IMPLICIT LANGUAGE APTITUDE

A discussion of implicit language aptitude needs to start with language aptitude in general. Language aptitude is a set of cognitive abilities that are predictive of learning rate and ultimate attainment in second language (L2) learning. According to meta-analytic and narrative syntheses of the research (Li, Reference Li2016; Skehan, Reference Skehan2015), aptitude is a strong predictor of L2 attainment, with an average correlation of approximately .5 between composite aptitude scores and outcome measures (course grades and scores on standardized proficiency tests). Aptitude’s predictive power is strong in comparison with other individual difference variables such as motivation and anxiety, whose average correlations with outcome measures are roughly .3 (Masgoret & Gardner, Reference Masgoret and Gardner2003) and –.3 (Teimouri et al., Reference Teimouri, Goetze and Plonsky2019; Zhang, Reference Zhang2019), respectively. However, the predictive power of aptitude is not uniform across learner groups and learning conditions. Specifically, it has been found to be more strongly correlated with initial than advanced L2 learning (Li, Reference Li2015); it is more likely to be drawn upon by adult learners than child language learners (DeKeyser, Reference DeKeyser2000; Granena & Long, Reference Granena2012); high-aptitude learners benefit more from inductive instruction while low-aptitude learners more from deductive instruction (Erlam, Reference Erlam2005; Hwu & Sun, Reference Hwu, Wei and Sun2014). More generally, as with cognitive abilities for general academic learning, traditional language aptitude has been found to be a set of abilities more likely to be implicated in conscious learning (Li, Reference Li2015), thus motivating the need to identify and validate abilities for unconscious learning—implicit language aptitude.

A brief account of the history and measurement of implicit aptitude is necessary before synthesizing the research findings. Kaufman et al. (Reference Kaufman, DeYoung, Gray, Jiménez, Brown and Mackintosh2010) were among the first to investigate implicit learning as a cognitive ability for second language learning. The primary objective of the study, however, was to examine the associations between implicit learning ability and academic achievements, not just language learning. In their study, implicit learning ability was measured through a serial reaction time (SRT) task in which learners responded to a dot that appeared at different locations on a computer screen. The locations where the dot appeared were based on two sequences: a target sequence and a control sequence. The target sequence appeared more frequently (in 85% of the stimuli) than the control sequence (15%), and learners’ test performance was calculated as the difference between their mean reaction time for the target and control sequences. Because of the nature of the stimuli used in the SRT, the underlying ability that is tested is called sequence learning. Granena (Reference Granena2012) introduced SRT into the field of second language acquisition as a measure of implicit language aptitude. Granena’s study also found that SRT loaded onto the same factor as LLAMA_D, a subtest of the LLAMA aptitude battery that measures the ability for phonological encoding. Since Granena’s study, there has been a steady growth of interest in implicit aptitude in SLA research, and SRT and LLAMA_D have become the default measures of implicit aptitude.

The studies on implicit aptitude can be divided into two large categories: naturalistic and classroom, with the former referring to settings where the second language is the primary language of the community (e.g., learning Spanish in Spain), and the latter to settings where the second language is primarily learned in the classroom (e.g., learning Spanish in the United States). The studies can also be divided into correlational and experimental depending on whether the purpose is to explore the relationship between aptitude and L2 proficiency without manipulation of instructional treatments (correlational) or the relationship between aptitude and the effects of different instructional treatments (experimental). Five published studies were conducted in naturalistic settings, and all are correlational (Granena, Reference Granena2013; Granena & Long, Reference Granena and Long2013; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015, Reference Suzuki and DeKeyser2017; Yi, Reference Yi2018). Studies using SRT as a measure of implicit aptitude showed that (a) implicit aptitude correlated with late Chinese-Spanish bilinguals’ implicit knowledge measured through a word-monitoring test but the effects were restricted to linguistic structures involving agreement between sentence elements (e.g., subject-verb agreement) rather than structures involving form-meaning mapping (e.g., subjunctive mood) (Granena, Reference Granena2013); (b) implicit aptitude was not a predictor of the Japanese proficiency of L1 Chinese speakers who stayed in Japan for around four years as a whole cohort (Suzuki & DeKeyser, Reference Suzuki and DeKeyser2017), but when they were divided into short- and long-residence learners based on whether they lived in Japan for less or more than 30 months, it was a near-significant predictor of long-residence learners’ implicit knowledge measured using a word-monitoring test (Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015); and (c) implicit aptitude was not a predictor of Chinese ESL learners’ collocation judgments in a US context (Yi, Reference Yi2018). Studies using LLAMA_D as a measure of implicit aptitude showed that LLAMA_D scores were correlated with late bilinguals’ acquisition of lexis and collocations (Granena & Long, Reference Granena and Long2013) and early bilinguals’ learning of explicit knowledge about structures involving agreement instead of form-meaning mapping (Granena, Reference Granena2013).

What do we make of the findings of the naturalistic studies? First, it would seem that SRT is more likely to be predictive of the L2 proficiency of the learners who live in the country for a longer period and who therefore have more exposure to the L2. Although Granena (Reference Granena2013) found SRT predictive of late bilinguals’ L2 attainment, the learners in the study had lived in the country for more than 10 years. Second, SRT seems more likely to be predictive of implicit knowledge than explicit knowledge, given that all significant results were found for implicit knowledge. Third, LLAMA_D seems important for vocabulary learning and for the learning of explicit grammar knowledge.

Next, we focus on classroom studies, where L2 learning occurs primarily in the classroom through instructed learning. Correlational classroom studies demonstrated that SRT was correlated with high school students’ L2 French and German scores (Kaufman et al., Reference Kaufman, DeYoung, Gray, Jiménez, Brown and Mackintosh2010) and advanced L2 Spanish learners’ reading and listening proficiency (Linck et al., Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson, Smith, Bunting and Doughty2013).Footnote ¹ LLAMA_D was found to predict the fluency of L2 Spanish speech production (Granena, Reference Granena2019) and EFL learners’ pronunciation development (Saito et al., Reference Saito, Suzukida and Sun2019). Furthermore, Granena (Reference Granena2019) found that SRT, labeled as “implicit learning” ability, interacted with a latent factor called “implicit memory” that consisted of LLAMA_D and Available Long-Term Memory (ALTM) such that among learners with high implicit learning ability, implicit memory was predictive of lexical variety in speech production. The findings of the correlational classroom studies seem to suggest that (a) SRT is predictive of advanced proficiency, with the caveat that the proficiency level of the learners in Kaufman et al.’s (Reference Kaufman, DeYoung, Gray, Jiménez, Brown and Mackintosh2010) study is unclear, and (b) LLAMA_D is facilitative of the phonological aspects of L2 proficiency.

The experimental classroom studies investigated whether SRT was predictive of treatment effects in different learning conditions. Using a pretest-treatment-posttest design, the studies by Granena and Yilmaz (Reference Granena and Yilmaz2019) and Yilmaz and Granena (Reference Granena2019) reported that SRT was predictive of the effects of implicit feedback measured using a self-paced reading task and an untimed written grammaticality judgment test in the learning of Spanish gender agreement but not in the learning of object differential marking; SRT did not predict the effects of explicit feedback while phonological short-term memory did. Both Hamrick (Reference Hamrick2015) and Tagarelli et al. (Reference Tagarelli, Ruiz, Moreno Vega and Rebuschat2016) investigated the relationship between SRT and the learning of novel structures built in the learners’ native language—called the semiartificial grammar paradigm. However, the two studies showed very different results. Hamrick’s study investigated the associations between SRT and LLAMA_B (a test of rote memory), on one hand, and the incidental learning of three Persian structures translated into English matrix sentences by native English speakers, on the other. During the treatment, learners read each sentence and rated whether it was easy or difficult—the purpose was to focus learners’ attention on meaning and encourage incidental learning; they were not informed that there would be a test. The researcher found a significant effect for LLAMA_B at the time of the immediate posttest and for SRT at the time of the delayed posttest. Tagarelli et al. (Reference Tagarelli, Ruiz, Moreno Vega and Rebuschat2016) conducted a similar study with a different semiartificial language: English words with German word order. They investigated three linguistic structures that differed in complexity and two treatment types: incidental and instructed. In both learning conditions, learners were told to read some sentences, judge whether they were semantically plausible, and read them aloud. In the incidental condition, learners were simply told that they would be tested after learning the materials, whereas in the instructed condition, they were taught rules, asked to write two sentences following the rules, and encouraged to search for rules when learning the materials. The only significant result was a strong negative correlation between SRT and the learning of the most complex structure in the incidental conditions. SRT also showed a negative correlation with other outcome measures, although the correlations were nonsignificant. The negative correlations may be due to the explicit nature of the treatments—even in the incidental conditions learners were informed of an upcoming test, which, according to Hulstijn (Reference Hulstijn2005), constitutes explicit instruction. Therefore, it would seem that implicit aptitude may have a negative impact on explicit learning. However, Tagarelli et al. (Reference Tagarelli, Ruiz, Moreno Vega and Rebuschat2016) administered two SRT tasks and used reaction time data from one task but accuracy data from the other. Thus, the results should be interpreted with caution.

To sum up, the findings of experimental classroom research, together with other strands of research, seem to suggest the following about implicit aptitude. First, sequence learning tested through SRT is predictive of the ultimate attainment of naturalistic learners who lived in the country where the second language is spoken for a longer period (Granena, Reference Granena2013; Granena & Long, Reference Granena and Long2013; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015), but not learners who lived in the country for a shorter period (less than three years) (Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015; Yi, Reference Yi2018). It would seem that more exposure to the second language is more likely to result in implicit knowledge, which in turn is more likely to be correlated with implicit aptitude. This finding is also supported by correlational classroom research showing a significant correlation between sequence learning and advanced L2 proficiency (Linck et al., Reference Linck, Osthus, Koeth and Bunting2014), which is more likely to be implicit than beginning level L2 proficiency. Second, sequence learning is sensitive to the nature of the instruction learners receive in that implicit instruction draws on implicit aptitude while explicit instruction involves explicit aptitude (Granena & Yilmaz, Reference Granena and Yilmaz2019; Hamrick, Reference Hamrick2015; Yilmaz & Granena, Reference Granena2019). It should be pointed out that the learners investigated in the reviewed experimental studies were beginners, suggesting that implicit aptitude has a role to play even at beginning stages of L2 learning, at least under tightly controlled experimental conditions. Third, sequence learning is correlated with implicit knowledge measured by word-monitoring tasks and oral production tasks (Granena, Reference Granena2013, Reference Granena2019; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015), but the experimental studies also showed that sequence learning is correlated with learners’ grammaticality judgment scores (Yilmaz & Granena, Reference Granena2019), which have been considered to represent explicit knowledge in SLA research (Ellis, Reference Ellis2005). Fourth, implicit memory measured by LLAMA_D is predictive of the learning of collocations and the phonological aspects of the L2 system (Granena & Long, Reference Granena and Long2013; Yi, Reference Yi2018). It is also correlated with learners’ metalinguistic knowledge (Granena, Reference Granena2013). These findings suggest that the ability LLAMA_D represents is likely explicit, or at least not entirely implicit. Fourth, sequence learning is predictive of structures involving linear relationships between sentence elements (agreement structures), but not structures involving form-meaning mapping (nonagreement structures). This finding is obtained by both naturalistic and experimental classroom studies by Granena and Yilmaz (Granena, Reference Granena2013; Yilmaz & Granena, Reference Granena2019). However, the distinction between agreement and nonagreement structures seems to be partly based on whether the linguistic structure involves meaning, but nearly all structures involve meaning to some extent and the so-called nonagreement structures such as the subjunctive mood also involve agreement between different sentence elements.

The review of the literature shows that to date, SRT and LLAMA_D have been utilized to measure implicit aptitude. However, the research has demonstrated that SRT (a) involves stimuli that are semantically vacuous, (b) concerns only the linear relationship between sentence elements, and (c) is less likely to be predictive of structures involving form-meaning mapping. LLAMA_D has demonstrated characteristics of explicit aptitude in that it is predictive of metalinguistic knowledge and of the lexical aspects of L2 knowledge. There is therefore a need to identify new measures of implicit aptitude that overcome the limitations of existing measures, and priming is an ideal candidate. In the following sections, we introduce the literature on syntactic priming regarding its effects and nature and its potential as a measure of implicit aptitude.

SYNTATIC PRIMING

Priming refers to facilitated performance due to a previous event. Priming has been examined primarily from the perspective of whether it is effective in influencing subjects’ linguistic production. The purpose of this study is to investigate priming as a cognitive ability for language learning. In this case, learners’ performance in a priming task will serve as an independent variable hypothesized to predict learning outcomes. To use priming tasks as measures of a cognitive ability, it is important to have a thorough understanding of the literature on priming, including whether priming is effective, what factors constrain its effectiveness, and how priming tasks have been designed.Footnote ² The best way to start the literature review is to summarize the findings of the meta-analysis conducted by Mahowald et al. (Reference Mahowald, James, Futrell and Gibson2016). The meta-analysis aggregated the results of all empirical research on syntactic priming and found a significant effect for priming in the absence of lexical boost (the same verb is used in the prime and the target), with a weighted mean odds ratio of 1.67 (95% CI: 1.63, 1.72). The finding suggests that when there is no lexical overlap between the prime and the target, participants are .67 times more likely to produce the target structure in primed conditions compared with unprimed conditions. The meta-analysts clarified that an odds ratio of 1.67 is equivalent to .28 if converted to Cohen’s d, which is a small effect based on Cohen’s criteria (1988). However, when there is a lexical overlap, the mean odds ratio became 3.26, which means the chances of producing the primed structure are 2.26 times larger under primed conditions than unprimed conditions. This effect is .6 in Cohen’s d, which constitutes a medium effect.

In addition to overall effects, the meta-analysts performed moderator analyses to ascertain whether the effects of priming varied as a function of systematic methodological differences between the sampled studies. We would like to focus on several moderators that are key to understanding the major paradigms of priming research and that are relevant to the priming tasks used in our own study. First, in terms of the impact of the language of priming tasks, no significant differences were found between L1 and L2 priming, but lexical boost had a significantly larger effect in L2 priming than in L1 priming. Second, regarding the effect of lags (fillers between the prime and the target), different findings were obtained when the studies were divided into nontreatment and treatment studies. In nontreatment studies where primes and targets were not manipulated such as those following Bock’s model (Reference Bock1986), priming effects decreased significantly with the increase of the number of lags, and the insertion of lags also made the influence of lexical boost disappear. Typically in these studies, the participant responds to an experimental trial by repeating a sentence (the prime) containing the linguistic structure to be primed and then describing a picture (the target). The most examined linguistic structures in these studies are the dative construction and transitive verbs in English. The dative construction has two variants or two alternating structures: prepositional object, as in “The professor gave a book to the student,” and double object, as in “The professor gave the student a book.” Transitive verbs can be used in either the active voice: “The lightning bolt hit the house,” or the passive voice: “The house was hit by the lightning bolt.” In these studies, the same participants respond to trials for both alternatives of the same structure, and some of the studies involve more than one linguistic structure. The priming tasks used in this study followed the methods in this paradigm.

Regarding the effect of lags, an opposite pattern was obtained from the analysis of the results of the treatment studies conducted by Kaschak and associates (e.g., Kaschak et al., Reference Kaschak, Kutta and Jones2011). Unlike the nontreatment studies, these treatment studies demonstrated that the number of lags was a positive predictor of priming effects such that increasing the number of lags led to larger priming effects. These studies are typically interventional, consisting of a pretest, a training session, and a posttest. In the training phase, participants were “biased” toward one variant, such as the prepositional object of the dative construction, by responding to target trials that sought to manipulate their language production (such as by filling out a template that only allows the prepositional object, “The teacher gave a book____.”). Also, in these studies participants only receive training on one variant of a linguistic structure. The methodological variation may account for the opposite effects of lags between treatment and nontreatment studies. In studies following Bock’s model, the same learners were primed for multiple structures and for more than one variant of the same structure, and the effects of priming may be limited to trials where there was no lag because trials containing the other variant may cancel the longer-term effects of the variant in question. In studies following Kaschak’s model, there was no distraction resulting from cross-priming because there was only one structure in the treatment. Lags in the treatment studies increased the space between primes and targets, alleviating participants’ processing burden and leading to greater priming effects—a phenomenon called spacing effects.

One important moderator that has figured prominently in the research but that was not investigated in Mahowald et al.’s (Reference Mahowald, James, Futrell and Gibson2016) meta-analysis is the linguistic target of priming, that is, some structures are more easily primed than others. For example, it has been found that “the datives generally yielded clearer patterns of priming [and larger effects] than the transitives” (Bock & Griffin, Reference Bock and Griffin2000, p. 183). Furthermore, within a particular target structure, one variant may show larger effects than the other. For example, in English, the prepositional object has consistently generated greater priming effects than the direct object, and the passive voice showed greater effects than the active voice. In either case, it is the less frequent variant, such as the prepositional object and the passive, that led to more priming gains—a phenomenon called “inverse frequency” (e.g., Bock & Loebell, Reference Bock and Loebell1990; Kaschak et al., Reference Kaschak, Kutta and Jones2011).

SYNTATIC PRIMING AS IMPLICIT LEARNING

The primary objective of the present study is to examine the validity of priming as an ability for implicit learning, and therefore a fundamental question for such an initiative is whether priming implicates implicit learning. To answer this question, it is necessary to evaluate the truthfulness of two theoretical accounts of the mechanism of priming: the “transient activation account” and the “implicit learning account” (Bock & Griffin, Reference Bock and Griffin2000, p. 180). The transient activation account states that priming is a matter of temporary activation of long-term memory, and that its effects are short-lived. Evidence for this view should show that the effects of priming will disappear or decline steeply if primes and targets are separated. However, this did not happen. The studies by Bock and Griffin (Reference Bock and Griffin2000) and Bock et al. (Reference Bock, Dell, Chang and Onishi2007) show that long lags (number of fillers between the target and prime) did not cause substantial decline of effects. Bock and Griffin (Reference Bock and Griffin2000) investigated the number of lags as an independent variable and found that the effects of priming could last as long as 10 lags after primes were presented. The effects were strongest at lag 2, followed by lags 1, 0, and 10, suggesting that a shorter lag is not equivalent to a stronger effect. One caveat is that Mahowald et al.’s (Reference Mahowald, James, Futrell and Gibson2016) meta-analysis showed a negative effect of a longer lag on priming effects. However, the counterevidence for the caveat is that the negative effect was restricted to studies where the priming of alternative structures may have cancelled the long-term effects of priming. The studies where participants received training on a single structure showed a positive effect for a larger number of lags, and these studies also showed that the treatment effects could last as long as one week. Also, the moderator—number of lags—was created by the meta-analysts based on the methodological features of the primary research, and most of the studies did not examine lags as an independent variable. Therefore, the finding is synthesis-based, not study-based, and synthesis-based findings are suggestive rather than conclusive (Li et al., Reference Li, Shintani and Ellis2012).

The “implicit learning account” holds that priming involves implicit learning because it does not require any effortful, conscious information retrieval or processing.Footnote ³ Bock and Griffin (Reference Bock and Griffin2000) stated:

Similarly, Pickering and Ferreira (Reference Pickering and Ferreira2008) believed that the learning that happens in syntactic priming is “tacit, incidental, and automatic” (p. 447). One argument that Bock and her colleagues have made based on a series of studies they conducted is that priming involves syntactic abstraction that is unrelated to semantic connections between primes and targets. Thus, in this view it is syntax rather than semantics that is behind the phenomenon of priming, and as the researchers argued, syntax is likely abstract and implicit, while semantics, which relates to episodic memory, is likely explicit. The claim that priming is a matter of syntax rather than semantics rests on the following empirical evidence. Bock (Reference Bock1986) reported that participants were more likely to produce passive sentences following passive primes and that manipulating the animacy (which concerns meaning) of the agent of the prime did not have any impact on whether they used the passive when describing target pictures. Similar findings were obtained by other studies such as Huang et al. (Reference Huang, Pickering, Yang, Wang and Branigan2016), which reported that manipulating the animacy of the recipient had little influence on the production of the dative alternatives.

Another argument for an implicit learning account is that it is syntax rather than lexical repetition that is at work, and lexicon is more likely to be explicit. This claim has been examined in two streams of research. One relates to lexical boost, which refers to the finding that the repetition of the same verb between the prime and the target leads to larger priming effects. Although the effects of lexical boost are likely due to explicit learning, studies where there was no lexical overlap between primes and targets also demonstrated significant priming effects. Also, the effects of lexical boost disappear once lags are inserted between primes and target (Mahowald et al., Reference Mahowald, James, Futrell and Gibson2016). The other stream of research concerns the speculation that priming occurs because of participants’ repeated exposure to the same particle that induces them to produce the linguistic target. Bock and Loebell (Reference Bock and Loebell1990) examined this possibility by investigating the priming effects of two sentence structures on the production of prepositional objects. Both structures contain “to” but have different constituent structures. One is the prepositional object, as in “Susan brought a book to Susan,” and the other is the infinitive, as in “Susan brought a book to study.” The results showed that only primes with prepositional objects were effective in leading to more frequent production of prepositional objects, not primes with infinitives, suggesting that the driving force for priming is the abstract syntactic rule rather than a mere repetition of the particle “to.”

One way to establish the validity of a construct is to identify its relationship with competing variables or variables in a different paradigm and with variables in the same paradigm which supposedly tap into the same or similar construct—the former is known as divergent validity and the latter convergent validity. Evidence for the divergent validity of priming as an implicit learning mechanism is provided by Ferreira et al.’s (Reference Ferreira, Bock, Wilson and Cohen2008) study that investigated learners with amnesia and that showed a separation between the learning mechanism of priming and that of explicit learning. The study showed that these learners performed as well as normal controls (learners with no learning disability) in syntactic priming, but their performance in the recognition of the meaning of priming sentences was significantly worse than normal controls. The researchers interpreted the findings as suggesting that the learners had intact procedural memory that contributed to their excellent performance in syntactic priming but they suffered from deficits in declarative memory that resulted in their poor recall of past events. Ferreira et al.’s (Reference Ferreira, Bock, Wilson and Cohen2008) interpretations were based on the assumption that procedural memory underlies implicit learning whereas declarative memory is responsible for explicit learning.

While priming has been shown to differ from explicit learning—evidence for divergent validity, there has been scant evidence for convergent validity. Kaschak et al. (Reference Kaschak, Kutta and Jones2011) is perhaps the only psycholinguistic study that investigated the relationship between priming and other implicit learning tasks. In this study, learners were divided into two groups receiving priming training in the two variants of the dative construction: prepositional object and double object. They also completed a Simon task as a measure of implicit learning, where they reproduced colored squares two seconds after they appeared on a computer screen. Unbeknownst to the participants, the locations of the squares were based on a fixed sequence, and the task consisted of a study phase and a test phase including new and old strings. The participants’ implicit learning scores were calculated by subtracting their accuracy scores on the new strings from the scores for the old strings. For both the prepositional object and double object, the contribution of implicit learning (performance on the Simon task) to priming effects was near-significant, p = .07, .06, respectively, with implicit learning being a negative predictor of the priming of the direct object (the authors did not report the direction of the correlation for the prepositional object). The authors interpreted the overall effect of implicit learning as being nonsignificant, while pointing out that (a) measures of implicit learning are typically poorly correlated or uncorrelated, and (b) the Simon task may have tapped into a different kind of ability from the ability underlying syntactic priming.

To sum up, there is theoretical justification and empirical evidence for the implicit nature of the learning that results from syntactic priming. Priming does not impose any demand for effortful, conscious information retrieval or processing on the participant. Priming involves abstract syntactic processes that are unrelated to semantics and lexicon (Bock & Loebell, Reference Bock and Loebell1990; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016). Priming effects are sustainable and long-lasting (Mahowald et al., Reference Mahowald, James, Futrell and Gibson2016). Similar to the findings on other implicit learning tasks, learners with cognitive impairments in explicit memory performed as well as learners with no cognitive disability when completing tasks of syntactic priming (Ferreira et al., Reference Ferreira, Bock, Wilson and Cohen2008; Kaschak et al., Reference Kaschak, Kutta and Jones2011). However, more research is needed on the convergent validity of priming—how it is related to other implicit learning tasks—and there has been no research on its predictive validity, that is, whether it can serve as a predictor of learning outcomes. This study will address these issues.

SYNTATIC PIRMING AS IMPLICIT LANGUAGE APTIUTDE

Syntactic priming is an ideal candidate of implicit aptitude because it involves implicit learning, and because it involves form-meaning mapping, a defining feature of language learning. Although priming studies (e.g., Bock & Loebell, Reference Bock and Loebell1990; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016) have shown that manipulating meaning did not have an impact on the effectiveness of priming, there is no denying that for priming to happen, the target must be consistent with the prime in terms of the basic event or discourse structure. For example, for a prime of double object (“The professor gave the student a book”) to be successful, the event of the target (picture) must depict an event that has the basic elements of a sentence containing the double object. In other words, priming is verbal and happens in context, and thus, unlike sequence learning, which is nonverbal, priming is not semantically vacuous. Therefore, priming matches the mechanism of the processing of a natural language.

The mechanism of priming fits the currently popular meaning-based approaches to language instruction that emphasize the importance of learning a second language through exposure to input and through using language as a tool to communicate meaning, rather than excessive grammar instruction and language-focused mechanical drills—characteristics of traditional language instruction. Priming has the following attributes that coincide with meaning-based instruction:

• • Priming is an unobtrusive technique in that it does not impose a processing demand and yet it exerts some kind of influence by orienting the learner toward the target feature.

• • Priming affords positive evidence that the learner can incorporate in their own interlanguage.

• • Priming provides a syntactic template that alleviates learners’ onus in selecting and retrieving the appropriate sentence-building procedure, thereby freeing up learners’ cognitive resources for form-meaning mapping.

• • Priming provides a forum where learners have an opportunity to practice retrieved or newly acquired linguistic knowledge in immediate production.

In light of the congruity between the mechanisms of priming and meaning-oriented instruction, priming can serve the dual function of a learning task (McDonough, Reference McDonough2006) and a measure of the cognitive ability for implicit learning that occurs in meaning-oriented approaches. To date, priming has been investigated for the former purpose, not the latter (but see Woltz (Reference Woltz2003) for a discussion of semantic priming as an ability for implicit learning). However, the findings about priming as a learning task accord with current explanations about the role of explicit and implicit learning in major theoretical models of second language acquisition (DeKeyser, Reference DeKeyser, VanPatten, Keating and Wulff2020; Long, Reference Long2015). In L2 research, L2 priming has been found to be more likely to occur for structures learners have more previous knowledge about (McDonough, Reference McDonough2006; McDonough & Fulga, Reference McDonough and Fulga2015), suggesting that it facilitates learning that happens at more advanced stages—a feature of implicit learning. The research also demonstrates that in L2 priming tasks, the effects of lexical boost (namely, priming is more effective when the prime and the target contain the same key words) were evident among beginners instead of advanced learners (Kim & McDonough, Reference Kim and McDonough2008), and that the effects of priming with lexical boost were short-lived while the effects of priming without lexical boost were sustainable (Jackson, Reference Jackson2018; McDonough, Reference McDonough, Trofimovich and McDonough2011). These findings suggest that priming with no lexical boost, which belongs to the implicit paradigm, happens in advanced L2 learning and generates long-term effects, and that priming with lexical boost, which suggests explicit learning, is characteristic of initial learning. The findings corroborate the claims of current SLA theories about explicit and implicit learning, which are more likely to occur at initial and advanced stages, respectively.

THIS STUDY

The primary objective of this study is to determine whether priming is a cognitive ability for implicit learning. To establish the construct validity of priming as a cognitive ability for implicit learning, it is important to collect evidence for three types of validity: convergent validity, divergent validity, and predictive validity. Convergent validity refers to whether priming is correlated with other measures of implicit aptitude, such as LLAMA_D and SRT, which have been used as tests of implicit aptitude in previous research (e.g., Granena, Reference Granena2013). Divergent validity concerns whether a certain variable is not or less correlated with variables in another domain—one that is hypothesized to be distinct from the domain the investigated variable falls into. In this case, priming as a measure of implicit aptitude should be uncorrelated or less correlated with cognitive abilities for explicit learning, which have been measured using traditional aptitude tests such as MLAT, LLAMA (B, E, F), and PLAB (Li, Reference Li2016). Predictive validity pertains to whether priming is predictive of learning outcomes measured using tests of L2 attainment. To examine the three types of validity of the construct of syntactic priming as a device for implicit learning, the following research questions were formulated:

INSTRUMENTS

A total of six tests were utilized to measure learners’ implicit and explicit aptitude and their L2 English proficiency. The measures of cognitive aptitude, which served as predictor or independent variables, included syntactic priming, serial reaction time, and the LLAMA aptitude battery, which consists of four subtests (B, D, E, and F). Among the tests, syntactic priming, serial reaction time, and LLAMA_D were hypothesized to measure implicit aptitude, and the three remaining subtests of the LLAMA were intended to measure explicit aptitude. The measures of outcome variables included (a) an untimed written grammaticality judgment test, (b) a metalinguistic knowledge test, and (c) an elicited imitation test (Ellis, Reference Ellis2005). The first two (untimed grammaticality judgment and metalinguistic knowledge) were intended to measure explicit knowledge, which is analyzed, metalinguistic, conscious, and accessible under conditions without time constraint. The elicited imitation test was posited to measure implicit knowledge—knowledge that is tacit, intuitive, unconscious, and accessed in spontaneous production (but see Suzuki & DeKeyser [Reference Suzuki and DeKeyser2015] who argued that elicited imitation measures automated explicit knowledge instead of implicit knowledge). The tests are described in the following sections.

TESTS OF COGNITIVE ABILITIES (PREDICTING VARIABLES)

Syntactic Priming

As pointed out in earlier sections, priming research has centered on whether learning occurs during a priming task. However, in this study priming was examined as an individual difference variable and the focus is on whether it is predictive of learning outcomes. The priming task was developed in the learners’ L1 instead of L2 to prevent the impact of their L2 proficiency on their priming performance.Footnote ⁴ The task stimuli targeted two linguistic structures: the dative construction (prepositional object vs. double object) and transitive verbs (active voice vs. passive voice), which have been investigated in many priming studies including those by Bock and associates (e.g., Bock et al., Reference Bock, Dell, Chang and Onishi2007). The two structures, exemplified in Table 1, also exist in Chinese (Cai et al., Reference Cai, Pickering, Wang and Branigan2015). Similar to English, Chinese has two variants for the dative construction: prepositional object and double object, with the former being formed by “Subject + Verb + Direct Object + Preposition + Indirect Object,” and the latter by “Subject + Verb + Indirect Object + Direct Object.” Transitive verbs also have two variants: active voice and passive voice. In active voice, the subject is the performer of the action, whereas in passive voice the subject is the receiver of the action. In Chinese, the passive construction is formed by “Subject (receiver of action) + Bei (passive marker, similar to ‘by’) + Agent (performer of action) + Verb.” It is noteworthy that in the field of priming, there has been little research on Chinese, and the limited research (Cai et al., Reference Cai, Pickering, Yan and Branigan2011; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016) has examined only the dative construction, not transitive verbs (active/passive voice).

TABLE 1.

Examples of the dative construction and transitive verbs

Following previous studies (Bock, Reference Bock1986; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016), the priming task consisted of two phases: a study phase and a test phase. During the study phase, the learners listened to some sentences and viewed some pictures, and they were informed that they needed to memorize the materials and identify them later in the test phase. The materials consisted of 50 items, half of which were sentences and half pictures. The items were presented in a random order, with the constraint that no more than two of the same type occurred consecutively. The study phase aimed primarily to make the learners believe that the whole activity was a memory task where items studied initially would be subsequently tested. The test phase, which is the core component of the priming task, contained 128 items, including 64 target items and 64 distractors. The target items did not appear in the study phase, and all the items presented in the study phase served as distractors in the test phase. Among the 64 target items, 32 concerned the dative construction, and 32 related to transitive verbs. Among the 32 items for each structure, 16 pertained to one variant (direct object or active voice), and 16 to the other variant (prepositional object or passive voice). Each target item consisted of two components: a prime followed by a target. The prime was an audio sentence that contained the target structure, and the learner was told to repeat the sentence and decide whether it appeared in the study phase. The target was a picture that the learner was asked to describe verbally using provided clues (names of people and objects). After the verbal description, the learner was required to judge whether the picture was presented in the study phase. The expectation was that the learner would reproduce the target structure in their verbal description after being exposed to the structure in the first event—the prime. The distracting items followed the same format as the target items, but they were sentences with intransitive verbs (e.g., “wushi zai tiaowu” meaning “The witch was dancing”), which are unrelated to the two linguistic structures. Figure 1 illustrates the steps and components of an item in the priming task.

FIGURE 1.

The template of a priming trial.

The researchers created the stimuli by consulting previous priming studies (Cai et al., Reference Cai, Pickering, Yan and Branigan2011; Huang et al., Reference Huang, Pickering, Yang, Wang and Branigan2016) as well as Chinese grammar books (Li & Thompson, Reference Li and Thompson1981), corpora, newspapers, and dictionaries. Each noun and verb used for both the audio and picture stimuli appeared exactly twice in the task. For the picture stimuli for both linguistic structures, half had the agent on the left and half on the right, to prevent the potential influence of the positions of the agent and patient on learners’ utterances. For transitive verbs, whether the agent and patient are human or nonhuman was controlled, which led to four configurations: human agent + human patient (e.g., “The pirate is chasing the dancer”); human agent + nonhuman patient (“The father broke the glasses”); nonhuman agent + human patient (“The clock awakened the singer”); and nonhuman agent + nonhuman patient (“The rocket hit the plane”). The job title of the character, the name of the object, and the verb of the action were provided in the picture. Also, dative pictures had a preamble stating the subject and the verb of the sentence to prevent the use of a third, alternative structure, the ba- structure. Scoring of the priming task was based on the number of picture description responses that were consistent with the primes, that is, a response was considered as primed if the target description had the same structure as the prime sentence (i.e., a passive response following a passive prime). For each of the four structures—prepositional object, direct object, active, and passive—the maximum score was 16, and the minimum was zero.

Serial Reaction Time Task

A serial reaction time task, which was developed by the researchers, was used to measure sequence learning, a default measure of implicit aptitude in SLA (Granena, Reference Granena2013; Kaufman et al., Reference Kaufman, DeYoung, Gray, Jiménez, Brown and Mackintosh2010; Linck et al., Reference Linck, Hughes, Campbell, Silbert, Tare, Jackson, Smith, Bunting and Doughty2013). During the task, the learners responded to a black dot that appeared on four horizontally displayed locations. The locations of the dot were based on two 12-digit number sequences: a target sequence 1–2–1–4–3–2–4–1–3–4–2–3–) and a control sequence (1–2–4–3–1–4–2–1–3–2–3–4–). The target sequence appeared in 85% of the stimuli and the control sequence in 15%. The stimuli were divided into eight blocks, each consisting of 10 12-digit sequences. Each sequence generated an average of 11 triplets (such as 121, 214, 143, etc.), yielding 110 triplets in total for each block. The control sequence was built into the stimuli by varying the third digit of a target triplet (e.g., from 121 to 124), and as much as possible, the control triplets were evenly distributed among the eight blocks of trials, with each block including an average of 16.5 control triplets (15% of 110). Altogether, the task comprised of 880 triplets, 748 of which were target triplets and 132 control triplets. The learners were told to press one of the four keys corresponding to the locations on the computer screen as quickly as possible, and they were informed that it was a game where speed and accuracy were both important. The test was scored by subtracting the learners’ mean reaction time for the target stimuli from their mean reaction time for the control stimuli: SRT score = control−target (Granena, Reference Granena2013; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015). In this scoring method, a larger value represents higher sequence learning ability because it suggests that the learner was faster in responding to the target stimuli (because he/she had learned the rules) and slower in responding to the control stimuli which were less familiar due to their lower frequency.

LLAMA

The LLAMA aptitude battery, which is modeled on the MLAT (Carroll & Sapon, Reference Carroll and Sapon2002), is a free online test kit that has been extensively used to measure language aptitude in recent research. Created based on a language spoken in Central America, the test is intended to be language-neutral to learners who are unfamiliar with the language. It has four subtests: B, D, E, and F, each with a study phase and a test phase. LLAMA_B is a test of rote memory, during which the learner has 2 minutes to study 20 picture-word associations and is then tested on the same stimuli. LLAMA_D, which has been claimed to be a measure of implicit aptitude, requires the learner to listen to 10 sound sequences in the study phase and then distinguish old and new sequences in the test phase. LLAMA_E measures the ability to learn sound-symbol associations. During the study phase, the learner listens to the recordings of 24 syllables and studies the scripts of the syllables. In the testing phase, the learner listens to some syllable combinations and chooses the correct spellings for each syllable sequence. LLAMA_F measures the ability for inductive learning or language analytic ability. The learner studies 20 sentences and their meanings represented by pictures for the purpose of extracting the grammar rules underlying the sentences and is then tested on the rules. The score of each subtest is automatically generated by the software program.

THE RELATIONSHIP BETWEEN SYNTACTIC PRIMING AND L2 PROFICIENCY

Based on the preceding measurement model regarding the structure of cognitive ability and on the findings of previous research (Granena, Reference Granena2013; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2017), a structural model mapping the relationship between aptitude measures and outcome measures was hypothesized (Figure 4). In this model, explicit aptitude is predictive of explicit knowledge and the two measures of implicit aptitude are predictive of implicit knowledge. However, the model was not confirmed, and an alternative model was identified (Figure 5 and Table 6), which showed a good fit, X² = 14.87, p = .92, NFI = .91, CFI = 1.00, and RMSEA = .00. In this model, explicit aptitude formed by LLAMA_B, _D, _E, and _F is significantly predictive of L2 proficiency, a latent factor represented by the three proficiency tests. Elicited imitation, which was hypothesized to measure implicit knowledge, was found to load onto the same factor as the two measures of explicit knowledge. For the untimed written grammaticality judgment test, scores based on ungrammatical items were used rather than composite scores or scores based on grammatical items because ungrammatical items showed a better fit for the data. Regarding the two measures of implicit aptitude, sequence learning was negatively predictive of metalinguistic knowledge, but priming was not a significant predictor of any outcome measure.

TABLE 6.

The structural model for cognitive abilities and L2 proficiency

* p < .05.

FIGURE 4.

Hypothesized structural model for cognitive ability and L2 proficiency.

FIGURE 5.

Identified structural model for cognitive ability and L2 proficiency.