096 08Kunihiro KUSANAGI










The purpose of the present study is to investigate whether or not the notional

number attraction phenomenon is evident during L2 learners’ online processing of subject–verb agreement. L2 sentence processing studies have frequently reported the L2 learners’ online insensitivity to number mismatches, and it has been claimed that L2 learners have difficulty in representing grammatical number features, or they have a

tendency to fail to access the number features in online tasks. L2 learners’ sensitivity to “notional number”, however, has not been considered by researchers. Hence, this research conducted a self-paced reading study targeting highly proficient Japanese learners of English (N = 28). The participants read the three types of sentences; (a)

control (e.g., everyone in the room was/were…), (b) notional attraction (e.g., everyone in the team was/were…), and (c) grammatical attraction (e.g., everyone in the halls was/ were…). In order to examine the participants’ sensitivity to number attractions, the differences in the reading times between the number matched and the mismatched verbs

(was/were) were compared among the attraction types. The observed reading time differences in the grammatical and notional attraction condition were different than in the control condition, indicating that both attraction phenomena were evident. L2


Graduate School, Nagoya University Japan Society for the Promotion of Science


Graduate School, Nagoya University Japan Society for the Promotion of Science

The Notional Number Attraction in English as a

Foreign Language: A Self-Paced Reading Study


learners’ imperfect representation of number features, which we call representation vulnerability, was discussed.

Keywords: sentence processing, number agreement, notional number

1. Introduction

1.1. Background of the Present Study

Unlike sentence processing in the first language, that of second/foreign language (L2) learners exhibits great variety among individuals (e.g., Clahsen & Felser, 2006).

The difference might be explained by the L2 learners’ first language, which is one of the most important factors affecting L2 acquisition and sentence processing. In addition to the L1 influence, developmental factors are also worth considering because they may make the system underlying L2 learners’ sentence processing unsteady, which is not the

case for adult native speakers. Individual traits such as working memory capacity, age, learning environment, and numerous other psychological traits can also be important factors to explain the variance (e.g., Robert & Meyer, 2012). More importantly, the strongest factors that affect the way L2 learners process the input may be the

grammatical structure and linguistically related issues. In other words, it is quite likely that L2 learners, particularly Japanese learners of English, struggle with certain grammatical structures such as number agreement (detailed below) and English articles. What is more, such linguistic factors interact with task-related factors in a highly

complex manner (Task–Structure Interaction; e.g., Kusanagi & Yamashita, 2013; Tamura & Kusanagi, 2014). That is, the effect of the task condition affects the performance, depending on the linguistic factors. Kusanagi and Yamashita (2013) and its partial replication study, Tamura and Kusanagi (2014), investigated this task–structure

interaction by using grammaticality judgment tests with time constraints. In both studies, the effect of time pressure was observed in some of the target structures but not in the others, indicating that not only do task characteristics and linguistic factors impinge on L2 learners’ performance but also there is a clear interaction between task-related factors

and linguistic structures.


2007; Jiang, Novokshanova, Masuda, & Wang, 2011). See the sentences in (1a) and (1b), where quantifier–noun number mismatches occur. A series of Jiang’s studies reported

that L2 learners with some specific L1s (Chinese and Japanese) did not show an increase in reading times in the number mismatched sentences, as can be seen in (1a) and (1b), indicating that those learners might be insensitive to the number agreement.

(1a) She picked a few of her [*dress/dresses] and left quickly. (1b) The professor noticed a few of his [friend/*friends] in the picture.

        (Jiang, 2007)

Another interesting finding in Jiang’s studies was that the participants in these studies performed almost perfectly to detect the errors in offline grammatical judgment tasks, but were unable to do so in the online self-paced reading tasks. These contrasting

findings led Jiang to claim that the L2 learners’ knowledge about number agreement was not fully integrated (Jiang, 2007). In other words, the L2 learners had only rudimentary implicit knowledge, or their skills remained unautomatized. Meanwhile, as Jiang (2004, 2007) reported, the L2 learners performed similarly to the native speakers of English

when processing sentences measuring the knowledge of verb-subcategorization. The different performance among the structures that L2 learners exhibited was explained in terms of the selective integration of linguistic knowledge (Jiang, 2007).

1.2. Theoretical Framework of the Study

Regarding the acquisition of number features, linguistics-based second language acquisition researchers have offered numerous hypotheses such as (a) The Missing Surface Inflection Hypothesis (e.g., Prévost & White, 2000), (b) The Representation Deficit Hypothesis (e.g., Hawkins, 2005), (c) The Morphological Congruency Hypothesis (Jiang et al., 2011), (d) The Transfer Hypothesis (Nicol & Greth, 2003), and (e) The Interpretability Hypothesis (Tsimpli & Dimtrakopoulou, 2007). These hypotheses, which will be briefly reviewed later on, explain the reasons for difficulty in acquiring number


not capable of mapping these features onto the surface morphemes (Prévost & White, 2000) due to cognitive overload. This can be called the computational difficulty position.

This position is slightly different from that which argues that the problem with morphology in L2 acquisition lies in impairment of interlanguage grammar itself; that is, L2 learners struggle with morphological manipulation at the level of abstract features or categories.

From another point of view, the Representation Deficit Hypothesis and Interpretability Hypothesis can be called the first language influence position because they put more emphasis on the learners’ L1s; if a learner does not have a given formal

feature (also called an uninterpretable one in the framework of the Interpretability Hypothesis) in his/her L1, the person cannot acquire the feature after the critical period (Hawkins, 2005; Tsimpli & Dimtrakopoulou, 2007, for instance).

Similarly, the Morphological Congruency Hypothesis proposed by Jiang et al.

(2011) also looks at the role of L1 in L2 acquisition, though it focuses instead on the congruency of morphological systems between an L1 and an L2. Taking an instance in the case of sensitivity to number agreement, it poses that the existence of the morphological systems with subject–verb agreement or inflection for grammatical

number in L1s can be used to predict one’s attainability of native-like processing skills. Nicol and Greth (2003) similarly claim that bilinguals (including L2 learners) tend to transfer their L1 strategies to L2 subject–verb agreement. This framework predicts that the speakers of L1s which do not have subject–verb agreement processes cannot transfer

their processing strategy from the first language, and such miss-transfers and non-transfers result in non-native-like performance.

More generally, The Shallow Structure Hypothesis (SSH, Clahsen & Felser, 2006), based on their comprehensive review of L1 and L2 processing studies, proposed that L2


The present study was undertaken to provide empirical evidence for the inquiry from a relatively new angle. We focus on L2 learners’ sensitivity to notional numbers in

subject–verb agreement processing, of which very little is reported in the literature of L2 studies.

1.3. Focus of the Present Study

In a traditional framework of studies on subject–verb agreement, there are three roles of grammatical constituents involved in subject–verb agreement. The first one is called the agreement controller. This controller is, in most cases, the head noun (subject)

in a sentence. Predicates (main verbs) also serve in the role of agreement target, of which the number must be matched with the number of the agreement controller.

In the cases that a subject forms a complex noun phrase (NP) in which a local prepositional phrase (PP) is attached, the local noun has the role of an agreement

attractor, which is fundamentally irrelevant to (target-like) number agreement, but sometimes affects even native speakers’ number agreement processes (this phenomenon is also called broken agreement, see Bock & Cutting, 1992; Bock & Eberhard, 1993; Jiang, 2004; Pearlmutter, Garnsey, & Bock, 1999). Specifically, it is well-known that

mismatches of numbers between an agreement controller and an agreement attractor cause more agreement errors in production data and delay in reading time in native speakers’ online comprehension. The tree diagram in (2) represents the three roles involved in subject-verb agreement of numbers.






in Det Nattractor

the roomsg







Vtarget NP

likepl Det N


Interestingly, L2 learners were not affected by the effect of the numbers of local nouns (agreement attractors), unlike native speakers (Jiang, 2004). In other words, the

grammatical attraction phenomenon was not evident in the case of online reading comprehension of L2 learners, at least with some specific L1 backgrounds such as Chinese and Japanese. Thus, it is supposed that L2 learners are prone to underutilize the grammatical number features; however, there remains a question of whether L2 learners

make use of notional number features in processing the number agreements.

Some words, such as everyone, are, needless to say, grammatically singular, but notionally (conceptually or semantically) plural since they refer to multiple referents―

people in the same group. This can be applied to almost all collective nouns such as family, team, and band. Although it is well known that subject–verb number agreement in English is purely syntax-driven, such a notional number of a local noun is sometimes considered to affect native speakers’ sentence processing (e.g., Bock & Cutting, 1993;

Bock & Eberhard, 1993; Eberhard, 1999; Hoshino, Dussias, & Kroll, 2010; Humphrey & Bock, 2005). This can be called the notional number attraction phenomenon. However, very little is known about the phenomenon in the case of L2 learners.

The studies of Jiang (2004, 2007) and others (e.g., Lim & Christianson, 2014) on

L2 learners’ online insensitivity to number mismatches lead us to anticipate that L2 learners are possibly independent of the notional number attraction phenomenon as well as the grammatical one. Meanwhile, SSH leads us to predict that L2 learners’ number matching processes will be affected by the notional number of the attractor adjacent to

the verb and not by the grammatical one, since L2 learners’ online processing is supposed to be guided by lexical, semantic and pragmatic information.

Moreover, Inagaki’s (2014) study provided empirical evidence that Japanese EFL learners tended to rely on semantic information when judging mass-count distinction in

English. Inagaki explained that this might be due to L1 influence; in other words, in Japanese, whether or not a noun is mass or count is determined semantically because Japanese does not have a grammatical marker representing number features. In English, however, it is the syntactic cue that is important for identifying mass-count distinctions.


2. Research Question

Given the assumptions on the basis of the previous research mentioned above, it is

plausible to presuppose that L2 learners mistakenly utilize the notional number of nouns during their online sentence processing, while the grammatical number of nouns is considered not to be fully represented in L2 learners’ mental lexicons, or is inaccessible during online tasks.

Thus, the present study addressed the following research questions.

1. Are Japanese L2 learners affected by notional number attraction during online

sentence processing?

2. Are Japanese L2 learners affected by grammatical number attraction during online sentence processing?

In order to answer the research questions above, the present study adopted the reaction time procedure. If an increase in reading times was observed when the Japanese learners of English read the sentences in which notionally plural corrective nouns function as attractors, this might be empirical evidence that Japanese L2 learners are not

insensitive to all types of number agreement phenomena. It means that there is a possibility that Japanese learners of English might be thinking about the number features of nouns conceptually during their online reading processes. Besides, if the reading times did not differ in the case of grammatical number attraction conditions, it would be a

striking contrast to the case of notional number attraction conditions.

3. The Present Study

3.1. Participants

The participants of the present study were composed of graduate and undergraduate university students who lived in Japan (N = 28). They usually learn and use English for academic purposes and it is their most fluent foreign language. The population we targeted to measure were highly proficient L1-Japanese learners (EFL learners), and all


education. Table 1 summarizes the demographic information about the sample. Since almost half of the sample had previous experience of taking a computer-based self-paced

reading task, it would reduce the noise which might happen during the experiment and skew the result of the study. All the participants received financial compensation.

The sample size of the present study was 28. The previous studies which adopted similar designs, such as Jiang (2004, 2007) also had the same size as the present study.

Moreover, a priori power analysis was conducted in order to estimate the required sample size. The research design of the present study was expected to adopt a MANOVA followed by an ANOVA, and it is the ANOVA that requires a larger sample size which

meets the estimated medium effect size, error probabilityα = .05, and targeted statistical power, 1-β = .80. Therefore, a priori simulation for ANOVA was conducted. The result showed that the total sample size which met all the criteria mentioned above was exactly 28, indicating that the sample size, 28, was not too small to detect the middle level of

effect sizes.

Table 1. The Descriptive Statistics of the Participants’ Demographic Information

M SD Skewness Kurtosis

TOEIC 779.70 127.37 -0.17 -0.57

Age 24.14 3.88 1.47 3.18


Reading 3.14 1.04 -0.30 -0.86

Writing 3.23 0.92 -0.10 -1.14

Listening 3.05 1.05 0.18 -0.40

Speaking 2.91 0.92 -0.61 -0.15

Vocabulary 2.73 1.03 0.04 0.02

Grammar 3.05 1.17 -0.29 -0.47

Note. The self-report proficiency ratings can range from one to five. N = 28.

3.2. Task


the words were presented by black underlines. Then, once the right arrow key was pressed, the first word appeared. Each time the button was pressed, the word on the

monitor disappeared and the next word appeared as described in Figure 1. The time to press the button for each word was measured by the program in milliseconds.

After reading each of the filler sentences, yes/no questions about the contents of the sentences were attached in order to confirm the comprehension levels of the participants

(detailed in a later section).

3.3. Stimuli

In total, the present study created six types of stimuli as summarized in Table 2. There were three types of attraction conditions: (a) control (no attraction), (b) notional attraction, and (c) grammatical attraction. Each attraction type had two levels; (a)

grammatical (number matched), and (b) ungrammatical (number mismatched).

In all the types of stimuli, we used only a single word, everyone, for the subjective head noun (the agreement controller) of the sentences, in order to observe the effect of attractions without any other types of effect caused by agreement controllers. The word

everyone is grammatically singular, but notionally plural. The word quite often has PPs attached, such as everyone in the room, and everyone in the family, and these phrases are likely to be pragmatically plausible.


The nouns in attached PPs varied among the three attraction conditions. In the control condition, the local nouns were common nouns of which the grammatical and

notional number were singular (e.g., room, office, restaurant, school, house, bar, park, and theater). In the notional attraction condition, collective nouns were used (e.g., team, company, audience, crew, family, class, and band). These words were grammatically singular, but notionally plural. In the grammatical attraction condition, inflected normal

words such as stands, cars, films, pictures, and pages were used. These words became both grammatically and notionally plural.

The agreement targets also had two versions; matched and mismatched. We used

only the past tense of the copula (was for matched, and were for mismatched) to reduce the variance of reading times caused by diversity of lexical items. In all of the conditions, since the agreement controller everyone was grammatically singular, the agreement targets had to be singular as well. The combinations of the conditions and their examples

are summarized in Table 2.

Table 2. The Conditions and Examples of the Stimuli

Attraction Agreement Attractor Target Example


Control Matched [sg] [sg] [sg] Everyone in the room was

Mismatched [sg] [sg] [pl] Everyone in the room were

Notional Matched [sg] [pl] [sg] Everyone in the team was

Mismatched [sg] [pl] [pl] Everyone in the team were

Grammatical Matched [pl] [pl] [sg] Everyone in the halls was

Mismatched [pl] [pl] [pl] Everyone in the halls were

Note. G = grammatical number, N = notional number.


attention of the participants from the target grammatical phenomenon, the stimuli list included 24 other sentences as filler items, which were equal to exactly half the total

number of items.

The comprehension questions were attached to 24 sentences out of 48. The mean accuracy score of the participants was .96, by which we judged that the participants had engaged well in the reading tasks, and they had been successfully made to focus on the

contents of the stimuli.

In order to compare the reading times among the conditions, we set the five target regions, as can be seen in (3). The first region (region A) was set at the position of the

determiner in the PPs. Region B was for the agreement attractor where the words varied. Region C, which was the main interest region in the present study, was located at the position of the agreement targets. Moreover, two more regions (D and E) were set in order to observe the delayed and spillover effects of reading times.

(3) Everyone |in |A the |B room |C was/were |D writing |E the …

3.4. Analysis

To implement data cleaning on the reading times, we conducted outlier processing. We removed more than M ± 2SD reactions from the data. The removed data was almost 12% of the entire data. The data without outliers was still positively skewed and did not fit the normal distribution. Therefore, the log-transformation method was used so that

further parametric analyses could be safely conducted. By using a transformation with the base of 10, the data followed the normal distribution.

In order to detect the reading time differences, we first conducted a MANOVA whose design was five regions × three types of attractions × two target versions (matched

and mismatched). Then, ANOVAs for the data of the main interest and its next region (region C and D) were performed. In addition, in order to discuss the strength of the attraction effects more directly, the point and interval estimation method for the effect sizes (Cohen’s d, Hedge’s g, and r) was used.

4. Results


summarized in Table 3. Figure 2 graphically represents the time courses of the participants’ reading times. Apparently, there were greater variances in reading times in

the main interest region, which was probably due to the attraction effects. In addition, variances were also found in region D, where the delayed and spillover effects should emerge.

Table 3. The Descriptive Statics of the Reading Time Data

Attraction Agreement



Control Matched 321 81 490 159 466 100 585 163 466 111

Mismatched 315 65 486 161 549 173 645 219 436 95

Notional Matched 334 86 511 210 569 182 732 244 448 88

Mismatched 338 77 513 188 558 183 699 187 453 102

Grammatical Matched 326 72 455 151 532 197 643 176 449 103

Mismatched 293 76 467 128 583 200 644 192 449 107

Note. N = 28.

Then, the transformed data were submitted to a MANOVA. The results based on the Figure 2. The line plot representing the time courses of the reading time.

The data were based on the raw reading times (ms). 800







Mean Reading Time (ms) Control Matched

Region A Region B Region C (Target)

Region D Region E Control Mismatched Notional Matched

Grammatical Matched


Pillai’s trace were that the main effects of the attractions on the five regions were statistically significant, F(10, 102) = 4.21, p < .01, ηp2 = .29, the main effects of

grammaticality (matched or mismatched) did not reach statistical significance, F(5, 23) = 1.23, p = .33, ηp

2 = .21, and the interactions were also statistically insignificant, F(10,

102) = 0.25, p = .15, ηp

2 = .13. As the overall tendency, there was no statistically

significant interaction. Then, we analyzed the effects in the main interest region (region

C), and the next region (region D) to find the delayed and spillover effects.

The ANOVAs for the regions C and D were performed using the Greenhouse– Geisser’s adjustment because of the violations of the assumption of sphericity with some

variables. The results in region C showed that the main effect of attractions was statistically significant, F(2, 54) = 3.81, p = .03,ηp

2 = .12, the main effect of

grammaticality was not statistically significant, F(1, 27) = 0.94, p = .34, ηp

2 = .03, and

the interaction was statistically significant, F(2, 54) = 4.34, p = .02, ηp2 = .14. Since the

interaction reached statistical significance, analyses of the simple main effects for the interaction were conducted. The results were that the simple main effect of grammaticality in the control condition was statistically significant, F(1, 27) = 5.49, p = .03, ηp2 = .17, the simple main effect of grammaticality in the notional attraction

condition was not statistically significant, F(1, 27) = 0.69, p = .41, ηp

2 = .02, and the

simple main effect of grammaticality in the grammatical attraction condition was also not statistically significant, F(1, 27) = 0.06, p = .81, ηp

2 < .01. Subsequently, the simple

main effect of attractions in the grammatical (matched) condition was statistically

significant, F(1, 27) = 6.90, p < .01,ηp

2 = .20, while the simple main effect of

attractions in the ungrammatical (mismatched) condition was not statistically significant, F(10, 102) = 1.45, p = .24, ηp

2 = .01. Since the main effect of attractions was also

statistically significant, multiple comparisons were performed using Holm’s adjustment

of alpha levels. The results showed that only the difference between the control and grammatical attraction conditions was statistically significant, t(27) = 3.45, p < .01.

Next, the results of the ANOVA in region D were that the main effect of attractions was statistically significant, F(2, 54) = 9.88, p < .01, ηp2 = .27, the main effect of

grammaticality did not reach statistical significance, F(1, 27) = 0.56, p = .46, ηp 2 = .02,

and the interaction was also not statistically significant, F(2, 45) = 1.33, p = .27, ηp 2 =


comparisons were performed in the same way as for region C. The results showed that the difference between the control and notional attraction conditions was statistically

significant, t(27) = 4.17, p < .01, and this was also true between the notional and grammatical attractions, t(27) = 3.53, p < .01. Figures 3 and 4 graphically represent the results in region C and D respectively.

Furthermore, we estimated the effect sizes for each attraction. In region C, the effect sizes in the control condition showed a medium level, d = 0.48 [0.16, 0.79], g = 0.46 [0.16, 0.77], r = .53. The notional number attraction showed none to a small level, d = 0.07 [-0.33, 0.47], g = 0.07 [-0.32, 0.46], r = .07, and the grammatical attraction

exhibited a small level, d = 0.25 [-0.09, 0.60], g = -0.09 [-0.02, 0.59], r = .28.

In region D, the control condition showed a small level, d = 0.48 [0.16, 0.79], g = 0.46 [0.16, 0.77], r = .53, while the notional and grammatical attraction conditions did not show a larger level of effect sizes. d = 0.08 [-0.21, 0.37], g = 0.08 [-0.20, 0.36], r =

.11, d = 0.08 [-0.21, 0.37], g = 0.08 [-0.20, 0.36], r = .11, respectively. The scatter plots in Figure 5 show the correspondences of each case in the three attraction conditions in regions C and D.

Figure 3. Plot representing the result in region C.

Figure 4. Plot representing the result in region D.






Control Notional Attraction Condition

Matched Mismatched

Grammatical Attraction

Log Transformed Mean Reading Times





Control Notional Attraction Condition

Matched Mismatched

Grammatical Attraction


5. Discussion

5.1. Representation Vulnerability

The results indicated that both the notional and grammatical number attraction

phenomena in L2 were found, showing the insignificant reading time differences between the notional and grammatical attraction conditions in the main interest region, and the next region. This means that the participants were affected by the effects of both the notional and grammatical numbers of local nouns within PPs (agreement attractors)

on their real-time reading process.

As has been discussed in the background section, L2 learners were supposed to face difficulty in representing number features or accessing such morphosyntactic features in online tasks. Nevertheless, the participants in the present study obviously showed their

online sensitivity to number mismatches in the control condition as the reading time of the singular copula was faster than that of the plural. Moreover, the results in the grammatical attraction condition showed insignificant reading times, which may suggest Figure 5. Scatter plots representing the correspondences of the mean reading times of individuals in each condition. The line with breaks represents y = x. The cases above the line indicate the participants who read the mismatched copula longer.

2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Control at C

Matched Mismatched 2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Notional Attraction at C

Matched Mismatched 2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Grammatical Attraction at C

Matched Mismatched 2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Control at D

Matched Mismatched 2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Notional Attraction at D

Matched Mismatched 2.4 2.4 2.6 2.8 3.0 3.2

2.6 2.8 3.0 3.2 Grammatical Attraction at D



that the participants’ online sentence processing was attracted by the number features of local nouns, similarly to native speakers. This may indicate that the participants

represented and fully accessed both of the grammatical number features of the controller (everyone) and the target (was), against the observations of several previous studies (e.g., Jiang, 2004, 2007; Jiang et al., 2011). Thus, the findings in the present study, unlike the previous studies, do not fully support the hypotheses regarding to the critical roles of

L1s in the acquisition and the online processing of grammatical number (the L1 influence position). Rather, our hypothetical argument here is that L2 learners might basically be capable of representing and accessing the number features just as native

speakers do. This assumption may support the computational difficulty position, such as the missing surface inflection hypothesis (e.g., Prévost and White, 2000).

However, it was also observed that the participants were guided by the attraction of notional numbers, and its effects were greater than the counterpart of grammatical

attraction. This clearly supports the claims in SSH, which are that L2 learners’ processing relies more on lexical, semantic, and pragmatic information than syntactic and morphosyntacitic information. This semantic-based processing tendency that the learners demonstrated seems to align with the findings of Inagaki’s (2014) study. Inagaki

pointed out that Japanese learners were likely to decide about the countability of nouns conceptually on account of L1 influence. From this point of view, it might be premature to conclude that there was no L1 influence in considering the mechanism of Japanese L2 learners’ sentence processing. The L1 influence is not ‘critical’ but it does affect the way

learners deal with number agreement. What actually happens in L2 online sentence processing is much more complicated than is expected.

This contradictory interpretation might be resolved if we suppose that Japanese L2 learners’ number representation and its utilization were easily interchangeable between

the grammatical and notional numbers, being influenced by a semantic-driven processing tendency which can be explained both by SSH or L1 influence. To put it differently, it is possible to infer that the participants mistakenly utilized the notional numbers instead of the grammatical ones that were solely responsible for number agreement in English. This

observation led us to assume that L2 learners’ representation of number features is not deficient but simply vulnerable.


nature of L2 learners’ processing than just L1 influences. It is an established fact that L2 learners have difficulty in attaining native-like grammatical performance. However, L2

learners exhibit a greater variety of performance than do native speakers, heavily depending on tasks, situations, purposes of language use, and grammatical structures in use. This may be because, at every moment, L2 learners flexibly and strategically compensate their vulnerable grammatical information or formal features with cues of

notional, semantic, conceptual and contextual information in order to construct an appropriate situation model in their minds.

5.2. Limitations and Future Directions

Several limitations should be noted in interpreting the results of this study. First, it should be acknowledged that the result of the present study should not be generalized to L2 learners of English in general because the population of this study was not all L2

learners from different backgrounds. In addition, the sample of this study could not represent all Japanese L2 learners. Instead, this study focused on proficient Japanese learners of English with mean scores of TOEIC of around 700–800. Since it is plausible that learners’ proficiency might affect the way they process sentences online, it is

necessary for future research to take into account the effect of English language proficiency1. Nonetheless, this does not mean that the sample size of the present study was too small, as ensured by the priori power analysis.

Second, as to the issue of generalizing the results of the present study, L1 influence,

which was repeatedly used in this article, should be carefully interpreted. Since only Japanese learners of English participated in the study, the observed increase in reading time in the notional attraction condition might not be due to Japanese learners’ tendency to give number features to a noun semantically. Future research should investigate

learners from different L1 backgrounds other than Japanese in order to make sure that conceptually-driven number agreement processing is peculiar to Japanese learners of English.

Third, it should be noted that in all the stimuli the agreement controller was


agreement controllers had been different nouns, the results might have been completely different. In addition, it is desirable to see various agreement patterns. The agreement

controllers, attractors, and targets can all be either singular or plural; therefore, the possible total number of agreement patterns is eight, theoretically. In order to comprehensively reveal how L2 learners process number agreements, further studies which incorporate as many of the factors mentioned above as possible are needed.

Lastly, it should be pointed out that although the present study adopted the reading time procedure with self-paced reading tasks, it is necessary for future research to investigate the L2 learners’ performance in different types of task because task

characteristics are one of the critical factors affecting learners’ performance, as Lim and Christianson (2014) pointed out.

6. Conclusion

Since the current study is exploratory in nature, a number of limitations discussed in the earlier section should be addressed in future research. Nonetheless, it can be said that the main contribution of the present study is to provide the first empirical evidence for the notional number attraction phenomenon in L2 learners’ online sentence processing.

The result of the self-paced reading tasks provide suggestive data that Japanese L2 learners seem to have a tendency to process number features conceptually and semantically, and this results in making their number representation vulnerable. However, the results also indicate that Japanese L2 learners might not be totally

insensitive to number agreement errors because an increase in reading time was, to some extent, observed in the control conditions and grammatical attraction conditions. Further investigation is necessary to get a clear picture of L2 learners’ number agreement processes by the use of various types of agreement and vocabulary with different L1



1. One reviewer recommended providing some proficiency information of the


604.4 in Experiment 2, and 618.56 in Jiang’s (2007) study. Although direct comparison of the TOEFL and TOEIC scores cannot be made, it was considered that

the participants of Jiang’s (2004) study were much more proficient than the participants in the present study.


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