BACKGROUND
Effective and efficient reading in an L2 partly depends on learners’ sensitivity to grammar,Footnote 1 which encodes crucial information such as plurality or the assignment of subject and object roles. L2 learnersFootnote 2 may have different knowledge of this grammar, access to that knowledge, and/or processing strategies when compared to speakers from other language backgrounds, such as native speakers (NSs) or learners with higher proficiencies or different first languages. Such L2 phenomena can result in misinterpretations or processing problems. According to some theories, processing problems can even impede acquisition itself (O’Grady, Reference O’Grady2005; Sharwood-Smith & Truscott, Reference Sharwood-Smith and Truscott2005; VanPatten, Reference VanPatten, VanPatten and Williams2007). Thus, it is important to understand L2 learners’ sensitivity to L2 morphosyntax and the variables that influence their processing performance.
Some suggest that the degree of similarity between L1 and L2 features may explain variation in L2 learners’ sensitivity to morphosyntax. If L1 properties do influence L2 processing performance, structures shared between the L1 and L2 would be expected to generate relatively less processing effort than those that are not (Tolentino & Tokowicz, Reference Tolentino and Tokowicz2011). This view is common to the morphological congruency hypothesis (Jiang, Novokshanova, Masuda, & Wang, Reference Jiang, Novokshanova, Masuda and Wang2011), according to which L2 acquisition of morphemes is dependent on L1 properties, and the unified competition model (MacWhinney, Reference MacWhinney2005), which claims that L1 influenceFootnote 3 is “pervasive in the arena of sentence interpretation” (p. 77). MacWhinney suggests that positive transfer occurs when linguistic features are represented in the same way in the L1 and L2. The weight of cues used to signal grammatical functions in the L1 may moderate these effects, as may the L2 proficiency level.
Not all theories attribute such importance to L1 influence. For example, the shallow structure hypothesis (Clahsen & Felser, Reference Clahsen and Felser2006a) foregrounds the universally “shallow” nature of processing of L2 syntax. Clahsen and Felser argue that L2 learners are unable to reliably analyze morphosyntactic information in online L2 input in the same way as NSs, and consequently rely more on lexico-semantic and pragmatic cues. This implies that L1 background is (at least relatively) insignificant compared to the general effects common to L2 syntactic processing (see also Bley-Vroman, Reference Bley-Vroman1990, Reference Bley-Vroman2009). Note, however, that although the shallow structure hypothesis does not readily predict a strong influence of the L1 in the domain of syntax (as noted elsewhere, e.g., Tolentino & Tokowicz, Reference Tolentino and Tokowicz2011), it does not completely rule out the possibility of L1 influence. Indeed, most recently, Clahsen and Felser (Reference Clahsen and Felser2018) describe their position on the question of L1 influence as “largely agnostic” (p. 697).
A number of methods have been used to investigate L1 influence in L2 processing, including the use of neurolinguistic and eye-movement data. For example, in a narrative review of nine studies providing evidence from functional magnetic resonance imaging and event-related potentials, Tolentino and Tokowicz (Reference Tolentino and Tokowicz2011) concluded that L1 features instantiated differently in the L2 and features unique to the L2 generated greater processing difficulty than those shared between the L1 and L2. The authors interpreted their findings as evidence in support of the unified competition model and against the shallow structure hypothesis.
Another, arguably more frequent and accessible, method used to investigate L1 influence on L2 morphosyntactic sensitivity has been the self-paced reading (SPR) paradigm (Just, Carpenter, & Wooley, Reference Just, Carpenter and Woolley1982). Indeed, a recent systematic review of L2 SPR research found 26 studies that were motivated by questions relating to L1 influence during L2 processing (Marsden, Thompson, & Plonsky, 2018). While L2 processing is of course investigated using a variety of methods, the findings from this methodological paradigm are the focus of the current study and, therefore, also of the “Background” section.
SPR RESEARCH INVESTIGATING ONLINE L2 MORPHOSYNTACTIC SENSITIVITY AND L1 INFLUENCE
In SPR tasks, sentences are presented word by word (or in groups of words) on a computer screen. The participant taps a key to see the next word (or multiword segment), and software measures the time taken between each key press. These reading times offer insights into the relative processing effort required to comprehend, with higher reading times usually indicating greater processing effort (for methodological reviews of SPR, see Jegerski, Reference Jegerski, Jegerski and VanPatten2014; Keating & Jegerski, Reference Keating and Jegerski2015; Marinis, Reference Marinis, Blunsworth and Blom2010; Marsden et al., Reference Marsden, Thompson and Plonsky2018; Roberts, Reference Roberts2012, Reference Roberts, Mackey and Marsden2016).
Studies using SPR have yielded conflicting findings regarding whether L2 learners are sensitive to L2 morphosyntax, with some suggesting similar sensitivity compared to NSs (e.g., Jackson, Reference Jackson2008; Jackson & Dussias, Reference Jackson and Dussias2009; Kaan, Ballantyne, & Wijnen, Reference Kaan, Ballantyne and Wijnen2015; Song, Reference Song2015), and others suggesting less sensitivity (e.g., Jiang, Reference Jiang2004, Reference Jiang2007; Marinis, Roberts, Felser, & Clahsen, Reference Marinis, Roberts, Felser and Clahsen2005). Conflicting findings have also been observed regarding whether L1-L2 similarity facilitates L2 processing: some studies have suggested that differential sensitivity to morphosyntax between learners of typologically different L1s is due to L1-L2 similarity/difference (e.g., Jackson, Reference Jackson, VanPatten and Jegerski2010; Jiang et al., Reference Jiang, Novokshanova, Masuda and Wang2011; Roberts & Liszka, Reference Roberts and Liszka2013); other studies have provided evidence against L1 influence (e.g., Felser, Roberts, Marinis, & Gross, Reference Felser, Roberts, Marinis and Gross2003; Gerth, Otto, Felser, & Nam, Reference Gerth, Otto, Felser and Nam2017; Papadopoulou & Clahsen, Reference Papadopoulou and Clahsen2003); and others have suggested that the L1 offers only a partial explanation of results (e.g., Juffs, Reference Juffs2005; Perpiñán, Reference Perpiñán2014).
This mixture of findings may be for a range of reasons. One might be that the kind of processing phenomena that the study taps into may affect the observation of morphosyntactic sensitivity and L1 influence. For example, a group of SPR studies investigating one type of ambiguity resolution—relative clause attachment—has fairly consistently suggested no L1 influence, at least among advanced L2 learners. This group of studies draws on cross-linguistic variation in preferences about relative clause attachment among monolinguals, and then explores whether L2 participants carry those L1 preferences into their L2. The phenomenon has been investigated by making the antecedent of a relative clause temporarily ambiguous until morphology disambiguates it, thus forcing a particular attachment to one of the nouns in the matrix clause. For example, in the sentence “Someone shot the servant of the actress who was on the balcony,” the relative clause (“who was on the balcony”) may refer to either the first determiner phrase (“the servant”) or the second (“the actress”). Studies have generally found a preference for the latter among English NSs (Papadopoulou & Clahsen, Reference Papadopoulou and Clahsen2003). In their study, Papadopoulou and Clahsen investigated the preferences of Spanish, German, and Russian learners of Greek. Because monolinguals of all these languages have a preference for attaching the relative clause to the first noun in the matrix clause, one would hypothesize that L2 learners process faster in morphosyntactic contexts that force this attachment than those that force the alternative attachment, if L1 preferences are transferred to L2 processing. The reading time data, however, suggested no processing preference for either attachment for any group (see Dussias, Reference Dussias2003; Felser et al., Reference Felser, Roberts, Marinis and Gross2003; Pan, Schimke, & Felser, Reference Pan, Schimke and Felser2015, for similar patterns of findings).
In addition to this type of ambiguity, SPR research has investigated other types of temporary and also global ambiguities (such as role assignment where ambiguity can remain after the sentence is complete). All such ambiguity resolution studies share the common characteristic that sentences are grammatical, and morphosyntactic sensitivity is monitored by whether a processing cost is incurred by morphosyntactic reanalysis at or after the disambiguation point. By contrast, other SPR studies insert ungrammatical morphosyntax into sentences, and monitor whether an interruption to the parse was caused by detection of the anomaly. The distinction between processing cost caused by grammatical (though potentially unexpected or incongruous) morphosyntax versus ungrammatical morphosyntax (violations) may lead to a systematic difference in the magnitude of effects, though this issue has not been systematically explored to date.
Another, closely related characteristic that varies between SPR studies is the wide range of linguistic features investigated. Studies investigating L1 influence have spanned filler-gap dependencies (e.g., Marinis et al., Reference Marinis, Roberts, Felser and Clahsen2005), subject-object role assignment (e.g., Gerth et al., Reference Gerth, Otto, Felser and Nam2017; Hopp, Reference Hopp2006, Reference Hopp2009, Reference Hopp2010), tense-aspect agreement (e.g., Roberts & Liszka, Reference Roberts and Liszka2013), verb-aspect licensing (e.g., Tokowicz & Warren, Reference Tokowicz and Warren2010), reduced relative versus main verb ambiguity (e.g., Rah & Adone, Reference Rah and Adone2010), relative clause attachment (e.g., Papadopoulou & Clahsen, Reference Papadopoulou and Clahsen2003), number agreement (e.g., Jiang et al., Reference Jiang, Novokshanova, Masuda and Wang2011), gender agreement (e.g., Renaud, Reference Renaud2014), and causative versus inchoative alternators (e.g., Juffs, Reference Juffs1998b). The type of linguistic feature may systematically affect the magnitude of sensitivity to morphosyntax or L1 influence, and, ideally, syntheses should take this variety into account. However, cross-study comparisons at the level of specific linguistic feature are difficult as the number of studies investigating one linguistic feature can be small. This reflects the paucity of replication in L2 research more generally (Marsden, Morgan-Short, Thompson, & Abugaber, Reference Marsden, Morgan-Short, Thompson and Abugaber2018; though see Juffs, Reference Juffs, Slabakova, Montrul and Prevost2006).
Nevertheless, one point of contrast in terms of linguistic feature that allows for cross-study comparisons with a larger k (k = number of studies) relates to whether the feature represents a processing preference or a grammatical principle that can be perceived as being invariable (often dichotomously, as “acceptable” or “unacceptable”). It is conceivable that studies of core, invariable morphosyntax render stronger effects than attachment preference studies, which may in turn render L1 effects more observable in the former studies. Indeed, a good number of such investigations have suggested L1 effects in, for example, tense-aspect agreement (Roberts & Liszka, Reference Roberts and Liszka2013), number marking (Chan, Reference Chan2012; Jiang et al., Reference Jiang, Novokshanova, Masuda and Wang2011), wh-dependencies (Jackson, Reference Jackson, VanPatten and Jegerski2010), reduced relative clauses (Rah & Adone, Reference Rah and Adone2010), and gender agreement (Tokowicz & Warren, Reference Tokowicz and Warren2010). For parsing preferences, however, it has been suggested that effects attributable to L1 influence may be confounded (and, arguably, potentially masked) by a complex range of issues, such as prosodic and lexico-semantic information (Jackson & Dussias, Reference Jackson and Dussias2009) or amount and age of exposure (Jegerski, Reference Jegerski2018).
SPR research also varies in terms of the study designs used to investigate L1 influence. Some have used within-subject designs, in which the linguistic conditions manipulated cross-linguistic similarity. Tokowicz and Warren (Reference Tokowicz and Warren2010), for example, investigated the sensitivity of beginner L1 English–L2 Spanish learners to violations of four features. Two were cross-linguistically similar (verb progressive aspect licensing), one was cross-linguistically different though present in both the L1 and L2 (possessive and definite determiner-noun number agreement), and one was unique to the L2 (determiner-noun gender agreement). Reading times provided evidence that the L2 learners were sensitive to grammatical violations of both similar and different features, but not those unique to the L2.
Other authors have used a between-group design, in which the performance of L2 groups with typologically different L1s is compared on the same linguistic feature across groups. For example, Juffs (Reference Juffs2005) investigated wh-movement with L1 Chinese, Japanese, and Spanish learners of English and NSs. Spanish, like English, instantiates wh-movement, but Japanese and Chinese are wh-in-situ. Reading times might thus reveal L1-attributable effects. Variation might also be caused by L1 word order, given that Spanish and Chinese are predominantly SVO, while Japanese is SOV. Speed differences were found (with Japanese being slowest and Spanish being fastest), but because all L2 learner groups experienced greater difficulty processing subject extractions than object extractions, regardless of L1 background, the author concluded that their parsing profiles could not solely be explained by L1-L2 convergence and divergence.
Still further SPR studies do not explicitly set out to test L1 influence within their study design but consider L1-L2 similarity as relevant to their predictions and/or post-hoc interpretation of results (e.g., Rah & Adone, Reference Rah and Adone2010; Song, Reference Song2015; VanPatten, Keating, & Leeser, Reference VanPatten, Keating and Leeser2012). For example, Jiang (Reference Jiang2004) investigated how advanced L1 Chinese learners of English processed the bound inflectional morpheme, plural –s. Reading times for sentences like those in (1) and (2) below were compared:
(1) The key to the cabinet was rusty from years of disuse
(2) The key to the cabinets was rusty from years of disuse
Jiang found that the L1 Chinese learners did not show a statistically significant sensitivity to the plural morpheme, unlike native English speakers who slowed down due to the “broken agreement effect” in (2) compared to (1). Jiang suggested this might reflect, among other things, the fact that overt plural morphemes are rarely grammaticalized in Chinese (see also Chan, Reference Chan2012; Jiang, Reference Jiang2007).
It is conceivable that this variation in study design moderates the detection of L1 influence. First, within-subject manipulations of L1-L2 similarity may yield more valid results than between-group comparisons because a single L1 group acts as its own control, lessening the risk of sampling error and extraneous variables (Morris & DeShon, Reference Morris and DeShon2002). Second, they may yield larger effects, as illustrated by Plonsky and Oswald’s (Reference Plonsky and Oswald2014) L2 fieldwide meta-analysis that found stronger effects for within-group (median d = 1.06) than between-group contrasts (median d = .70). As yet, this issue has not been explored specifically for reading time research, probably largely due to the fact that the use and interpretation of effect sizes in such research has been relatively neglected.
USING AND INTERPRETING EFFECT SIZES IN L2 READING TIME RESEARCH
In addition to investigating the substantive issues mentioned previously, an important, more general purpose of the current study was to investigate the feasibility and usefulness of calculating and interpreting effect sizes in L2 reading time research. To date, null hypothesis significance testing (NHST; e.g., analyses of variance, t-tests, and, more recently, regression models with mixed effects) represents the default option for analyzing data in L2 SPR (Marsden et al., Reference Marsden, Thompson and Plonsky2018) and L2 research generally (Plonsky, Reference Plonsky2013, Reference Plonsky2015). The main concerns about NHST include: (a) because p is influenced by both the relationship and sample size being measured, it may lead to false conclusions. For example, in L2 SPR research samples are usually small, and so nonsignificant findings that are used to infer no L1 influence or no difference between L2 learners and NSs may in fact reflect a lack of statistical power rather than an absence of effects in the data; (b) p values channel research toward dichotomous conclusions, such as “L1 influence” versus “no L1 influence,” which are less nuanced than conclusions about the relative strength of L1 influence for specific conditions, learners, or features (VanPatten & Jegerski, Reference VanPatten and Jegerski2010); (c) the cutoff values for rejecting the null hypothesis, such as .05, are arbitrary and yet contribute to the oft-cited “file drawer problem” (Rosenthal, Reference Rosenthal1979) whereby results not meeting this threshold are not submitted to and/or published by journals, leading to published findings that underrepresent the totality of evidence (Marsden et al., Reference Marsden, Thompson and Plonsky2018; Norris & Ortega, Reference Norris and Ortega2006; Plonsky & Oswald, Reference Plonsky and Oswald2014); and (d) an overreliance on NHST can also exacerbate a temptation to p-hack, whereby participants or items are added or removed to obtain a statistically significant result, a questionable practice that does not necessarily reveal a more informative observation.
Effect size reporting could be a step toward more nuanced evidence regarding sensitivity to L2 morphosyntax and L1 influence, providing (a) information about the magnitude of difference between two means; (b) evidence that is less confounded by sample size; and (c) standardized units of measurement (standard deviations) that can be aggregated systematically across studies. Standardized units are necessary because the raw data reported in two SPR tasks are difficult to compare due to differences between participants, stimuli, processing issues, and general processing speeds. Means in raw milliseconds (also a “standardized unit,” of course) could arguably be used to compare across studies. However, this would present several problems. First, it ignores variation in standard deviations, yielding a distorted interpretation of the size of differences found in different studies. (For example, two comparisons between means may render an identical raw difference, yet if one mean has a greater standard deviation, confidence in that mean is weaker. Because the calculation for d uses the standard deviation as its denominator, it expresses difference in terms of standard deviation units.) Second, and relatedly, different overall reading speeds between participant groups (due to, e.g., different proficiencies or literacy skills) distort the interpretation of raw millisecond reading time data. That is, the difference between two conditions within one participant group may be relatively small simply due to their overall fast reading speeds, whereas in another group it is much larger simply due to their slower reading speed; yet, the “small difference” may be meaningful while the “larger difference” may not. Third, some SPR tasks use residual reading times, which control for word-length differences between stimuli (see Keating & Jegerski, Reference Keating and Jegerski2015), and could not be directly compared with the raw reading time data from another study. Effect sizes are unaffected by this because the type of data (raw or residual) is constant within each dataset that yields an effect size.
Despite these potential benefits, the d family of effect sizes has rarely been reported in L2 reading time studies to date, so we do not have a clear picture of what constitutes a “small” or “large” effect (for exceptions, see Brysbaert & Stevens, Reference Brysbaert and Stevens2018; Jegerski, Reference Jegerski2018; Marsden et al., Reference Marsden, Thompson and Plonsky2018). Illustrating two consequences of this problem, we note that in Adesope, Lavin, Thompson, & Ungerleider’s (Reference Adesope, Lavin, Thompson and Ungerleider2010) meta-analysis, data had to be extracted to calculate effect sizes for the two reading time studies they included and, potentially more concerning, these effect sizes were aggregated with effects from test scores (accuracy, judgments, etc.), which are a different type of data. An illustration of how effect sizes can vary across data types is found in Hedge et al.’s (Reference Hedge, Powell, Bompas, Vivian-Griffiths and Sumner2018) meta-analysis of task performance, which observed reliably different effect sizes (with no correlation) when performance cost was measured by reaction times versus errors. As their comparability with other data types is not well understood, effect sizes from measurements with reading time data may constitute a moderator variable (“data type”) that should be considered when aggregating effects across data types.
Toward a framework of reference
To begin to address this gap, we sought to capture a “framework of reference” within which to interpret effect sizes for our main substantive areas of interest as discussed previously (i.e., sensitivity to L2 morphosyntax and L1 influence during online reading). Of course, for interpreting d, we could turn to Cohen’s (Reference Cohen1969) general guidelines, suggesting that an effect size of .2 is small, .5 is medium, and .8 is large, yet these were only intended as a rule of thumb for the social sciences (Howell, Reference Howell2013). In L2 research, Plonsky and Oswald (Reference Plonsky and Oswald2014) calculated generic medians, based on 25th, 50th, and 75th percentile cutoffs, that might reflect patterns of small (.4), medium (.7), and large (1.0) effects for between-subject differences, and .6 (small), 1.0 (medium), and 1.4 (large) for within-subject differences. However, given that effect sizes for reading time data are rarely reported, in the current study we needed to identify and contextualize the usefulness, magnitude, and meaningfulness of the effects found for our domain of interest.
One step toward gauging the usefulness of effect sizes is to explore the extent to which effects might vary as a function of corresponding results from NHSTs that were reported as “statistically significant” versus “not significant.” First, for example, d may reveal a reliable effect (albeit small) where NHST suggested a “nonsignificant difference.” Or alternatively, d may reveal an unreliable or negligible difference where NHST results suggested a “significant difference.” Second, the 95% confidence intervals (CIs) of effects for “significant” versus “nonsignificant” findings may overlap considerably or one effect may sit within the CI of the other effect, suggesting that although the effects may be different descriptively (as well as according to NHST), the difference between them may not be reliable (see Larson-Hall, Reference Larson-Hall2016; Plonsky, Reference Plonsky2015).
Another way of interpreting the magnitude and meaningfulness of effect sizes in SPR research is to ascertain the magnitude of differences in reading speeds between participant groups. Regardless of L1-L2 similarity of specific morphosyntax, different individuals use different amounts of time to interpret lexical and morphosyntactic information (Roberts, Reference Roberts and van Gompel2013). Despite variable reading speeds being a widely acknowledged phenomenon (see, e.g., Rayner, Schotter, Masson, Potter, & Treiman, Reference Rayner, Schotter, Masson, Potter and Treiman2016), we do not have any general estimate of the magnitude of difference that might be expected between the kinds of participant groups commonly used in L2 SPR studies. First, it is unclear to what extent L2 learners who are proficiency-matched but have different L1s (and different L1 scripts) vary in their reading speed. While some studies report significant group differences in reading speed (e.g., Pan et al., Reference Pan, Schimke and Felser2015), others do not, even when L1 scripts differ (e.g., Fender, Reference Fender2003). Second, it is unclear to what extent reading speed is associated with proficiency (compare, e.g., Roberts & Felser, Reference Roberts and Felser2011 with Kaan et al., Reference Kaan, Ballantyne and Wijnen2015). In terms of speed differences between advanced L2 learners and NSs, it has often been observed that L2 learners read more slowly (e.g., Marinis et al., Reference Marinis, Roberts, Felser and Clahsen2005; Roberts & Liszka, Reference Roberts and Liszka2013), although some studies suggest otherwise (e.g., Bel, Sagarra, Comínguez, & García-Alcaraz, Reference Bel, Sagarra, Comínguez and García-Alcaraz2016; Jegerski, Reference Jegerski2016; Kaan et al., Reference Kaan, Ballantyne and Wijnen2015). Thus, although not the main focus of our study, estimating the magnitude of speed differences in reading was not only useful methodologically (to identify a frame of reference for our main effect size estimates) but also of substantive interest.
THE PRESENT STUDY
In sum, the current study is a meta-analysis of morphosyntactic sensitivity and L1 influence during L2 SPR. There is sufficient primary evidence for such a meta-analysis, and existing results have not been consistent, possibly reflecting cross-study variation such as study design, processing issue, and linguistic feature, variables that can be submitted to systematic moderator analyses. More generally, given the potential benefits of effect size reporting, the present study is a preliminary attempt to calculate and aggregate effect sizes from reading time data. With a view to identifying a preliminary framework of reference, we investigate the magnitude of differences between (a) effects reported as “statistically significant” and “nonsignificant”; (b) reading speeds of proficiency-matched L2 learner groups with different L1s; and (c) reading speeds of L2 learner and NS groups. Critically, by using data from a single type of instrument (SPR) we improve cross-study comparability by reducing the effects of heterogeneity due to instrumentation, a frequent concern acknowledged by meta-analysts for interpreting findings (see Borenstein, Hedges, Higgins, & Rothstein, Reference Borenstein, Hedges, Higgins and Rothstein2009, pp. 379–380; Norris & Ortega, Reference Norris and Ortega2006, p. 16).
RESEARCH QUESTIONS
Our study addressed two substantive research questions:
RQ1: How sensitive are L2 learners and NSs to morphosyntactic violations and disambiguating information during SPR?
RQ2: To what extent is sensitivity to L2 morphosyntactic violations and disambiguating information influenced by L1 background?
It was anticipated that effects for RQ1 and RQ2 might vary as a function of four moderator variables: study design, processing phenomenon, sentence region, and linguistic feature.
Our third objective (henceforth RQ3) was to capture a frame of reference within which to consider effect sizes during SPR; to address this, we asked the following three subquestions:
RQ3a: To what extent do effect sizes differ for NHST results reported as “statistically significant” versus “not statistically significant”?
RQ3b: What is the average magnitude of difference in online reading speeds for proficiency-matched learners of the same L2 with different L1s?
RQ3c: What is the average magnitude of difference in online reading speeds between L2 learners (of any L1 background) and NSs?
METHOD
STUDY ELIGIBILITY CRITERIA AND DATA COLLECTION
For inclusion in our meta-analysis, we applied the following eligibility criteria:
• The study used SPR.
• Raw or residual group mean reading times, standard deviations and n were provided, or t statistics that could be converted to d. Footnote 4
• Participants were adult L2 learners. Studies with young bilinguals or L2 learners (e.g., Marinis, Reference Marinis, Belikova, Meroni and Umeda2007) and heritage speakers raised in a bilingual environment (e.g., Keating, Jegerski, & VanPatten, Reference Keating, Jegerski and VanPatten2016) were excluded.
• The study focused on morphosyntax. Investigations into other areas such as the lexicon (e.g., Bultena, Dijkstra, & van Hell, Reference Bultena, Dijkstra and van Hell2014) were excluded.
• The potential effect of L1-L2 similarity/difference of the target morphosyntax could be ascertained from the author’s predictions or post-hoc explanations (i.e., the study’s own design was not required to explicitly test L1 influence; see Supplementary Material 1).
• For inclusion in RQ3b, the study had to have at least two L2 groups with different L1s, with no statistically significant proficiency differences reported between the groups, as proficiency may be a confound (as noted by Hopp, Reference Hopp2006; Juffs, Reference Juffs2005). Nonmatching groups were excluded.
• For inclusion in RQ3c, the study had to have at least one L2 group (of any L1 background and any proficiency) and a NS comparison/control group.
The search for studies involved two phases. First, we extracted studies that met the criteria above from Marsden and colleagues' (2018) methodological synthesis of L2 SPR research, for which the search finished in June 2016. Our second phase involved both updating that body of studies to the following 12 monthsFootnote 5 and extending it, given that it had only included journal articles. For our meta-analysis we wanted to increase inclusivity to generate better representativeness and reduce vulnerability to any bias toward the publication of statistically significant results (as recommended by Plonsky & Oswald, Reference Plonsky and Oswald2014). Thus, we included both published and unpublished material, with a view to determining the possibility of publication bias in our sample.
Our search drew on a range of search techniques and online databases, as recommended by Plonsky and Brown (Reference Plonsky and Brown2015). The online databases were ERIC, LLBA, PsycINFO, ProQuest, Cambridge Core, Google, and Google Scholar. Relevant authors’ websites were also checked and a backward search of one extensive reference list was also carried out (Juffs & Rodriguez, Reference Juffs and Rodriguez2015). The LingRef (Cascadilla Proceedings Project) database was also consulted, as were the resources of the authors’ university library. The search terms self-paced reading, moving window, and subject-paced reading were cross-referenced with language, processing, learning, second, acquisition, and cross-linguistic influence.
We attempted to address the “missing data problem” (Plonsky, Reference Plonsky2013, p. 613) by contacting individual researchers, five of whom provided their data. However, 18 studies had to be excluded because either descriptive statistics or t statistics were not available or not reported for the sentence regions of interest. For three studies that reported mean reading times without standard deviations, we decided, because inclusivity was a priority and our study was exploratory (and following advice from a meta-analyst), to estimate their standard deviations based on those found for the same processing phenomenon and linguistic feature (see Supplementary Material 1).
In the end, for RQ1 and RQ2, which minimally required a single L2 learner group with any L1, 57 studies met the inclusion criteria (i.e., 57 study reports; see Supplementary Material 2). Fifty-four of these studies also included a NS group. Twenty-seven manipulated cross-linguistic similarity as either a within- (k = 11) or a between-subject (k = 16) variable. The remaining 30 studies involved at least one L2 learner group (each with one L1), using L1-L2 similarity/difference of the target feature(s) to either predict or interpret findings.
For the analyses for RQ3a, we used the same sample (K = 57) as for RQ1 and RQ2. For RQ3b, 27 studies met the criteria, all of which used two or more groups of L2 learners with different L1s. Due to reported proficiency differences between groups, four L2 groups were excluded from four separate studies within this set (see Table S2, Supplementary Material 1). All but one of the studies included in RQ3b was also used for RQ3c (k = 26), which required the inclusion of a NS group.
CODING THE STUDIES AND EXTRACTING SECONDARY DATA
A coding scheme (see Supplementary Material 3) was developed to record data about: bibliographic information (author, year, publication type), participant characteristics (proficiency as reported, L1, L2), and potential moderator variables (linguistic feature, processing phenomenon, sentence region, study design for manipulating L1-L2 similarity, i.e., within-subject, between-subject, none). To calculate effect sizes, we extracted sample sizes, mean group reading times for each condition, and standard deviations. For our analysis of the moderator variable “sentence region,” we coded whether each reading time was on the critical, postcritical, or wrap-up region.Footnote 6, Footnote 7 Finally, to investigate how effect sizes related to the findings from the studies’ NHST, we extracted whether the result was reported as “significant” or “not significant,” the t statistic (or equivalent), and corresponding p value. When available, these were recorded separately for the subject and item analyses.
CALCULATING EFFECT SIZES
Sensitivity to morphosyntax and L1 influence (RQ1 and RQ2)
For all effect sizes, Cohen’s d was calculated to measure the mean difference in every paired comparison. For the denominator, we used the pooled standard deviation (the average of two groups’ standard deviations weighted according to sample size [Lipsey & Wilson, Reference Lipsey and Wilson2001]), rather than the standard deviation of a control group, as the pooled standard deviation more accurately represents variance when two sample sizes are fairly similar (Coe, Reference Coe2002; Norris & Ortega, Reference Norris and Ortega2006), as was most often the case in our study sample.
To determine sensitivity to grammatical information during SPR, comparisons were drawn between the reading times for an experimental condition and its corresponding “baseline” condition. To identify each comparison, we adhered strictly to the primary study’s within-subject experimental manipulations between conditions. This was done separately for each morphosyntactic feature investigated. Effect sizes were calculated for L2 learners (for RQ1 and RQ2) as well as for the NSs (for RQ1).
We illustrate how this was done with two examples. First, in a study of anomaly detection with L1 French and L1 German learners of English, the reading times for past simple sentences with tense-aspect agreement (as in 3) were compared (within-subject) with ungrammatical equivalents (as in 4). If L2 learners are insensitive to the tense-aspect violation in (4), their reading times for the two conditions would be similar and the corresponding effect size would be relatively small/negligible. If, however, L2 learners detect the tense-aspect violation, a slowdown would be expected on the critical and, possibly, postcritical regions in (4) compared to (3). The increased processing cost in (4) would yield a meaningful and reliably larger effect size between the two conditions relative to that of participants who were insensitive to the morphology. Furthermore, as the primary study’s authors predict, if L2 learners’ sensitivity to tense-aspect violations is influenced by their L1, greater effect sizes would be observed for the French participants, whose L1 grammar marks tense-aspect agreement in the past simple, than for the Germans, whose L1 grammar does not. The size of the difference between reading times for the underlined regions in sentence (3) compared to (4) is given in [ ] for the L1 French and L1 German groups, respectively:
(3) Since / last / week / James / has / gone / swimming / every / day
(4) *Last / year / James / has [critical region d = .06; .21] / gone [postcritical d = .00; .18] / swimming [postcritical d = .58; .12] / every / day
(Roberts & Liszka, Reference Roberts and Liszka2013, p. 421 [d not in original])
A similar type of comparison of effects is illustrated using a different processing phenomenon: ambiguity resolution. L2 learners’ sensitivity to morphosyntax that disambiguates meaning during online processing can be tested by comparing their reading time in a temporarily ambiguous experimental condition with that in a comparison condition. For example, Juffs (Reference Juffs2005) investigated L2 learners’ assignment of subject (S) and object (O) roles in sentences with wh-interrogatives. L1 Chinese, L1 Japanese, and L1 Spanish learners of English read sentences such as (5) and (6):
(5) Whoi / did / the / woman / suggest / the / manager / liked e i / at / the / office?
(6) Whoi / did / the / woman / suggest e i / liked / the / manager / at / the / office?
In (5), the parser extracts an object in the embedded clause after “liked,” whereas in (6) it extracts a subject in the embedded clause after “suggest.” Because the parser seeks to build sentence structure as early as possible, it is possible that in (6), “who” is initially analyzed as the object of the main clause (“Who did the woman suggest”). This initial interpretation needs to be revised on reaching the verb of the embedded clause (“liked”) as this verb now requires a complementizer phrase. This would result in a slowdown in (6) compared to (5) at word six. If L2 learners’ sensitivity to wh-dependencies is influenced by L1-L2 correspondence, greater effect sizes would be observed for the Chinese and Japanese participants, whose L1s do not instantiate wh- movement, than for Spanish participants, whose L1 does. As per the primary study’s author’s predictions, syntactic reanalysis should be less costly for participants of Spanish, an L1 that builds wh- chains in a similar way to English. See sentences (7) and (8) to illustrate, with the d that we calculated provided in [ ] for L1 Japanese, L1 Chinese, and L1 Spanish, respectively.
(7) Whoi / did / the / woman / suggest / the / manager / liked e i / at / the / office?
(8) Whoi / did / the / woman / suggest e i / liked [critical region d = .66; .63; .36] / the / manager / at / the / office?
(Juffs, Reference Juffs2005, pp. 136–137 [d not in original])
Thus, to investigate whether L1 background moderates the magnitude of effect sizes, each comparison between conditions was coded for “potential L1 effect” or “no potential L1 effect,” as per the author of the primary study's manipulations and argumentation. These terms were used to code the predicted magnitude of effects based on L1-L2 correspondence of the target grammar feature: “potential L1 effect” refers to conditions in which L1-L2 convergenceFootnote 8 (e.g., L1 French, Roberts & Liszka, 2013) or nonconvergence (e.g., L1 Japanese, Juffs, 2005) was predicted to render a larger effect size, whereas comparisons coded for “no potential L1 effect” concerned conditions in which L1-L2 convergence (L1 Spanish, Juffs, 2005) or nonconvergence (L1 German, Roberts & Liszka, 2013) was predicted to render a smaller effect size. Note, L1-L2 convergence can facilitate sensitivity that either reduces or increases processing cost, depending on the study in question. Because larger effect sizes are not necessarily more desirable or indicative of “positive” L1 influence, we decided against using the terms positive and negative to describe potential and no potential L1 effect.
If a study did not provide the necessary information to allow coding for “(no) potential L1 effect,” data for that feature and participant group were excluded. As a result, data from eight studies were excluded for these comparisons (see Table S1, Supplementary Material 1). Further exclusions were also made for two studies in which effect sizes could not be calculated due to the absence of a comparison condition in the within-subject experimental manipulation.
An experienced second coder (the second author) checked (a) the coding for “(no) potential L1 effect” and (b) the suitability of conditions being compared in 14 studies (25% of the total sample). Once the coding scheme had been piloted (with approximately nine studies) and finalized, the extraction of data was then an objective process (e.g., extraction of bibliographic, unequivocal study design features, reading times, and reported statistics).
In sum, if L1-L2 convergence or nonconvergence affect online sensitivity to an L2 feature, reliably larger effect sizes are potentially expected for comparisons coded as “potential L1 effect” than for those coded as “no potential L1 effect.” If, however, the L1 does not influence online sensitivity to an L2 feature, effect sizes coded for “potential L1 effect” and “no potential L1 effect” would not differ reliably.
The size of effects corresponding to “statistically significant” and “nonsignificant” NHST results (RQ3a)
For this analysis, each effect size extracted for RQ1 and RQ2 was coded for whether the authors reported a difference that was statistically significant (or not) by both participants and items, by participants, and by items.Footnote 9 (Only two studies reported NHSTs that were only significant by items and these are therefore not reported here; see Supplementary Material 5.)
Between-group comparisons for speed differences (RQ3b and RQ3c)
To estimate speed differences between different learner groups for RQ3b, we compared the reading times of two groups of L2 learners with different L1s on the same SPR task for every condition and every critical, postcritical, or wrap-up region, as reported. Because we were interested in the size rather than the direction of difference, we recorded the absolute value of d (i.e., removed minus signs); whether one L1 group was faster or slower than another was not of interest here. Instead, we were interested in ascertaining preliminary estimates of basic speed differences between groups to contextualize the results from RQ1 and RQ2.
To estimate speed differences between L2 learners and NSs for RQ3c, comparisons were drawn between the reading times of each learner group and the NS group on the same SPR task for every condition and every critical, postcritical, or wrap-up region. Because we wanted to ascertain whether any difference between L2 learners and NSs reflected faster or slower reading, the direction of difference was also recorded (i.e., keeping minus signs).
To assess the reliability of both the individual effect sizes and of the grand weighted mean (see following text), 95% CIs were recorded. If the CIs of an effect size pass through zero, the effect size is unlikely to be reliably different to zero (Cumming, Reference Cumming2012; Larson-Hall, Reference Larson-Hall2016). When comparing two effect sizes, the difference between them is sometimes considered reliable if their CIs do not overlap (see, e.g., Lee & Huang, Reference Lee and Huang2008; Lee, Jang, & Plonsky, Reference Lee, Jang and Plonsky2015). Other guidelines suggest that “if the two CIs just touch, p is about 0.01, and the intervals can overlap by as much as about half the length of one CI arm before p becomes as large as 0.05” (Cumming, Reference Cumming2009, p. 205; see also Cumming, Reference Cumming2012; Greenland et al., Reference Greenland, Senn, Rothman, Carlin, Poole, Goodman and Altmann2016, p. 344). According to a third approach, the difference between two means is reliable if “the mean of one group falls … outside the CI for the other group’s mean” and vice versa (Plonsky, Reference Plonsky2015, p. 40; see also Greenland et al., Reference Greenland, Senn, Rothman, Carlin, Poole, Goodman and Altmann2016). In describing and interpreting differences between our effect sizes, we considered these three criteria together. Furthermore, in Supplementary Material 6, we provide detailed interpretations of the results of the present study according to these three criteria.
AVERAGING AND WEIGHTING
For within-subject comparisons (RQ1 and RQ2), an average effect size estimate was first calculated based on comparisons for each unique sample. We repeated this for each unique sample and then averaged the results to calculate an overall estimate of learners’ sensitivity to L2 morphosyntax and an overall estimate of NS sensitivity to morphosyntax. For RQ1, these averages were based on 130 effect size estimates for the L2 learners and 61 effect size estimates for NSs.Footnote 10, Footnote 11 Effects were coded for linguistic feature, processing issue, and sentence region, as potential moderators of sensitivity to L2 morphosyntax.
Having calculated an overall effect size estimate for RQ1, the effect sizes were then separated, for each unique sample, according to “potential L1 effect” (91 effect sizes) and “no potential L1 effect” (62 effect sizes) for RQ2. Effects were coded for study design, linguistic feature, processing issue, and sentence region, as potential moderator variables of the average “potential L1 effects” and “no potential L1 effects.”
For RQ3a, which investigated the size of within-subject effects corresponding to “statistically significant” and “nonsignificant” NHST results, the effect sizes, calculated by unique samples, were averaged to provide a grand estimated effect for L2 participants (and also separately for “potential L1 effect” and “no potential L1 effect”) and for NSs. The grand estimated effects for differences reported significant by “participants and items,” “significant by participants,” and “not significant” were based, respectively, on 16, 21, and 42 effect sizes for L2 learners, and on 21, 14, and 15 effect sizes for NSs.
For RQ3b, we averaged effect sizes of speed differences between two groups of L2 learners of different L1s on the same SPR task. We note that for those studies with three or more L1 groups (k = 8), this method used the results from the same L1 group more than once (e.g., L1a vs. L1b, L1a vs. L1c). Although this may underestimate the overall level of sampling error in summary figures (Borenstein et al., Reference Borenstein, Hedges, Higgins and Rothstein2009), it was deemed necessary to retain the relevant sample-level variability. The alternative, which would have involved aggregating all comparisons to calculate a single effect size for each study (Lipsey & Wilson, Reference Lipsey and Wilson2001), would have reduced the detail in our data by collapsing the size and L1 characteristics of each L1 subsample. Our process yielded 55 effect size estimates for RQ3b.
For RQ3c, an average was calculated from each set of effect sizes for an L2 versus NS comparison on the same SPR task. This generated 73 effect size estimates, a higher figure than for RQ3b because (a) comparisons in studies with two L2 groups (k = 13) contributed two effect sizes in RQ3c (L1a vs. NSs, L1b vs. NSs), but only one in RQ3b (L1a vs. L1b) and (b) as noted previously, effect sizes were not calculated between L2 groups for which the authors had reported proficiency differences.
In meta-analytic research, statistical models (e.g., with fixed and random effects) may be chosen to combine effects across studies (see Borenstein, Hedges, & Rothstein, Reference Borenstein, Hedges and Rothstein2007). However, this was not deemed to be important in the present study, as recommendations for L2 meta-analysis downplay the importance of homogeneity tests, citing their low degree of statistical power and consequent risk of inaccurate conclusions (Oswald & Plonsky, Reference Oswald and Plonsky2010). We opted instead for an approach based on averaging, weighting, and theoretically motivated moderator analyses.
In line with good practice in meta-analysis, effect sizes from larger samples should represent a greater proportion of the grand effect size (Borenstein et al., Reference Borenstein, Hedges and Rothstein2007). To this end, we used inverse variance weighting, whereby the weight of a given effect size is proportionate to the inverse of the sampling error variance of the two groups.Footnote 12
MODERATOR ANALYSES
Following the grand effect size calculations for RQ1 and RQ2, moderator analyses were undertaken, as follows:
Processing issue (RQ1 and RQ2)
To investigate whether morphosyntactic sensitivity and L1 influence varied as a function of different processing phenomena, our moderator analysis isolated anomaly detection (involving violations) and ambiguity resolution (involving grammatical sentences only).
Linguistic feature (RQ1 and RQ2)
Online sensitivity to morphosyntax may vary as a function of linguistic feature. In our study sample, 18 types of linguistic feature were investigated and we calculated average effect sizes for each. This allowed us to isolate studies that investigated morphosyntax that aligns (or not) with parsing preferences (in studies of relative clause attachment), from other studies where the morphosyntax was operationalized as being invariably grammatical or not (i.e., less reliant on preferences). As noted in the preceding text, results from parsing preference studies often seem to have suggested no L1 influence, while results from the latter may be more likely to observe L1 effects.
For RQ2, we were able to explore the extent to which eight linguistic feature types moderated L1 influence, that is, those that had been investigated both in contexts where there was potential and no potential L1 influence, though k was small for some of these features.
Sentence region (RQ1 and RQ2)
To date, we have little general sense of the extent to which heavier processing costs are observed on one region compared to another, although L2 processing effects have often been thought to manifest themselves in “spillover” sentence regions after the critical region. Thus, we separated critical, postcritical, and wrap-up regions as a potential moderating variable.
Study design (RQ2 only)
Within-subject manipulations of cross-linguistic similarity, where the same participants yield effects for both “potential L1 effect” and “no potential L1 effect,” may yield larger indicators of L1 influence than between-subject manipulations (in between-group and single group designs), where the comparisons for each unique sample were coded as either “potential L1 effect” or “no potential L1 effect.” Thus, our moderator analysis investigated the extent to which aggregated results for potential versus no potential L1 effects were influenced by these different study designs.
Although proficiency level was also of interest it could not be investigated as a moderator because diverse proficiency measures were used (see Supplementary Material 4), an issue also noted in a recent meta-analysis of lexical activation in bilingual sentence processing (Lauro & Schwartz, Reference Lauro and Schwartz2017; see also Thomas, Reference Thomas, Norris and Ortega2006). Furthermore, the majority of our studies in our sample (k = 44) involved L2 participants at advanced or near-native level, reflecting researchers’ interest in the later stages/end state of L2 acquisition and their concern that L2 participants comprehend the sentences (Marsden et al., Reference Marsden, Thompson and Plonsky2018).
VISUAL DATA INSPECTION FOR INFLUENCE OF PUBLICATION BIAS AND SAMPLE SIZE ON EFFECTS
The distribution of effects was visually inspected using a funnel plot of the effect sizes for sensitivity to L2 morphosyntax (RQ1), where asymmetries may indicate the presence of (unplanned) moderator variables in the data. Figure 1 shows that a greater number of effect sizes appear to the left of the overall unweighted mean (d = .21). This plot does not very clearly reflect the traditional funnel shape with effects falling evenly on either side of the mean. Instead, it suggests a tendency for studies to have small effects with effect sizes clustering below the mean effect size (as also found by Plonsky, Reference Plonsky2011), and that this mean effect size is affected by a smaller number of studies with large effect sizes. The plot illustrates the expected influence of sample size, in that effects found for small samples are more widely distributed (with a wider range of effects) than those of larger samples. However, it also suggests a dispersion of effects that is not similar either side of the mean, with a smaller range in the dispersion found to the left of the mean compared with the range of effects found above the mean. This might indicate some kind of suppression of effects that might be observed in the “true” population (for further explanation see Oswald & Plonsky, Reference Oswald and Plonsky2010). However, it seems unlikely that this is due to a publication bias that generally suppresses small or negative effects. Figure 1 shows that effects from published sources were unlikely to be larger than those found for unpublished sources and, in fact, that fewer unpublished studies found effects below the mean (k [unique samples] = 7) than above the mean (k [unique samples] = 14).
FIGURE 1. Scatter plot of unweighted effect sizes (M=0.21) as a function of sample size and publication status
We also checked for publication bias by carrying out a moderator analysis of RQ2 results, relating to the influence of L1, as this is a controversial theoretical issue that might conceivably engender a publication bias (one way or the other). The overall mean effect found from published sources (journal articles, edited book chapters) was d = .18 (.13, .24), k (unique samples) = 109 and this was similar to the mean from unpublished sources (i.e., conference proceedings, dissertations, working papers; d = .29 [.15, .43], k [unique samples] = 21), with the mean from published sources sitting within the 95% CIs of the mean from unpublished sources and substantial overlap in the CIs of both means. In line with what Figure 1 indicated, this did not suggest a reliable effect of publication type in the expected direction of larger effects in published compared to unpublished work. However, the slightly larger effect in unpublished sources (with a mean sitting above the 95% CIs of the mean from published sources) may tentatively indicate a borderline trend to publish or submit for publication findings that suggest little or no influence of L1 compared to findings that suggest a larger influence of L1 on morphosyntactic sensitivity during reading. However, it may also suggest other tendencies, such as a greater use of anomaly detection in unpublished studies (see results for RQ2).
Another indication of other types of bias relate to the reporting of nonsignificant effects and the relationship between the results of NHST in the primary studies and the effect sizes that we extracted—issues that are examined for RQ3a.
RESULTS
SENSITIVITY TO L2 MORPHOSYNTAX (RQ1)
For RQ1, summary results are reported first, followed by moderator analyses. Our general estimate of sensitivity to morphosyntactic information during L2 SPR was d = .20 (.15, .25), based on 130 unique samples and 3,052 L2 participants. The 95% CIs did not pass through zero, suggesting a reliable effect that would normally fall around one fifth of a standard deviation unit. Expressed in another way, this suggests that advanced L2 learners changed their processing speeds by about one fifth of a standard deviation unit when elements in a sentence coerced sensitivity to morphosyntactic information (overall, across anomaly detection and ambiguity resolution). Among the NS groups, we found the magnitude of overall sensitivity to be d = .28 (.21, .35), drawn from 61 unique samples and 1,494 participants. Note that the means of the L2 learners and NSs fall just outside each other’s CIs and the CIs overlapped by approximately half an average CI arm, suggesting that the two grand effect sizes are likely to be reliably different.
These are general estimates, irrespective of potential L1 effect, processing phenomenon, linguistic feature, or sentence region. L1 influence is examined in RQ2 (see following text). In Table 1 we present analyses for the other three potential moderators. As shown in Table 1, for L2 participants we did not find processing phenomenon was a reliable moderator as CIs for the effects overlapped considerably and the means sat within each other’s CIs. For NS groups, by contrast, processing phenomena did seem to moderate effects, with evidence of a greater sensitivity during anomaly detection than ambiguity resolution—note that the means sat outside each other’s CIs and the CIs only overlapped very slightly. Furthermore, the difference between NSs and L2 speakers for anomaly detection seemed to be reliable, with means that sat outside each other’s CIs and CIs that only touched end to end. For ambiguity resolution, however, there was no evidence of a reliable difference in effects between L2 learners and NSs.
TABLE 1. Results of moderator analyses for morphosyntactic sensitivity
In terms of linguistic feature, studies were highly heterogeneous, with 18 different linguistic features investigated across our sample. Results are presented in full in Supplementary Material 5, showing a low number of unique samples for some features. In Table 1 we present the five most frequently investigated. Of these five features, only relative clause attachment studies were not reliable, with CIs that passed through zero for both L2 learners and NSs, suggesting that core, invariable morphosyntax might have yielded stronger effects than attachment preference studies. Notably, two of the highest effect sizes were .49 and .44, for number and gender agreement, respectively, among NSs. However, these effects were largely derived from anomaly detection studies (k [unique samples] = 15 out of 16), suggesting these higher effects may (also) be attributable to the nature of the processing phenomenon investigated (i.e., sensitivity to violations) rather than to (only) the specific type of morphosyntax.
In terms of sentence region, there was tentative evidence that this moderated effects for L2 learners, given that effect sizes for critical and postcritical regions were reliable (and similar to each other) as their CIs that did not pass through zero, in contrast to sentence-final wrap-up segments that had CIs passing through zero. However, the effect sizes for critical and postcritical regions did not fall outside the CIs of wrap-up regions and their CIs overlapped those of wrap-up regions by almost one average arm length, suggesting a lack of reliable difference. For NSs, effect sizes for all regions were reliable and not reliably different to each other.
MORPHOSYNTACTIC SENSITIVITY MODERATED BY L1 BACKGROUND (RQ2)
Regarding the extent to which L1 background moderated the online sensitivity of L2 learners, effect sizes were averaged separately, and by unique samples, for conditions coded as “potential L1 effect” (d = .23 [.17, .29], k [unique samples] = 90), and “no potential L1 effect” (d = .19 [.12, .26], k [unique samples] = 64). The two means fell within each other’s CIs and overlapped considerably, suggesting no reliable difference and, therefore, no reliable, general effect of cross-linguistic influence.
The results of three of our four moderator analyses for RQ2 are presented in Table 2. Effects from studies with a within-subject design were not reliably higher than those from studies with a between-subject design, both for potential L1 effect and no potential L1 effect, with at least one mean that fell inside the CIs of the other and CIs that overlapped considerably. For studies with a within-subject design, effect sizes did not differ reliably as a function of potential L1 effect versus no potential L1 effect, suggesting no reliable L1 influence. However, for studies with a between-subject design, effect sizes with potential L1 effect appeared to be reliably greater than those with no potential L1 effect, with means that fell outside the other’s CIs and CIs that overlapped by around half an average arm length. With regard to processing issue, effects in anomaly detection studies for conditions with potential L1 effect were reliably larger than those with no potential L1 effect, with means that fell outside the other’s CIs and CIs that only overlapped slightly. In contrast, effect sizes for ambiguity resolution studies did not appear to differ reliably. Comparing these two processing phenomena, effect sizes with potential L1 effect for anomaly detection appeared to be reliably greater than those with potential L1 effect for ambiguity resolution, with means that sat outside the CIs of the other and CIs that overlapped by about half an average arm length.
TABLE 2. Results of moderator analyses for potential L1 effect and no potential L1 effect
a These results were very similar when split into single versus multigroup designs.
The other analyses investigated whether effect sizes for specific sentence regions and linguistic features were moderated by potential L1 effect versus no potential L1 effect. In terms of sentence region, effects did not appear to be moderated by L1 background as the means for potential L1 effect and no potential L1 effect overlapped considerably for every sentence region, though there was tentative evidence of a trend toward larger effects on postcritical regions with potential L1 effect, given that each mean fell outside each other’s CIs. In the analyses by linguistic feature, it was possible to analyze 8 of the 18 linguistic features for potential L1 effect versus no potential L1 effect (the remaining 10 could be coded only for either potential L1 effect or no potential L1 effect; see Table S1, Supplementary Material 5). For every feature, the effect sizes for potential L1 effect versus no potential L1 effect clearly overlapped in their CIs, suggesting little meaningful difference. Two findings aligned with a pattern that could tentatively support a prediction that the L1 influences effects: tense-aspect agreement yielded a reliable effect for potential L1 effect (d = .41 [.10, .73], k [unique samples] = 4), but not for no potential L1 effect (d = .11 [–.23, .46], k [unique samples] = 3); similarly, number agreement yielded a reliable effect size for potential L1 effect (d = .30 [.15, .44], k [unique samples] = 11) but not for no potential L1 effect (d = .10 [–.07, .26], k [unique samples] = 10). However, the considerably overlapping CIs do not provide convincing evidence of a reliable influence of L1 for either feature, and mean effects for tense-aspect agreement were based on small number of samples. We also note that the effect size for relative clause attachment with potential L1 effect (d = .02 [–.15, .18], k [unique samples] = 13) was not reliable, with CIs that passed through zero. This differed reliably from subject-object role assignment with potential L1 effect (d = .31 [.17, .46], k [unique samples] = 21), with means that fell outside the CIs of the other and CIs that only overlapped marginally, suggesting that potential L1 influence might have been moderated by the type of morphosyntax (core, invariable morphosyntax vs. morphosyntax used for interpretation preferences).
Summing up the results for RQ2, no reliable difference was found between our grand effect sizes for “potential L1 effect” and “no potential L1 effect.” However, the results of moderator analyses only aligned to some extent with this. In particular, the effects for anomaly detection studies seemed to be moderated by potential L1 effect as those effects were reliably larger than those for no potential L1 effect. The average effect size for anomaly detection also seemed reliably larger than for ambiguity resolution, at least for conditions with potential L1 influence. In the analyses by study design, potential L1 effect appeared to yield reliably larger effects than no potential L1 effect for between-subject study designs, but not for within-subject study designs. Finally, the magnitude of effects for different sentence regions and linguistic features was largely unaffected by L1 background, though two linguistic features with potential L1 effect, relative clause attachment and subject-object role assignment, did differ reliably, in line with the suggestion that the type of morphosyntax might moderate L1 influence.
AN INITIAL CONTEXTUAL FRAMEWORK FOR INTERPRETING EFFECT SIZES OF READING TIMES IN L2 SPR (RQ3)
RQ3 sought to explore the usefulness and meaningfulness of the effect sizes mentioned previously, and in SPR research more generally, through three analyses.
First, for RQ3a, we estimated the average effect sizes for within-group contrasts of overall sensitivity to morphosyntax that had been reported as “statistically significant” or “not significant.” For contrasts reported as significant by both participants and items analyses, the mean effect size was d = .64 (.49, .79), k (unique samples) = 16 and by participants d = .55 (.44, .67), k (unique samples) = 23; both these effects were reliable. For findings reported as not statistically significant, mean d = .17 (.08, .25), k (unique samples) = 42. That is, the effect was considerably smaller and, reassuringly, its CIs did not overlap with those of the mean effect for “statistically significant” findings. This suggests a reliable difference between the magnitudes of differences that had been originally reported as statistically significant and nonsignificant. However, note that the CIs for the effects for “nonsignificant” differences did not pass through zero, suggesting some small but reliable, and therefore potentially meaningful, effect, of approximately one sixth of a standard deviation unit. This pattern was the same regardless of whether the context was “potential L1 effect” or “no potential L1 effect” (see Table S2, Supplementary Material 5). For NSs the results patterned similarly: for comparisons reported as statistically significant by both participants and items analyses, d = .64 (.52, .76), k (unique samples) = 21 and by participant analyses, d = .46 (.29, .62), k (unique samples) = 14. Again, these effects were reliably larger than for comparisons reported as not significant, d = .18 (.02, .33), k (unique samples) = 15. Again, however, the 95% CIs did not pass through zero, suggesting a small (just under a fifth of a standard deviation unit) but reliable difference for within-subject comparisons where “null” NHST results could be (mis-)represented as evidence of no differences.
For RQ3b we estimated the size of difference between the processing speeds of two groups of participants with different L1 backgrounds learning the same L2 undertaking the same SPR task. A grand weighted average effect size of d = .35 (.26, .43) was found, from 55 effect size estimates from 75 unique samples with a total of 1,540 participants. The CIs suggest that this comparison is statistically reliable. This is a relatively crude between-group comparison of basic speed differences, regardless of the experimental manipulation or L1-L2 convergence. The result also appears to be reliably greater than the estimated mean magnitude of general L2 morphosyntactic sensitivity, with nonoverlapping CIs.
RQ3c addressed the general magnitude of difference between the processing speeds of an L2 learner group (of any L1) and an NS group undertaking the same SPR task. We found a weighted average effect size of d = .53 (.46, .60), comprising 73 effect size estimates from 99 unique samples with 2,145 participants. Strikingly, this grand effect size is reliably larger (in raw terms, by about a fifth of a standard deviation) than that found for differences between learner groups with different L1s.
DISCUSSION
In answer to RQ1, we found that learners showed a reliable sensitivity to L2 morphosyntax of about one fifth of a standard deviation unit. When set against benchmarking used both traditionally (Cohen’s) and in the specific field of L2 research (Plonsky & Oswald’s), this is a small to very small effect. However, given that two of our largest average effect sizes were for NSs processing anomalies in gender agreement (d = .44) and number agreement (d = .49) we cannot conclude that .20 is necessarily “small” in the context of reading times during SPR. The magnitude of sensitivity was different between L2 learners (generally advanced/near-native) and NSs, with means that fell outside of each other’s CIs and CIs that overlapped by around half an average arm length. In answer to RQ2, our overall results did not suggest any kind of general L1 influence during L2 SPR, and in this sense they are consistent with observations that L1 effects in online L2 morphosyntactic processing are “elusive” (VanPatten & Jegerski, Reference VanPatten and Jegerski2010, p. 9) and “more limited than one might expect” (Clahsen & Felser, Reference Clahsen and Felser2006b, p. 565).
The main findings, both for overall morphosyntactic sensitivity and L1 effects, were partially altered by our moderator analyses. On the one hand, with regard to study design, the results for within-subject manipulations of L1-L2 similarity, anticipated to be the most likely to yield observable and reliable results for RQ2, did not vary reliably as a function of “potential L1 effect,” and because 25 unique samples were used in this moderator analysis, the finding cannot be entirely attributed to a lack of statistical power. Nevertheless, we did find a reliable effect of the L1 in our studies with between-subject designs. This might possibly reflect systematic “cluster” effects due to various known and unknown shared group characteristics, as our grand effect size for speed differences between L2 learners of different L1s (d = .35) also suggests. The results of moderator analyses by processing issue also diverged to some extent from the overall findings; L2 learners’ general sensitivity seemed to be unaffected by processing issue, yet their sensitivity was reliably greater during anomaly detection than ambiguity resolution in comparisons with potential L1 effect, suggesting that the type of processing phenomenon under investigation may have moderated L1 influence.
One challenge that arose in discussing our results was how to interpret whether two effect sizes were similar or different. Different guidelines rendered different conclusions for a number of our analyses, and this is clearly indicated in Supplementary Material 6. For example, for L2 learners versus NSs during anomaly detection, the CIs touched end to end. If entirely nonoverlapping CIs are used to determine a difference between means (e.g., Lee & Huang, Reference Lee and Huang2008), this difference was not reliable. However, if overlap of about half an average CI arm (Cumming, Reference Cumming2009, Reference Cumming2012) or one mean falling outside the 95% CIs of the other (Plonsky, Reference Plonsky2015) is used, then the difference was reliable (see Supplementary Material 6). Similarly, for L2 learners with potential L1 effect versus no potential L1 effect during anomaly detection, the difference was unreliable according to nonoverlapping CIs, but reliable according to the other two criteria. In other words, according to two of the three criteria, the results suggested a reliable native/nonnative difference and reliable L1 influence for L2 learners during anomaly detection.
Interestingly, the three preceding criteria also yield different interpretations for results in the moderator analyses by linguistic feature. Although these analyses did not generally yield many meaningful differences, the CIs of our grand effect size for L2 learners in studies of relative clause attachment overlapped only very slightly with that those of wh-dependencies and subject-object role assignment (see Supplementary Material 6). These differences were not reliable according to the criteria of nonoverlapping CIs, but were reliable according to the criteria of Cumming (Reference Cumming2009) and Plonsky (Reference Plonsky2015). Consequently, we do not rule out the possibility that sensitivity differed for core morphosyntax versus processing preferences, at least for these features, though we remain cautious given that these analyses were based on relatively few unique samples. More generally, our findings suggest that the use of nonoverlapping CIs to determine the reliability of a difference between two means is relatively conservative compared to other criteria (Schenker & Gentleman, Reference Schenker and Gentleman2001). We hope that future meta-analyses, given a wider body of sample studies and greater consensus in the field on interpreting reliable differences between effect sizes, can reach firmer conclusions about the processing phenomena investigated in this article.
In terms of informing theories about the nature of online processing among advanced L2 learners, we offer several insights, albeit tentatively. Overall, we found different morphosyntactic sensitivity among advanced L2 learners and NSs and our analysis of the subset of anomaly detection studies also found a reliable difference between advanced L2 learners and NSs, lending some evidence to the notion that shallow morphosyntactic processing may happen more often among L2 learners than NSs (Clahsen & Felser, Reference Clahsen and Felser2006a, Reference Clahsen and Felser2018; Clahsen, Felser, Neubauer, Sato, & Silva, Reference Clahsen, Felser, Neubauer, Sato and Silva2010). Also aligning to some extent with the idea that “the hypothesized tendency [for L2 learners] to underuse grammatical information … is independent of a learner’s L1” (Clahsen & Felser, Reference Clahsen and Felser2018, p. 697), we found no reliable evidence for general L1 influence on morphosyntactic sensitivity. However, shedding some light on the current agnostic position of the Shallow Structure Hypothesis, we found that in this set of anomaly detection studies, L1-L2 similarity tended to slightly, but reliably, increase sensitivity to morphosyntax compared to when the L2 feature did not share characteristics with the L1, at least among the advanced L2 learners in our study sample. Future studies are required to further probe these tendencies, including evidence from different data-elicitation techniques.
Another avenue for further investigation might be the extent to which making an explicit judgment about grammaticality after having read the sentence affects online reading times. Most (k = 40) studies included only a comprehension question at the end of (some) sentences, but 12 sought grammaticality judgments after reading the sentence (Dekydtspotter, Edmonds, Fultz, & Renaud, Reference Dekydtspotter, Edmonds, Fultz, Renaud, Iverson, Ivanov, Judy, Rothman, Slabakova and Tryzna2010; Jackson & Dussias, Reference Jackson and Dussias2009; Jackson & van Hell, Reference Jackson and van Hell2011; Juffs, Reference Juffs1998a, Reference Juffs1998b, Reference Juffs2005, Reference Juffs, Slabakova, Montrul and Prevost2006; Juffs & Harrington, Reference Juffs and Harrington1995; Jung, Reference Jung2010; Lakshmanan et al., Reference Lakshmanan, Kim, McCreary, Park, Suen, Lee, Bowles, Ionin, Montrul and Tremblay2009; Perpiñán, Reference Perpiñán2014; Renaud, Reference Renaud2014). A further two studies used the “stop making sense” paradigm that elicits an explicit judgment about meaning during reading. These 14 studies cover a wide range of processing and grammatical phenomena and present too small a sample for a useful moderator analysis in the current study. However, we urge future researchers to explore the possibility that explicit judgments alter differences between NSs and L2 learners or any L1 effects.
We acknowledge that our substantive findings for RQ1 and RQ2 remain somewhat tentative given that they draw on results from one data elicitation method among a number used in L2 processing and from learners of advanced L2 proficiency, who were also very often highly literate, educated, and proficient readers in their L1 and L2 (see Hulstijn, Reference Hulstijn2019; Marsden et al., Reference Marsden, Thompson and Plonsky2018). Indeed, the lack of strong and consistent L2 learner-NS differences or L1 influence across all our moderator analyses (only really emerging for anomaly detection) may in part be due to the high levels of proficiency and literacy of L2 participants in our study sample. Such high-proficiency L2 learners may reach a stage where L1 effects are only very selectively observable, at least on SPR tasks. Indeed, a number of studies using SPR have found that proficiency moderated the sensitivity of learners to features that do not exist or are different in their L1. For example, Sagarra and Herschensohn (Reference Sagarra and Herschensohn2010, Reference Sagarra and Herschensohn2011) found that intermediates and NSs experienced slowdowns for gender agreement violations, while beginners did not. Similarly, Jackson (Reference Jackson2008) found that advanced learners of German, unlike intermediates, were able to use case marking to determine the subject of a sentence, even though their L1 English largely relies on word order for this. These findings are broadly consistent with those of Bel, Sagarra, Comínguez, and Garcia-Alcaraz (Reference Bel, Sagarra, Comínguez and García-Alcaraz2016), Hopp (Reference Hopp2010), Jackson and Dussias (Reference Jackson and Dussias2009), Jackson and van Hell (Reference Jackson and van Hell2011), and Lee, Lu, and Garnsey (Reference Lee, Lu and Garnsey2013). We acknowledge, nevertheless, that as most studies in our synthesis involved advanced and near-native proficiencies, further research with other proficiencies is needed to ascertain the extent to which L1 influence during L2 SPR is attenuated by proficiency. Such an agenda will certainly require increased parity in the proficiency measures used across studies (Thomas, Reference Thomas, Norris and Ortega2006).
A further issue to be considered when accounting for the effects found for RQ1 and RQ2 is the extent to which general reading speeds play a role. Evidence from Kaan et al. (Reference Kaan, Ballantyne and Wijnen2015) suggested that faster readers showed smaller and more delayed grammaticality effects than slower readers, both among NSs and L2 learners. After controlling for differences in reading speed between the L2 learners, who read faster overall than NSs, the authors found that L2 learners and NSs patterned similarly in their sensitivity to grammaticality. Our findings of slower reading among L2 learners and overall lower sensitivity among L2 learners versus NSs do not seem to align with Kaan et al.’s more fine-grained study. More research is needed to understand the interaction of reading speed and sensitivity to morphosyntax. The individual difference of reading speed may in fact exert more of an influence over online reading behaviors than “native versus L2 learner” status or specific L1-L2 morphosyntactic relations.
Our RQ3 explored a broader framework for interpreting effect sizes in SPR research. First, effect sizes for results reported as “statistically significant” were reliably greater than those reported as “not significant,” both for L2 and NSs, with the upper-bound 95% CI reaching a maximum of d = .76 for NSs. Interestingly, however, results reported as “not significant” rendered a small but reliable effect, with a maximum upper-bound CI of about one third of a standard deviation unit, suggesting the presence of small differences where NHST results were, for all intents and purposes, broadly interpreted as indicating “no difference.” This pattern of findings was similar regardless of L1 background. Because nonsignificant effects were not widely reported for our comparisons (L2 learners: k [unique samples] = 42 out of 130; NSs: k [unique samples] = 15 out of 61), we wonder how much this figure would have changed if authors had reported all nonsignificant effects. This highlights the need for effect size reporting to move SPR research beyond the arbitrary dichotomy and sample-size dependency inherent in NHST toward more nuanced interpretations. It may also indicate the need for an increased use of Bayes factors to enable L2 researchers to make more accurate inferences about accepting null hypotheses (discussed by Dienes, Reference Dienes2014, and see Morgan-Short et al., Reference Morgan-Short, Marsden, Heil, Issa, Leow, Mikhaylova, Mikołajczak, Moreno, Slabakova and Szudarski2018, for an example). Our effect sizes can also be used to establish “priors” for future Bayesian analyses (i.e., estimates of intervals of effect sizes within which future results may occur [Norouzian, de Miranda, & Plonsky, Reference Norouzian, de Miranda and Plonsky2018]).
The other analyses for RQ3 provided general estimates of processing speed differences between groups that are typically included in SPR research. The grand effect size for proficiency-matched L2 learner groups of different L1s (d = .35) may be small according to existing L2 benchmarks for between-group comparisons, and yet it was larger than our estimated grand effect for overall morphosyntactic sensitivity among L2 learners. As previously noted, such between-group speed differences may possibly reflect “cluster” effects, highlighting the importance and difficulty of establishing parity between learner participant groups.
The grand effect size for comparisons between L2 learners (of any L1) and NSs (d = .53) was reliably greater than differences between L2 groups. This supports the notion of a “general L2 processing effect” (Roberts, Reference Roberts2007, p. 124) where L2 processing tends to be slower than L1 processing, regardless of variables such as linguistic feature, processing issue, and cross-linguistic similarity.Footnote 13 These findings are noteworthy; even though our dataset drew largely on participants with advanced or near-native L2 proficiency, they still exhibited overall slower processing on the sentence regions examined. This likely reflects the demands of real-time access to and integration of morphosyntactic, lexico-semantic and pragmatic information, while activating/inhibiting representations of several languages. Our nonoverlapping CIs for speed differences among L2 learners compared to L2 learners versus NSs suggest that this general effect may exist regardless of individual differences between L2 learners, but, as noted in the preceding text, this remains to be investigated further.
Overall, the most basic but perhaps the most significant of our findings is that SPR reading time data seem to generate considerably smaller effect sizes than those for L2 research more generally, as observed in the many meta-analyses to date. One explanation for low effect sizes may be the stage of maturity of the subdomain (discussed by Plonsky & Oswald, Reference Plonsky and Oswald2014). This yields two opposing accounts, depending on where the subdomain is in its temporal trajectory: differences between SPR effect sizes might become more apparent as hypotheses, design, and instrumentation are refined or the effect sizes may be nearing the end of a process of regressing to the mean. Given (a) the relatively small number of studies to date (compared to other subdomains, such as motivation or grammar instruction), (b) the clustering of our sample of studies within the last decade, and (c) Marsden et al.’s (Reference Marsden, Thompson and Plonsky2018) observations that refinement of design and instrumentation is required, it seems that SPR effect sizes may be likely to grow. Given that SPR use in L2 research is relatively new, future research might also consider the magnitude of effects in L1 research, although we remain unaware of any systematic synthesis to date in mainstream psycholinguistic research. Our study is one example, among others in SLA research, where L2 researchers are at the cutting edge of exploring innovative approaches to improving our toolkit for understanding language learning phenomena.
However, another, or possibly additional, explanation for smaller effects might be the observation that variation in reaction time data is often large relative to the mean; tasks with a mean of 600ms often have a standard deviation in the region of 150–200ms (Brysbaert & Stevens, Reference Brysbaert and Stevens2018). Thus, larger standard deviations, serving as the denominator, decrease effect sizes. Furthermore, a positive correlation tends to exist between the size of a mean and standard deviation (Brysbaert & Stevens, Reference Brysbaert and Stevens2018), and so studies that report mean reading times by multiword segments, which are inevitably longer than by single word times, may increase variance/decrease precision of observed effects. Because nearly half of our SPR studies elicited reading times on multiword segments (k = 26 out of 57), this may have attenuated effects.
In sum, our findings suggest that caution should be exercised when comparing effect sizes between measurement types if the size of the standard deviation relative to the mean varies systematically as a function of measurement type. On the basis of the low effect sizes found in our meta-analysis, this may be the case with reading times from SPR compared to measurements used more widely in the field, such as judgment or production tests.
Effect sizes are of course not new to L2 research; several journals including Language Learning, Language Learning and Technology, Studies in Second Language Acquisition, TESOL Quarterly, and The Modern Language Journal require their reporting; and the American Psychological Association’s publication manual (2010, p. 34) states that they are “almost always necessary” to communicate the practical significance of results. We note, however, that some researchers have expressed concern that the d family of effect sizes are rendered redundant or inappropriate by the increasing, and highly appropriate, use of linear mixed effects models (MEMs) for analysis of SPR data. Nonetheless, we suggest that the d family is still meaningful and appropriate, for reasons that include (a) Marsden et al. (Reference Marsden, Thompson and Plonsky2018) found only seven such SPR studies to date, illustrating that there is some way to go before these models are incorporated into the field’s statistical toolkit; (b) linear MEMs, although bringing many benefits to quantitative analyses, represent a type of NHST and so could still raise some of the concerns outlined here and elsewhere; (c) reporting d effect sizes allows meta-analyses to be done on paired variables of theoretical and practical interest; and (d) there is no mathematical reason why data cannot be set out differently for separate analyses, one using models and one calculating means for the d family of effect sizes. To expand on this last point, for MEMs, data are laid out with each data point from each item (trial) from each participant in a separate row. For the d family of effect sizes, data from individual items (trials) are aggregated across individual learners (for “by participant” analyses) and across individual items (for “by item” analyses). Each approach (MEM and d family of effect sizes) has advantages and disadvantages. Aggregating across items or participants loses the capacity to calculate random effects of items or participants (though in a carefully designed experiment, the stimuli are created in such a way so as to reduce the effect of “item” and participants are selected to a degree that is at least theoretically and ecologically satisfactory, for example, by proficiency, age, or L1). The advantage is that aggregating across items or individuals allows us to infer the magnitude of effects between pairs of theoretically interesting variables. Thus, the two analyses are complementary and such a belts-and-braces approach can only strengthen argumentation.
The use of effect sizes such as d appears particularly compelling given the current lack of meta-analysis in L2 processing research, leaving effects in individual studies somewhat “quarantined from related research data” (Ellis, Reference Ellis, Norris and Ortega2006, p. 303). As such, our study makes a first step toward a more contextualized understanding of effect sizes based on reaction time data, and suggests that effects fall somewhat lower than those found by other syntheses of L2 and social science research. Providing more fine-grained benchmarks, such as effects at the 25th, 50th, and 75th percentiles, would require a larger set of studies than our sample, as low numbers of studies within each percentile could render misleading benchmarks at this stage. We leave that task to future research, but we suspect that the ranges will be considerably tighter than other benchmarks that have been proposed.
Finally, we note that a limitation of the present study, and of meta-analyses in general, is that the quality of meta-analytic findings is affected by cross-study heterogeneity in areas such as study design, sample demographics, instrument construction, data-cleaning procedures (e.g., outlier removal, treatment of nonnormal distributions), analysis, and transparency. As Marsden et al. (Reference Marsden, Thompson and Plonsky2018) found, there is a pressing need to standardize the use and reporting of SPR research. While improving our understanding of L2 processing, it will also facilitate future research syntheses. However, in defense of our approach, note that our effect sizes were calculated for each experimental manipulation of conditions within each study, which means that decisions about data cleaning (such as the use of trimmed, transformed, residual, or aggregated data) were all held constant for each effect size calculation. This reduces some potential threats caused by interstudy methodological heterogeneity.
CONCLUSION
As effect sizes are best interpreted in the context of a specific domain, the results reported in the present study could offer a preliminary reference framework for future SPR research. These include (a) an overall reliable sensitivity to morphosyntax for L2 learners of d = .20 (.15, .25), which was likely meaningfully less than for NSs (d = .28 [.21, .35]); (b) a reliable sensitivity to anomaly detection for L2 learners of d = .19 (.09, .29), which seemed reliably less than for NSs (d = .41 [.29, .54]); (c) larger effect sizes in anomaly detection studies in conditions with L1-L2 similarity (d = .34 [.21, .48]); (d) a lack of L1 influence in ambiguity resolution studies; (e) speed differences between L2 learner groups with different L1s (d = .35 [.26, .43]); and (f) speed differences between L2 learner groups and NSs (d = .53 [.46, .60]).
These findings should be understood as estimates of processing performance at advanced stages of L2 learning. They can help orient researchers toward situating future SPR research within a preliminary framework of reference. For example, the average effect sizes we found may inform subsequent power analyses to determine sample sizes required to obtain a particular effect size. Principally, we hope our findings help contextualize future within- and between-subject comparisons for a range of different research questions that are unlikely to lend themselves to binary distinctions. Further synthesis of the growing body of L2 processing research will certainly be helped by the field’s gradually increasing disposition to report and interpret effect sizes and share raw data.
SUPPLEMENTARY MATERIAL
To view supplementary material for this article, please visit https://doi.org/10.1017/S0272263119000196
