" is the common plural maker; however, some other mark- ers are also used. There are a number of nouns that are pluralized with "an", "at", and "in" (e.g., moalem (teacher), Moaleman (teachers); heyvan (animal), heyvanat (animals); mosafer (traveler), mosaferin (travellers)). As Ghomeshi (2003, P.57) noted, "Choice of plural marking is therefore rather complicated and may be determined by factors such as register, level of education of the speaker, etc." However, the pattern changes in the cases in which a cardinal number is used. In Per- sian we have: a Se mard Three person ( Three persons) b Se medad Three pencil ( Three pencils) As the examples indicate, the singular noun is used with the numeral se (three). According to Ghomeshi, in Persian, plural marker and overt numerals cannot co-occur, except when the noun phrase involving numerals is definite. See the examples below: a Do ta Sib xord-am Two CL-apple eat. past-1SG (I ate two apples.) b *Do ta sibha khordam Two-CL apple-PL eat. past-1SG (I ate two apples.) Do ta sibha ro khordam Two-CL apple-PL-OM eat. past-1SG (I ate the two apples.) As the examples represent, unlike in English, Persian, except in the case of definite nouns, does not allow the pluralization of the noun in Numeral + Noun construction. Besides, the two languages differ in the case of Numeral + Classifier + Noun structure. This aspect is discussed more deeply in the following section. Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 4 of 21 Numeral + Mass / Count Nouns As in English, some nouns in Persian are typically semantically individuated into separate units (count nouns); however, others cannot be partitioned (mass nouns). In English, mass nouns are typically accompanied by a measure phrase in order to be countable (a loaf of bread, two glasses of water). Likewise, in Persian, mass nouns are referred to via classifiers. However, an important property of Persian, which is not seen in English, is that the classifier in Persian is not marked for plurality and the sin - gular form of the classifier is used. For example, we have: a Se livan Shir Three glass milk ( Three glasses of milk) b Se kase berenj Three bowl rice ( Three bowls of rice) Returning now to count nouns, in Persian and not in English, it has been shown that they may optionally appear with classifiers. For example: a Do jeld ketab Two CL-volume book ( Two books) b Se ras asb Three CL-head horse ( Three horses) c Se nafar kargar Three CL-person worker ( Three workers) However, there is another marker, "ta", which is used in the place of a more specific one. Ta can be used with both count and mass nouns. For instance, we have: a Se ta namak Three-CL salt ( Three salts) b Se-ta ketab Three—CL book ( Three books) Here, se ta namak means three packs of salt, indicating that the type of coercion effect found in English is not absent in Persian and is identified in classifier construc - tions (Ghomeshi, 2003). Hence, the difference between the two languages is that English uses number mor - phology whereas Persian uses count-classifiers. According to Doetjes (1996), in some languages such as English, number morphology is the grammatical marker whereas in languages that lack number morphology (e.g., Chinese), the grammatical marker is the (count) classifier. As such, count classifiers and number morphology both can indicate number. According to this speculation, in the case of Persian, it seems that T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 5 of 21 as Persian, unlike English, lacks number morphology, it uses count classifiers. This is also confirmed by Ghomeshi (2003): We have seen that English realizes the count/mass and the singular/ plural distinc- tion at the level of NumP. Chinese and Persian make the count/mass distinction at the level of the classifier. However, Persian, unlike Chinese, also has plural marking, the absence of which is often tied to the use of obligatory classifiers (P.67). To sum up, we can say that in Persian, just as one must say three unit (of ) (a mass noun), one might say three unit (of ) (a count noun). However, in English, no classifier is used with the count nouns, and plurality is indicated through number morphology. Hence, it seems that Persian differs from English in that it lacks a NumP projection. However, in the case of non-referential nouns acting as classifiers following a numeral, there is no difference between English and Persian concerning the use of plural marker. See the examples below: a Yek taraneye 10 daghighei A song ten minute (A ten-minute song) b Yek masire dah maili A way ten mile (A ten-mile way) In referential nouns, there is just one noun which is the head noun and the numeral and classifier are plurality markers. However, in non-referential nouns, there are two nouns in a noun phrase. For instance, in a ten-mile way, the two nouns in this example are "mile" and "way". The head noun is "way" and "mile" is the noun modifier. In fact, the English noun phrase a ten-mile way originates from the clause a way which is ten miles. Since "mile" becomes the head noun modifier for "way", it cannot be pluralized. The same is true for Persian. Taken together, based on the Transfer Hypothesis, such differences discussed above might be challenging for L2 learners of either English or Persian having acquired the other one as L1. Hence, through this study, the author aimed to investigate how first language (Persian) knowledge of plurality constrains and determines processing of simi- lar L2 (English) structures. More specifically, this paper aimed to assess the predictions of Transfer Hypothesis in low-proficient Persian learners’ processing of English plural morphemes in structures involving numerals. Therefore, the authors’ prediction is that if there is an L1 effect, respondents will read more slowly at the critical regions (show longer reaction times (RTs)) and require more time answering the following question (longer response latency). Such predictions can be more specifically summarized as below: Hypotheses H1 As Persian lacks number morphology, Persian speaking learners with low profi - ciency in English will not expect the plural form of the noun in the Numeral + Count Noun construction. Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 6 of 21 H2 Persian speaking learners of English with low proficiency might tend to use sin - gular classifier (instead of the plural one) in the Numeral + Classifier + Mass Noun con- struction in English. H3 As Persian also inserts classifiers into Numeral + Count Noun construction, Persian speaking learners of English with low proficiency might consider the Numeral + Classi- fier + Count Noun as acceptable in English. H4 As there is no difference between the two languages in the case of Non-referential Nouns acting as classifiers following a numeral, Persian learners with low Proficiency in English will not have problem processing the structure in English; that is, there should not be long reaction times, response latencies at such regions, or inaccurate responses. Both correct and incorrect sentences were used to test these predictions. Learner reac- tion times, response latencies, and their answers to the comprehension questions could provide clues to support or refute these predictions. Method Subjects A total of 99 English learners, from three language institutes in Iran and with the age range of 13 to 18, took part in the first phase of the study which aimed to measure their English proficiency. Of these, 71 subjects whose proficiency was recognized to be low (A2, according to DIALANG), were selected to participate in the second phase of the study, i.e., the main experiment. The other subjects who were recognized to be beginners or at a higher level were excluded from the study. This decision was made based on the prediction that lower-level L2 learners are more likely to reveal L1 transfer in L2 processing, following reports in the literature regarding the role of proficiency. Instruments Proficiency test The DIALANG (available at http:// www. lancs. ac. uk/ resea rchen terpr ise/ DIALA NG/ about), which is a test based on the Common European Framework of Reference (CEFR), was used to assess the subjects’ proficiency level. In DIALANG, the test results are reported on a six- level scale from A1, being the lowest level (beginner) to C2, the highest level (native-like). The self‑paced online reading task The present study used an online psycholinguistic experimental method (using IBEX, avail - able at: http:// spell out. net/ ibexf arm) to examine the role of L1 transfer in the processing of L2 English plurality. Twenty-five self-paced English sentences, covering the four areas dis - cussed, were utilized as the stimuli for the main experiment. The experimental sentences were either grammatical or contained errors. Each sentence was proceeded by the compre- hension question (The sentence was…?, with two options of correct/incorrect answer). T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 7 of 21 Procedure The initial test (comprising 35 items) underwent some revisions by the researcher. To ensure the content validity of the test, two experts were requested to provide feedback. They were asked to examine the suitability of the items and underlying constructs and the fitness of the items to the four structures of concern. They were also asked to check for bias and language issues, such as mechanics, word choice, etc. In general, it was con- curred that the designed test was an appropriate tool for measuring the intended struc- tures. The comments were used to revise the test accordingly, enhancing the validity of the instrument. Reliability statistics were derived from pilot testing conducted on 20 stu- dents and the test was finalized with 25 items (see Appendix A). Subjects went through two separate phases. Through the first phase, in which they were physically present, their proficiency was assessed using DIALANG. Initially, they were explained about DIALANG orally. Before the main test, DIALANG presents a short placement test comprising 75 vocabulary items through which existing words in English have to be distinguished from non-words. Following, based on their perfor- mance score, subjects receive one of the three versions of the grammar test with varying difficulty levels. Those learners who achieved A2 on the grammar test were considered low-proficient and were selected to take part in the main experiment. The second phase, i.e., the main experiment, was the self-paced online reading task in which subjects were again physically present. The experiment was conducted in a quiet room. IBEX was used to present the material and to record their choices and reaction times. Prior to the experiment, they were told about how to work on IBEX. They were also asked to judge each sentence based on grammar rules; however, they were not told about the grammatical point tested. Attempts were made to choose the simplest vocab- ularies; nevertheless, to avoid possible interference of lexical difficulty in grammatical processing of the sentences, the meaning of likely difficult words was explained prior to the experiment. The self-paced online reading task started off with three practice items which were presented to familiarize the subjects with the self-paced reading format. All items were randomly presented to the learners. For the dashed sentences in IBEX, each trial involves a set of dashes on the computer screen in place of the words. The first press of the space bar replaces the first dash with the first word in the sentence. Upon the following space-bar presses, the next word appears, and the preceding word disap- pears. Each sentence was followed by a comprehension question with two options (cor- rect/incorrect) for the answer. The processing time of each word, i.e., the time between space bar presses and the time subjects took to answer each comprehension question were recorded by the computer. After the experiment was completed, the results were automatically sent to the server. Cronbach’s analysis indicated an acceptable reliability index of 0.88. Analyses were performed on reaction times on critical regions and accuracy rate of the response to the comprehension question proceeding the stimuli. The analyses mainly focused on how each type of condition and sentence (ill-formed vs. well-formed) was processed by the subjects. Prior to the analysis, all response times longer than 10000 ms and shorter than 400 ms were eliminated. In the next step, reaction times 3SD above and below the mean for each condition were excluded. Critical regions for the read- ing time analyses were defined and reading times on these words were averaged. The Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 8 of 21 Table 1 The overall accuracy per condition Condition Percentage (%) Num + Count noun 30 Num + CL + Count noun 8 Num + CL + mass noun 14 Num + Non-ref N + N 6 Total 58 Table 2 The overall mean reaction times per region across the four conditions Condition region Num + Count N Num + CL + mass N Num + CL + Count N Num + Non- ref N + N Preceding word 1463 (525)* 1532 (624) 1884 (631) 1336 (550) Numeral 1564 (630) 1514 (622) 1667 (838) 1480 (505) Classifier 2015 (802) 1966 (617) 2180 (1417) Noun 2294 (975) 2473 (1011) 2865 (1353) 2070 (864) Following word 1714 (830) 2313 (939) 2678 (1102) 1498 (641) Response 3275 (1935) 4363 (2324) 4499 (1467) 3463 (1641) *Standard deviations are added in parentheses preceding and subsequent words were also analyzed to capture a better understanding of the potential processing of the critical regions. RT preceding the numeral was pre- sented to ensure baseline for comparison. RT on the word following the noun was pre- sented to determine whether subjects experienced any delayed or prolonged processing of the experimental conditions. According to Barber and Carreiras (2005), late process- ing effects are important because differences between constituents often have a delayed onset. In addition, performance on comprehension questions was analyzed to gain understanding of the processing outcome. Results The overall analysis The overall accuracy rate and mean reading times for each condition and sentence type are presented in this section. Based on the analysis of the accuracy rates (see Table 1), just 58% of the answers to the question presented after the stimuli was correct, meaning that the test had been some- what hard for these low-proficient learners. As Table (1) shows, sentences involving the Numeral + Count Noun structure seemed to be the easiest (30% of the total accuracy) and sentences involving the Non-referential Noun could be considered as the most dif- ficult one, with only 6% of the total accuracy. Further analysis was conducted to find out whether RTs on different regions differed for different conditions. See Table 2 for the results. Analysis of reaction times on critical regions for each condition suggested the longest RTs in the case of Numeral + Count Noun structure to be on noun, for both Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun struc- tures on classifier, noun, and following word and for the Numeral + non-referential T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 9 of 21 Table 3 Univariate tests (the effect of condition) Dependent variable Sum of squares df Mean Square F Sig. Partial Eta squared RT, preceding word Contrast 1,673,495.00 2 836,747.00 2.000 .097 .021 Error 77,090,163.00 217 355,254.00 RT, numeral Contrast 1,009,156.00 2 504,578.00 1.000 .000 .011 Error 88,528,771.00 217 407,966.00 RT, classifier Contrast 1,636,138.00 2 818,069.00 .000 .000 .007 Error 227,388,578.00 217 1,047,873.00 RT, noun Contrast 17,751,056.00 2 8,875,528.00 8.039 .000 .069 Error 239,591,590.00 217 1,104,108.00 RT, following word Contrast 17,851,576.00 2 8,925,788.00 11.000 .000 .094 Error 172,529,518.00 217 795,066.00 RT, response Contrast 62,088,647.00 2 31,044,323.00 7.000 .001 .067 Error 870,034,153.00 217 4,009,373.00 Table 4 Overall mean RTs for each region per sentence type Sentence Type Preceding word Numeral Classifier Noun Following word Response Ill-formed 1458 (550) 1540 (676) 2093 (1970) 2520 (1122) 1953 (954) 3911 (2125) Well- formed 1456 (722) 1512 (528) 1066 (892) 2132 (919) 1763 (862) 3698 (1891) Noun + Noun structure on classifier and noun. Hence, in the case of the second and third conditions, there seems to be prolonged processing after the noun. Except for the Numeral + non-referential Noun + Noun structure, the results on RTs seem to be consistent with those of accuracy rates, with the lowest RTs on critical regions for Numeral + Count Noun, indicating the ease of processing and the longest RTs for Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun struc- tures, proving them to be more challenging. In addition, based on the overall results (see Table 3, below), there was an effect of condition for all regions except for preceding word (p = 0.097). The main effect seemed to be for the following word [Wilks’ Lambda = 0.000, F(2, 217) 11.000, p = 0.0005, partial eta squared = 0.094], indicating almost large effect size, for the noun [Wilks’ Lambda = 0.000, F(2, 217) 8.039, p = 0.0005, partial eta squared = 0.069], indicating moderate effect size, and for the response [Wilks’ Lambda = 0.000, F(2, 217) 7.000, p = 0.001, partial eta squared = 0.067], suggesting moderate effect size. As indicated in Table 4, below, reaction times for ill-formed sentences seemed to be longer than those for well-formed sentences. Univariate analysis suggested the effect of sentence type on RTs per region; however, the effect size seemed to be small (see Table 5 ). The larger effect of sentence type seems to be for noun, [Wilks’ Lambds = 0.087, F (2, 218) 7.400, p = 0.018, partial eta squared = 0.065], indicating a moderate effect size. Accuracy rates per sentence type are illustrated for each condition in Table 6, below. A more detailed individual analysis of each condition is provided in the following section. Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 10 of 21 Table 5 Univariate analysis (the effect of sentence type) Dependent variable Sum of squares df Mean square F Sig. Partial Eta squared Preceding word Contrast 118.00 1 118.00 1.000 .000 .000 Error 78,763,539.00 218 361,300.00 Numeral Contrast 33,923.00 1 33,923.00 1.083 .000 .000 Error 89,504,004.00 218 410,568.00 Classifier Contrast 659,746.00 1 659,746.00 1.000 .000 .003 Error 228,364,970.00 218 1,047,545.00 Noun Contrast 6,482,275.00 1 6,482,275.00 7.400 .018 .065 Error 250,860,371.00 218 1,150,735.00 Following word Contrast 1,560,871.000 1 1,560,871.00 1.000 .000 .008 Error 188,820,223.00 218 866,147.00 Response Contrast 1,959,027.00 1 1,959,027.00 1.080 .000 .002 Error 930,163,773.00 218 4,266,806.00 Table 6 Accuracy rates per sentence type for each condition condition sentence type Num + Count Num + CL + Mass Num + CL + Count Num + Cl + Non- N (%) N (%) N ref N (%) Overall accuracy 30 14 8% 6 Ill-formed 70 32 41% 26 Well-formed 91 73 – 36 Individual analysis for each condition per sentence type Numeral + Count Noun As mentioned in the previous sections, unlike English, Persian lacks number mor- phology. Hence, it was predicted that L2 English learners might expect depluralizing of nouns in sentences including Numeral + Count Noun structure. Ten items tested Numeral + Count Noun structure, with 7 items ill-formed in English and 3 items con sistent with the English grammar. Included in the ill-formed items was the deplural- ized form of the noun, which is consistent with the structure in Persian. Below, the analyses of the accuracy rate and reaction times are provided. Accuracy analysis Concerning the first condition, i.e., the Numeral + Count Noun structure, as mentioned above (Table 1), the overall accuracy, in comparison to the whole test, was 30%. The overall accuracy for well-formed sentences involving the Numeral + Count Noun structure was 91% and for the ill-formed sentences was 70%. High accuracy of the responses indicated that these low-proficient learners were aware of the structure. This provided evidence that learning the Numeral + Count Noun structure had been easy. This also ruled out the strong effect of L1 in the processing of this structure at this stage of the learners’ language development. Reaction time analysis As Table 2 indicated, the overall average RT was 1463 ms for preceding word, 1546 ms for numeral, 2294 ms for noun, and 1714 ms for following T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 11 of 21 word. The overall mean RT for response to the subsequent question was 3275 ms. Mean reaction time for ill-formed and well- formed sentences including the mean RTs for the critical regions, preceding and following words, and for the response to the subsequent comprehension question are presented in Table 7, below. As mentioned earlier, the overall analysis indicated differences in RTs for different sen - tence types. In accordance with the general results obtained, for the Numeral + Count Noun structure, the data collected on mean RTs to the ill-formed sentences differed from those on the well-formed. Paired sample t-test was employed to see whether the RT differences for regions within and between sentence types were significant. Analysis indicated them not to have occurred by chance (see Tables 11 and 12 in Appendix B). As RTs for the noun took longer in ill-formed sentences, compared to the well-formed sentences, it can be claimed that the inaccurately depluralized nouns received generally longer response latencies than the accurately pluralized nouns (2208 ms vs. 1760 ms), indicating learners’ awareness of the discrepancy. Generally, RTs to the preceding word and the numeral did not differ very much (mean difference = 197). The longest RTs seemed to be on the noun. Besides, looking at the results for mean RT to the word fol- lowing the noun, it can be inferred that participants showed no prolonged processing of the structure in both ill-formed and well-formed sentences. The nearly high accuracy rate mentioned above also supports participants’ sensitivity to the structure and reduces the probability of L1 transfer. Numeral + Classifier + Mass Noun As discussed, in English, mass nouns are always singular and appear with morphol- ogy only for taxonomic reading or for known quantities. The condition is the same in Persian; however, in Persian, the classifier accompanying the mass noun is not plural - ized. Hence, Persian speaking learners of English with low proficiency might tend to use singular classifier (instead of the plural one) in the Numeral + Classifier + Mass Noun structure in English. The prediction was that such influence might be observed while processing the structure in English. Seven items tested participants’ knowledge of mass nouns in English. Two items were compatible with the English structure, in which Clas- sifier (PL) + Mass Noun (SIG) was used, as in she gave her two bowls of soup, and five items, ill-formed in English, were in accord with the Persian grammar, in which the sin- gular form of the classifier was used along with the singular mass noun, as in she drank two glass of cold water. Results for accuracy rate and reaction time are presented below. Analysis of accuracy The overall accuracy of the Numeral + Classifier + Noun structure is 14%. Only 32% (see Table 6, above) of the responses to the ill-formed sentences were correct, meaning that the majority of subjects recognized the structure Numeral + Classi fier(SIG) + Mass Noun (SIG) as acceptable in English (68% inaccurate response). This can Table 7 Mean RTs for Numeral + Count Noun for sentence types Condition W1 Num Noun W2 Response Ill-formed sentences 1312 (299) 1701 (756) 2208 (591) 1718 (594) 3329 (1434) Well-formed sentences 1510 (499) 1606 (466) 1760 (532) 1340 (432) 3012 (1334) Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 12 of 21 be an evidence for the negative transfer of the corresponding structure from L1. However, the unexpected point is that 73% of the responses to the grammatically well-formed sen- tences was accurate too. Reaction time analysis As indicated in Table 2, the overall average RT was 1532 ms for preceding word, 1514 ms for numeral, 2015 ms for classifier, 2473 ms for the mass noun, and 2313 ms for following word, with the longest RTs on noun and following word. The overall mean RT for response to the subsequent question was 4363 ms. Mean reaction time for each sentence type are presented in Table 8, below. As with the Numeral + Count Noun, RT analysis in the Numeral + Classifier + Mass Noun structure also revealed an effect of sentence type, with faster RTs for well-formed than for ill-formed structures. Paired sample t-test analysis indicated the differences to be meaningful (see Tables 11 and 12 in Appendix B). However, there were not strict differences between the mean RTs for the word and the numeral preceding the classi - fier (1384 ms and 1391 ms for the ill-formed and 1601 ms and 1596 ms for the well- formed sentences, respectively). However, there were larger RT differences between the classifiers (2129 ms vs. 1641 ms), longer for the depluralized classifier in the ill- formed sentences. This might be due to the participants’ recognition of the mismatch between the preceding numeral and the depluralized classifier, a structure which is inaccurate in English. Hence, being sensitive to number agreement, participants exhibit longer RTs when number agreement is violated. There were longer RTs on the mass noun, the word following the noun, and the response to the subsequent question, as well. Besides, as mentioned in the analysis of the accuracy rates, 68% of the response to the ill-formed sentences indicated them as correct. This might be an indication of the L1 influence; that is, participants, after having struggled with the structure, have finally nativized the structure. Therefore, the decision of whether to mark the classifier as correct or not might have been contaminated by the influence of Persian in which the singular form of the classifier is used. Numeral + Classifier + Count Noun It was discussed in the review of the literature that Persian uses classifiers to make nouns countable and numerals are often accompanied by a classifier even in the case of a count noun. However, as mentioned, in English, count nouns can be directly modi- fied by numerals. Accordingly, it was predicted that Persian speaking learners of Eng - lish with low proficiency might expect to have classifiers into the Numeral + Count Noun structure in English. Three items were testing the participants’ reaction to the erroneous Numeral + Classifier + Count Noun structure in English. The presented stimuli included an erroneous part such as in three person workers. Table 8 Mean RTs for Num + CL + Mass Noun structure per sentence types Condition W1 Num CL Noun W2 response Ill-formed sentence 1384 (497) 1391 (622) 2129 (686) 2558 (944) 2410 (817) 4360 (1956) Well-formed sentence 1601 (791) 1596 (534) 1641 (627) 2200 (1045) 2109 (945) 3763 (1929) T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 13 of 21 Accuracy analysis As mentioned in the previous sections, the overall accuracy of the Numeral + Classifier + Noun structure was 8%. Fifty-nine percent of the responses to the ill-formed structures indicated them to be correct, yielding an accuracy rate of 41%. This is consistent with the results obtained from the analysis of the sentences testing mass nouns. Therefore, L1 Persian L2 English learners might have difficulty processing the Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun structures, and it seemed that these L2 learners processed plurality in the case of these two structures more natively. Reaction time analysis The overall average RT was 1884 ms for preceding word, 1667 ms for numeral, 1966 ms for classifier, 2865 ms for count noun, and 2678 ms for following word. The overall mean RT for response to the subsequent question was 4449 ms. The longest RTs were found to be on classifier, noun, and following word. The analyses for the critical regions are presented in Table 9. Analysis of t-test within and between each sentence type also indicated the differences to be significant (see Tables 11 and 12 in Appendix B). As was the case with the ill-formed sentences involving mass noun, discussed in the previous section, in Numeral + Clas- sifier + Count Noun structure, the classifier accompanying the noun got longer RTs. This might be because of the quantifier that precedes the classifier and the kind of mismatch rec - ognized on the part of the subjects. There were also longer RTs on the noun, the following word, and the response to the question which might indicate learners having challenge with the structure. An error rate of 59% suggested that these L2 learners might have processed the structure as in Persian. Numeral + Classifier + Non‑referential noun + Noun As there is no difference between the two languages with respect to the use of plural marker in the case of non-referential nouns acting as classifiers, it was predicted that Persian learn - ers with low proficiency in English would have no problem processing this structure. That is, there should not be long reaction times, response latencies at such regions, or inaccurate responses. Here, if L1 played any role, it would be facilitatory. Five items, two of which were grammatically correct in both Persian and English, i.e., the singular form of the classifiers was used as in she listened to a ten-minute song, were used to test the prediction. The other three items, in which the pluralized form of the classifier was used as in she had two-inches nails, were inaccurate in both languages. See below for the analysis of accuracy rate and reaction times. Accuracy analysis As indicated earlier, in Table 2, the overall accuracy for all sentences involving the non-referential noun structure in themselves was 6%. Overall accuracy rates (see Table 6) were quite low for both well- formed sentences (36%) and ill-formed sentences (26%). These low accuracy rates might show that learners have not acquired the rule in Eng - Table 9 Mean RTs for Num + CL + Count N (Ill-formed) Condition W1 Num CL Noun W2 Response Sentence (ill-formed) 1884 (631) 1677 (838) 1966 (617) 2865 (1353) 2678 (1102) 4449 (1467) Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 14 of 21 lish. There seemed to be no clear evidence for L1 effect which was expected to be positive and lead the learners to process the structure more easily and produce more correct answers. Analysis of the reaction times might provide more information. Reaction time analysis According to Table (2), the overall average RT was 1336 ms for preceding word, 1480 ms for numeral, 2180 ms for classifier, 2070 ms for noun, and 1498 ms for following word. The overall mean RT for response to the subsequent question was 3463 ms. For both ill-formed and well-formed sentences, the longest reaction times were recorded for classifier and noun with no delayed RTs (see Table 10). T-test analysis indicated the differences to be meaningful (see Tables 11 and 12 in Appendix B). However, for this structure, an effect of sentence type, contrary to that of the previous structures, was observed. Namely, there were longer RTs on the main regions for the well-formed sentences. The results were in accord with those of accuracy rate analysis which yielded low accuracy rates. Seventy-four percent of the responses indicated the ill-formed sentences as accurate, and only 36% of the response to the well- formed sentences was correct. This indicated that learners tended to use the pluralized form of the noun for the non-referential noun. Discussion This study aimed to investigate whether L1 morpho-syntactic features affect L2 pro - cessing. The evidence presented above suggests that Persian plurality influences English processing; however, there are situations in which the answer to this question is not so clear. Generally, accuracy rates differed significantly across the four conditions, with the lowest accuracy rates for the structure including Numeral + Non-ref Noun + Noun and the highest accuracy for Numeral + Count Noun construction, indicating them to be, respectively, the most challenging and the easiest structures at the learners’ present level of proficiency. Nevertheless, the state of the accuracy rates across the conditions was not completely into the expected direction. In the case of Numeral + Count Noun, accuracy rates above expectations were obtained. Concerning the second and third con- ditions, i.e., Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun structures, expectations came out to be true as participants mostly considered the ill- formed structures as correct. However, it needs to be pointed that in these two cases, accuracy rates for the well-formed sentences were also moderately high. These contra - dictory results might be task- oriented; that is, exposing the subjects to the erroneous structure might have deviated them from the correct structure and has triggered the influence of the Persian structure. To find the reasons for such a discrepancy, employing different tasks such as on-line production tasks in future experiments could be insight - ful. In the case of the fourth condition, for which L1 and L2 do not contradict, accuracy rates below expectations were observed. Table 10 Mean RTs for Num + CL + Non-ref N (Ill-formed) Condition W1 Num Cl Noun W2 Response Ill-formed sentence 1285 (470) 11,578 (579) 2460 (489) 2052 (882) 1450 (495) 2953 (1533) Well-formed sentence 1386 (611) 1518 (541) 2302 (395) 2200 (715) 1595 (812) 3666 (1902) T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 15 of 21 RT patterns were also somewhat variant across the four conditions. In all four cases, RTs were slow in the word preceding the numeral and high on classifier and noun; how - ever, they mostly differed in the degree of RT on the word proceeding the noun, indicat - ing prolonged processing or lack of it. The results indicated that L2 learners will process the Numeral + Classifier + Mass Noun and Numeral + Classifier + Count Noun struc- tures in a prolonged fashion. For Numeral + Count Noun and Numeral + Non-referential Noun + Noun, no such delays were recorded; hence, the reaction times surfaced quickly after the noun itself. It was also found that these low-level L2 learners required longer time to answer the comprehension questions on the sentences with Numeral + Classi- fier + Mass Noun and Numeral + Classifier + Count Noun structures than for those on the Numeral + Count Noun and Numeral + Non-referential Noun + Noun structures. There was also a moderate effect of sentence type in all analyses with longer RTs for the ill-formed sentences than the well-formed ones for the first three conditions and the reverse for the fourth. As such, it can be concluded that the tendency towards a transfer effect was more observable in the results for Numeral + Classifier + Mass Noun and Numeral + Clas- sifier + Count Noun conditions. As evidence for lack of L1 effect, the results for Numeral + Count Noun were more conclusive. It might be speculated that in the case of easy-to-learn rules, L2 influences might overrule and L1 effect might not even arise. However, the results obtained for the fourth condition were not significant enough to confirm or disconfirm any transfer effect. It was predicted that as there is no difference between the two languages in the case of Non-referential nouns acting as classifiers fol - lowing a numeral, Persian learners with low proficiency in English will not face difficulty processing the structure in English. In the case of this structure, learners’ sensitivity to number or overgeneralization of the Numeral + Count Noun(PL) rule to this structure might have played an inhibitory role in activating the L1 influence or employing the cor - rect rule in English, leading the learners to the erroneous processing of the structure. Therefore, the results in the by-item analysis of this structure might indicate that there are other factors in addition to L1 effect in L2 learners’ processing of such structures, as also claimed by some researchers (e.g., Cohen & Brooks-Carson, 2001; Kellerman, 1983; Mahmoud, 2000). Hence, further research is demanded to spot other possible factors in processing of these structures in English. Taken together, the results disconfirmed the claims proposed in the literature that L1 plays no role in the L2 process of acquisition (e.g., Ellis, 1994; Felser et al., 2003; Papado- poulou & Clahsen, 2003). Findings also fit well with the research reports in L2 process - ing that low proficient learners seem not to be able to acquire the structures properly (e.g., De La Colina & Garcia Mayo, 2007; Di Camilla & Anton, 2012; Elston-Güttler et al., 2005; Frenck-Mestre, 2002; Storch & Wigglesworth, 2003; Su, 2001; Swain & Lapkin, 2000; Tian & Jiang, 2021). Nevertheless, further tracking of the participants’ language development could provide more insightful understanding of their L1 transfer effect. Conclusion The purpose of this study was to find out whether Persian, as the first language, affects the processing of L2 English structures indicating plurality. Four different conditions were studied. It was hypothesized that if L1 transfer occurs, RTs to the incongruent Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 16 of 21 conditions should be slower than those to the congruent condition. Hence, in the case of interfering transfer (conditions I, II, &III), participants were expected to read more slowly at the critical regions, take more time responding to the proceeding question, and accept the erroneous structures as correct. In the case of L1 effect being positive, i.e., the fourth condition discussed, faster RTs and higher accuracy rates were expected. An overview of accuracy rates and RTs across the conditions revealed that it is likely that these L2 learners did not utilize transfer to a similar extent across the four conditions and the two sentence types. However, if L2 learners were using their English-derived knowledge with structures indicating plurality, they would have been expected to react equivalently to all conditions, sentence types, and number mismatches. Hence, the find - ings suggested that these L2 learners employed at least some degree of transfer in their parsing of number mismatches. This study can have implications for instructional practices in L2 classrooms. Students’ success in using the structures will be more feasible if they are aware of the differences between L1 and L2. As such, teachers can help students understand the differences and take actions to make benefits of L1 and reduce the impeding effects. They can also be trained to reflect on the way they process the structure in both L1 and L2. Nevertheless, it is possible that a larger sample population would have produced more robust data. Therefore, future directions should include conducting experiments employing different tasks with a much larger sample size and with regard to different proficiency levels. Particularly, similar procedure should be conducted with near-native speakers as well as advanced groups to determine more accurately the true nature of L1 effect in L2 processing. Appendix A List of the sentences used for the experiment 1. There are two store near my grandmother’s house. 2. There are more than one billion cell in our body. 3. Petersons had two boy, John eight and Tom two years older. 4. The building has twenty floors with a big parking space. 5. I read the first three book immediately after the school. 6. She has bought two nice spacious bags from a bargain. 7. She ate the second three sandwich one hour later. 8. She brought two fantastic novel to the classroom. 9. These three flowers are originally from Netherlands. 10. Our house has three closet with a spacious bathroom. 11. We are in need for two kilo meat for the dinner. 12. She drank two cup of coffee after the lunch. 13. She gave her two bowls of soup. 14. The factory needed two kind of salt. 15. We waste about two hundred liter water every day. 16. She got two box of apple for the picnic. 17. Losing two pounds of weight every day is dangerous for the health. T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 17 of 21 18. She bought two volume of book yesterday. 19. Three person workers were paving the way. 20. She drank two glass of cold water. 21. She listened to a ten minute song yesterday. 22. He had to walk a hundred miles way from home to work. 23. She had two inches nails before she went to school. 24. He climbed a one thousand meter mountain last week. 25. We had a two hours walk from school to home. Appendix B See Tables 11 and 12. Table 11 Paired samples test (within sentence type analysis) Paired Differences t df Sig. (2-tailed) Mean Std. Std. Error 95% Confidence Interval of Deviation Mean the Difference Lower Upper Pair 1 C1_W1_ − 190.00000 751.00000 74.00000 − 339.00000 − 42.00000 − 2.000 100 .012 ILL - C1_ NUM_ILL Pair 2 C1_ − 597.00000 991.00000 97.00000 − 790.07688 − 404.00000 − 6.000 103 .000 NUM_ILL - C1_N_ ILL Pair 3 C1_N_ILL 204.00000 1010.00000 99.00000 608.00000 1001.00000 8.000 103 .000 -C1_W2_ ILL Pair 4 C1_W1_ − 3.00000 442.00000 66.01752 − 136.00000 129.00000 − .059 44 .000 WELL -C1_ NUM_ WELL Pair 5 C1_ − 279.00000 427.00000 63.00000 − 408.00000 − 151.00000 − 4.000 44 .000 NUM_ WELL -C1_N_ WELL Pair 6 C1_N_ 293.00000 508.00000 75.00000 140.00000 446.00000 3.000 44 .000 WELL -C1_W2_ WELL Pair 7 C2_W1_ − 22.00000 553.00000 64.00000 − 150.00000 105.00000 .000 73 .000 ILL -C2_ NUM_ILL Pair 8 C2_ − 645.00000 796.00000 92.00000 − 830.00000 − 461.00000 − 6.000 73 .000 NUM_ILL -C2_CL_ ILL Pair 9 C2_ − 420.05405 1281.00000 148.00000 − 716.00000 − 123.00000 − 2.000 73 .006 CL_ILL -C2_N_ ILL Pair 10 C2_N_ILL 416.00000 1047.00000 120.00000 175.00000 657.00000 3.000 74 .001 -C2_W2_ ILL Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 18 of 21 Table 11 (continued) Paired Differences t df Sig. (2-tailed) Mean Std. Std. Error 95% Confidence Interval of Deviation Mean the Difference Lower Upper Pair 11 C2_W1_ 114.00000 606.00000 110.00000 − 111.00000 340.00000 1.034 29 .000 WELL - C2_ NUM_ WELL Pair 12 C2_ − 141.00000 724.00000 132.00000 − 412.00000 128.00000 − 1.072 29 .000 NUM_ WELL -C2_CL_ WELL − 551.00000 1105.00000 201.00000 − 964.00000 − 138.00000 − 2.000 29 .011 Pair 13 C2_CL_ WELL -C2_N_ WELL Pair 14 C2_N_ 274.00000 1172.00000 214.07205 − 162.00000 712.00000 1.000 29 .052 WELL -C2_W2_ WELL Pair 15 C3_W1 - − 153.00000 840.00000 126.00000 − 408.00000 102.00000 − 1.000 43 .000 C3_NUM Pair 16 C3_NUM − 291.00000 858.08024 127.00000 − 549.06251 − 33.00000 − 2.000 44 .028 - C3_CL Pair 17 C3_CL - − 908.00000 1269.00000 189.00000 − 1290.00000 − 527.00000 − 4.000 44 .000 C3_N Pair 18 C3_N - 802.00000 1683.00000 253.00000 290.00000 1314.00000 3.000 43 .063 C3_W2 Pair 19 C4_W1_ − 129.00000 634.00000 94.00000 − 320.00000 60.00000 − 1.000 44 .000 ILL -C4_ NUM_ILL Pair 20 C4_ − 695.00000 1642.00000 244.00000 − 1189.00000 − 202.08511 − 2.000 44 .007 NUM_ILL -C4_CL_ ILL Pair 21 C4_ 100.00000 1865.00000 278.00000 − 460.00000 660.00000 .000 44 .000 CL_ILL -C4_N_ ILL Pair 22 C4_N_ILL 670.00000 914.00000 136.00000 395.00000 945.00000 4.000 44 .000 -C4_W2_ ILL Pair 23 C4_W1_ − 232.00000 440.00000 81.00000 − 400.09564 − 64.00000 − 2.000 28 .008 WELL -C4_ NUM_ WELL Pair 24 C4_ − 783.00000 1041.00000 193.00000 − 1179.00000 − 387.00000 − 4.051 28 .000 NUM_ WELL -C4_CL_ WELL Pair 25 C4_CL_ 239.00000 1263.00000 234.00000 − 241.00000 720.00000 1.021 28 .000 WELL -C4_N_ WELL Pair 26 C4_N_ 466.00000 1070.00000 198.00000 59.00000 873.00000 2.000 28 .026 WELL -C4_W2_ WELL T aghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 19 of 21 Table 12 Paired samples test (between sentence type analysis) Paired Differences t df Sig. (2-tailed) Mean Std. Std. Error 95% Confidence Interval of Deviation Mean the Difference Lower Upper Pair 1 C1_W1_ − 197.00000 534.00000 79.00000 − 358.00000 − 37.00000 − 2.000 44 .017 ILL - C1_W1_ WELL Pair 2 C1_ 195.00000 795.00000 119.00000 − 46.00000 437.00000 1.000 43 .000 NUM_ILL -C1_ NUM_ WELL Pair 3 C1_N_ILL 447.00000 883.00000 133.00000 178.00000 716.06608 3.000 43 .002 -C1_N_ WELL Pair 4 C1_W2_ 235.00000 759.00000 114.00000 4.00000 465.00000 2.053 43 .046 ILL - C1_W2_ WELL Pair 5 C1_ 316.00000 1772.00000 264.00000 − 215.00000 849.00000 1.000 44 .000 RES_ILL -C1_RES_ WELL Pair 6 C2_ − 237.00000 981.09918 179.00000 − 603.00000 129.00000 − 1.000 29 .000 W1_ILL -C2_W1_ WELL Pair 7 C2_ − 108.00000 687.09369 127.00000 − 370.00000 152.00000 .000 28 .000 NUM_ILL -C2_ NUM_ WELL Pair 8 C2_ 488.00000 928.00000 172.00000 135.00000 842.04547 2.000 28 .008 CL_ILL -C2_CL_ WELL Pair 9 C2_N_ILL 358.00000 1506.00000 274.00000 − 203.00000 920.00000 1.000 29 .000 -C2_N_ WELL Pair 10 C2_ 4.03448 1136.00000 211.09594 − 428.00000 436.00000 .019 28 .000 W2_ILL -C2_W2_ WELL Pair 11 C2_ 596.00000 2767.00000 505.00000 − 436.00000 1630.00000 1.000 29 .000 RES_ILL -C2_RES_ WELL Pair 12 C4_ .00000 578.00000 107.00000 − 220.00000 219.00000 − .007 28 .000 W1_ILL -C4_W1_ WELL Pair 13 C4_ 59.00000 745.00000 138.00000 − 224.00000 343.00000 .000 28 .000 NUM_ILL -C4_ NUM_ WELL Pair 14 C4_ 164.00000 2289.00000 425.00000 − 706.00000 1035.00000 .000 28 .000 CL_ILL -C4_CL_ WELL Pair 15 C4_N_ILL − 148.00000 1113.00000 206.00000 − 571.00000 275.07894 .000 28 .000 -C4_N_ WELL Taghizadeh Asian. J. Second. Foreign. Lang. Educ. (2023) 8:9 Page 20 of 21 Table 12 (continued) Paired Differences t df Sig. (2-tailed) Mean Std. Std. Error 95% Confidence Interval of Deviation Mean the Difference Lower Upper Pair 16 C4_ − 145.00000 785.00000 145.00000 − 444.00000 153.00000 .000 28 .000 W2_ILL -C4_W2_ WELL Pair 17 C4_ − 713.06897 2644.00000 491.00000 − 1719.00000 292.00000 − 1.000 28 .000 RES_ILL -C4_RES_ WELL Abbreviations CEFR C ommon European framework of reference CL Classifier L1 First language L2 Second language N Noun Num Numeral PL Plural RT Reaction time SG Singular W1 Preceding word W2 Following word Acknowledgements The author is indebted to the students and colleagues who participated and cooperated in this study and the two experts who examined the validity of the instrument designed. She also expresses deep gratitude to the reviewers for their insightful comments. Author contributions The author of the paper was responsible for data collection, data analysis, preparing, and reviewing the work. The author read and approved the final manuscript. Funding No funding was received for the current study. Availability of data and materials The data will be available upon e-mail request to the author. Declarations Ethics approval and consent to participate The present study adhered to ethical considerations in educational research by obtaining informed consent from the participants and also by ensuring them regarding the confidentiality of the collected data. Competing interests The author declares no competing interest. Received: 8 August 2022 Accepted: 31 October 2022 References Barber, H., & Carreiras, M. (2005). Grammatical gender and number agreement in Spanish: An ERP comparison. Journal of Cognitive Neuroscience, 17(1), 137–153. https:// doi. org/ 10. 1162/ 08989 29052 880101 Barto-Sisamout, K., Nicol, J., Witzel, J., & Witzel, N. (2009). 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Journal
Asian-Pacific Journal of Second and Foreign Language Education – Springer Journals
Published: Mar 15, 2023
Keywords: Reaction time; Accuracy rate; L1 transfer effect; Plurality