Introduction
A key point of interest in second language (L2) acquisition research relates to the circumstances under which first language (L1) and L2 processing converge. L1–L2 differences have repeatedly been observed in the processing of syntactically complex constructions, such as those involving movement dependencies (e.g., Berghoff, Reference Berghoff2020; Felser & Roberts, Reference Felser and Roberts2007; Marinis et al., Reference Marinis, Roberts, Felser and Clahsen2005). These differences have been attributed to a variety of factors, including reduced sensitivity to abstract syntactic structure in L2 compared to L1 speakers and reduced L2 compared to L1 processing automaticity. At the same time, certain theoretical accounts (Clahsen & Felser Reference Clahsen and Felser2006a, Reference Clahsen and Felser2006b, Reference Clahsen and Felser2018; Ullman, Reference Ullman2001) and empirical findings (Pliatsikas & Marinis, Reference Pliatsikas and Marinis2013; Pliatsikas et al., Reference Pliatsikas, Johnstone and Marinis2017) suggest that L1–L2 processing convergence may be more likely among (early) L2 learners with naturalistic exposure to the L2. The ability to draw conclusions in this area is currently limited by the dearth of studies that have examined such learners and these studies’ focus on only one type of movement dependency, namely long-distance wh-dependencies. The present study extends this body of literature by exploring the processing of indirect-object dependencies among L2 learners drawn from a context in which naturalistic L2 exposure is extensive and typically begins at an early age. To this end, the study replicates Felser and Roberts (Reference Felser and Roberts2007), which examined indirect-object dependency processing among classroom learners in a foreign-language context.
Background
The focus of this study is on sentences such as (1), taken from Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007, p. 185), where the peacock originates structurally after the direct object the nice birthday present but appears earlier in the sentence.
Processing a sentence such as (1) poses a challenge because after the moved element (i.e., the filler) the peacock is encountered, it must be retained in short-term memory until it can be linked to the element that licenses it, in a process termed “filler integration.” A number of online studies of filler-gap dependency processing have observed a tendency, among L1 speakers, to reactivate the filler at clause boundaries and at its original position (Chow & Zhou, Reference Chow and Zhou2019; Fernandez et al., Reference Fernandez, Höhle, Brock and Nickels2018; Gibson & Warren, Reference Gibson and Warren2004; Nicol, Reference Nicol, Altmann and Shillcock1993; Nicol & Swinney, Reference Nicol and Swinney1989; although see Roberts et al., Reference Roberts, Marinis, Felser and Clahsen2007 and Miller, Reference Miller2014 for contrary findings). This processing pattern is in line with a Chomskyan account of movement (Chomsky, Reference Chomsky1986), in which the filler moves through these positions on its way to its surface structure destination, leaving behind a silent copy of itself—a “trace”—at each position.
Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007) used a cross-modal picture priming task to investigate whether L1 speakers—children aged 5 to 7 years and adults—reactivated the moved element at its original position, termed the “gap” position. While listening to sentences such as (1), participants were shown pictures that were either identical or unrelated to the entity denoted by the filler at either the gap position or a control position 500 milliseconds earlier in the sentence. They then had to decide whether the depicted entity was alive or not alive, with their reaction times (RTs) to this decision serving as the dependent variable. Both adults and children with relatively high working-memory capacity, as measured in adults by a reading span task and in children by a listening span task, showed reduced RTs to identical versus unrelated targets at the gap position. RTs to identical targets were also lower at this position than at the control position. This finding suggests that the moved element was reactivated at the gap position, thus facilitating responses in the decision task. Participants with relatively lower working-memory scores, however, showed no difference in RTs to identical versus unrelated targets (adults) or a disadvantage for identical versus unrelated targets (children) at the gap position.
Felser and Roberts (Reference Felser and Roberts2007) used the same task and materials employed by Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007) to investigate the L2 processing of indirect-object dependencies among L1 Greek speakers. These participants had first been exposed to the L2 between the ages of 6 and 11 in a classroom setting and did not consider themselves bilingual; further, they had been living in the United Kingdom for an average of 2.9 years at the time of testing (Felser & Roberts, Reference Felser and Roberts2007, p. 18). The results for the L2 speakers differed from those obtained for the L1 speakers in Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007) in two respects: first, there were no working memory effects on processing behavior among the L2 speakers; and second, they showed an advantage for identical versus unrelated targets at both sentence positions. The latter result suggests that instead of selectively reactivating the moved element at the gap position, the participants may have actively maintained the moved element in working memory during processing, which facilitated their responses at both the gap and the control locations. Importantly, as indirect-object dependencies are formed in essentially the same way in Greek and English, the results do not suggest a transfer of L1 processing strategies to the L2.
Miller (Reference Miller2014, Reference Miller2015) investigated whether the reduced automaticity of L2 compared to L1 processing might inhibit trace reactivation in L2 speakers. From this perspective, delays in L2 lexical access lead to a delay in the construction of syntactic representations, and it is this delay in L2 processing that precludes the observation of a trace reactivation effect. As such, this account predicts that if experimental stimuli are designed in such a way that L2 lexical access is facilitated, L2 speakers will show sensitivity to movement traces during real-time processing.
In Miller (Reference Miller2014, Reference Miller2015), fillers were denoted by L1–L2 cognates (e.g., English–French gorilla–gorille), with the rationale that the facilitative effect of cognates on lexical access (see e.g., Costa et al., Reference Costa, Caramazza and Sebastian-Galles2000) would mitigate the potential confounding effects of reduced L2 processing automaticity. In line with this prediction, Miller’s (Reference Miller2014) intermediate L2 learners showed RT patterns consistent with trace reactivation at the gap position. Miller (Reference Miller2015) obtained similar results with indirect-object cleft sentences in which the filler crossed a clause boundary, where a subset of learners showed evidence of filler reactivation at both the clause boundary and the gap position.
Miller’s (Reference Miller2014, Reference Miller2015) findings are suggestive of a role for processing automaticity in facilitating the construction of fully specified syntactic representations. In turn, they predict that the construction of such representations should also be more likely given the presence of individual characteristics associated with greater processing automaticity. One such characteristic is L2 exposure, which has been proposed to exert a practice effect on the L2 system, leading L2 processing to become more proceduralized (e.g., Ullman, Reference Ullman2001). Indeed, a few studies have observed differences in L2 processing across L2 learners with classroom L2 exposure and naturalistic L2 exposure (e.g., Dussias & Sagarra, Reference Dussias and Sagarra2007). Regarding the processing of movement dependencies specifically, Pliatsikas and Marinis (Reference Pliatsikas and Marinis2013; see also Pliatsikas et al., Reference Pliatsikas, Johnstone and Marinis2017), in their study of long-distance wh-dependency processing, observed trace reactivation at the clause boundary among L2 learners with naturalistic L2 exposure (an average of 9 years), but not among L2 learners whose exposure was limited to the classroom. Some accounts of L2 processing—for example, the Shallow Structure Hypothesis (Clahsen & Felser, Reference Clahsen and Felser2006a, Reference Clahsen and Felser2006b, Reference Clahsen and Felser2018)—additionally attribute a central role to age of L2 acquisition (AoA) in increasing sensitivity to morphosyntactic information during L2 processing. There is variation in the literature regarding the timing of the so-called sensitive period for grammar, with some studies reporting an offset at around age six (Long, Reference Long1990) and others only at the end of adolescence (Hartshorne et al., Reference Hartshorne, Tenenbaum and Pinker2018; Johnson & Newport, Reference Johnson and Newport1989). Here, too, though, type of exposure is crucial: Research has established that AoA is less relevant for L2 outcomes in instructed L2 settings in which L2 exposure is limited (Muñoz, Reference Muñoz2006).
This article reports on a close replication of Felser and Roberts (Reference Felser and Roberts2007) conducted in South Africa with L1 Afrikaans–L2 English speakers with AoAs ranging from 1–14 (mean 5.3 years). We refer to these as “early” L2 learners because the maximum AoA still falls within the upper bound of the proposed sensitive period for grammar. While South Africa has 11 official languages, English is a prominent societal language (Posel & Zeller, Reference Posel and Zeller2016). Exposure to English often commences before it is formally introduced as a school subject and is not limited to the classroom context, with studies indicating that Ln speakers of English use this language extensively with both family and friends (Berghoff, Reference Berghoff2021; Coetzee van Rooy, Reference Coetzee-Van Rooy2013). At the same time, however, L2 English speakers are not immersed in the L2 in South Africa, and the L1 is typically maintained alongside English (Berghoff, Reference Berghoff2021; Coetzee van Rooy, Reference Coetzee-Van Rooy2012; Posel et al., Reference Posel, Hunter and Rudwick2020). The consequences of such societally multilingual settings for language processing remain poorly understood. This study aims to extend our knowledge in this domain by investigating whether L2 learners of this background show evidence of trace reactivation at the gap position during indirect-object dependency processing.
Method
Participants
The study’s participants were 22 L1 Afrikaans–L2 English speakersFootnote 1 (mean age 20.75 years, standard deviation [SD] 1.06 years, range 19–23 years) who were students at a university in South Africa. All had normal or corrected-to-normal vision. The study was approved by the university’s research ethics committee (project number 0382) and informed consent was obtained from all participants prior to the beginning of the experiment. Participants received course credit for their participation.
Language background information was obtained using the Language Experience and Proficiency Questionnaire (LEAP-Q; Marian et al., Reference Marian, Blumenfeld and Kaushanskaya2007). Participants’ English proficiency was assessed using a C-test consisting of three short texts, each of which contained 20 incomplete words with the first half of their letters provided. The participants’ scores were comparable to those obtained from a sample of 53 L1 English speakers who were students at the same university (mean 76.92%, SD 11.6%).Footnote 2
One participant who indicated their age of first exposure to English as 0 years was removed from further analyses. The characteristics of the remaining participants are summarized in Table 1.
Table 1. Participant characteristics
a The relevant question in the LEAP-Q here is “Please list what percentage of the time you are currently and on average exposed to each language” (italics in original).
b The lowest values for L2 Exposure and the three self-rated variables all come from one participant. This participant obtained a C-test score of 80%, suggesting that their self-ratings were not reliable. The value of 2 for L2 Exposure is also implausible, given the study’s context. Due to the small sample, however, we did not wish to exclude this participant’s data.
c Self-ratings are on a scale of 0 to 10, with 0 indicating “none” and 10 indicating “perfect.”
Working memory was assessed using a computerized reading span task (Stone & Towse, Reference Stone and Towse2015; von Bastian et al., Reference von Bastian, Locher and Ruflin2013). In the task, participants were presented with a set of sentences and had to judge each sentence as either “makes sense” or “does not make sense.” Each sentence was followed by a number that had to be remembered until the end of the set of sentences, at which point the participant had to provide all of the numbers they had seen in that set in order of appearance. The number of sentences in a set ranged from two to five, and scoring was done based on the proportion of numbers the participant recalled correctly. The data from one participant who scored 0 on this task was removed from further analyses. The mean proportion correct of the remaining participants was 53.97% (SD 13.9%, range 26–76%).
Materials
The task involved 20 experimental sentences, which were identical to those used in Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007) and Felser and Roberts (Reference Felser and Roberts2007). As in these studies, the task also contained 60 filler sentences similar in length to the experimental sentences, 12 of which were similar in structure to the experimental sentences, but where the visual target was displayed at a position other than the two critical test points.
The 80 sentences were recorded by a female L1 English speaker using Audacity (Audacity Team, 2019). All but two of the target pictures were obtained from Snodgrass and Vanderwart’s (Reference Snodgrass and Vanderwart1980) dataset.Footnote 3 Each experimental sentence was paired with a visual target that was either identical to the referent of the indirect object or unrelated.
In each experimental sentence, the visual target (identical or unrelated) appeared at one of two critical points: the offset of the direct object noun phrase (i.e., the gap position) or a pregap control position 500 milliseconds prior to this offset. This yielded four experimental conditions, illustrated in (2) (Felser & Roberts, Reference Felser and Roberts2007, p. 20). It is noted that, like English, Afrikaans is also a wh-movement language in which the indirect object canonically follows the direct object (de Stadler, Reference de Stadler1995).
The experimental items were divided across four presentation lists, so that each participant saw only one version of each experimental sentence. The 20 experimental items in each list were combined with the 60 fillers and pseudorandomized.
Procedure
The cross-modal picture priming task was designed and administered in PsychoPy (Peirce et al., Reference Peirce, Gray, Simpson, MacAskill, Höchenberger, Sogo, Kastman and Lindeløv2019). Participants performed the task on a laptop with a 15-inch screen (resolution: 1366 × 768). At the beginning of the session, the experiment administrator told the participant to listen carefully to the prerecorded sentences, which were presented over headphones, and watch the screen for a picture of an animal or an object that would be displayed at an undetermined point during the sentence. They were instructed further that when a picture appeared, they had to decide as quickly as possible whether the animal/object was alive or not alive and indicate their choice by pressing either the green (“yes”) or red (“no”) key on the keyboard. As in Felser and Roberts (Reference Felser and Roberts2007), the task also included 38 comprehension questions, which were distributed across the experiment and auditorily presented. The experiment was preceded by a short practice round to allow participants to familiarize themselves with the procedure. The task included four self-timed breaks and on average took around 30 minutes to complete. After the completion of the experiment, participants completed the working memory task, the LEAP-Q, and the C-test.
Analysis
RTs were analyzed using Bayesian regression. A key advantage of the Bayesian approach (see e.g., Norouzian et al., Reference Norouzian, Miranda and Plonsky2019) is that it allows for the strength of evidence both for and against the null hypothesis to be evaluated. In contrast, the conventional null hypothesis significance testing approach does not provide evidence in favor of the null hypothesis, as the failure to obtain a significant effect may be due to, for example, a lack of statistical power, rather than the nonexistence of the effect. Another benefit offered by this approach is the ability to specify, by means of so-called priors, the expected direction and magnitude of an effect based on extant research findings or expert opinion. Here, we use Felser and Roberts’s (Reference Felser and Roberts2007) results as a basis for the specification of informative priors for the effects of target type, sentence position, and their interaction. Details of the prior specification are provided in Appendix A. Because Felser and Roberts (Reference Felser and Roberts2007) report no effect of working memory, we use noninformative priors for this term; noninformative priors were also used for the standard deviations. For robustness purposes, we also reran the models using informative priors based on the results of Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007). The Bayes factors remain robust. These results are available upon request.
All models were fit with four chains, each of which contained 10,000 samples following a warmup of 2,000 samples. For each model parameter, we report the parameter estimate b; the 95% credible interval, or the range within which b can be taken to fall with 95% certainty; and the evidence ratio P(b). Following Jeffreys (Reference Jeffreys1998), we consider an evidence ratio of 0.3 or smaller as substantial evidence for the absence of an effect and an evidence ratio of 3 or greater as substantial evidence for the presence thereof.
Results
Accuracy
Accuracy scores were 80.13% (SD 7.4%, range 67.6–94.6%) on the end-of-trial comprehension questions and 96.3% (SD 3.7%, range 84.2–100%) on the aliveness decision task. These results are comparable to those of Felser and Roberts (Reference Felser and Roberts2007) and Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007).
Reaction Times
In line with previous studies (Felser & Roberts, Reference Felser and Roberts2007; Roberts et al., Reference Roberts, Marinis, Felser and Clahsen2007), only trials in which the aliveness decision was correct were analyzed, which led to the removal of 3.7% of the data. No RTs on this task exceeded 2,000 milliseconds, nor were there any individual outliers exceeding two SDs from each participant’s mean per condition; thus, no additional data points were omitted.
Table 2 provides the means and SDs of the participants’ RTs per condition. As is evident, RTs to identical targets were shorter than those for unrelated targets at both the control and trace position, but the advantage for identical targets is slightly larger at the trace position (52 vs. 44 ms).
Table 2. Mean RTs (SD) to visual targets per condition
Log-transformed RTs were analyzed using a Bayesian linear mixed regression model fit with the brms package (version 2.16.3, Bürkner, Reference Bürkner2017) in the R environment for statistical computing (version 4.1.2, R Core Team, 2021). The model included Position (Control or Trace, sum contrast coded as –0.5 and 0.5), Target Type (Unrelated or Identical, sum contrast coded as –0.5 and 0.5), and Working Memory Score (scaled and centered around the mean) as fixed effects, as well as the interaction between Working Memory Score, Target Type, and Position. Model comparisons indicated that adding C-test score, L2 Exposure, and AoA did not improve the fit of the model; therefore, no additional predictors were included. The random effects structure included random intercepts for participants and items and by-participants and by-items random slopes for Position, Target Type, and their interaction. Model results are provided in Table 3. Bayes factors indicating the extent of support for the existence of an effect in the direction specified in the model output were calculated using the “hypothesis” function from the brms package; in each case, the Bayes factor indicates the ratio of the hypothesis (e.g., b > 0) to its complement (e.g., b < 0; see Winter & Bürkner, Reference Winter and Bürkner2021). The estimates of robust effects (Bayes factor ≥ 3) are indicated in bold.
Table 3. Model results: RTs to visual targets
Note: Estimate = parameter estimate; Est. Error = standard error; CI L = lower end of the 95% credible interval; CI U = upper end of the 95% credible interval. Parameter estimates in bold are effects that are reliably present (Bayes factor ≥ 3).
There was a reliable effect of Target Type, which indicates that RTs were faster for identical versus unrelated targets (P(b < 0) = 10.39). There was also a reliable effect of Working Memory Score, such that participants with higher working memory had lower RTs overall (P(b < 0) = 3.13). In addition, there were reliable interactions between Working Memory Score and Target Type (P(b > 0) = 15.1) and between Working Memory Score, Target Type, and Position (P(b < 0) = 6.6). The former effect indicates that participants with higher working-memory scores showed less of an RT advantage for identical compared to unrelated target pictures; the latter effect indicates that participants with higher working-memory scores showed a larger RT advantage for identical pictures at the gap compared to the control position.
Given the interactions between Working Memory Score and the factors of interest, we split participants into two groups based on the median working memory score (55.7%). This yielded two groups of 10 participants each. Importantly, these groups did not differ significantly in terms of either AoA or L2 exposure (ps > .2). Table 4 shows the mean RTs (SDs) per working memory group in the four conditions.
Table 4. Mean RTs (SDs) to visual targets in low-span and high-span participants
We then analyzed RTs in the low-span and high-span participants separately, again using Bayesian linear mixed regression models with Position and Target Type as fixed effects and the same maximal random effects structure reported in the preceding text. As in the main analysis, informative priors based on Felser and Roberts’s (Reference Felser and Roberts2007) results were used for the effects of Target Type, Position, and their interaction. Model results are provided in Table 5, with the estimates of effects that are reliably present marked in bold. Figures 1 and 2 illustrate the posterior distributions of the model parameters (i.e., estimates of the distributions that take the new data into account) for the low- and high-span groups, respectively.
Table 5. Model results for low-span and high-span participants
Figure 1. Posterior distributions: Low-span group.
Figure 2. Posterior distributions: High-span group.
Table 5 indicates that for the low-span participants, the only reliable effect was an RT advantage for identical compared to unrelated targets (P(b < 0) = 69.95). The data are inconclusive regarding a potential advantage for identical targets at the gap relative to the control position (Target Type × Position: P(b < 0) = 1). For the high-span participants, the only effect that is reliably present is the Target Type × Position interaction (P(b < 0) = 3.1).
Discussion and Conclusion
This article’s aim was to extend previous research on indirect-object dependency resolution to a group of L2 speakers that is understudied in the L2 processing literature, namely early L2 acquirers with extensive (though nonimmersive) naturalistic L2 exposure. This focus was motivated by accounts of L2 processing that predict greater processing automaticity among L2 learners of this profile (e.g., Clahsen & Felser, Reference Clahsen and Felser2006a, Reference Clahsen and Felser2006b, Reference Clahsen and Felser2018; Ullman, Reference Ullman2001), as well as previous studies that have found increased sensitivity to abstract syntactic structure among learners in naturalistic exposure environments (e.g., Pliatsikas & Marinis, Reference Pliatsikas and Marinis2013). We conducted a close replication of Felser and Roberts (Reference Felser and Roberts2007). In contrast to these authors, but like Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007), we observed a working memory effect on our participants’ response patterns. Follow-up analyses indicated that while low-working-memory participants responded more quickly to identical targets at both the gap and the earlier control position, high-working-memory participants’ RTs to identical targets were lower at the gap than the control position.
The low-working-memory participants’ processing pattern, which mirrors that of Felser and Roberts’s (Reference Felser and Roberts2007) participants, would be consistent with a strategy in which the filler was actively maintained in working memory, leading to lower RTs at both test positions. However, we note a caveat here, which arises due to the affordances of the Bayesian analysis: Specifically, the data do not provide evidence that the low-working-memory group did not show a position-specific RT advantage to identical targets; the Bayes factor was inconclusive at 1. We therefore cannot comment further on whether trace reactivation occurred among this group.
There does, however, appear to be a difference in processing pattern between our low-span L2 group and the low-span L1 group in Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007), who did not show an advantage for identical targets at either position. This difference suggests that even among individuals who share relatively lower working-memory capacity, L1 and L2 processing of movement dependencies may differ. The divergence here may be attributable to different allocations of cognitive resources during processing: For example, Williams (Reference Williams2006) found that L2 speakers with relatively low working-memory capacity, unlike L1 speakers, seemed not to process input incrementally when they also had to perform a memory task, suggesting that the L2 speakers had directed their cognitive resources toward the memory task.
Our high-span group’s processing pattern is compatible with a strategy in which the filler is selectively reactivated at the gap position. This finding is in line with the proposal that when a filler is encountered, the parser predicts an upcoming syntactic gap, and retrieval of the filler from memory is triggered when such a gap is reached (e.g., Frazier, Reference Frazier1987). In this respect, our high-working-memory participants showed the same processing pattern as the high-working-memory L1 groups (both adults and children) in Roberts et al. (Reference Roberts, Marinis, Felser and Clahsen2007). In turn, this finding aligns with the results of Miller (Reference Miller2014, Reference Miller2015), in that it shows that L2 learners can make use of abstract syntactic structure during real-time processing. In Miller’s (Reference Miller2014, Reference Miller2015) studies, however, it was a task characteristic, specifically the use of cognates as visual targets, that seemed to facilitate sensitivity to the gap. The present results, like those of Pliatsikas and Marinis (Reference Pliatsikas and Marinis2013), provide an indication that this sensitivity can arise in the absence of targeted attempts to elicit it. Considering, however, that our low- and high-span groups did not differ in terms of AoA or L2 exposure, we cannot say that either of these factors is decisive in engendering trace reactivation. Ultimately, working memory capacity seemed to be the deciding factor in this regard.
Our observation of a working memory effect bears on another important question in SLA, namely whether individual cognitive differences are equally relevant to L2 outcomes across early and late L2 learners. Theories of SLA and L2 processing in which AoA plays a central role (e.g., Clahsen & Felser, Reference Clahsen and Felser2006a, Reference Clahsen and Felser2006b, Reference Clahsen and Felser2018) typically do not discuss the potential effects of individual differences on early learners’ L2 attainment, with the implicit assumption being that an early start to learning and sufficient exposure should together ensure acquisition success. However, some studies have observed effects of, for example, language aptitude on L2 outcomes among early learners (Abrahamsson & Hyltenstam, Reference Abrahamsson and Hyltenstam2008; Granena, Reference Granena2014). Our results align with these findings and highlight the complex, multifactorial nature of early L2 acquisition (cf. Granena, Reference Granena2014). Future research might aim to shed additional light on the interplay between environmental and individual-level variables among early L2 learners, particularly with respect to the parsing of complex syntactic structures.
Acknowledgments
I would like to thank Emanuel Bylund as well as the journal editors and two anonymous reviewers for their constructive feedback on the manuscript.
Data Availability Statement
The experiment in this article earned an Open Data badge for transparent practices. The materials are available at https://doi.org/10.7910/DVN/SGFGKO.
Appendix A
Informative priors for the effects of Target Type, Position, and their interaction were based on the reaction times in Felser and Roberts (Reference Felser and Roberts2007). Each prior was normally distributed with a mean equal to the result obtained in Felser and Roberts (Reference Felser and Roberts2007) and a standard deviation equal to the mean. The prior distributions are visualized in Figure A1.
Figure A1. Prior distributions.
Open access
