Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm

Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm Merel Muylle, Sarah Bernolet, Robert J. Hartsuiker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4351475/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2024 Read the published version in Journal of Cultural Cognitive Science → Version 1 posted 10 You are reading this latest preprint version Abstract A series of artificial language (AL) learning studies investigated the development of shared syntactic representations during early stages of second language (L2) acquisition. In this paradigm, the sharing of syntax is measured by means of structural priming: if structures are shared between two languages, a sentence in one language (e.g., the AL) should prime the structure of a sentence in another language (e.g., Dutch) and vice versa. According to Hartsuiker and Bernolet’s ( 2017 ) developmental theory, syntactic representations evolve gradually from item-specific to more abstract with increasing L2 proficiency. The current study tested this hypothesis by focusing on individual differences in AL proficiency during the first day of acquisition in a re-analysis of three AL studies. We predicted that individuals with higher AL proficiency levels show more cross-linguistic priming than those with lower levels. AL proficiency was indeed related to the magnitude of structural priming, although the strongest evidence for modulation of priming by proficiency was obtained for item-specific priming. Additionally, we observed that working memory (WM) capacity and L1 proficiency predicted AL proficiency and priming in general. Finally, WM capacity predicted the magnitude of priming in ditransitive sentences, but not in transitive sentences, suggesting a larger role for WM in ditransitive vs. transitive priming. structural priming artificial language learning sentence production individual differences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction One of the important issues in second language (L2) acquisition research is how individuals acquire and represent L2 syntactic structures in early and later stages of L2 acquisition. Many bilingual theories assume that bilingual speakers can, at least at some point during the learning trajectory, share certain linguistic representations across their languages (e.g., Dijkstra & van Heuven, 2002 ; Flege & Bohn, 2021 ; Kroll & Stewart, 1994 ; Nicoladis, 2006 ; Paradis & Navarro, 2003 ). This is also the case for the syntax, where structures that share similarities between languages may be merged into abstract, cross-linguistic syntactic representations (Hartsuiker et al., 2004 ). Evidence for such shared representations stems from the observation that bilingual speakers are influenced by recently encountered structures in one language when formulating or comprehending sentences in the other language. For instance, if a Dutch-English bilingual is confronted with a passive sentence in Dutch (e.g., “de kok wordt aangeraakt door de clown” [the cook is being touched by the clown]), he or she will be more likely to produce a sentence in the passive voice in English (e.g., “the lawyer was fired by the salesman”), instead of the highly preferred active voice (e.g., “the salesman fired the lawyer”). This phenomenon of repeating or predicting recently-encountered syntactic structures across sentences, regardless of differences in meaning, is referred to as syntactic or structural priming (Bock, 1986 ) and showcases prediction of the upcoming structure as an important mechanism in both language comprehension and production (Jaeger & Snider, 2013 ; Pickering & Garrod, 2007 ). Since such priming also occurs in the absence of lexical, phonological, or meaning overlap, it is often considered evidence for the existence of abstract syntactic representations (for a review see Pickering & Ferreira, 2008 ). Hence, when priming also occurs across languages, one could argue that these abstract representations are shared or at least connected between languages (Hartsuiker et al., 2004 ; van Gompel & Arai, 2018 ). Interestingly, cross-linguistic structural priming has been observed with a wide variety of language pairs, such as English-Dutch (Bernolet et al., 2007 , 2013 ; Desmet & Declercq, 2006 ; Schoonbaert et al., 2007 ), English-French (Hartsuiker et al., 2016 ), English-Polish (Fleischer et al., 2012 ), English-Mandarin (Chen et al., 2013 ; Huang et al., 2019 ), English-Korean (Hwang et al., 2018 ; Shin & Christianson, 2009 ; Song & Do, 2018 ), English-Irish (Favier et al., 2019 ), English-Swedish (Kantola & van Gompel, 2011 ), English-Spanish (Flett et al., 2013 ; Hartsuiker et al., 2004 ), Cantonese-Mandarin (Cai et al., 2011 ; Huang et al., 2019 ; Liu et al., 2022 ), Dutch-German (Bernolet et al., 2007 ), Dutch-French (Hartsuiker et al., 2016 ), and Spanish-Swedish (Montero-Melis & Jaeger, 2020 ) (see Muylle et al., 2023 , for an overview of studies). Some of these studies found that structural priming patterns may differ across L2 proficiency levels (e.g., Bernolet et al., 2013 ; Favier et al., 2019 ; Hartsuiker & Bernolet, 2017 ; Kim & McDonough, 2008 ; Montero-Melis & Jaeger, 2020 ), although the direction of the effect seems to depend on whether there is lexical overlap between prime-target pairs or not. For instance, Bernolet and colleagues ( 2013 ) correlated the magnitude of structural priming effects with L2 proficiency and observed that cross-linguistic priming effects became larger with increasing proficiency, whereas within-L2 priming effects in the presence of lexical overlap became smaller as proficiency increased. On the other hand, a recent study that investigated priming between Cantonese and Mandarin did not observe any effect of proficiency (Liu et al., 2022 ), which suggests that proficiency has no effect on the sharing of syntax between highly similar languages. Since the sharing of syntax seems to be influenced by proficiency, there are two contrasting views on how L2 learners develop syntactic representations in the new language: either from separate to shared representations (e.g., Hartsuiker & Bernolet, 2017 ) or from shared to separate representations (e.g., De Bot, 1992 ; Montero-Melis & Jaeger, 2020 ). The first view assumes that L2 learners start with building language-specific syntactic representations in their L2, which then gradually become shared with the L1 when these are similar enough. The second view states that L2 learners use their L1 knowledge to organize the L2 and with increasing proficiency, the L2 representations will become separated from the L1, resulting in more native-like processing in the L2. When it comes to more abstract forms of priming (i.e., priming across languages and/or in the absence of lexical overlap), existing evidence seems to be more compatible with the view that learners start with separate, language-specific representations that become shared over time (see Muylle et al., 2023 , for a discussion). This idea was further elaborated in Hartsuiker and Bernolet's (2017) developmental theory of shared syntactic representations. According to this theory, L2 learners go through a number of different stages to establish shared syntactic representations across languages. At the start of L2 learning, the learner does not have any L2 syntactic representations yet, while some lexical representations may already be in place. In order to produce sentences, the learner will therefore use explicit strategies to combine the lexical items by either a) copying and editing utterances from a more proficient speaker, or b) transferring L1 syntax to the L2 without knowing whether this will result in a grammatical utterance. Hence, learners in the first stage may show within-language and cross-linguistic priming, but only when they are able to recall the prime sentence. In a second stage, which could already take place after limited exposure, the learner may develop item-specific syntactic representations. For instance, they encounter the verb "give" with a noun-phrase (NP) containing the object, followed by a prepositional phrase (PP) containing the recipient (e.g., "the cook gave a ball to the clown"), so they learn that "give" is complemented with NP + PP. Assuming that L2 syntactic knowledge is gathered through exposure (using a form of Hebbian learning), it is highly likely that the learner will first develop syntactic representations for frequent verbs and structures, and only later (i.e., in the third stage of the theory) for less frequent verbs and structures. Learners in the second and third stage will show item-specific priming, but only for the lexical items for which they already have L2 syntactic representations in place. Importantly, such priming would take place irrespective of whether the learner is able to recall the prime sentence, since this is no longer required to formulate sentences in the L2. In the fourth stage, the learner starts to generalize syntactic structures across lexical items (e.g., the verbs "give" and "send" can both be followed by NP + PP), which means that they will show priming within the L2, regardless of whether lexical items are repeated between prime and target. Finally, in the fifth stage, learners will merge the new L2 syntactic representations with the existing L1 representations. From that moment on, they will show priming from the L1 to the L2 and vice versa, both in presence and absence of meaning overlap between the prime and target. 1.1 The PP02 learning paradigm The predictions of Hartsuiker and Bernolet’s ( 2017 ) developmental theory were tested in a series of experiments that used an artificial language (AL) learning paradigm to study structural priming within and across languages from the onset of L2 acquisition (Muylle et al., 2021a , 2021b , 2021c ; Muylle, Bernolet, et al., 2020). In this paradigm, L1 Dutch speakers learned to produce intransitive, transitive, and ditransitive sentences in the AL (named “PP02”) by means of five different tasks that were administered sequentially. The first task was a vocabulary learning task in which participants learned the AL nouns to describe depicted human figurines (e.g., cook, witch) and objects (e.g., ball, hat). Next, they watched short action movie clips (taken from the normed stimulus set by Muylle, Wegner, et al., 2020 ) in which the human figurines interacted with each other and with objects, while listening to a sentence in the AL that described these actions. Following this exposure task, participants did a movie-sentence matching task in which they were asked to watch two movie clips on the screen and match a presented AL sentence with the correct movie clip. Next, there was a sentence production task in which the participants described movie clips in the AL and received feedback. Finally, intra-lingual and cross-linguistic priming effects were assessed by means of a structural priming task. This task consisted of two parts. First, participants had to judge whether a Dutch or AL sentence (i.e., the prime) matched a movie clip and after that, they saw a new movie and were asked to describe the action (i.e., the target) in Dutch or the AL, depending on a visual cue. Transitive primes could be in the active or passive voice and ditransitive primes could have a prepositional object (PO, e.g., "the cook is giving a book to the clown") or double-object (DO) dative structure (e.g., "the cook is giving the clown a book"). Intransitive sentences (e.g., the clown is waving") acted as fillers. The AL learning paradigm has proven to be successful in eliciting structural priming effects both within the AL and between the AL and Dutch from the first day of learning. In a multiple-session study, two experiments tested structural priming effects across five AL learning sessions (Muylle et al., 2021a ). The first experiment used a relatively easy version of PP02 (e.g., no articles or verb conjugations), whereas in the second experiment, we administered a more difficult PP02 version (e.g., with articles and verb conjugations). Participants in both experiments were very fast in acquiring the AL and they already showed priming within the AL and from the AL to Dutch from the first day of learning. Priming from Dutch to the AL was present on the first day for transitives as well, but for ditransitives, there was only an effect from the second day onward. These results suggest that L2 learners may indeed start with L2-specific syntactic representations that are only later merged with L1 representations. However, since most priming effects were already present on Day 1 of testing, subsequent experiments using this paradigm only included one or two sessions. A second series of studies used the PP02 learning paradigm to explore how similar the structures have to be across languages in order to be shared in the L2 learner's mind (Muylle et al., 2021c ; Muylle, Bernolet, et al., 2020). One study (Muylle, Bernolet, et al., 2020) compared priming effects within the AL and between the AL and Dutch (that has SVO order and no overt case marking) across different versions of PP02 (i.e., a version with SVO order, one with SOV order, and one with case marking) in a between-subjects design and found similar effects across all versions, suggesting that differences in the presence of overt case marking or in word order across languages do not influence the extent to which learners share the representations of these languages. The other study (Muylle et al., 2021c ) tested SVO and SOV PP02 structures in a within-subjects design and found that the presence of the SVO order in the AL hindered the learning and sharing of the SOV order. Finally, Muylle et al. ( 2021b ) studied the role of L1 and AL frequency distributions in priming from PP02 to L1 Dutch by comparing a PP02 version that had a DO bias versus one that had a PO bias in a between-subjects design. Here, immediate priming was not affected by the bias in the AL, but only by the Dutch bias (i.e., a PO bias). However, participants in the DO bias condition were more likely to produce DO structures in Dutch overall than participants in the PO condition, showing that the AL exerted an influence on the preference in Dutch, regardless of the immediate prime. Crucially, this experiment could not clarify whether learners first build L2 presentations for the most frequent structures and only later for less frequent structures, as predicted by the developmental theory. In sum, the AL learning paradigm has proven to be a useful tool to mimic second language acquisition in a highly controlled context. Nevertheless, it is important to bear in mind that there are also important differences with natural language learning. For instance, since the participants need to be able to acquire the AL in the course of a few hours, the vocabulary size and number of different structures have to be sufficiently small. In addition, the participants' motivation and the learning context are different from learners' motivations and learning environments/contexts in real-life situations. 1.2 A different approach to proficiency The multiple-session AL learning experiments (Muylle et al., 2021a ) were not fully conclusive on whether L2 representations evolve gradually from being item-specific to abstract with increasing L2 proficiency as proposed by the developmental theory. An issue with these studies was that there was already evidence for the sharing of syntax across the L1 and the AL (assessed by structural priming) on the first day of AL learning. In other words, the sharing went so fast that it was not possible to observe the early stages of its development using an AL learning paradigm. These studies considered proficiency a feature that develops over time. Indeed, AL proficiency (i.e., average AL accuracy scores) increased from the first to the second session, but then reached a plateau. Another way to deal with proficiency is by comparing priming effects in participants with high AL accuracy scores with those in participants that have low scores. In other words, the effect of AL proficiency on priming can also be studied by testing individual differences in learning on the first day of AL acquisition. Although several studies used the AL learning paradigm (Muylle et al., 2021a , 2021b , 2021c ; Muylle, Bernolet, et al., 2020), the influence of AL accuracy on priming was not addressed in the individual studies because of the limited number of participants. However, in the current study, we combined the data from these methodologically similar studies into one big data frame. This allowed us to test Hartsuiker and Bernolet’s hypotheses on a sample of 336 participants. In addition, we investigated whether other individual differences such as L1 proficiency and working memory (WM) play a role in the learning of the AL and in the sharing of syntax across the AL and L1. 1.3 Working memory One important variable to consider is WM capacity, since explicit memory mechanisms (which are restricted by WM capacity) play a crucial role in the first stage of the developmental theory. In this stage, learners have no L2 syntactic representations in place yet on which they can rely to formulate sentences. Therefore, they may imitate more proficient L2 speakers by copying and editing their utterances. Generally speaking, the developmental theory assumes a more important role for explicit memory processes in early vs. later stages of L2 acquisition, based on the finding that the magnitude of L2 priming with lexical overlap decreases with increasing proficiency (Bernolet et al., 2013 ; Hartsuiker & Bernolet's 2017 re-analysis of Schoonbaert et al., 2007 ). Coumel and colleagues ( 2024 ) recently tested this prediction by studying L2 priming with repeated verbs across low and high proficiency L2 speakers of French in a dual-task design (i.e., a letter series recall task between prime and target). In both groups, priming effects became smaller when the intervening task was more difficult (i.e., when letter series became longer). Interestingly, an exploratory comparison across proficiency levels suggested that less proficient speakers relied more on explicit memory of the prime than more proficient speakers, who fell back to their own preferences to formulate sentences. Apart from the developmental theory, the well-established lexical boost effect to priming (i.e., larger priming effects when there is lexical overlap between prime and target compared to when there is no such overlap) is often explained in terms of explicit memory mechanisms and hence also depends on WM capacity (e.g., Bock & Griffin, 2000 ; Chang et al., 2006 , 2012 ; Reitter et al., 2011 ; Zhang et al., 2020 ). To assess WM capacity in the AL learning studies, we mostly administered the forward and backward digit span tests (WAIS-IV subtests; Wechsler, 2008 ), except for the first multiple-session experiment, where we used the Operation Span (OSPAN) test (Oswald et al., 2015 ). In the digit span tests, participants repeat increasingly longer number sequences (either forward in the forward digit span task or backward in the backward digit span task) for as long as they can correctly recall them. The results of these tests provide an approximation of how many items can be retained in WM in the correct order. Because the backward digit span test requires transformation of the retained information, it may be more informative about the processing capacity of the participant’s WM than the forward digit span (Alloway et al., 2006 ). In addition, the backward digit span may involve visuo-spatial processing (e.g., Li & Lewandowsky, 1995 ; St Clair-Thompson & Allen, 2013 ). In addition, we inspected whether digit span scores predicted priming. Participants with a high forward digit span score tended to show more priming than those with a lower score, but this effect was only present in ditransitive and not in transitive structures. Since ditransitive sentences contain more phrasal constituents than transitive sentences, it is not surprising that processing ditransitives requires more WM capacity, and hence, learners with higher WM capacity have more resources left to maintain the prime structure in WM than learners with lower WM capacity. The backward digit span, on the other hand, showed a less straightforward relation with priming. In general, participants with high backward digit span scores showed smaller priming effects than participants with lower scores, which seems opposite to the effect of the forward digit span. However, a closer inspection of the data revealed that backward digit span scores mainly affected priming within L1 Dutch or from the AL to Dutch when there was lexical overlap. One explanation could be that participants with lower span scores rely more on explicit memory when formulating a new sentence and hence show more priming in conditions with lexical overlap. Participants with higher span scores, however, may have a better memory of sentences preceding the prime, which enables them to develop abstract syntactic representations at a faster pace. As such they rely less on explicit memory of the prime to formulate sentences and fall back on more implicit priming mechanisms (cf. Coumel et al., 2023). Indeed, priming effects that are supported by explicit memory of the prime tend to be larger than those that are not (e.g., Hartsuiker et al., 2008 ). Since the analyses of the multiple-session study indicated a rather complicated relation between WM capacity and priming, we did not further test the relation between digit span scores and priming in the subsequent AL learning studies. Instead, these data were mainly used to control for individual differences between groups in between-subjects designs. Therefore, the current re-analysis also aimed to further examine the effects of the forward and backward digit span score. In addition, we studied the effect of the digit span scores on accuracy in the AL. 1.4 L1 proficiency Another factor that may influence the learning of the AL is the proficiency in the L1. Many studies have shown that there is a strong relationship between L1 and foreign language learning ability (e.g., Cummins, 1984 ; Dufva & Voeten, 1999 ; Hulstijn & Bossers, 1992 ; Sparks & Ganschow, 1993 ). Concretely, it has been argued that mastering a certain skill (e.g., phonology) in the L1 serves as a basis for learning that same skill in the L2, and that there is a basic language learning mechanism that underlies learning in both L1 and L2 (e.g., Sparks, 1995 ; Sparks & Ganschow, 1993 ). As such, it may be interesting to study whether L1 proficiency also predicts accuracy in the AL. A positive relationship would further support the AL learning paradigm as an appropriate method to study natural L2 acquisition. In all studies using this paradigm, we measured the participant's score on the Dutch LexTALE test, a short vocabulary test that consists of 60 lexical decision trials (Lemhöfer & Broersma, 2012 ). Although the LexTALE test provides an estimation of the vocabulary size in particular, the scores correlate well with other language proficiency measures (Lemhöfer & Broersma, 2012 ). In the current re-analysis, we therefore also included the Dutch LexTALE score as a covariate in our models. 1.5 Research questions and hypotheses In this re-analysis, we took together all (priming) results from the AL studies to answer the following three research questions: 1) How do individual differences influence accuracy in the AL? For this, AL accuracy was taken as a dependent variable and we tested whether forward digit span, backward digit span, and LexTALE Dutch score influenced accuracy scores in the various AL tasks. Since L1 learning abilities are related to foreign language learning abilities (e.g., Cummins, 1984 ; Dufva & Voeten, 1999 ; Hulstijn & Bossers, 1992 ; Sparks & Ganschow, 1993 ), we expected an effect of the L1 LexTALE score on AL accuracy. 2) How do individual differences influence structural priming in general? Here, we tested whether both digit span scores, the LexTALE Dutch score, and AL accuracy predicted priming effects overall. Since several theories assume a role for WM in priming (e.g., Bock & Griffin, 2000 ; Chang et al., 2006 , 2012 ; Hartsuiker & Bernolet, 2017 ; Reitter et al., 2011 ; Zhang et al., 2020 ), we expected that both digit span scores would affect priming. For AL accuracy, we also predicted a positive effect, since Hartsuiker and Bernolet ( 2017 ) predicted more priming with increasing proficiency in most of the conditions. 3) How does AL accuracy influence structural priming in the different priming conditions? To investigate the assumption of Hartsuiker and Bernolet's developmental theory that L2 syntactic representations become more abstract with increasing proficiency and finally become shared with existing L1 representations, we tested whether priming effects became larger with increasing PP02 proficiency. Concretely, we expected more priming for high vs. low proficiency participants in all priming conditions, except for the related PP02-PP02 condition. In other words, we predicted an interaction between Prime structure , Relatedness , Target Language , and AL accuracy . On the one hand, priming effects should be larger within the AL than from the AL to Dutch and should be larger for related prime-target pairs (i.e., with verb overlap) compared to unrelated pairs (i.e., without verb overlap). Moreover, this effect of relatedness is thought to be larger within the AL (i.e., the lexical boost effect) than between languages (i.e., the translation equivalent boost effect). On the other hand, priming effects should be larger with increasing proficiency, but only for the more abstract types of priming and not for related PP02-PP02 priming. 2 Methods 2.1 Data inclusion and exclusion Priming data were gathered from Muylle et al.'s 2020 , 2021b , and 2021c studies, but only from Day 1 and only for PP02-PP02 and PP02-Dutch conditions (which were present in all these studies). All studies tested priming in ditransitives (PO vs. DO) and in transitives (active vs. passive). In addition, we included the participants' AL accuracy, forward digit span, backward digit span, and LexTALE Dutch scores. The multiple-session study (Muylle et al., 2021a ) was not included in these analyses because the experiments in that study were quite different from the other studies in various ways (e.g., amount of vocabulary, priming conditions, etc. …). 2.2 Participants In total, this dataset includes experimental data of 336 participants. All of them were university students with L1 Dutch. They had normal or corrected-to-normal hearing and vision. None of them reported having language or learning disorders. 2.3 Variables Group . This categorical variable with 7 levels indicates the individual between-subjects conditions to which participants belonged: CM1: case marking study – baseline condition, CM2: case marking study – case marking condition, CM3: case marking study – SOV condition, BL1: blocking study – Exp 1, BL2: blocking study – Exp 2, FR1: frequency study – DO bias condition, FR2: frequency study – PO bias condition. All groups were exposed to active, passive, DO, and PO primes. LexTALE. This is the score on the Dutch LexTALE test (Lemhöfer & Broersma, 2012 ), that measures vocabulary size in Dutch (the participants’ L1). Forward digit span. This continuous score was measured with a classical spoken digit span task, in which participants were asked to repeat increasingly longer sequences of numbers in the correct order. Backward digit span. This score was measured via the same task as the forward digit span, but here participants repeated number sequences in reverse order. AL accuracy. This continuous score was computed by adding accuracy scores of a) the final presentation of each noun item ( N = 12) in the vocabulary learning block, b) the matching block ( N = 50 or 90, depending on the study), c) the sentence production block ( N = 20 or 24, depending on the study), and d) PP02 target trials in the priming block ( N = 80, 60 or 74, depending on the study), and then dividing this sum by the total number of observations. Prime structure. We conducted separate analyses for transitives and ditransitive sentences. Prime structure was a categorical variable with two levels: for the transitives, this was active vs. passive, and for the ditransitives this was PO vs. DO. 2.4 Analyses and results 2.4.1 How do individual differences influence accuracy in the AL? In order to answer this question, we built generalized linear mixed effects models (beta family) using the glmmTMB package (Brooks et al., 2017 ) in R (R Development Core Team, 2017 ) with AL accuracy as outcome variable. The random effects structure consisted of a random intercept for Group (we did not include an intercept for participant, since all variables were between subjects). By adding this random intercept to the model, we could control for differences between conditions across experiments (e.g., accuracy tends to be lower in the CM2 compared to the CM1 group, because the PP02 version is more difficult). Inclusion of random slopes led to singularity issues, so they were removed. For the fixed effects, we added LexTALE , Forward digit span , and Backward digit span to the model, using a forward modelling strategy. Concretely, we started with models that only included a main effect of one of these variables (Model 1–3). Next, we added one of the other main effects to the model (Model 4–6). Finally, we tested a model with all three main effects (Model 7). The different models are presented in Table 1 . [1] Table 1 Forward models of individual differences in AL accuracy Fixed effects structure ß Z P 1) LexTALE 0.02 3.33 < .001 2) Forward digit span 0.18 5.45 < .001 3) Backward digit span 0.17 5.16 < .001 4) LexTALE + Forward digit span 0.01 0.17 2.55 5.02 < .05 < .001 5) LexTALE + Backward digit span 0.01 0.17 3.11 5.03 < .01 < .001 6) Forward digit span + Backward digit span 0.14 0.12 3.77 3.34 < .001 < .001 7) Forward digit span + Backward digit span + LexTALE 0.12 0.12 0.01 3.40 3.40 2.63 < .001 < .001 < .01 From these results, it can be deducted that the forward and backward digit span were the strongest predictors for AL accuracy. The backward digit span explained some additional variance on top of the forward digit span (despite the high correlation between the forward and backward digit span: r = .43, t (333) = 8.60, p < .001). Furthermore, the LexTALE explained additional variance on top of the forward and backward digit span (despite the high correlation between LexTALE and forward digit span: r = .18, t (333) =, p < .001) [2] . Hence, we maintained the Forward digit span + Backward digit span + LexTALE as the final model. The individual main effects are depicted in Fig. 1 . 2.4.2 How do individual differences influence structural priming in general? Transitives were treated separately from ditransitives in these analyses for practical reasons and because it allows to find effects of individual differences that are specific for one of both structures. Transitives. To find out which individual differences influenced structural priming, we built generalized linear mixed effects models using the afex package (Singmann et al., 2016 ) in R with Active response as binomial outcome variable (0 = passive, 1 = active). We started from the maximal random effects structure, as suggested by Barr, Levy, Scheepers, & Tily ( 2013 ), which was reduced in case of singularity (or other non-convergence) issues following the recommendations of Bates, Kliegl, Vasishth, & Baayen ( 2015 ). The maximal model consisted of a random intercept of Participant and Group , and a random slope of Prime structure over participants and over groups. Similar to the previous analyses, we used forward modelling to determine the variables that contributed to the priming effect. If one of these variables affects priming, there should be an interaction with Prime structure . An overview of the different models can be found in Table 2 . Table 2 Forward models of individual differences in structural priming for transitives Fixed effects Random effects Wald’s Z p Prime structure * AL accuracy (Prime structure | Participant) + (1 | Group) 4.76 < .001 Prime structure * LexTALE (Prime structure | Participant) + (Prime structure || Group) 2.28 < .05 Prime structure * Forward digit span (Prime structure || Participant) + (Prime structure || Group) 1.59 .11 Prime structure * Backward digit span (Prime structure || Participant) + (Prime structure || Group) -0.86 .39 Prime structure * AL accuracy + Prime structure * LexTALE + Backward digit span (1 | Participant) + (1 | Group) 6.54 3.02 -2.64 < .001 < .01 < .01 Prime structure * AL accuracy * LexTALE + Backward digit span (1 | Participant) + (1 | Group) -0.36 -2.65 .72 < .01 As can be seen from the table, both AL accuracy and Dutch LexTALE scores predicted structural priming independently. In contrast, the digit span scores did not have a significant effect on priming. However, because the analyses with the backward digit span model have shown that there was a main effect of Backward digit span on the proportion of active responses (Wald’s Z = -2.29, p < .05), this main effect was included in the more complicated models. Given that there was no significant three-way interaction between Prime structure , LexTALE , and AL accuracy , the model with Prime structure * AL accuracy + Prime structure * LexTALE + Backward digit span as fixed effects was kept as the final model. The effects of the individual difference measures on priming are visualized in Fig. 2 . Ditransitives. The same analyses were conducted for the ditransitives as for the transitives, but now the outcome variable was PO response . An overview of the forward models can be found in Table 3 . Table 3 Forward models of individual differences in structural priming for ditransitives Fixed effects Random effects Wald’s Z p Prime structure * AL accuracy (1 | Participant) + (Prime structure | Group) -2.70 < .01 Prime structure * LexTALE (1 | Participant) + (Prime structure | Group) -0.21 .84 Prime structure * Forward digit span (1 | Participant) + (Prime structure || Group) -2.89 < .01 Prime structure * Backward digit span (1 | Participant) + (Prime structure | Group) -1.76 .078 Prime structure * AL accuracy + Prime structure * Forward digit span (1 | Participant) + (Prime structure || Group) -1.84 -2.16 .066 < .05 Prime structure * AL accuracy * Forward digit span (1 | Participant) + (Prime structure || Group) -0.43 .66 Here, the only variables that predicted structural priming in isolation were AL accuracy and Forward digit span . Indeed, the higher the AL accuracy and forward digit span scores, the larger the priming effects. However, when those variables appeared together in the model, the effect of AL accuracy disappeared (although it was still marginally significant). There was no three-way interaction with Prime structure . Hence, the model with Prime structure * AL accuracy + Prime structure * Forward digit span was kept as the final model. We plotted the effects of the individual difference measures on priming in Fig. 3 . 2.4.3 How does AL accuracy influence structural priming in the different priming conditions? We performed separate confirmatory analyses for transitive and ditransitive structures. As before, we used generalized linear mixed effects models, starting from the maximal random effects structure, namely a random intercept for Participant and Group and a random slope for Prime structure * Target language over participants and groups ( Relatedness was not included in the random effects because participants in the first experiment of the blocking study were only tested on unrelated priming). The fixed effects consisted of the Prime structure * Relatedness * Target language * AL accuracy interaction. Again, the outcome variable was Active response for the transitives and PO response for the ditransitives. Furthermore, to assess priming and the effect of AL accuracy in the different conditions, we performed post-hoc pairwise contrasts using the phia package in R (De Rosario-Martinez, 2015 ). Transitives. The final random model consisted of a random intercept for Participant and Group and an uncorrelated random slope of Prime structure and Target language over participants. An overview of the fixed effects can be found in Table 4 . Table 4 Transitive model output Summary of the fixed effects in the multilevel logit model ( N = 10976; log-likelihood = -2911.9) Fixed effect Coefficient SE Wald’s Z p (Intercept) 3.42 (0.387) 8.85 < .001 Prime structure 1.46 (0.070) 21.02 < .001 Relatedness -0.25 (0.051) -4.89 < .001 Target language 0.09 (0.077) 1.16 .24 AL accuracy 3.06 (1.142) 2.68 < .01 Prime structure * Relatedness 0.76 (0.051) 14.95 < .001 Prime structure * Target language -0.13 (0.054) -2.45 < .05 Relatedness * Target language 0.08 (0.051) 1.52 .13 Prime structure * AL accuracy 1.83 (0.531) 3.44 < .001 Relatedness * AL accuracy 0.06 (0.396) 0.15 .88 Target language * AL accuracy 0.36 (0.632) 0.57 .57 Prime structure * Relatedness * Target language -0.13 (0.050) -2.59 < .01 Prime structure * Relatedness * AL accuracy 1.43 (0.391) 3.66 < .001 Prime structure * Target language * AL accuracy 0.73 (0.412) 1.77 .076 Relatedness * Target language * AL accuracy 0.05 (0.393) 0.13 .90 Prime structure * Relatedness * Target language * AL accuracy -0.11 (0.385) -0.29 .77 There was no significant four-way interaction between Prime structure , Relatedness , Target language , and AL accuracy , but there was a significant interaction between Prime structure , Relatedness , and AL accuracy (see Fig. 4 ). Pairwise contrasts indicate that priming effects became larger with increasing AL accuracy, but only in the related conditions (related: χ 2 (1) = 21.49, p < .001; unrelated: χ 2 (1) = 0.42, p = .52). In addition, there was a marginally significant interaction between Prime structure , Target language , and AL accuracy , in the sense that the (positive) slope of accuracy on priming was steeper for Dutch ( χ 2 (1) = 13.44, p < .001) compared to PP02 targets ( χ 2 (1) = 2.89, p = .088). Furthermore, the three-way interaction between Prime structure , Relatedness , and Target language confirms the priming pattern of the individual AL studies that a) there is a lexical boost effect to priming (related PP02-unrelated PP02: χ 2 (1) = 180.72, p < .001), b) a translation equivalent boost effect to priming (related Dutch-unrelated Dutch: χ 2 (1) = 68.37, p < .001), which is weaker than the lexical boost effect (related-unrelated in Dutch vs. PP02: χ 2 (1) = 6.73, p < .01), and c) priming within PP02 is stronger than from PP02 to Dutch (PP02-Dutch: χ 2 (1) = 6.19, p < .05). There was a significant priming effect in all priming conditions (related PP02: χ 2 (1) = 476.39, p < .001; unrelated PP02: χ 2 (1) = 53.03, p < .001; related Dutch: χ 2 (1) = 210.10, p < .001; unrelated Dutch: χ 2 (1) = 47.24, p < .001) Ditransitives. Here, the random effects structure in the final model consisted of a random intercept of Participant and Group and an uncorrelated random slope of Prime structure * Target language over participants and of Prime structure + Target language over groups. The model output is presented in Table 5 . Table 5 Ditransitive model output Summary of the fixed effects in the multilevel logit model ( N = 11293; log-likelihood = -3799.4) Fixed effect Coefficient SE Wald’s Z p (Intercept) 1.85 (0.324) 5.70 < .001 Prime structure -0.76 (0.071) -10.75 < .001 Relatedness 0.00 (0.043) -0.08 .94 Target language 1.37 (0.249) 5.50 < .001 AL accuracy -1.46 (1.147) -1.28 .20 Prime structure * Relatedness -0.36 (0.039) -9.20 < .001 Prime structure * Target language 0.52 (0.040) 12.96 < .001 Relatedness * Target language 0.02 (0.043) 0.57 .57 Prime structure * AL accuracy -1.92 (0.408) -4.70 < .001 Relatedness * AL accuracy 0.23 (0.404) 0.57 .57 Target language * AL accuracy -1.94 (1.062) -1.83 .067 Prime structure * Relatedness * Target language 0.29 (0.041) 7.08 < .001 Prime structure * Relatedness * AL accuracy -1.27 (0.350) -3.62 < .001 Prime structure * Target language * AL accuracy 0.85 (0.359) 2.36 < .05 Relatedness * Target language * AL accuracy 0.45 (0.404) 1.10 .27 Prime structure * Relatedness * Target language * AL accuracy 0.83 (0.365) 2.26 < .05 In this model, there was a significant four-way interaction between Prime structure , Relatedness , Target language , and AL accuracy (see Fig. 5 ). Post-hoc pairwise comparisons revealed a significant positive slope of AL accuracy on the priming effect, but only for the related PP02 condition ( χ 2 (1) = 46.51, p < .001). The other conditions also showed larger priming effects with increasing AL proficiency, but this was not significant (unrelated PP02: χ 2 (1) = 1.54, p = .43; related Dutch: χ 2 (1) = 2.50, p = .34; unrelated Dutch: χ 2 (1) = 0.81, p = .43). Also here, we found significant priming effects in all priming conditions, but these appeared to be weaker compared to the transitives, especially for the Dutch targets (related PP02: χ 2 (1) = 352.50, p < .001; unrelated PP02: χ 2 (1) = 43.68, p < .001; related Dutch: χ 2 (1) = 7.15, p < .05; unrelated Dutch: χ 2 (1) = 4.43, p < .05). In line with the individual experiments, there was a lexical boost effect ( χ 2 (1) = 143.46, p < .001), and no translation equivalent boost effect ( χ 2 (1) = 1.23, p = .27). 3 Discussion The first aim of this study was to find out how certain individual differences influence AL accuracy. Concretely, we assessed the role of L1 proficiency, assessed by the Dutch LexTALE test, and WM capacity, as measured by the forward and backward digit span, using forward modelling strategies. Participants with a high forward or backward digit span had higher AL accuracy scores than those with a lower span. Interestingly, both span tasks contributed to the effect independently, in line with studies arguing that forward and backward digit span measure (partially) different abilities (e.g., Alloway et al., 2006 ; Li & Lewandowsky, 1995 ; St Clair-Thompson & Allen, 2013 ). On the one hand, the forward digit span may reflect the ability to maintain phonological information in WM (e.g., St Clair-Thompson & Allen, 2013 ), which could facilitate vocabulary acquisition, and thus leave more cognitive resources available to learn the AL's syntax. The backward digit span, on the other hand, is thought to involve visuo-spatial processing (e.g., Li & Lewandowsky, 1995 ; St Clair-Thompson & Allen, 2013 ), which could enhance the acquisition of the AL's syntax (including positioning of phrasal constituents in the sentence). LexTALE scores also exerted an independent positive effect on AL accuracy, despite the high correlation with the forward digit span. This result indicates that participants with a high digit span score tended to have a larger vocabulary size in L1 and also had a higher accuracy in the AL. Verbal WM has indeed been found to be associated with L1 and L2 vocabulary learning (e.g., Baddeley et al., 1998 ; Gathercole et al., 1999 ; Majerus et al., 2008 ; Service, 1992 ). The current findings corroborate previous results that L1 proficiency influences foreign language proficiency (e.g., Cummins, 1984 ; Dufva & Voeten, 1999 ; Hulstijn & Bossers, 1992 ; Sparks & Ganschow, 1993 ) and also pinpoint an important role for WM in language learning abilities. Moreover, the significant relation between LexTALE scores and AL accuracy further indicates that the PP02 learning paradigm resembles natural L2 acquisition. The second aim of the current study was to identify the participant variables that mediate structural priming in general. Again, we studied the role of L1 proficiency and WM capacity, but also of AL proficiency. This was done separately for transitive and ditransitive structures. For the transitives, the results indicated that participants with higher Dutch LexTALE scores and higher AL accuracy scores show more structural priming than those with lower scores. In addition, participants with high backward digit span scores were more likely to produce passive sentences compared to those with lower scores. Since the formulation of passives requires more transformations of lexical items compared to actives (e.g., creating a past participle and adding an auxiliary verb), it stands to reason that participants who have more WM capacity available are more likely to produce passive sentences. Both digit span scores did not have an effect on structural priming in transitive sentences. For the ditransitives, there was no effect of LexTALE scores and backward digit span, but there was an effect of forward digit span and AL accuracy on priming. However, the effect of AL accuracy became only marginally significant when the forward digit span was included in the model as well. Thus, it seems that the effect of AL accuracy on priming can be explained by WM capacity as assessed by the forward digit span. This effect may be specific for ditransitive structures which have more constituents than transitive structures. As such, participants with a higher WM capacity might be better in retaining all information from the prime sentence, resulting in priming. The final goal of this study was to test the prediction of the developmental theory of shared syntactic representations (Hartsuiker & Bernolet, 2017 ) that abstract priming effects will become larger with increasing L2 proficiency, as has been attested in the study by Bernolet, Hartsuiker, and Pickering ( 2013 ) and a reanalysis of the study by Schoonbaert, Hartsuiker, and Pickering ( 2007 ). For this, we assessed whether there was a significant slope of accuracy on the magnitude of the priming effect in the different priming conditions. The theory predicts a positive slope for unrelated PP02-PP02 priming, and related and unrelated PP02-Dutch priming. In contrast, the theory does not assume a positive slope for related PP02-PP02, because this type of priming is hypothesized to be present from the earliest phases of L2 learning. For the transitives, we found a significant positive slope for the related conditions and for both target languages, although the effect was stronger for Dutch targets. These results are largely in line with Hartsuiker & Bernolet's developmental theory because they show that people with high AL proficiency show more between-language priming than those with low proficiency. However, a positive slope was also observed for related PP02-PP02 priming, in contrast to the findings with natural L2s (Bernolet et al., 2013 ; Schoonbaert et al., 2007 ), that did not show an increase in related L2-L2 priming with ascending L2 proficiency. A possible explanation for this contradictory result may be found in the fact that the current data reflect priming on the first day of AL learning, whereas participants in the other studies had been exposed to the L2 for years. Indeed, both low- and high-proficiency participants in the current study had only limited exposure to the AL and did not have the time to start relying less on the specific prime as they became more proficient. In fact, participants in the AL study that are sensitive to priming (or use priming as a strategy) may be better learners of the AL in this short time period. This idea is in line with the observation that the learning of difficult structures (in this case, the passive) can be sped up by means of a structural priming intervention with lexical overlap between primes and targets (Muylle et al., 2021c ; van Lieburg et al., 2023 ). The results for the ditransitives are quite different from the transitives. We only found a significant slope of AL accuracy on ditransitive priming for the related PP02-PP02 condition. The absence of an effect for abstract priming can be reconciled with the fact that many of the AL studies did not find cross-linguistic priming for ditransitives on the first day of learning (Muylle et al., 2021a , 2021c ; Muylle, Bernolet, et al., 2020). The cross-linguistic priming effects for ditransitives in the current data set are significant, but clearly much weaker than for transitives, leaving little room to detect differences across participants. It is possible that the majority of participants did not develop shared syntactic representations for ditransitives after one learning session. As such, even highly proficient participants may not show strong evidence for priming yet. However, participants with high AL accuracy did show more related PP02-PP02 priming than those with lower accuracy. This finding is similar to what has been observed for the transitives and there is probably a similar explanation. In addition, the other analyses showed that WM capacity as assessed by the forward digit span has a strengthening effect on priming (and on AL learning) in ditransitives, so people wo have a higher WM capacity are better in maintaining the prime sentence in WM and hence show more related priming and better learning. Taken together, the results for the transitives and ditransitives show a clear relation between related PP02-PP02 priming and AL accuracy. In the early stages of learning, strong related priming coincides with better AL learning. This indicates that item-specific structural priming may play an important role in L2 learning, as suggested by Muylle et al. ( 2021c ) and van Lieburg et al. ( 2023 ). Thus, although the developmental theory predicts that more proficient L2 speakers will rely less on item-specific priming than less proficient ones, L2 speakers who show more item-specific priming at the earliest phases of learning may be better L2 learners. These findings have practical implications for L2 education. Indeed, structural priming can serve as a tool to teach L2 structures in the classroom, even when these structures are not similar to L1 structures. 4 Conclusions The current study combined the data from three PP02 learning studies into one big analysis. This re-analysis revealed some interesting insights. First, participants with high L1 proficiency and high WM capacity, in terms of recalling numbers in the correct (forward digit span) or reverse (backward digit span) order, had an advantage when learning the AL. Moreover, the effect of the forward digit span could not be fully explained by the effect of the backward digit span, which means that there are different mechanisms underlying both tasks that are responsible for the learning advantage, in line with previous research. Second, both AL and L1 proficiency predicted priming in transitives. The higher the proficiency in both languages, the more priming, in line with Hartsuiker and Bernolet's developmental theory. However, the only factor that reliably predicted priming in ditransitives was the forward digit span score, which shows that WM capacity may play a more important role in the acquisition of complex vs. easier structures. Finally, AL proficiency had the strongest relation with priming effects in PP02-PP02 conditions with verb overlap, although participants with a high AL accuracy score also tended to show more priming under more abstract priming conditions. The positive relation between AL proficiency and priming shows that priming can improve the acquisition of L2 syntax and hence should be used as a tool in L2 education. Declarations Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Funding This work was supported by a grant from the Research Foundation – Flanders (FWO) [grant number G049416N]. Author Contribution Conceptualization: M.M., S.B., R.H.; Methodology: M.M., R.H.; Formal analysis and investigation: M.M.; Writing - original draft preparation: M.M.; Writing - review and editing: M.M., S.B., R.H.; Funding acquisition: S.B., R.H.; Supervision: S.B., R.H. 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R: A language and environment for statistical computing. In Vienna, Austria . https://doi.org/R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org. Reitter, D., Keller, F., & Moore, J. D. (2011). A computational cognitive model of syntactic priming. Cognitive Science , 35 (4), 587–637. https://doi.org/10.1111/j.1551-6709.2010.01165.x Schoonbaert, S., Hartsuiker, R. J., & Pickering, M. J. (2007). The representation of lexical and syntactic information in bilinguals: Evidence from syntactic priming. Journal of Memory and Language , 56 (2), 153–171. https://doi.org/10.1016/j.jml.2006.10.002 Service, E. (1992). Phonology, Working Memory, and Foreign-language Learning. The Quarterly Journal of Experimental Psychology Section A , 45 (1), 21–50. https://doi.org/10.1080/14640749208401314 Shin, J.-A., & Christianson, K. (2009). Syntactic processing in Korean–English bilingual production: Evidence from cross-linguistic structural priming. Cognition , 112 (1), 175–180. https://doi.org/10.1016/j.cognition.2009.03.011 Singmann, H., Bolker, B., Westfall, J., Aust, F., Højsgaard, S., Fox, J., Lawrence, M. A., Mertens, U., & Love, J. (2016). afex: Analysis of factorial experiments. In R package version 0.16-1. https://CRAN.R-project.org/package=afex . Song, Y., & Do, Y. (2018). Cross-linguistic structural priming in bilinguals: priming of the subject-to-object raising construction between English and Korean. Bilingualism: Language and Cognition , 21 (1), 47–62. https://doi.org/10.1017/S1366728916001152 Sparks, R. L. (1995). Examining the linguistic coding differences hypothesis to explain individual differences in foreign language learning. Annals of Dyslexia , 45 (1), 187–214. https://doi.org/10.1007/BF02648218 Sparks, R. L., & Ganschow, L. (1993). Searching for the Cognitive Locus of Foreign Language Learning Difficulties: Linking First and Second Language Learning. The Modern Language Journal , 77 (3), 289–302. https://doi.org/10.1111/j.1540-4781.1993.tb01974.x St Clair-Thompson, H. L., & Allen, R. J. (2013). Are forward and backward recall the same? A dual-task study of digit recall. Memory & Cognition , 41 (4), 519–532. https://doi.org/10.3758/s13421-012-0277-2 van Gompel, R. P. G., & Arai, M. (2018). Structural priming in bilinguals. Bilingualism: Language and Cognition , 21 (3), 448–455. https://doi.org/10.1017/S1366728917000542 van Lieburg, R., Sijyeniyo, E., Hartsuiker, R. J., & Bernolet, S. (2023). The development of abstract syntactic representations in beginning L2 learners of Dutch. Journal of Cultural Cognitive Science , 7 (3), 289–309. https://doi.org/10.1007/s41809-023-00131-5 Wechsler, D. (2008). Wechsler Adult Intelligence Scale - Fourth edition administration and scoring manual . Pearson. Zhang, C., Bernolet, S., & Hartsuiker, R. J. (2020). The role of explicit memory in syntactic persistence: Effects of lexical cueing and load on sentence memory and sentence production. PLOS ONE , 15 (11), e0240909. https://doi.org/10.1371/journal.pone.0240909 Footnotes Interactions were not significant. Backward digit span scores were not correlated with LexTALE scores ( p = .28) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4351475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298978435,"identity":"66855b6e-61af-4257-9d96-7fee7dc3100d","order_by":0,"name":"Merel Muylle","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLCCByCCnbmB4QOEz0ZYSwKIYGZsYJwB0UCCFmYeYrTotvc+fJDAsC1xw2HGxse2bbVy5vINbA8+4NFidua4sUECw22Qlmbj3LbjxpZtDOyGM/BpuZHGJgHV0iad23YsccMxBjZpHnxa7j9j/wHV0v7bsu1YPVjLH7y2sLExwGxhZmyrSTAAacHnfbMzacwSCQa3jWcC/SLZc+6A4YZjie2GPfi0HD/G+OFDxW3ZvuPNBz/8KKuTNzh8+NiDH/isAQMDBgaFA2DWYSBmbCCoAQzkIerqiFM9CkbBKBgFIwoAAPBiUu9pgtE5AAAAAElFTkSuQmCC","orcid":"","institution":"Ghent University","correspondingAuthor":true,"prefix":"","firstName":"Merel","middleName":"","lastName":"Muylle","suffix":""},{"id":298978437,"identity":"af0a9be9-5f5c-473f-bfa4-b9c96c92bc27","order_by":1,"name":"Sarah Bernolet","email":"","orcid":"","institution":"University of Antwerp","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"","lastName":"Bernolet","suffix":""},{"id":298978439,"identity":"c15075b3-2600-46a1-8d69-01af772a4e26","order_by":2,"name":"Robert J. Hartsuiker","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"J.","lastName":"Hartsuiker","suffix":""}],"badges":[],"createdAt":"2024-04-30 21:08:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4351475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4351475/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s41809-024-00156-4","type":"published","date":"2024-11-18T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56196742,"identity":"a1e3c4e6-1b18-4597-bfde-0db06b9a7cf6","added_by":"auto","created_at":"2024-05-09 18:13:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53083,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of the forward digit span, backward digit span, and LexTALE Dutch score on AL accuracy.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/8c6b6f6e3216f99f9dfc188f.jpg"},{"id":56195777,"identity":"36751aef-6bc1-41ee-97f6-ad3beff3c964","added_by":"auto","created_at":"2024-05-09 18:05:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62180,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of AL accuracy, the forward digit span, backward digit span, and LexTALE Dutch score on transitive priming\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/ca8983ba73a2ae580efd6ae1.jpg"},{"id":56195776,"identity":"5ddb0fca-a7fc-4a8e-aaa8-e101cfecf567","added_by":"auto","created_at":"2024-05-09 18:05:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56032,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of AL accuracy, the forward digit span, backward digit span, and LexTALE Dutch score on transitive priming\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/67d017168555657180c9a92e.jpg"},{"id":56195779,"identity":"a4f66304-009a-416b-96a2-c7880425e4ca","added_by":"auto","created_at":"2024-05-09 18:05:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63252,"visible":true,"origin":"","legend":"\u003cp\u003eThe slope of AL accuracy on the transitive priming effect across priming conditions\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/0b9d56ba3d289068a2bfa932.jpg"},{"id":56195780,"identity":"a264e675-aa26-45b5-91b4-3708e43782ab","added_by":"auto","created_at":"2024-05-09 18:05:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61358,"visible":true,"origin":"","legend":"\u003cp\u003eThe slope of AL accuracy on the ditransitive priming effect across priming conditions\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/97d766dac33ceeb346034c70.jpg"},{"id":69834878,"identity":"f41cd16d-9cd9-4c90-84ce-ce24ebedf107","added_by":"auto","created_at":"2024-11-25 16:09:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1272548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4351475/v1/3d3514b2-762e-418b-b9b9-93755856c1da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOne of the important issues in second language (L2) acquisition research is how individuals acquire and represent L2 syntactic structures in early and later stages of L2 acquisition. Many bilingual theories assume that bilingual speakers can, at least at some point during the learning trajectory, share certain linguistic representations across their languages (e.g., Dijkstra \u0026amp; van Heuven, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Flege \u0026amp; Bohn, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kroll \u0026amp; Stewart, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Nicoladis, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Paradis \u0026amp; Navarro, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This is also the case for the syntax, where structures that share similarities between languages may be merged into abstract, cross-linguistic syntactic representations (Hartsuiker et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Evidence for such shared representations stems from the observation that bilingual speakers are influenced by recently encountered structures in one language when formulating or comprehending sentences in the other language. For instance, if a Dutch-English bilingual is confronted with a passive sentence in Dutch (e.g., \u0026ldquo;de kok wordt aangeraakt door de clown\u0026rdquo; [the cook is being touched by the clown]), he or she will be more likely to produce a sentence in the passive voice in English (e.g., \u0026ldquo;the lawyer was fired by the salesman\u0026rdquo;), instead of the highly preferred active voice (e.g., \u0026ldquo;the salesman fired the lawyer\u0026rdquo;). This phenomenon of repeating or predicting recently-encountered syntactic structures across sentences, regardless of differences in meaning, is referred to as \u003cem\u003esyntactic\u003c/em\u003e or \u003cem\u003estructural priming\u003c/em\u003e (Bock, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and showcases prediction of the upcoming structure as an important mechanism in both language comprehension and production (Jaeger \u0026amp; Snider, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pickering \u0026amp; Garrod, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Since such priming also occurs in the absence of lexical, phonological, or meaning overlap, it is often considered evidence for the existence of abstract syntactic representations (for a review see Pickering \u0026amp; Ferreira, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Hence, when priming also occurs across languages, one could argue that these abstract representations are shared or at least connected between languages (Hartsuiker et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; van Gompel \u0026amp; Arai, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, cross-linguistic structural priming has been observed with a wide variety of language pairs, such as English-Dutch (Bernolet et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Desmet \u0026amp; Declercq, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schoonbaert et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), English-French (Hartsuiker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), English-Polish (Fleischer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), English-Mandarin (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), English-Korean (Hwang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shin \u0026amp; Christianson, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Song \u0026amp; Do, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), English-Irish (Favier et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), English-Swedish (Kantola \u0026amp; van Gompel, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), English-Spanish (Flett et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hartsuiker et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), Cantonese-Mandarin (Cai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Dutch-German (Bernolet et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Dutch-French (Hartsuiker et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and Spanish-Swedish (Montero-Melis \u0026amp; Jaeger, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (see Muylle et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, for an overview of studies).\u003c/p\u003e \u003cp\u003eSome of these studies found that structural priming patterns may differ across L2 proficiency levels (e.g., Bernolet et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Favier et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hartsuiker \u0026amp; Bernolet, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim \u0026amp; McDonough, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Montero-Melis \u0026amp; Jaeger, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), although the direction of the effect seems to depend on whether there is lexical overlap between prime-target pairs or not. For instance, Bernolet and colleagues (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) correlated the magnitude of structural priming effects with L2 proficiency and observed that cross-linguistic priming effects became larger with increasing proficiency, whereas within-L2 priming effects in the presence of lexical overlap became smaller as proficiency increased. On the other hand, a recent study that investigated priming between Cantonese and Mandarin did not observe any effect of proficiency (Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which suggests that proficiency has no effect on the sharing of syntax between highly similar languages.\u003c/p\u003e \u003cp\u003eSince the sharing of syntax seems to be influenced by proficiency, there are two contrasting views on how L2 learners develop syntactic representations in the new language: either from separate to shared representations (e.g., Hartsuiker \u0026amp; Bernolet, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or from shared to separate representations (e.g., De Bot, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Montero-Melis \u0026amp; Jaeger, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The first view assumes that L2 learners start with building language-specific syntactic representations in their L2, which then gradually become shared with the L1 when these are similar enough. The second view states that L2 learners use their L1 knowledge to organize the L2 and with increasing proficiency, the L2 representations will become separated from the L1, resulting in more native-like processing in the L2. When it comes to more abstract forms of priming (i.e., priming across languages and/or in the absence of lexical overlap), existing evidence seems to be more compatible with the view that learners start with separate, language-specific representations that become shared over time (see Muylle et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, for a discussion).\u003c/p\u003e \u003cp\u003eThis idea was further elaborated in Hartsuiker and Bernolet's (2017) developmental theory of shared syntactic representations. According to this theory, L2 learners go through a number of different stages to establish shared syntactic representations across languages. At the start of L2 learning, the learner does not have any L2 syntactic representations yet, while some lexical representations may already be in place. In order to produce sentences, the learner will therefore use explicit strategies to combine the lexical items by either a) copying and editing utterances from a more proficient speaker, or b) transferring L1 syntax to the L2 without knowing whether this will result in a grammatical utterance. Hence, learners in the first stage may show within-language and cross-linguistic priming, but only when they are able to recall the prime sentence. In a second stage, which could already take place after limited exposure, the learner may develop item-specific syntactic representations. For instance, they encounter the verb \"give\" with a noun-phrase (NP) containing the object, followed by a prepositional phrase (PP) containing the recipient (e.g., \"the cook gave a ball to the clown\"), so they learn that \"give\" is complemented with NP\u0026thinsp;+\u0026thinsp;PP. Assuming that L2 syntactic knowledge is gathered through exposure (using a form of Hebbian learning), it is highly likely that the learner will first develop syntactic representations for frequent verbs and structures, and only later (i.e., in the third stage of the theory) for less frequent verbs and structures. Learners in the second and third stage will show item-specific priming, but only for the lexical items for which they already have L2 syntactic representations in place. Importantly, such priming would take place irrespective of whether the learner is able to recall the prime sentence, since this is no longer required to formulate sentences in the L2. In the fourth stage, the learner starts to generalize syntactic structures across lexical items (e.g., the verbs \"give\" and \"send\" can both be followed by NP\u0026thinsp;+\u0026thinsp;PP), which means that they will show priming within the L2, regardless of whether lexical items are repeated between prime and target. Finally, in the fifth stage, learners will merge the new L2 syntactic representations with the existing L1 representations. From that moment on, they will show priming from the L1 to the L2 and vice versa, both in presence and absence of meaning overlap between the prime and target.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 The PP02 learning paradigm\u003c/h2\u003e \u003cp\u003eThe predictions of Hartsuiker and Bernolet\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) developmental theory were tested in a series of experiments that used an artificial language (AL) learning paradigm to study structural priming within and across languages from the onset of L2 acquisition (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Muylle, Bernolet, et al., 2020). In this paradigm, L1 Dutch speakers learned to produce intransitive, transitive, and ditransitive sentences in the AL (named \u0026ldquo;PP02\u0026rdquo;) by means of five different tasks that were administered sequentially. The first task was a vocabulary learning task in which participants learned the AL nouns to describe depicted human figurines (e.g., cook, witch) and objects (e.g., ball, hat). Next, they watched short action movie clips (taken from the normed stimulus set by Muylle, Wegner, et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in which the human figurines interacted with each other and with objects, while listening to a sentence in the AL that described these actions. Following this exposure task, participants did a movie-sentence matching task in which they were asked to watch two movie clips on the screen and match a presented AL sentence with the correct movie clip. Next, there was a sentence production task in which the participants described movie clips in the AL and received feedback. Finally, intra-lingual and cross-linguistic priming effects were assessed by means of a structural priming task. This task consisted of two parts. First, participants had to judge whether a Dutch or AL sentence (i.e., the prime) matched a movie clip and after that, they saw a new movie and were asked to describe the action (i.e., the target) in Dutch or the AL, depending on a visual cue. Transitive primes could be in the active or passive voice and ditransitive primes could have a prepositional object (PO, e.g., \"the cook is giving a book to the clown\") or double-object (DO) dative structure (e.g., \"the cook is giving the clown a book\"). Intransitive sentences (e.g., the clown is waving\") acted as fillers.\u003c/p\u003e \u003cp\u003eThe AL learning paradigm has proven to be successful in eliciting structural priming effects both within the AL and between the AL and Dutch from the first day of learning. In a multiple-session study, two experiments tested structural priming effects across five AL learning sessions (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The first experiment used a relatively easy version of PP02 (e.g., no articles or verb conjugations), whereas in the second experiment, we administered a more difficult PP02 version (e.g., with articles and verb conjugations). Participants in both experiments were very fast in acquiring the AL and they already showed priming within the AL and from the AL to Dutch from the first day of learning. Priming from Dutch to the AL was present on the first day for transitives as well, but for ditransitives, there was only an effect from the second day onward. These results suggest that L2 learners may indeed start with L2-specific syntactic representations that are only later merged with L1 representations. However, since most priming effects were already present on Day 1 of testing, subsequent experiments using this paradigm only included one or two sessions.\u003c/p\u003e \u003cp\u003eA second series of studies used the PP02 learning paradigm to explore how similar the structures have to be across languages in order to be shared in the L2 learner's mind (Muylle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Muylle, Bernolet, et al., 2020). One study (Muylle, Bernolet, et al., 2020) compared priming effects within the AL and between the AL and Dutch (that has SVO order and no overt case marking) across different versions of PP02 (i.e., a version with SVO order, one with SOV order, and one with case marking) in a between-subjects design and found similar effects across all versions, suggesting that differences in the presence of overt case marking or in word order across languages do not influence the extent to which learners share the representations of these languages. The other study (Muylle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e) tested SVO and SOV PP02 structures in a within-subjects design and found that the presence of the SVO order in the AL hindered the learning and sharing of the SOV order.\u003c/p\u003e \u003cp\u003eFinally, Muylle et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) studied the role of L1 and AL frequency distributions in priming from PP02 to L1 Dutch by comparing a PP02 version that had a DO bias versus one that had a PO bias in a between-subjects design. Here, immediate priming was not affected by the bias in the AL, but only by the Dutch bias (i.e., a PO bias). However, participants in the DO bias condition were more likely to produce DO structures in Dutch overall than participants in the PO condition, showing that the AL exerted an influence on the preference in Dutch, regardless of the immediate prime. Crucially, this experiment could not clarify whether learners first build L2 presentations for the most frequent structures and only later for less frequent structures, as predicted by the developmental theory.\u003c/p\u003e \u003cp\u003eIn sum, the AL learning paradigm has proven to be a useful tool to mimic second language acquisition in a highly controlled context. Nevertheless, it is important to bear in mind that there are also important differences with natural language learning. For instance, since the participants need to be able to acquire the AL in the course of a few hours, the vocabulary size and number of different structures have to be sufficiently small. In addition, the participants' motivation and the learning context are different from learners' motivations and learning environments/contexts in real-life situations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 A different approach to proficiency\u003c/h2\u003e \u003cp\u003eThe multiple-session AL learning experiments (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) were not fully conclusive on whether L2 representations evolve gradually from being item-specific to abstract with increasing L2 proficiency as proposed by the developmental theory. An issue with these studies was that there was already evidence for the sharing of syntax across the L1 and the AL (assessed by structural priming) on the first day of AL learning. In other words, the sharing went so fast that it was not possible to observe the early stages of its development using an AL learning paradigm. These studies considered proficiency a feature that develops over time. Indeed, AL proficiency (i.e., average AL accuracy scores) increased from the first to the second session, but then reached a plateau.\u003c/p\u003e \u003cp\u003eAnother way to deal with proficiency is by comparing priming effects in participants with high AL accuracy scores with those in participants that have low scores. In other words, the effect of AL proficiency on priming can also be studied by testing individual differences in learning on the first day of AL acquisition. Although several studies used the AL learning paradigm (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Muylle, Bernolet, et al., 2020), the influence of AL accuracy on priming was not addressed in the individual studies because of the limited number of participants. However, in the current study, we combined the data from these methodologically similar studies into one big data frame. This allowed us to test Hartsuiker and Bernolet\u0026rsquo;s hypotheses on a sample of 336 participants. In addition, we investigated whether other individual differences such as L1 proficiency and working memory (WM) play a role in the learning of the AL and in the sharing of syntax across the AL and L1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Working memory\u003c/h2\u003e \u003cp\u003eOne important variable to consider is WM capacity, since explicit memory mechanisms (which are restricted by WM capacity) play a crucial role in the first stage of the developmental theory. In this stage, learners have no L2 syntactic representations in place yet on which they can rely to formulate sentences. Therefore, they may imitate more proficient L2 speakers by copying and editing their utterances. Generally speaking, the developmental theory assumes a more important role for explicit memory processes in early vs. later stages of L2 acquisition, based on the finding that the magnitude of L2 priming with lexical overlap decreases with increasing proficiency (Bernolet et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hartsuiker \u0026amp; Bernolet's 2017 re-analysis of Schoonbaert et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Coumel and colleagues (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) recently tested this prediction by studying L2 priming with repeated verbs across low and high proficiency L2 speakers of French in a dual-task design (i.e., a letter series recall task between prime and target). In both groups, priming effects became smaller when the intervening task was more difficult (i.e., when letter series became longer). Interestingly, an exploratory comparison across proficiency levels suggested that less proficient speakers relied more on explicit memory of the prime than more proficient speakers, who fell back to their own preferences to formulate sentences. Apart from the developmental theory, the well-established lexical boost effect to priming (i.e., larger priming effects when there is lexical overlap between prime and target compared to when there is no such overlap) is often explained in terms of explicit memory mechanisms and hence also depends on WM capacity (e.g., Bock \u0026amp; Griffin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Reitter et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assess WM capacity in the AL learning studies, we mostly administered the forward and backward digit span tests (WAIS-IV subtests; Wechsler, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), except for the first multiple-session experiment, where we used the Operation Span (OSPAN) test (Oswald et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In the digit span tests, participants repeat increasingly longer number sequences (either forward in the forward digit span task or backward in the backward digit span task) for as long as they can correctly recall them. The results of these tests provide an approximation of how many items can be retained in WM in the correct order. Because the backward digit span test requires transformation of the retained information, it may be more informative about the processing capacity of the participant\u0026rsquo;s WM than the forward digit span (Alloway et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In addition, the backward digit span may involve visuo-spatial processing (e.g., Li \u0026amp; Lewandowsky, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; St Clair-Thompson \u0026amp; Allen, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, we inspected whether digit span scores predicted priming. Participants with a high forward digit span score tended to show more priming than those with a lower score, but this effect was only present in ditransitive and not in transitive structures. Since ditransitive sentences contain more phrasal constituents than transitive sentences, it is not surprising that processing ditransitives requires more WM capacity, and hence, learners with higher WM capacity have more resources left to maintain the prime structure in WM than learners with lower WM capacity.\u003c/p\u003e \u003cp\u003eThe backward digit span, on the other hand, showed a less straightforward relation with priming. In general, participants with high backward digit span scores showed smaller priming effects than participants with lower scores, which seems opposite to the effect of the forward digit span. However, a closer inspection of the data revealed that backward digit span scores mainly affected priming within L1 Dutch or from the AL to Dutch when there was lexical overlap. One explanation could be that participants with lower span scores rely more on explicit memory when formulating a new sentence and hence show more priming in conditions with lexical overlap. Participants with higher span scores, however, may have a better memory of sentences preceding the prime, which enables them to develop abstract syntactic representations at a faster pace. As such they rely less on explicit memory of the prime to formulate sentences and fall back on more implicit priming mechanisms (cf. Coumel et al., 2023). Indeed, priming effects that are supported by explicit memory of the prime tend to be larger than those that are not (e.g., Hartsuiker et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the analyses of the multiple-session study indicated a rather complicated relation between WM capacity and priming, we did not further test the relation between digit span scores and priming in the subsequent AL learning studies. Instead, these data were mainly used to control for individual differences between groups in between-subjects designs. Therefore, the current re-analysis also aimed to further examine the effects of the forward and backward digit span score. In addition, we studied the effect of the digit span scores on accuracy in the AL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 L1 proficiency\u003c/h2\u003e \u003cp\u003eAnother factor that may influence the learning of the AL is the proficiency in the L1. Many studies have shown that there is a strong relationship between L1 and foreign language learning ability (e.g., Cummins, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Dufva \u0026amp; Voeten, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hulstijn \u0026amp; Bossers, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sparks \u0026amp; Ganschow, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Concretely, it has been argued that mastering a certain skill (e.g., phonology) in the L1 serves as a basis for learning that same skill in the L2, and that there is a basic language learning mechanism that underlies learning in both L1 and L2 (e.g., Sparks, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Sparks \u0026amp; Ganschow, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). As such, it may be interesting to study whether L1 proficiency also predicts accuracy in the AL. A positive relationship would further support the AL learning paradigm as an appropriate method to study natural L2 acquisition. In all studies using this paradigm, we measured the participant's score on the Dutch LexTALE test, a short vocabulary test that consists of 60 lexical decision trials (Lemh\u0026ouml;fer \u0026amp; Broersma, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Although the LexTALE test provides an estimation of the vocabulary size in particular, the scores correlate well with other language proficiency measures (Lemh\u0026ouml;fer \u0026amp; Broersma, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In the current re-analysis, we therefore also included the Dutch LexTALE score as a covariate in our models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e1.5 Research questions and hypotheses\u003c/h2\u003e \u003cp\u003eIn this re-analysis, we took together all (priming) results from the AL studies to answer the following three research questions:\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1) How do individual differences influence accuracy in the AL?\u003c/h3\u003e\n\u003cp\u003eFor this, AL accuracy was taken as a dependent variable and we tested whether forward digit span, backward digit span, and LexTALE Dutch score influenced accuracy scores in the various AL tasks. Since L1 learning abilities are related to foreign language learning abilities (e.g., Cummins, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Dufva \u0026amp; Voeten, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hulstijn \u0026amp; Bossers, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sparks \u0026amp; Ganschow, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), we expected an effect of the L1 LexTALE score on AL accuracy.\u003c/p\u003e\n\u003ch3\u003e2) How do individual differences influence structural priming in general?\u003c/h3\u003e\n\u003cp\u003eHere, we tested whether both digit span scores, the LexTALE Dutch score, and AL accuracy predicted priming effects overall. Since several theories assume a role for WM in priming (e.g., Bock \u0026amp; Griffin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hartsuiker \u0026amp; Bernolet, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Reitter et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we expected that both digit span scores would affect priming. For AL accuracy, we also predicted a positive effect, since Hartsuiker and Bernolet (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) predicted more priming with increasing proficiency in most of the conditions.\u003c/p\u003e\n\u003ch3\u003e3) How does AL accuracy influence structural priming in the different priming conditions?\u003c/h3\u003e\n\u003cp\u003eTo investigate the assumption of Hartsuiker and Bernolet's developmental theory that L2 syntactic representations become more abstract with increasing proficiency and finally become shared with existing L1 representations, we tested whether priming effects became larger with increasing PP02 proficiency. Concretely, we expected more priming for high vs. low proficiency participants in all priming conditions, except for the related PP02-PP02 condition. In other words, we predicted an interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eRelatedness\u003c/em\u003e, \u003cem\u003eTarget Language\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e. On the one hand, priming effects should be larger within the AL than from the AL to Dutch and should be larger for related prime-target pairs (i.e., with verb overlap) compared to unrelated pairs (i.e., without verb overlap). Moreover, this effect of relatedness is thought to be larger within the AL (i.e., the lexical boost effect) than between languages (i.e., the translation equivalent boost effect). On the other hand, priming effects should be larger with increasing proficiency, but only for the more abstract types of priming and not for related PP02-PP02 priming.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data inclusion and exclusion\u003c/h2\u003e \u003cp\u003ePriming data were gathered from Muylle et al.'s \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e, and \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e studies, but only from Day 1 and only for PP02-PP02 and PP02-Dutch conditions (which were present in all these studies). All studies tested priming in ditransitives (PO vs. DO) and in transitives (active vs. passive). In addition, we included the participants' AL accuracy, forward digit span, backward digit span, and LexTALE Dutch scores. The multiple-session study (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) was not included in these analyses because the experiments in that study were quite different from the other studies in various ways (e.g., amount of vocabulary, priming conditions, etc. \u0026hellip;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eIn total, this dataset includes experimental data of 336 participants. All of them were university students with L1 Dutch. They had normal or corrected-to-normal hearing and vision. None of them reported having language or learning disorders.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Variables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGroup\u003c/b\u003e. This categorical variable with 7 levels indicates the individual between-subjects conditions to which participants belonged: CM1: case marking study \u0026ndash; baseline condition, CM2: case marking study \u0026ndash; case marking condition, CM3: case marking study \u0026ndash; SOV condition, BL1: blocking study \u0026ndash; Exp 1, BL2: blocking study \u0026ndash; Exp 2, FR1: frequency study \u0026ndash; DO bias condition, FR2: frequency study \u0026ndash; PO bias condition. All groups were exposed to active, passive, DO, and PO primes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLexTALE.\u003c/b\u003e This is the score on the Dutch LexTALE test (Lemh\u0026ouml;fer \u0026amp; Broersma, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), that measures vocabulary size in Dutch (the participants\u0026rsquo; L1).\u003c/p\u003e \u003cp\u003e\u003cb\u003eForward digit span.\u003c/b\u003e This continuous score was measured with a classical spoken digit span task, in which participants were asked to repeat increasingly longer sequences of numbers in the correct order.\u003c/p\u003e \u003cp\u003e \u003cb\u003eBackward digit span.\u003c/b\u003e This score was measured via the same task as the forward digit span, but here participants repeated number sequences in reverse order.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAL accuracy.\u003c/b\u003e This continuous score was computed by adding accuracy scores of a) the final presentation of each noun item (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12) in the vocabulary learning block, b) the matching block (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50 or 90, depending on the study), c) the sentence production block (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20 or 24, depending on the study), and d) PP02 target trials in the priming block (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;80, 60 or 74, depending on the study), and then dividing this sum by the total number of observations.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrime structure.\u003c/b\u003e We conducted separate analyses for transitives and ditransitive sentences. Prime structure was a categorical variable with two levels: for the transitives, this was active vs. passive, and for the ditransitives this was PO vs. DO.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analyses and results\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 How do individual differences influence accuracy in the AL?\u003c/h2\u003e \u003cp\u003eIn order to answer this question, we built generalized linear mixed effects models (beta family) using the glmmTMB package (Brooks et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in R (R Development Core Team, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) with \u003cem\u003eAL accuracy\u003c/em\u003e as outcome variable. The random effects structure consisted of a random intercept for \u003cem\u003eGroup\u003c/em\u003e (we did not include an intercept for participant, since all variables were between subjects). By adding this random intercept to the model, we could control for differences between conditions across experiments (e.g., accuracy tends to be lower in the CM2 compared to the CM1 group, because the PP02 version is more difficult). Inclusion of random slopes led to singularity issues, so they were removed.\u003c/p\u003e \u003cp\u003eFor the fixed effects, we added \u003cem\u003eLexTALE\u003c/em\u003e, \u003cem\u003eForward digit span\u003c/em\u003e, and \u003cem\u003eBackward digit span\u003c/em\u003e to the model, using a forward modelling strategy. Concretely, we started with models that only included a main effect of one of these variables (Model 1\u0026ndash;3). Next, we added one of the other main effects to the model (Model 4\u0026ndash;6). Finally, we tested a model with all three main effects (Model 7). The different models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003csup\u003e[1]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eForward models of individual differences in AL accuracy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects structure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e\u0026szlig;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1) LexTALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2) Forward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3) Backward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4) LexTALE +\u003c/p\u003e \u003cp\u003eForward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003cp\u003e5.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5) LexTALE +\u003c/p\u003e \u003cp\u003eBackward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6) Forward digit span +\u003c/p\u003e \u003cp\u003eBackward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7) Forward digit span +\u003c/p\u003e \u003cp\u003eBackward digit span +\u003c/p\u003e \u003cp\u003eLexTALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e0.12\u003c/p\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003cp\u003e3.40\u003c/p\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom these results, it can be deducted that the forward and backward digit span were the strongest predictors for AL accuracy. The backward digit span explained some additional variance on top of the forward digit span (despite the high correlation between the forward and backward digit span: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.43, \u003cem\u003et\u003c/em\u003e(333)\u0026thinsp;=\u0026thinsp;8.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Furthermore, the LexTALE explained additional variance on top of the forward and backward digit span (despite the high correlation between LexTALE and forward digit span: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003et\u003c/em\u003e(333) =, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003csup\u003e[2]\u003c/sup\u003e. Hence, we maintained the \u003cem\u003eForward digit span\u0026thinsp;+\u0026thinsp;Backward digit span\u003c/em\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eLexTALE\u003c/em\u003e as the final model. The individual main effects are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 How do individual differences influence structural priming in general?\u003c/h2\u003e \u003cp\u003eTransitives were treated separately from ditransitives in these analyses for practical reasons and because it allows to find effects of individual differences that are specific for one of both structures.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransitives.\u003c/b\u003e To find out which individual differences influenced structural priming, we built generalized linear mixed effects models using the afex package (Singmann et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in R with \u003cem\u003eActive response\u003c/em\u003e as binomial outcome variable (0\u0026thinsp;=\u0026thinsp;passive, 1\u0026thinsp;=\u0026thinsp;active). We started from the maximal random effects structure, as suggested by Barr, Levy, Scheepers, \u0026amp; Tily (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which was reduced in case of singularity (or other non-convergence) issues following the recommendations of Bates, Kliegl, Vasishth, \u0026amp; Baayen (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The maximal model consisted of a random intercept of \u003cem\u003eParticipant\u003c/em\u003e and \u003cem\u003eGroup\u003c/em\u003e, and a random slope of \u003cem\u003ePrime structure\u003c/em\u003e over participants and over groups. Similar to the previous analyses, we used forward modelling to determine the variables that contributed to the priming effect. If one of these variables affects priming, there should be an interaction with \u003cem\u003ePrime structure\u003c/em\u003e. An overview of the different models can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eForward models of individual differences in structural priming for transitives\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Prime structure | Participant) +\u003c/p\u003e \u003cp\u003e(1 | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * LexTALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Prime structure | Participant) + (Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Forward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Prime structure || Participant) + (Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Backward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Prime structure || Participant) + (Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy +\u003c/p\u003e \u003cp\u003ePrime structure * LexTALE +\u003c/p\u003e \u003cp\u003eBackward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) + (1 | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.54\u003c/p\u003e \u003cp\u003e3.02\u003c/p\u003e \u003cp\u003e-2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy * LexTALE\u0026thinsp;+\u0026thinsp;Backward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) + (1 | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003cp\u003e-2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs can be seen from the table, both AL accuracy and Dutch LexTALE scores predicted structural priming independently. In contrast, the digit span scores did not have a significant effect on priming. However, because the analyses with the backward digit span model have shown that there was a main effect of \u003cem\u003eBackward digit span\u003c/em\u003e on the proportion of active responses (Wald\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e = -2.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), this main effect was included in the more complicated models. Given that there was no significant three-way interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eLexTALE\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e, the model with \u003cem\u003ePrime structure * AL accuracy\u0026thinsp;+\u0026thinsp;Prime structure * LexTALE\u0026thinsp;+\u0026thinsp;Backward digit span\u003c/em\u003e as fixed effects was kept as the final model. The effects of the individual difference measures on priming are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDitransitives.\u003c/b\u003e The same analyses were conducted for the ditransitives as for the transitives, but now the outcome variable was \u003cem\u003ePO response\u003c/em\u003e. An overview of the forward models can be found in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eForward models of individual differences in structural priming for ditransitives\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * LexTALE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Forward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Backward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure | Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy +\u003c/p\u003e \u003cp\u003ePrime structure * Forward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003cp\u003e-2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.066\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy * Forward digit span\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1 | Participant) +\u003c/p\u003e \u003cp\u003e(Prime structure || Group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHere, the only variables that predicted structural priming in isolation were \u003cem\u003eAL accuracy\u003c/em\u003e and \u003cem\u003eForward digit span\u003c/em\u003e. Indeed, the higher the AL accuracy and forward digit span scores, the larger the priming effects. However, when those variables appeared together in the model, the effect of \u003cem\u003eAL accuracy\u003c/em\u003e disappeared (although it was still marginally significant). There was no three-way interaction with \u003cem\u003ePrime structure\u003c/em\u003e. Hence, the model with \u003cem\u003ePrime structure * AL accuracy\u0026thinsp;+\u0026thinsp;Prime structure * Forward digit span\u003c/em\u003e was kept as the final model. We plotted the effects of the individual difference measures on priming in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 How does AL accuracy influence structural priming in the different priming conditions?\u003c/h2\u003e \u003cp\u003eWe performed separate confirmatory analyses for transitive and ditransitive structures. As before, we used generalized linear mixed effects models, starting from the maximal random effects structure, namely a random intercept for \u003cem\u003eParticipant\u003c/em\u003e and \u003cem\u003eGroup\u003c/em\u003e and a random slope for \u003cem\u003ePrime structure * Target language\u003c/em\u003e over participants and groups (\u003cem\u003eRelatedness\u003c/em\u003e was not included in the random effects because participants in the first experiment of the blocking study were only tested on unrelated priming). The fixed effects consisted of the \u003cem\u003ePrime structure * Relatedness * Target language * AL accuracy\u003c/em\u003e interaction. Again, the outcome variable was \u003cem\u003eActive response\u003c/em\u003e for the transitives and \u003cem\u003ePO response\u003c/em\u003e for the ditransitives. Furthermore, to assess priming and the effect of AL accuracy in the different conditions, we performed post-hoc pairwise contrasts using the phia package in R (De Rosario-Martinez, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransitives.\u003c/b\u003e The final random model consisted of a random intercept for \u003cem\u003eParticipant\u003c/em\u003e and \u003cem\u003eGroup\u003c/em\u003e and an uncorrelated random slope of \u003cem\u003ePrime structure\u003c/em\u003e and \u003cem\u003eTarget language\u003c/em\u003e over participants. An overview of the fixed effects can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransitive model output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSummary of the fixed effects in the multilevel logit model (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10976; log-likelihood = -2911.9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.387)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.051)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.385)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no significant four-way interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eRelatedness\u003c/em\u003e, \u003cem\u003eTarget language\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e, but there was a significant interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eRelatedness\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Pairwise contrasts indicate that priming effects became larger with increasing AL accuracy, but only in the related conditions (related: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;21.49, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; unrelated: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.52). In addition, there was a marginally significant interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eTarget language\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e, in the sense that the (positive) slope of accuracy on priming was steeper for Dutch (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;13.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared to PP02 targets (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;2.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.088).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the three-way interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eRelatedness\u003c/em\u003e, and \u003cem\u003eTarget language\u003c/em\u003e confirms the priming pattern of the individual AL studies that a) there is a lexical boost effect to priming (related PP02-unrelated PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;180.72, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), b) a translation equivalent boost effect to priming (related Dutch-unrelated Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;68.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), which is weaker than the lexical boost effect (related-unrelated in Dutch vs. PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;6.73, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.01), and c) priming within PP02 is stronger than from PP02 to Dutch (PP02-Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;6.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). There was a significant priming effect in all priming conditions (related PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;476.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; unrelated PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;53.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; related Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;210.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; unrelated Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;47.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001)\u003c/p\u003e \u003cp\u003e \u003cb\u003eDitransitives.\u003c/b\u003e Here, the random effects structure in the final model consisted of a random intercept of \u003cem\u003eParticipant\u003c/em\u003e and \u003cem\u003eGroup\u003c/em\u003e and an uncorrelated random slope of \u003cem\u003ePrime structure * Target language\u003c/em\u003e over participants and of \u003cem\u003ePrime structure\u0026thinsp;+\u0026thinsp;Target language\u003c/em\u003e over groups. The model output is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDitransitive model output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSummary of the fixed effects in the multilevel logit model (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11293; log-likelihood = -3799.4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u0026rsquo;s \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * Target language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.359)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatedness * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrime structure * Relatedness * Target language * AL accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this model, there was a significant four-way interaction between \u003cem\u003ePrime structure\u003c/em\u003e, \u003cem\u003eRelatedness\u003c/em\u003e, \u003cem\u003eTarget language\u003c/em\u003e, and \u003cem\u003eAL accuracy\u003c/em\u003e (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Post-hoc pairwise comparisons revealed a significant positive slope of AL accuracy on the priming effect, but only for the related PP02 condition (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;46.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The other conditions also showed larger priming effects with increasing AL proficiency, but this was not significant (unrelated PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.43; related Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;2.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.34; unrelated Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.43).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlso here, we found significant priming effects in all priming conditions, but these appeared to be weaker compared to the transitives, especially for the Dutch targets (related PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;352.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; unrelated PP02: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;43.68, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; related Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;7.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05; unrelated Dutch: \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;4.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). In line with the individual experiments, there was a lexical boost effect (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;143.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and no translation equivalent boost effect (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e(1)\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.27).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThe first aim of this study was to find out how certain individual differences influence AL accuracy. Concretely, we assessed the role of L1 proficiency, assessed by the Dutch LexTALE test, and WM capacity, as measured by the forward and backward digit span, using forward modelling strategies.\u003c/p\u003e \u003cp\u003eParticipants with a high forward or backward digit span had higher AL accuracy scores than those with a lower span. Interestingly, both span tasks contributed to the effect independently, in line with studies arguing that forward and backward digit span measure (partially) different abilities (e.g., Alloway et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Li \u0026amp; Lewandowsky, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; St Clair-Thompson \u0026amp; Allen, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). On the one hand, the forward digit span may reflect the ability to maintain phonological information in WM (e.g., St Clair-Thompson \u0026amp; Allen, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which could facilitate vocabulary acquisition, and thus leave more cognitive resources available to learn the AL's syntax. The backward digit span, on the other hand, is thought to involve visuo-spatial processing (e.g., Li \u0026amp; Lewandowsky, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; St Clair-Thompson \u0026amp; Allen, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which could enhance the acquisition of the AL's syntax (including positioning of phrasal constituents in the sentence).\u003c/p\u003e \u003cp\u003eLexTALE scores also exerted an independent positive effect on AL accuracy, despite the high correlation with the forward digit span. This result indicates that participants with a high digit span score tended to have a larger vocabulary size in L1 and also had a higher accuracy in the AL. Verbal WM has indeed been found to be associated with L1 and L2 vocabulary learning (e.g., Baddeley et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Gathercole et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Majerus et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Service, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The current findings corroborate previous results that L1 proficiency influences foreign language proficiency (e.g., Cummins, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Dufva \u0026amp; Voeten, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hulstijn \u0026amp; Bossers, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Sparks \u0026amp; Ganschow, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and also pinpoint an important role for WM in language learning abilities. Moreover, the significant relation between LexTALE scores and AL accuracy further indicates that the PP02 learning paradigm resembles natural L2 acquisition.\u003c/p\u003e \u003cp\u003eThe second aim of the current study was to identify the participant variables that mediate structural priming in general. Again, we studied the role of L1 proficiency and WM capacity, but also of AL proficiency. This was done separately for transitive and ditransitive structures.\u003c/p\u003e \u003cp\u003eFor the transitives, the results indicated that participants with higher Dutch LexTALE scores and higher AL accuracy scores show more structural priming than those with lower scores. In addition, participants with high backward digit span scores were more likely to produce passive sentences compared to those with lower scores. Since the formulation of passives requires more transformations of lexical items compared to actives (e.g., creating a past participle and adding an auxiliary verb), it stands to reason that participants who have more WM capacity available are more likely to produce passive sentences. Both digit span scores did not have an effect on structural priming in transitive sentences.\u003c/p\u003e \u003cp\u003eFor the ditransitives, there was no effect of LexTALE scores and backward digit span, but there was an effect of forward digit span and AL accuracy on priming. However, the effect of AL accuracy became only marginally significant when the forward digit span was included in the model as well. Thus, it seems that the effect of AL accuracy on priming can be explained by WM capacity as assessed by the forward digit span. This effect may be specific for ditransitive structures which have more constituents than transitive structures. As such, participants with a higher WM capacity might be better in retaining all information from the prime sentence, resulting in priming.\u003c/p\u003e \u003cp\u003eThe final goal of this study was to test the prediction of the developmental theory of shared syntactic representations (Hartsuiker \u0026amp; Bernolet, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) that abstract priming effects will become larger with increasing L2 proficiency, as has been attested in the study by Bernolet, Hartsuiker, and Pickering (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and a reanalysis of the study by Schoonbaert, Hartsuiker, and Pickering (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For this, we assessed whether there was a significant slope of accuracy on the magnitude of the priming effect in the different priming conditions. The theory predicts a positive slope for unrelated PP02-PP02 priming, and related and unrelated PP02-Dutch priming. In contrast, the theory does not assume a positive slope for related PP02-PP02, because this type of priming is hypothesized to be present from the earliest phases of L2 learning.\u003c/p\u003e \u003cp\u003eFor the transitives, we found a significant positive slope for the related conditions and for both target languages, although the effect was stronger for Dutch targets. These results are largely in line with Hartsuiker \u0026amp; Bernolet's developmental theory because they show that people with high AL proficiency show more between-language priming than those with low proficiency. However, a positive slope was also observed for related PP02-PP02 priming, in contrast to the findings with natural L2s (Bernolet et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Schoonbaert et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), that did not show an increase in related L2-L2 priming with ascending L2 proficiency. A possible explanation for this contradictory result may be found in the fact that the current data reflect priming on the first day of AL learning, whereas participants in the other studies had been exposed to the L2 for years. Indeed, both low- and high-proficiency participants in the current study had only limited exposure to the AL and did not have the time to start relying less on the specific prime as they became more proficient. In fact, participants in the AL study that are sensitive to priming (or use priming as a strategy) may be better learners of the AL in this short time period. This idea is in line with the observation that the learning of difficult structures (in this case, the passive) can be sped up by means of a structural priming intervention with lexical overlap between primes and targets (Muylle et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; van Lieburg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results for the ditransitives are quite different from the transitives. We only found a significant slope of AL accuracy on ditransitive priming for the related PP02-PP02 condition. The absence of an effect for abstract priming can be reconciled with the fact that many of the AL studies did not find cross-linguistic priming for ditransitives on the first day of learning (Muylle et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e; Muylle, Bernolet, et al., 2020). The cross-linguistic priming effects for ditransitives in the current data set are significant, but clearly much weaker than for transitives, leaving little room to detect differences across participants. It is possible that the majority of participants did not develop shared syntactic representations for ditransitives after one learning session. As such, even highly proficient participants may not show strong evidence for priming yet. However, participants with high AL accuracy did show more related PP02-PP02 priming than those with lower accuracy. This finding is similar to what has been observed for the transitives and there is probably a similar explanation. In addition, the other analyses showed that WM capacity as assessed by the forward digit span has a strengthening effect on priming (and on AL learning) in ditransitives, so people wo have a higher WM capacity are better in maintaining the prime sentence in WM and hence show more related priming and better learning.\u003c/p\u003e \u003cp\u003eTaken together, the results for the transitives and ditransitives show a clear relation between related PP02-PP02 priming and AL accuracy. In the early stages of learning, strong related priming coincides with better AL learning. This indicates that item-specific structural priming may play an important role in L2 learning, as suggested by Muylle et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e) and van Lieburg et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, although the developmental theory predicts that more proficient L2 speakers will rely less on item-specific priming than less proficient ones, L2 speakers who show more item-specific priming at the earliest phases of learning may be better L2 learners. These findings have practical implications for L2 education. Indeed, structural priming can serve as a tool to teach L2 structures in the classroom, even when these structures are not similar to L1 structures.\u003c/p\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe current study combined the data from three PP02 learning studies into one big analysis. This re-analysis revealed some interesting insights. First, participants with high L1 proficiency and high WM capacity, in terms of recalling numbers in the correct (forward digit span) or reverse (backward digit span) order, had an advantage when learning the AL. Moreover, the effect of the forward digit span could not be fully explained by the effect of the backward digit span, which means that there are different mechanisms underlying both tasks that are responsible for the learning advantage, in line with previous research. Second, both AL and L1 proficiency predicted priming in transitives. The higher the proficiency in both languages, the more priming, in line with Hartsuiker and Bernolet's developmental theory. However, the only factor that reliably predicted priming in ditransitives was the forward digit span score, which shows that WM capacity may play a more important role in the acquisition of complex vs. easier structures. Finally, AL proficiency had the strongest relation with priming effects in PP02-PP02 conditions with verb overlap, although participants with a high AL accuracy score also tended to show more priming under more abstract priming conditions. The positive relation between AL proficiency and priming shows that priming can improve the acquisition of L2 syntax and hence should be used as a tool in L2 education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by a grant from the Research Foundation \u0026ndash; Flanders (FWO) [grant number G049416N].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: M.M., S.B., R.H.; Methodology: M.M., R.H.; Formal analysis and investigation: M.M.; Writing - original draft preparation: M.M.; Writing - review and editing: M.M., S.B., R.H.; Funding acquisition: S.B., R.H.; Supervision: S.B., R.H.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe full dataset and R scripts are available on the Open Science Framework (https://osf.io/35qz9).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlloway, T. 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Cross-linguistic structural priming in bilinguals: priming of the subject-to-object raising construction between English and Korean. \u003cem\u003eBilingualism: Language and Cognition\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(1), 47\u0026ndash;62. https://doi.org/10.1017/S1366728916001152\u003c/li\u003e\n\u003cli\u003eSparks, R. L. (1995). Examining the linguistic coding differences hypothesis to explain individual differences in foreign language learning. \u003cem\u003eAnnals of Dyslexia\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 187\u0026ndash;214. https://doi.org/10.1007/BF02648218\u003c/li\u003e\n\u003cli\u003eSparks, R. L., \u0026amp; Ganschow, L. (1993). 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The development of abstract syntactic representations in beginning L2 learners of Dutch. \u003cem\u003eJournal of Cultural Cognitive Science\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 289\u0026ndash;309. https://doi.org/10.1007/s41809-023-00131-5\u003c/li\u003e\n\u003cli\u003eWechsler, D. (2008). \u003cem\u003eWechsler Adult Intelligence Scale - Fourth edition administration and scoring manual\u003c/em\u003e. Pearson.\u003c/li\u003e\n\u003cli\u003eZhang, C., Bernolet, S., \u0026amp; Hartsuiker, R. J. (2020). The role of explicit memory in syntactic persistence: Effects of lexical cueing and load on sentence memory and sentence production. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(11), e0240909. https://doi.org/10.1371/journal.pone.0240909\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Interactions were not significant.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Backward digit span scores were not correlated with LexTALE scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.28)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cultural-cognitive-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cucs","sideBox":"Learn more about [Journal of Cultural Cognitive Science](http://link.springer.com/journal/41809)","snPcode":"41809","submissionUrl":"https://submission.nature.com/new-submission/41809/3","title":"Journal of Cultural Cognitive Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"structural priming, artificial language learning, sentence production, individual differences","lastPublishedDoi":"10.21203/rs.3.rs-4351475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4351475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA series of artificial language (AL) learning studies investigated the development of shared syntactic representations during early stages of second language (L2) acquisition. In this paradigm, the sharing of syntax is measured by means of structural priming: if structures are shared between two languages, a sentence in one language (e.g., the AL) should prime the structure of a sentence in another language (e.g., Dutch) and vice versa. According to Hartsuiker and Bernolet\u0026rsquo;s (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) developmental theory, syntactic representations evolve gradually from item-specific to more abstract with increasing L2 proficiency. The current study tested this hypothesis by focusing on individual differences in AL proficiency during the first day of acquisition in a re-analysis of three AL studies. We predicted that individuals with higher AL proficiency levels show more cross-linguistic priming than those with lower levels. AL proficiency was indeed related to the magnitude of structural priming, although the strongest evidence for modulation of priming by proficiency was obtained for item-specific priming. Additionally, we observed that working memory (WM) capacity and L1 proficiency predicted AL proficiency and priming in general. Finally, WM capacity predicted the magnitude of priming in ditransitive sentences, but not in transitive sentences, suggesting a larger role for WM in ditransitive vs. transitive priming.\u003c/p\u003e","manuscriptTitle":"Individual differences in the acquisition of shared syntactic representations: A re-analysis of studies using an artificial language learning paradigm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-09 18:04:47","doi":"10.21203/rs.3.rs-4351475/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-29T12:23:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-21T09:58:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-05T17:59:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335054824097055398964621930861349394688","date":"2024-05-19T06:56:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15838723543477004033711676577121261454","date":"2024-05-18T08:19:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47402632200252489753779264176490832979","date":"2024-05-17T16:58:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-03T05:21:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-02T15:25:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-02T01:59:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cultural Cognitive Science","date":"2024-04-30T20:54:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cultural-cognitive-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cucs","sideBox":"Learn more about [Journal of Cultural Cognitive Science](http://link.springer.com/journal/41809)","snPcode":"41809","submissionUrl":"https://submission.nature.com/new-submission/41809/3","title":"Journal of Cultural Cognitive Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"192251ac-6405-4891-af99-6cd1d06d29eb","owner":[],"postedDate":"May 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-25T16:02:06+00:00","versionOfRecord":{"articleIdentity":"rs-4351475","link":"https://doi.org/10.1007/s41809-024-00156-4","journal":{"identity":"journal-of-cultural-cognitive-science","isVorOnly":false,"title":"Journal of Cultural Cognitive Science"},"publishedOn":"2024-11-18 15:57:36","publishedOnDateReadable":"November 18th, 2024"},"versionCreatedAt":"2024-05-09 18:04:47","video":"","vorDoi":"10.1007/s41809-024-00156-4","vorDoiUrl":"https://doi.org/10.1007/s41809-024-00156-4","workflowStages":[]},"version":"v1","identity":"rs-4351475","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4351475","identity":"rs-4351475","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}