Sanz, C. & Grey, S. (2015). Effects of conditions on L2 development: Moving beyond accuracy. In P. Rebuschat (Ed.), Explicit and implicit pedagogical conditions (pp. 301-324). Amsterdam:

2nd proofs

PAGE P r o o f s

© John bEnJAmins PublishinG comPAny

doi 10.1075/sibil.48.13san© 2015 John Benjamins Publishing Company

Effects of conditions on L2 development

Moving beyond accuracy

Cristina Sanz & Sarah GreyGeorgetown University

Most research on the effects of implicit and explicit conditions, especially that which is pedagogically oriented, has been limited in terms of outcome measures. This stems from an over-reliance on accuracy data as the only dependent variable in measuring the differential effects of conditions. Accuracy data provide information on the static outcome, or product, of an input condition, but are unable to inform us about the dynamic processing profiles that underlie this product. This chapter outlines a more detailed perspective on the contributions of explicit and implicit conditions in second language (L2) development. Specifically, it highlights recent research which has used accuracy data in combination with online measures of processing in order to better characterize the effects of conditions on L2 learning and development.

Introduction

Research in the field of second language acquisition (SLA) has two basic goals: (1) To explain the nature of L2 knowledge, and (2) to explain the differences in rate and final attainment of that knowledge among L2 learners. Perhaps because SLA research has often been motivated by urgent needs in second language pedagogy (especially in early studies, e.g. Krashen, 1981; Felix, 1981) most of the work has con-centrated on the second goal, and specifically on the role of context - naturalistic vs. classroom contexts, or pedagogical variables under the ‘explicit-implicit’ umbrella – for explaining differences in rate and final L2 attainment. Research has examined the nature of the input that feeds learning (N. Ellis, 2002; Krashen, 1985; Schmidt, 1990; VanPatten, 2004, 2005) and the role of attention during the processing of said input (Williams, 2005). However, an understanding of the effects of context and processes is necessarily interpreted within the limits of our understanding of what is affected in the learner’s L2 system. In this way, the first and the second goals, i.e. the nature of L2 knowledge and intra-learner variation, cannot be separated.

2nd proofs

PAGE P r o o f s


302 Cristina Sanz & Sarah Grey

The interest in learning processes has not been accompanied by process-oriented methodology. With rare exceptions (DeKeyser, 1996, 1997; Robinson, 1997; Robinson & Ha, 1993) the majority of SLA studies have relied on accuracy data, which is an indicator of the product of learning. The sole reliance on accuracy as well as the nature of the tasks implemented to elicit it have been a problem from the start, a problem that has defined the field. Critiques of Krashen’s 1985 Input Hypothesis came from different sides – methodological (McLaughlin, 1987) as well as theoretical (White, 1991) – and evidence against it was quickly found (Long, 1983). Of relevance here is Larsen-Freeman’s work (1975), showing that conclusions from the morpheme studies were an artifact of the elicitation task the researchers had implemented (Burt, Dulay, & Hernandez-Chavez, 1975). Tests that were more or less ‘communicative’ led to dif-ferent morpheme orders, which Krashen used as evidence in favor of the Monitor Hypothesis (Krashen, 1985), according to which, research tasks that allow for time and do not lead the learner to focus on content (i.e. tasks that do not pressure the learner to rely on ‘acquired’1 automatic, un-verbalizable knowledge) do not provide a reliable picture of the effects of pedagogical conditions. Thus, the litmus test for any new advance in language pedagogy research was the communicative nature of the test implemented to evaluate its efficacy and its ability to elicit changes in accuracy, which were interpreted as growth in implicit/acquired knowledge.

‘Communicative’ has been defined in many ways – open-ended, suprasentential, meaningful – and has been operationalized as oral or written story retelling, responses to prompts, or as interviews (for an elaborated discussion, see Sanz, 1996). Timed tasks were not popular except in the case of grammaticality judgment tests, which, even as the name suggests, are not the most meaning-focused of elicitation tasks. To honor the time-pressure argument, oral elicitation tasks were preferred over written elicitation tasks. However, communicative tasks, whether timed or untimed, are still problematic for the researcher, as they tend to show a significant amount of intra- and inter-learner variation. At a theoretical level, variation is to be expected since SLA studies language development, which is by nature fluid and variable. However, given the inherent lack of control over the nature and quality of outcome data in communicative tasks, it is difficult to find patterns and to explain variation in learning outcomes at an empirical level, especially when the elicitation tasks have not been created along pre-specified criteria, such as mode (when all participants complete oral and written versions of the

1. Krashen (Krashen, 1981, 1985) pioneered the Acquisition-Learning distinction in SLA, where acquired knowledge was considered to be automatic, implicit knowledge derived from exposure to meaningful input and learned knowledge was considered to be explicit knowl-edge derived largely from classroom exposure. However, most current theorists and practitio-ners in adult language acquisition research no longer make such a distinction and the terms are often used interchangeably.

2nd proofs

PAGE P r o o f s


Effects of implicit and explicit conditions on L2 development 303

same task) or amount of information to be conveyed (e.g. 2 vs. 7 events, Sanz, 1997) and thus may lack internal validity. Variation is also a burden for popular statistical procedures, as it makes it more difficult for analyses to yield significant results; as a consequence, open-ended tests are left only for methodological risk-takers. This is evi-denced in meta-analyses on the effects of explicit and implicit pedagogical conditions -in lay terms, with or without an explicit focus on form over meaning-on language development, which highlight the relatively small proportion of studies that imple-ment tasks that are considered ‘communicative’ (Li, 2010; Norris & Ortega, 2000, 2012; Spada & Tomita, 2010). The number of studies that operationalize effects of conditions on L2 development as changes in any measure other than accuracy is even smaller.

Below we include a flow chart that represents the steps involved in language pro-cessing for acquisition and that we use to illustrate different ways of addressing this puzzle. One way is to shift the focus to place it earlier in the process; i.e. from Set III to sets I and II (Figure 1), and to investigate the role of attention and learning with and without attention (Schmidt, 1992; Williams, 2005) by using techniques that try to avoid set III and its product, such as those discussed in chapters in this volume by Leow and by Gass and Winke. The advantage researchers have here is that they are in control. In their effort to observe attention during input processing, researchers can manipulate the input and implement technology (e.g. mouse-tracking or eye- tracking). Low-tech and easier to implement are learner verbalizations, which are prone to interpretative difficulty and have been shown to alter the very processes they are meant to uncover (e.g. Sanz, Lin, Lado, Bowden & Stafford, 2009). Naturally, this research is limited in that it cannot address the status of or access to knowledge resulting from the processes under study.

Set I Set II Set III

Input Intake Developingsystem Output

Figure 1. Input to output processes

A second solution is to develop elicitation tasks that avoid the problems detailed earlier in the section. Rod Ellis (R. Ellis, 1994, 2004, 2005; R. Ellis, et al. 2009; Han & R. Ellis, 1998) has proposed seven features along which to distinguish explicit and implicit knowledge and develop replicable assessments: (1) Degree of awareness, (2) Time available, (3) Focus of attention, (4) Systematicity, (5) Certainty, (6) Metalan-guage, and (7) Learnability. A number of their studies have evaluated the reliability and internal validity of a test battery that operationalizes implicit/explicit knowledge based on these criteria (e.g. Elder, 2009; R. Ellis, 1994, 2004, 2005; R. Ellis, et al. 2009; Erlam, 2005; Han & Ellis, 1998; Philp & Tognini, 2009). The reliability of all tests has

2nd proofs

PAGE P r o o f s



proven to be relatively high, with Cronbach’s alpha coefficients and inter-rater agree-ment exceeding 0.80 for all tests. Furthermore, it has consistently been found that the three tests measuring implicit knowledge (Elicited Oral Imitation, Oral Narrative Test & Timed Grammaticality Judgment Test) and the two measuring explicit knowl-edge (Untimed Grammaticality Judgment Test & Metalinguistic Knowledge Test) load on separate factors in exploratory and confirmatory principal component factor analy-ses. Ellis (R. Ellis, 1994, 2004; R. Ellis, et al. 2009) and Philp and Tognini (2009) con-clude that of all tests, the elicited oral imitation test appears to be the best measure of implicit knowledge, while the untimed grammaticality judgment test seems to be the most effective measure of explicit knowledge (confirmed by Johnson Serafini, 2013). Likewise, Ortega (Ortega, 2000; Ortega, Iwashita, Rabie, & Norris, in preparation) has developed an oral imitation task that is highly reliable (α > 0.95) and has been shown to strongly correlate with a standardized measure of oral proficiency (Simulated Oral Proficiency Interview – SOPI; r = 0.87 to 0.91) in L2 Spanish learners at a range of pro-ficiency levels. The learner’s task is to repeat grammatical sentences (n = 30) increasing in syllable length (7–17 syllables). One important strength of this task is that scoring considers both content and grammar instead of the all-or-nothing approach that looks at only the form and often results only in accuracy scoring.

A third solution, which works best in tandem with the appropriately designed and utilized tasks outlined above, is to rely on more than one dependent measure of development, and specifically one that provides complementary process-level informa-tion to the already popular product-level information (accuracy). Collecting multiple, complementary sources of data allows researchers to more reliably capture the complex nature of L2 learning and development, especially with respect to the patterns and vari-ability that permeate SLA (Dussias, 2010; Felser, 2005; Marinis, 2003; Roberts, 2012). As will become clear from the studies below, group-level differences (or lack thereof) as assessed by accuracy alone do not always (and perhaps rarely) account for the full range of effects of conditions on L2 development. Consequently, a great deal of information is left unattended when using accuracy data as the only dependent measure of effects and then drawing conclusions about the relative effectiveness of instructional conditions. Discussed here are two effective process-level measures that have been uniquely useful in moving beyond accuracy to elucidate the dynamic effects of different instructional conditions on L2 development: event-related potentials (ERPs) and latency.

Alternatives to accuracy: ERPs and latency measures

Event-related potentials refer to small changes in the electrical activity of the brain, which are recorded from electrodes placed on the scalp. ERPs are able to offer precise temporal information (millisecond timing) about language processing while learners

2nd proofs

PAGE P r o o f s



are engaged with a task (either auditory or visual), and provide the additional benefit of not having to use a potentially interfering secondary task (for in-depth discussions, see Handy, 2005; Kaan, 2007; Luck, 2005, Morgan-Short this volume). Though ERPs have the advantage of excellent temporal precision, they are largely limited in their ability to provide spatial information (i.e. where in the brain the activity is occurring) and the fMRI (functional magnetic resonance imaging) technique is much better suited for that purpose. ERPs are not good candidates for language research involving speaking, as head, eye, and mouth movements must be minimized in order to acquire clean data and are thus largely limited to being a receptive measure of language pro-cessing (this is also somewhat true of fMRI, as subjects must remain still in the mag-net, but silent language production circumvents this issue).

Language research using ERPs often employs the violation paradigm (correct sentences compared to semantically or grammaticality incorrect sentences) in order to study the time-course of language processing in the brain. The ERP technique, together with this violation paradigm, has been used across different languages and linguistic domains in L2 research (Gillon Dowens, Guo, Guo, Barber, & Carreiras, 2011; Gillon Dowens, Vergara, Barber, & Carreiras, 2009; Hahne, Mueller, & Clahsen, 2006; McLaughlin, Osterhout, & Kim, 2004; Ojima, Nakata, & Kakigi, 2005; Osterhout, McLaughlin, Pitkänen, Frenck-Mestre, & Molinaro, 2006; Rossi, Gugler, Friederici, & Hahne, 2006; Sabourin & Stowe, 2008; Tokowicz & MacWhinney, 2005; Weber-Fox & Neville, 2001). However, it has only recently been used to study the effects of implicit and explicit instructional conditions on L2 learning and processing (Morgan-Short, Sanz, Steinhauer, & Ullman, 2010; Morgan-Short, Finger, Grey, & Ullman, 2012; Morgan-Short, Steinhauer, Sanz, & Ullman, 2012).

Morgan-Short and colleagues (Morgan-Short, et al. 2010 ; Morgan-Short, Steinhauer, et al. 2012) trained L2 learners under either an implicit condition (meaningful examples only) or explicit condition (meaningful examples + grammar rule information). The learners were monolingual English speakers trained on a natural language-like artificial language, and they were tested at two points during learning – low and high proficiency – using both behavioral (grammaticality judg-ment task, GJT; accuracy only) and neural (ERP) measures. Low proficiency was operationalized as above-chance performance on comprehension practice and high proficiency was operationalized as completion of all practice (both comprehension and production). The results of these studies indicated that both groups of learners were successfully able to learn the language under their respective training condi-tion (as assessed by GJT performance). Additionally, neither group of learners were different from each other in terms of performance accuracy on the GJTs, at either low or high proficiency or for the two linguistic structures of noun phrase gender agreement (Morgan-Short, et al. 2010) or word order (Morgan-Short, Steinhauer, et al. 2012).

2nd proofs

PAGE P r o o f s



However, the ERP patterns showed important differences in the two groups of learners, with the group in the implicit condition showing more language-related L1-like neural signatures than those in the explicit condition (see Morgan-Short, et al. 2010; Morgan-Short, Steinhauer, et al. 2012, Morgan-Short this volume for more details). In these studies, the accuracy data alone would not have been able to differ-entiate between the effects of the two training conditions, because accuracy showed improvement for both types of training but no differences between the two. The ERP results, however, were uniquely able to provide more dynamic information on the pro-cessing differences that arose between these training conditions, namely more L1-like processing in the implicit than explicit condition (see also Morgan-Short, Finger, et al. 2012). These studies mark an important first step in using ERPs to investigate the effects of implicit and explicit instructional contexts on L2 learning and processing. However, more research using ERPs is needed in order to build a reliably full pic-ture of the neurocognitive underpinnings of the knowledge that results from implicit and explicit conditions. More studies are needed that not only replicate and extend the above results, but especially investigate how individual differences (such as lan-guage experience, motivation, or aptitude) may interact with performance or neural outcomes.

Latency

Though ERPs are an excellent approach to the study of the effects of conditions, impor-tant insights about L2 processing can also be gathered from the use of reaction time (latency) measures, and at much less cost than ERPs. Reaction times (RTs), which are measured in milliseconds, have a long tradition of use in psychology research (e.g. Pachella, 1973; Posner & Boies, 1971; Reber, 1967, 1976), as well as in bilingual lexical processing and sentence processing (see references in Li & Moyer, 2008) but have only recently been utilized by SLA researchers to investigate L2 processing. Reaction time is the elapsed time between the presentation of a sensory stimulus and the subsequent behavioral response: pressing the enter key, an eye movement, voice onset, are the most common. Considered to be an index of speed of processing, reaction time indi-cates how fast the thinker can execute the mental operations needed to complete the task. Faster reaction time is seen as more efficient processing.

Reaction time is usually measured in one of these ways (for additional consid-erations on the reaction time technique as a research tool in SLA, see Jiang 2011; McDonough & Trofimovich, 2008): (a) Simple reaction time is the time required for an observer to respond to the presence of a stimulus (e.g. DeKeyser, 1996); (b) Recognition or Go/No-Go when the participant is required to press a button when one stimulus type appears and withhold a response when another stimulus type appears (e.g. in

2nd proofs

PAGE P r o o f s



a picture naming task that combined this technique with ERPs, Abdel Rahman, van Turennout, & Levelt, 2003) ; (c) Discrimination reaction time involves comparing pairs of displays presented simultaneously and then pressing one of two buttons accord-ing to a criterion of interest: correct, incorrect, or to identify the texts underlined or the picture as the subject or the agent of a sentence (e.g. GJT in Sanz, et al. 2009); (d) Choice reaction time require different responses to different stimuli. For example, when the subject presses one button corresponding to one stimulus and another but-ton for another stimulus (e.g. interpretation tasks in Lado, Bowden, Stafford, and Sanz, 2013, where participants chose between picture A or B, each corresponding to a differ-ent button, after hearing the target sentence). From the references we have included, it is clear that simple RT, choice and discrimination are the preferred methods. Since there is a considerable amount of variability in an individual’s response time, it is usu-ally the case that participants complete multiple response trials, from which a measure of the average response time can be calculated. (Note that we have not been able to identify in the SLA literature any study using the Go/No-Go technique.)

It should be noted that reaction times, though increasingly used, are not a com-pletely unambiguous measure, especially with respect to analysis. As noted by Hulstijn and colleagues (Hulstijn, Van Gelderen, & Schoonen, 2009), latency data have three basic characteristics: (a) they are positively skewed, (b) this positive skew increases with task difficulty, and (c) the spread of the distribution increases with the mean. Though the positive skewness of RTs is often considered ‘normal enough’ to be entered into factorial analyses of variance for statistical calculations (see Notes in Lachaud & Renaud, 2011) there are important factors that must be considered when evaluating RT data.

First, RT data should be filtered for outliers, where outliers are considered to be response times generated by processes that are not the ones being studied, such as fast guesses, guesses based on the subject’s estimate of the usual time to respond, multiple runs of the process that is the target of the study, subject’s inattention, or guesses based on failure to decide (Ratcliff, 1993). Outliers are often eliminated by using a standard ±2 standard deviation (SD) filter on the overall distribution of the data, though this may result in a non-neglible loss of information; it has thus been recommended that researchers use a larger cut-off (±3 SD) on both item and subject RTs, and run analyses both with this filter and without in order to determine how much the data is changed by the filter (Lachaud & Renaud, 2011).

A second important consideration in using latency measures is the handling of error data. RT data for items to which subjects responded incorrectly are not entered into statistical analysis. Error data are usually handled in one of two ways: (1) by taking a missing values-approach (whereby the RTs to error responses may be replaced with the item or subject mean), which may artificially reduce the variability in the data set, or (2) by eliminating the error (or outlier) data altogether, which reduces the number

2nd proofs

PAGE P r o o f s



of data points that contribute to the analysis (Ratcliff, 1993). In sum, the handling of both outliers and error data is capable of non-trivially changing the latency data, and subsequently affecting the reliability of the observed results. Nonetheless, with proper handling prior to statistical analyses, and a sufficient number of items, latency infor-mation offers the important advantage of providing a precise, real-time measure of L2 processing as it unfolds.

Many of the first SLA studies to use latency measures in their design were inter-ested in the development of automaticity, which was inherited from work in psychol-ogy on skill acquisition (e.g. Anderson, 1987, 1992) and these studies have mostly (e.g. Akamatsu, 2009; Segalowitz & Segalowitz, 1993; Segalowitz, Segalowitz, & Wood, 1998) but not exclusively (DeKeyser, 1996, 1997; Robinson, 1997; Robinson & Ha, 1993) focused on L2 lexical access. In L2 research, operationalizing automaticity as ‘faster processing’ appears to be the most dominant angle (Hulstijn, et al. 2009) and reaction time data evidencing faster mean RTs compared to some experimental base-line is usually considered evidence of increased automaticity and reduced reliance on effortful controlled, or monitoring, processing.

Compared to work on L2 automaticity, there is much less research on the effects of instructional conditions using latency measures. Studies which have used latency to specifically address the effects of different instructional conditions have not focused on automaticity per se and interpret reduced RTs more generally as a mea-sure of efficiency of processing to compare the effectiveness of different conditions. Thus, faster reaction times in one condition compared to another (barring decreases in accuracy) would be considered evidence of greater effectiveness for that condi-tion – in terms of improving the efficiency of processing. Irrespective of whether reduced response times index increased automatic processing or decreased monitor-ing, they are always considered a sign of efficiency when accuracy is maintained and reaction times speed up.

What follows is a review of the studies that have used latency to assess the devel-opment of L2 automaticity in different instructional conditions, and those that have used latency to assess the effects of instructional conditions on efficiency of processing more generally.

Automaticity

DeKeyser (1996) used reaction-time methodology to investigate how instructional context affects the L2, and specifically how type of practice affects L2 automaticity. In this study, six subjects were taught an artificial language, Autopractan, over the course of 8 weeks. These six subjects were given the same type of instruction, but were divided into three groups according to the type of practice they carried out. Four

2nd proofs

PAGE P r o o f s



grammar forms of Autopractan were the target of the study: noun number marking, noun case marking, verb gender marking, and verb instrumentality marking. One group practiced two forms in comprehension and another two in production, a second group practiced the opposite two forms in comprehension and production as the first group, and the third set of subjects practiced all four forms in both comprehension and production. During each session of the study, subjects’ performance was mea-sured using their responses to the meaning of sentence/picture combinations (both comprehension and production). Comprehension practice involved selecting a picture that matched an Autopractan sentence and production practice involved typing an Autopractan sentence that matched a picture.

The results of this small study showed that reaction times in both comprehension and production declined during the course of the study (over the 8 weeks) and that subjects were sensitive to the conditions under which they had practiced certain forms such that if they were tested in the opposite condition their reaction times increased. The results on error rates also showed an initial decline, but then leveled-off after the third session. Thus, the results from the reaction time data were more informative than those from accuracy data alone in terms of subjects’ sensitivity to practice and test conditions in learning Autopractan. Using this reaction time data, DeKeyser sug-gested that L2 automaticity had occurred, though he cautioned that the small number of subjects limited the impact of the results. In a follow-up study with a larger group of subjects (n = 61), DeKeyser (1997) again used Autopractan to investigate automatic-ity and followed a similar design as that reported in DeKeyser (1996). The results of this larger study echo those of DeKeyser (1996). Specifically, the response time data showed a steady decline across the experimental sessions and was sensitive enough to capture different effects based on learners’ familiarity with a specific practice condition (i.e. increased if the test was in a different condition than practice had been for the target item) while the error data, which also declined, was again interpreted as having been a less reliable measure of change for these subjects.

In another study which also investigated automaticity and instructional context, Robinson (1997) trained and tested sixty Japanese L1-English L2 learners on dative alternation in English; using novel artificial verbs to control for prior linguistic knowl-edge. The sixty learners were divided into four groups according to the type of training they received: implicit, incidental, enhanced, and instructed. In the implicit condition subjects were instructed to read and memorize the position of the words in the sen-tence. Subjects in the incidental condition were instructed to focus on the meaning of the sentences. In the enhanced condition, subjects were also instructed to focus on the meaning of the sentence, but were exposed to enhanced language input by which the target form had a box drawn around it. Finally, in the instructed condition, subjects were provided with the grammar rule that was the target of the study. Following train-ing, subjects’ knowledge was tested using a GJT. The results showed that the instructed

2nd proofs

PAGE P r o o f s



group outperformed all other groups in terms of accuracy on new (untrained) gram-matical items and also on untrained ungrammatical items. The instructed group also showed shorter response latencies than the other three groups for these items. For accuracy on untrained ungrammatical items the enhanced group outperformed the implicit group, but showed longer response latencies than the implicit group for these items. The implicit and incidental learners were no different from each other in accuracy on untrained grammatical or ungrammatical items, but the implicit group showed significantly faster reaction times than the incidental group on untrained ungrammatical items.

Considering the accuracy data alone, one might conclude that the enhanced con-dition outperformed the implicit condition and there is no difference in the effects of implicit versus incidental training (as operationalized in the study) on learning. However, when evaluating the reaction time data it is clear that the increased accu-racy in the enhanced group came at a cost to response time, suggesting that these learners were not efficiently processing the sentences, at least for new ungrammatical sentences. Similarly, the reaction time data on these items for the implicit compared to incidental learners suggests that, though the accuracy responses do not distinguish clearly between the two groups, the implicit group was processing the L2 material faster (perhaps more automatically) than the incidental group. Thus, this study, like DeKeyser (1996, 1997), highlights the importance of considering not just discrete accuracy-based outcomes, but also measuring the more dynamic component of response time in studies on the effects of context on L2 learning.

These studies explicitly focused on the construct of L2 automaticity, where auto-maticity generally refers to ‘faster processing’ and is considered to be qualitatively different from simply speeding up, i.e. applying control and explicit or attention-related procedures quickly (Hulstijn, et al. 2009; Segalowitz, 2003). Segalowitz and colleagues have suggested that mean reaction time data may not be able to capture differences between faster processing (automaticity) and the application of control-related procedures and suggest that the coefficient of variation (CV) is a more accu-rate index of such differences (Segalowitz & Segalowitz, 1993; Segalowitz, Segalowitz, & Wood, 1998; Segalowitz, 2003). This coefficient is calculated using participant mean reaction times and their standard deviations. According to Segalowitz and col-leagues, automaticity would be interpreted as decreased mean RTs (and standard deviations, which are collinear) and also decreased CVs, or in other words, a correla-tion among the three measures. Speeded up control procedures, on the other hand, would show only the decrease in mean RTs and standard deviations and no correla-tion with the CVs. Hulstijn et al. (2009) reviewed the existing studies that focused on these distinctions and also performed a series of analyses on their own RT data and found that the utility of CV information depends non-trivially on how the RT data is filtered (error data, outliers, and accuracy performance) and conclude that

2nd proofs

PAGE P r o o f s



an empirical distinction between automaticity and speed-up based on CV analyses may be premature. Note, however, that this discussion on the calculable differences between the constructs of automaticity and speed-up does not necessarily under-mine any arguments in favor of collecting RT data as a complement to accuracy mea-sures in studies on L2 processing.

L2 learning and development

Several studies have also used latency measures to examine L2 development more gen-erally (i.e. not necessarily as a focus of automatization), and specifically with respect to the effects of different instructional contexts. Sanz et al. (2009), for example, were interested in examining the issue of reactivity in verbal protocols (think-alouds) and its interaction with instructional contexts. Across two experiments the researchers compared the effects of grammar instruction and thinking-aloud on L2 develop-ment. In the first experiment, 24 naïve learners of Latin (English L1) were randomly assigned to either a Silent or Think-aloud condition while they interacted with an explicit grammar lesson on Latin case-assignment, followed by task-essential practice and explicit feedback. In the second experiment, another set of learners (n = 24) were again assigned to either Silent or Think-aloud groups, but differed from the subjects in Experiment 1 in that the instructional treatment involved no explicit grammar lesson (practice, feedback, and testing were identical to Experiment 1).

Learning was assessed in both experiments with three tasks: aural interpretation, timed grammaticality judgment, and sentence production. On all of these tasks both accuracy and reaction time data were recorded. The results from Experiments 1 and 2 revealed that all learners (Silent and Think-aloud) improved from pretest to immediate posttest on the three tasks. There were no differences between Think-aloud and Silent conditions in the magnitude of improvement for learners who received a grammar les-son (Experiment 1). However, the Think-aloud and Silent groups did differ for learners provided with no such information (Experiment 2). For these learners, thinking-aloud induced larger accuracy gains compared to being silent. Notwithstanding these find-ings for positive reactivity, the reaction time data revealed that, depending on the instructional context, thinking-aloud can be detrimental. Specifically, thinking-aloud significantly slowed response time on the grammaticality judgment task compared to the silent condition, which was interpreted as evidence that thinking aloud may slow down processing by favoring strategy use and metalinguistic reflection on the target language. This effect was only found for the group of learners who received a gram-mar lesson prior to practice. No such reaction time effect was found in Experiment 2. Were one to consider just the accuracy data from these two experiments it might seem as though the use of verbal protocols does not interfere with L2 development, and in

2nd proofs

PAGE P r o o f s



less explicit instructional contexts may in fact enhance it (Experiment 2). However, the latency data revealed that using verbal protocols in a more explicit instructional context may severely slow down learners’ processing (Experiment 1) and as such the reaction time data provide critical, cautionary information for research methodology (i.e. the use of think-alouds in experiments) that the accuracy data were incapable of elucidating.

In a study on the effects of different types of feedback, Lyster and Izquierdo (2009) used both oral production measures as well as RT data. The subjects in this study were twenty-five English L1/French L2 learners (intermediate level) who were all pro-vided with the same type and amount of instruction on the target form (French gender agreement). The learners differed as to whether, following instruction, they engaged in dyadic interaction involving recasts or prompts as feedback, where recasts were consid-ered less explicit forms of corrective feedback and prompts considered more explicit. Development was assessed by means of pre-, immediate and four-week delayed post-tests consisting of two oral production tasks and one computerized binary-choice task. The binary-choice task recorded both accuracy and reaction time data and required subjects to choose the article (masculine or feminine) that corresponded to a visually-presented French noun.

The authors were partially motivated in this study by the supposition that prompts allow for more opportunities to repair erroneous utterances as compared to recasts and thus initiate deeper levels of processing; thereby making prompts more effective than recasts in promoting L2 development. The results of the study revealed that both groups were provided with nearly the same amount of corrective feedback, but that the prompt group repaired nearly 100% of their errors while the recast group repaired less than 5%. This finding supported the authors’ argument that repairs (modified output) are more likely to occur in the more explicit prompt contexts than in less explicit recast contexts.

However, and contrary to previous studies which had revealed differences in the effectiveness of recasts compared to prompts (e.g. Ammar & Spada, 2006; R. Ellis, 2007; Lyster, 2004), the results of this study revealed no differential effects in the two types of feedback. Both groups improved significantly from pre- to immediate and delayed posttests on all three measures of development in terms of accuracy. For reaction times, both groups responded significantly faster at immediate and delayed tests than at pre-test, with no difference between immediate and delayed testing. Thus, in this study the accuracy data reveals that, despite group differences in terms of modified L2 output following erroneous utterances, the two types of feedback (more or less explicit) were equivalently effective in promoting L2 development. Additionally, the reaction time data provide evidence that the similar effectiveness applies not just to product-level measures (accuracy), but also to extended to process-oriented levels of analysis in terms of efficiency of processing for these interaction-based feedback contexts.

2nd proofs

PAGE P r o o f s



In another study on the effects of different types of feedback Lado et al. (2013) focused on negative evidence with or without grammatical explanation. As part of the Latin Project, the study investigated learners’ ability to accurately and efficiently inter-pret, judge and produce sentences in Latin, and implemented a design that avoided biases among the experimental groups by controlling time on task (amount of prac-tice) and by avoiding testing materials that favor planning and explicit processing. Moreover, by conducting separate analyses on accuracy and RT, and on trained and untrained items, they were uniquely able to compare groups’ initial language develop-ment in terms of both accuracy and efficiency, as well as their item and system learn-ing. Several interesting conclusions emerged.

In line with previous studies investigating the effectiveness of negative evidence with or without metalinguistic information (Nagata, 1993; Nagata & Swisher, 1995; Stafford, Bowden, & Sanz, 2011), Lado et al. (2013) conclude that both types of feed-back appear to lead to a more accurate ability to interpret, judge, and produce tar-get sentences in naïve learners of Latin. Providing metalinguistic information gives an initial advantage to these learners on accuracy when processing the target form is cognitively demanding, such as in an aural interpretation task, or when transfer from input- to output-based skills is involved. Two weeks were enough, however, to see most of that advantage disappear, as participants appeared to forget much of what they had learned from exposure to metalinguistic information. This is especially evident when processing items that were part of the treatment (trained) and could therefore be remembered (and consequently, be susceptible to forgetting). With their implementa-tion of RT measures, they also show that the more implicit condition, the one that did not receive metalinguistic information, leads to more efficient processing.

Also, the more implicit group showed more stable gains, compared with the losses by the less implicit group that received metalinguistic information. This dif-ference in stability of gains over time may reflect qualitatively different learning pro-cesses at work – more explicit processes in the less implicit feedback group and more implicit processes in the more implicit group (Li, 2010). One basic problem is that immediate post-tests are inherently incapable of capturing the full extent of the learn-ing that takes place in implicit conditions as it takes more time than learning that develops under explicit conditions, with the first possibly including a latent phase of experience-triggered memory consolidation following practice (see for example Roth, Kishon-Rabin, Hildesheimer, & Karni, 2005). Such consolidation processes would occur subsequent to immediate posttests and thus not be captured by immediate mea-sures of performance.

Importantly, in the case of the GJT, comparable accuracy levels combined with faster performance by the more implicit group also suggests that the two groups may have been engaged in qualitatively different processing that lead to quantitatively simi-lar accuracy outcomes, providing further evidence that different types of instruction

2nd proofs

PAGE P r o o f s



may have led to different types of processing. This behavior is reminiscent of R. Ellis’ (2005) claim, mentioned above, that a timed grammaticality judgment test may be one of the most reliable measures of implicit knowledge. As mentioned above, faster RTs are usually interpreted as a sign of increased automaticity, whereas slower RTs are taken to index reliance on slower, controlled processes, including monitoring – though independent of this distinction, faster RTs accompanied by maintained accuracy per-formance index increased efficiency, either of executing automatic procedures or con-trolled ones. Thus, the more implicit feedback group appears to have been engaged in more efficient, potentially more automatic and less monitored processing of the L2 on the GJT, even when accuracy was similar to the group that was provided with metalinguistic information. Consideration should be given to the fact that repeatedly, it is in the GJT, not in production or interpretation tasks, where differences in RT with comparable levels of accuracy have been identified.

Evidence for different cognitive processes underlying similar performance in dif-ferent instructional contexts was also found by Morgan-Short et al. (2010; Morgan-Short, Steinhauer, et al. 2012) in their ERP study. As mentioned above, the results showed that at high proficiency learners who were trained under either an explicit or implicit condition did not differ in their accuracy performance on a GJT. In contrast, the ERPs revealed striking differences between the groups’ neural activity. The explicit training (with metalinguistic information) resulted in some aspects of brain process-ing found in native speakers, namely those that are posited to involve strategic or con-trolled (conscious) processing (Hahne & Friederici, 1999; Kaan, Harris, Gibson, & Holcomb, 2000; Osterhout & Holcomb, 1992; Steinhauer & Connolly, 2008). The implicit training (without metalinguistic information), however, showed more native-like neurocognitive patterns than explicit training, specifically with respect to neu-ral patterns that are associated with more automatic processing (Friederici, Gunter, Hahne, & Mauth, 2004; Hahne & Friederici, 1999; van den Brink & Hagoort, 2004).

In a study on the role of explicit information (EI) in processing instruction (PI) interventions, Fernandez (2008) used reaction time methodology for the express purpose of investigating whether previous PI studies had overlooked potential benefits of EI in L2 learning by virtue of having had only accuracy-based (pretest- posttest) designs. The study was divided into two experiments: Experiment 1 focused on Spanish OVS word order and Experiment 2 focused on Spanish subjunc-tive in expressions of doubt. The participants were the same 84 learners in the two experiments, had English as their L1, and were divided into two groups: explicit information plus structured input (PI) or structured input only (SI). The design of both experiments consisted of computerized treatment during which accuracy and reaction time were recorded. In the PI group, explicit information about the target form was provided, followed by 30 SI items; the SI group received the same 30 items (no preemptive EI).

2nd proofs

PAGE P r o o f s



The results from Experiment 1 (Spanish word order) showed no differences between the two experimental groups, for accuracy, reaction time, or trials to crite-rion (correct response to 3 targets and 1 distracter in a row). In Experiment 2, results showed that more learners reached criterion in the PI group compared to the SI group, and that these learners also reached criterion sooner. The PI group was also found to be more accurate in their responses after reaching criterion than the SI group. In terms of response time, the PI group responded significantly faster than the SI group. The PI group, however, did not immediately begin processing the structure correctly (i.e. at trial 1) and, like the SI group, still had to work through several trials in order to begin responding quickly and correctly. The author suggested that EI may be beneficial in drawing attention to non-salient structures (such as subjunctive mood) and therefore may allow for quicker processing of the target structure, but notes that the SI group also showed learning. Thus, though in Experiment 1 no beneficial effects of explicit information were found, Experiment 2 revealed that learners provided with such information outperformed those not provided with any preemptive explicit informa-tion, both for discrete accuracy and also for response time.

Finally, two studies have used RT data to investigate whether study abroad con-texts differentially affect L2 processing. First, Segalowitz & Freed (2004) compared an at-home group (n = 18) and a study abroad group (n = 22) on L2 semantic clas-sification (and other measures), but found no differences in the effects on speed of processing between the two contexts. Sunderman and Kroll (2009) found a different pattern of results; their learners were divided between those that had studied abroad (n = 14) and those that had not (n = 34). The results from their study showed that study abroad experience was related to both higher accuracy and faster processing on a translation recognition task; though the authors also found that if an individual had higher working memory, they would be both faster and more accurate regardless of study abroad experience. Thus, the RT data from this study revealed an intriguing interaction between the effects of context (study abroad) and learner-internal differ-ences (working memory).

Leung and Williams (2011a) did not compare different instructional contexts, but did investigate implicit learning, and specifically focused on reaction time methodol-ogy to achieve this aim. In their study, twenty-five subjects (English L1) engaged with a semi-artificial linguistic system that paired novel determiners (gi, ro, ul, ne) with English nouns. The participants were informed that two determiners were used with adults and the other two with children, but were not told that the use of determiners also depended on the thematic role of the noun (agent, patient). Thus, their study sought to test whether this thematic information could be learned implicitly. During the experiment, participants were presented with pictures on a computer screen and instructed to (a) describe it, (b) indicate (with button-press) which side of the screen a named individual appeared and (c) reformulate the sentence in English.

2nd proofs

PAGE P r o o f s



The results of the study showed that twenty of the learners were not aware that the determiners also indexed thematic information for the nouns (as determined by a post-exposure probe for verbalizable knowledge). However, in spite of being unaware of this grammatical information, the reaction time data revealed that these learners were in fact sensitive to violations of determiner-thematic role pairings. Specifically, reaction times decreased steadily during exposure, but increased significantly in the exposure block containing determiner-thematic role violations. Accuracy during exposure remained constant for these unaware learners. The authors took these results to indicate that implicit learning of the form-meaning connection between the deter-miners and their thematic content had occurred. The five learners who reported being aware of the relationship between the determiners and agent-patient information had higher error rates than the unaware learners and their reaction time data failed to show the same sensitivity to violations of the regularity as the unaware group, though the results cannot fully be interpreted because of the small number of participants. In a similar study, Leung and Williams (2011b) found that RTs and error rates increased on the violation block for noun animacy form-meaning violations (Experiment 1), though no such behavioral change was found for relative size form-meaning violations (Experiment 2). Additionally, all learners (both aware and unaware) showed the RT and error rate increase on the violation block in Exp. 1. The authors interpreted these results as indicating that implicit learning may be limited by what kind of meaning is involved in the mappings.

In sum, the studies reviewed above provide compelling evidence that there is a non-negligible amount of information, and theoretical as well as empirical insight, that researchers lose when relying solely on accuracy measures in SLA research. In these studies, the process-level measures, such as ERPs (e.g. Morgan-Short, et al. 2010) and RTs (e.g. Lado, et al. 2013), were uniquely able to reveal meaningful differences in the effects of conditions on L2 learning and development that were not (or to a lesser extent) evidenced in the product-level measure of accuracy.

Conclusion

The implicit/explicit distinction became crucial for the field of SLA when the litmus test for the effectiveness of any pedagogical method or technique was defined as its capacity to affect ‘acquired’, meaning implicit, knowledge. The terms have been used to characterize pedagogical treatments as well. When referring to language process-ing and its product, the distinction, which has its origins in the cognitive psychology literature, often contrasts automatic vs. controlled processing (Schiffrin & Schneider, 1977) and procedural vs. declarative use of knowledge (Anderson, 1983). The role

2nd proofs

PAGE P r o o f s



of attention is key here, as it is posited to be involved in access to explicit knowledge during controlled processing, i.e. monitoring, while it is assumed not to be involved in fluent, effortless, fast and efficient spontaneous language use that results from access to implicit knowledge that is automatic (e.g. N. Ellis, 2005; R. Ellis, 2004, 2009).

While SLA researchers have borrowed the constructs, they have not borrowed the operationalizations: SLA has relied almost exclusively on accuracy measures, rather than on processing-oriented measures such as reaction time or ERP and fMRI data. Researchers have instead manipulated the nature of the elicitation tasks -more or less communicative, more or less controlled- to elicit production. It has been only recently that the field has incorporated another dependent measure, reaction time, which is better suited to characterize qualitative changes in processing and knowledge, espe-cially since ‘fluent, fast, efficient’ (all time-related adjectives), characterize the con-struct ‘implicit’.

Reaction time refers to the amount of time (in milliseconds) between the pre-sentation of a stimulus and the behavioral response of interest, and indexes how quickly the subject executes the mental procedures involved in completing the task. Generally, faster RTs are considered to reflect efficient, fast execution of the men-tal procedures involved, though there is still debate among some SLA researchers as to whether ‘faster’ is also ‘more automatic’ or is instead related to speeded-up control (explicit) procedures (e.g,. Hulstijn, et al. 2009; Segalowitz & Segalowitz, 1993). Reaction time data are highly useful in their ability to provide precise tem-poral information about processing as it unfolds during a task, but must be handled responsibly (i.e. filtering, outliers, analysis) in order to reach their maximum explan-atory potential.

The chapter has presented a number of situations in which reaction time adds significant value to the study of effects of more or less explicit conditions in language development. For example, reaction time data have been able to reveal that explicit but not implicit pedagogical conditions may lead to slowed performance, even when there are no differences in accuracy gains for the two types of conditions (Lado, et al. 2013). The slowed performance in the explicit condition may be due to learners in that group relying on the knowledge of the rule provided to them as part of the feedback. Accessing the metalinguistic knowledge before responding involves at least one extra processing step. An explicit condition likely leads learners to first retrieve rule knowl-edge (step 1) before they can compare knowledge to item (step 2). In the more implicit condition, on the other hand, there is no rule to retrieve per se (i.e. they do not have to “think” back to or recall it) so for learners in that condition it is merely a matter of compare knowledge to item where “knowledge” may be largely unspecified (i.e. respond based on ‘feel’). If attrition affects the more explicit group, then step 1 cannot happen

2nd proofs

PAGE P r o o f s



(rule knowledge has been forgotten), so step 2 is compromised and consequently costs cognitive time. However, if there is no reliance on step 1 (for the more implicit group) then step 2 is perhaps the first step in processing and costs comparatively less cogni-tive time.

Similarly, a slow-down in RT has proven to be useful to show an interaction between reactivity and condition; i.e. thinking aloud during a treatment that includes feedback with grammatical rules (+explicit) results in less efficient performance in posttets (Sanz, et al. 2009). Therefore, although verbalizations do not result in changes in accuracy, latency data provides evidence that thinking aloud alters the very pro-cesses verbalizations are expected to shine light upon.

RT is also informative when used to study the effects of practice during exposure to different conditions. Within the framework of skill acquisition, accuracy plateaus during the task but decreases in RT can be interpreted as suggesting that the learn-ers rely initially on their declarative knowledge to complete the tasks, but gradually, through practice, this knowledge becomes qualitatively different (procedural) knowl-edge, with the quantitative change in processing being reflected in the decreased RTs (DeKeyser, 1996, 1997).

Finally, as demonstrated in Leung and Williams (2011a, 2011b), RTs can provide a new angle from which to view questions about implicit learning in L2 acquisition. The gradual decrease in RTs throughout exposure followed by a sharp increase in RT when exposed to language violations offers a unique perspective on the underlying processing differences between aware and unaware learners and thus between process-ing associated with explicit and implicit learning processes.

We have reviewed here the ways in which finer-grained measures of processing, such as reaction times and ERPs, can reveal important differences between the effects of different types of conditions in SLA. Reaction times are capable of capturing subtle differences in processing as measured by behavioral responses to stimuli and offer an economical alternative to ERPs, which also provide precise temporal information about processing and additionally may reveal neural effects of conditions that are not visible or calculable in the behavioral data, as in Morgan-Short et al. (2010, Morgan-Short, Steinhauer, et al. 2012).

Both of these measures can be used as complements to accuracy-based data and future studies that move beyond accuracy and employ measures of reaction time or event-related potentials in their research on implicit and explicit conditions, learn-ing, and knowledge will undoubtedly make valuable contributions to field of SLA. As such, researchers in SLA who are interested in elucidating the effectiveness of pedagogical interventions in promoting additional language learning should strongly consider incorporating these and other process-level measures in their experimental designs.

2nd proofs

PAGE P r o o f s



References

Abdel Rahman, R., van Turennot, M., & Levelt, W.J.M. (2003). Phonologogical encoding is not contigent on semantic feature retrieval: An electrophysiological study on object naming. Journal of Experimental Psychology, 29(5), 850–860.

Akamatsu, N. (2009). The effects of training on automatization of word recognition in English as a foreign language. Applied Psycholinguistics, 29, 178–193.

Ammar, A., & Spada, N. (2006). One size fits all?: Recasts, prompts, and L2 learning. Studies in Second Language Acquisition, 28(4), 543–574. DOI: 10.1017/S0272263106060268

Anderson, J.R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.Anderson, J.R. (1987). Skill acquisition: Compilation of weak-method problem situations.

Psychological Review, 94(2), 192. DOI: 10.1037/0033-295X.94.2.192Anderson, J.R. (1992). Automaticity and the ACT theory. The American Journal of Psychology,

105, 165–180. DOI: 10.2307/1423026Burt, M., Dulay, H., & Hernandez-Chavez, E. (1975). Bilingual syntax measure. New York, NY:

Harcourt, Brace, Jovanovitch.DeKeyser, R.M. (1996). Exploring automatization processes. Tesol Quarterly, 30(2), 349–357.

DOI: 10.2307/3588151DeKeyser, R.M. (1997). Beyond explicit rule learning. Studies in Second Language Acquisition,

19(2), 195–221. DOI: 10.1017/S0272263197002040Dussias, P.E. (2010). Uses of eye-tracking data in second language sentence processing research.

Annual Review of Applied Linguistics, 30(1), 149–166. DOI: 10.1017/S026719051000005XElder, C. (2009). Validating a test of metalinguistic knowledge. In R. Ellis et al. (Eds.),

(pp. 113–138).Ellis, N.C. (2002). Frequency effects in language processing: A review with implications for the-

ories of implicit and explicit language acquisition. Studies in Second Language Acquisition, 24, 143–188.

Ellis, R. (1994). A theory of instructed second language acquisition. In N. Ellis (Ed.), Implicit and explicit learning of languages (pp. 79–114). London: Academic Press.

Ellis, R. (2004). The definition and measurement of L2 explicit knowledge. Language Learning, 54(2), 227–275. DOI: 10.1111/j.1467-9922.2004.00255.x

Ellis, R. (2005). Measuring implicit and explicit knowledge of a second language: A psychomet-ric study. Studies in Second Language Acquisition, 27(2), 141–172.

Ellis, R. (2007). The differential effects of corrective feedback on two grammatical structures. In A. Mackey (Ed.), Conversational interaction in second language acquisition: A series of empirical studies (pp. 339–360). Oxford: Oxford University Press.

Ellis, R., Loewen, S., Elder, C., Erlam, R., Philp, J., & Reinders, H. (2009). Implicit and explicit knowledge in second language learning, testing, and teaching. Bristol: Multilingual Matters.

Erlam, R. (2005). Language aptitude and its relationship to instructional effectiveness in second language acquisition. Language Teaching Research, 9(2), 147–171.

DOI: 10.1191/1362168805lr161oaFelix, S.W. (1981). The effect of formal instruction on second language acquisition. Language

Learning, 31(1), 87–112. DOI: 10.1111/j.1467-1770.1981.tb01374.xFelser, C. (2005). Experimental psycholinguistic approaches to second language acquisition.

Second Language Research, 21(2), 95–97. DOI: 10.1191/0267658305sr253ed

2nd proofs

PAGE P r o o f s



Fernandez, C. (2008). Reexamining the role of explicit information in processing instruction. Studies in Second Language Acquisition, 30(3), 277–305.

DOI: 10.1017/S0272263108080467Friederici, A.D., Gunter, T., Hahne, A., & Mauth, K. (2004). The relative timing of syntactic and

semantic processes in sentence comprehension. NeuroReport, 15(1), 165–169. DOI: 10.1097/00001756-200401190-00032Gillon Dowens, M., Guo, T., Guo, J., Barber, H., & Carreiras, M. (2011). Gender and number

processing in Chinese learners of Spanish -Evidence from Event Related Potentials. Neuro-psychologia, 49, 1651–1659. DOI: 10.1016/j.neuropsychologia.2011.02.034

Gillon Dowens, M., Vergara, M., Barber, H., & Carreiras, M. (2009). Morphosyntactic process-ing in late second-language learners. Journal of Cognitive Neuroscience, 22(8), 1870–1887. DOI: 10.1162/jocn.2009.21304

Hahne, A., & Friederici, A.D. (1999). Electrophysiological evidence for two steps in syntactic analysis: Early automatic and late controlled processes. Journal of Cognitive Neuroscience, 11(2), 194–205. DOI: 10.1162/089892999563328

Hahne, A., Mueller, J.L., & Clahsen, H. (2006). Morphological processing in a second language: Behavioral and event-related brain potential evidence for storage and decomposition. Jour-nal of Cognitive Neuroscience, 18(1), 121–134. DOI: 10.1162/089892906775250067

Han, Y., & Ellis, R. (1998). Implicit knowledge, explicit knowledge and general language profi-ciency. Language Teaching Research, 2(1), 1–23. DOI: 10.1177/136216889800200102

Handy, T.C. (Ed.). (2005). Event-related potentials: A methods handbook. Cambridge, MA: The MIT Press.

Hulstijn, J.H., Van Gelderen, A., & Schoonen, R. (2009). Automatization in second language acquisition: What does the coefficient of variation tell us? Applied Psycholinguistics, 30(4), 555–582. DOI: 10.1017/S0142716409990014

Jiang, N. (2011). Conducting reaction time research in second language studies. London: Routledge.Kaan, E. (2007). Event-related potentials and language processing: A brief overview. Language

and Linguistics Compass, 1(6), 571–591. DOI: 10.1111/j.1749-818X.2007.00037.xKaan, E., Harris, A., Gibson, E., & Holcomb, P. (2000). The P600 as an index of syntactic integra-

tion. Language and Cognitive Processes, 15(2), 159–201. DOI: 10.1080/016909600386084Krashen, S.D. (1981). Second language acquisition and second language learning. New York, NY:

Prentice-Hall.Krashen, S.D. (1985). The input hypothesis: Issues and implications. London: Longman.Lachaud, C.M., & Renaud, O. (2011). A tutorial for analyzing human reaction times: How to

filter data, manage missing values, and choose a statistical model. Applied Psycholinguistics, 32(2), 389–416. DOI: 10.1017/S0142716410000457

Lado, B., Bowden, H., Stafford, C.A., & Sanz, C. (2013). A fine-grained analysis of the effects of negative evidence with and without metalinguistic information in language development. Language Teaching Research, 18(3), 320–344. DOI: 10.1177/1362168813510382

Larsen-Freeman, D.E. (1975). The acquisition of grammatical morphemes by adult ESL stu-dents. Tesol Quarterly, 9(4), 409–419. DOI: 10.2307/3585625

Leung, J.H. C., & Williams, J.N. (2011a). The implicit learning of mappings between forms and contextually derived meanings. Studies in Second Language Acquisition, 33, 33–55.

DOI: 10.1017/S0272263110000525Leung, J.H. C., & Williams, J.N. (2011b). Constraints on implicit learning of grammatical form-

meaning connections. Language Learning, 1–29.

2nd proofs

PAGE P r o o f s



Li, S. (2010). The effectiveness of corrective feedback in SLA: A meta-analysis. Language Learn-ing, 60(2), 309–365. DOI: 10.1111/j.1467-9922.2010.00561.x

Li, W. & Moyer, M.G. (2008). Research methods in bilingualism and multilingualism. Malden, MA: Blackwell.

Long, M.H. (1983). Does instruction make a difference? Tesol Quarterly, 17, 357–382. DOI: 10.2307/3586253Luck, S.J. (2005). An introduction to the event-related potential technique. Cambridge, MA: The

MIT Press.Lyster, R. (2004). Differential effects of prompts and recasts in form-focused instruction. Studies

in Second Language Acquisition, 26, 399–432. DOI: 10.1017/S0272263104263021Lyster, R., & Izquierdo, J. (2009). Prompts versus recasts in dyadic interaction. Language Learn-

ing, 59(2), 453–498. DOI: 10.1111/j.1467-9922.2009.00512.xMarinis, T. (2003). Psycholinguistic techniques in second language acquisition research. Second

Language Research, 19(2), 144–161. DOI: 10.1191/0267658303sr217raMcDonough, K., & Trofimovich, P. (2008). Using priming methods in second language research.

New York, NY: Routledge.McLaughlin, B. (1987). Second-language acquisition in childhood: School-age children (Vol. 2).

Hillsdale, NJ: Lawrence Erlbaum Associates.McLaughlin, J., Osterhout, L., & Kim, A. (2004). Neural correlates of second-language word

learning: Minimal instruction produces rapid change. Nature Reviews Neuroscience, 7, 703–704. DOI: 10.1038/nn1264

Morgan-Short, K., Finger, I., Grey, S., & Ullman, M.T. (2012). Second language processing shows increased native-like neural responses after months of no exposure. PLoS ONE, 7(3), e32974. DOI: 10.1371/journal.pone.0032974

Morgan-Short, K., Sanz, C., Steinhauer, K., & Ullman, M.T. (2010). Second language acquisition of gender agreement in explicit and implicit training conditions: An event-related potential study. Language Learning, 60(1), 154–193. DOI: 10.1111/j.1467-9922.2009.00554.x

Morgan-Short, K., Steinhauer, K., Sanz, C., & Ullman, M.T. (2012). Explicit and implicit second language training differentially affect the achievement of native-like brain activation pat-terns. Journal of Cognitive Neuroscience, 24(4), 933–947. DOI: 10.1162/jocn_a_00119

Nagata, N. (1993). Intelligent computer feedback for second language instruction. Modern Lan-guage Journal, 77(3), 330–339. DOI: 10.1111/j.1540-4781.1993.tb01980.x

Nagata, N., & Swisher, M.V. (1995). A study of consciousness-raising by computer: The effect of metalinguistic feedback on second language learning. Foreign Language Annals, 28(3), 337–347. DOI: 10.1111/j.1944-9720.1995.tb00803.x

Norris, J.M., & Ortega, L. (2000). Effectiveness of L2 instruction: A research synthesis and quan-titative meta-analysis. Language Learning, 50(3), 417–528.

DOI: 10.1111/0023-8333.00136Norris, J.M., & Ortega, L. (2012). Assessing learner knowledge. In S. M. Gass & A. Mackey

(Eds.), Handbook of second language acquisition (pp. 573–589). New York, NY: Routledge.Ojima, S., Nakata, H., & Kakigi, R. (2005). An ERP study on second language learning after

childhood: Effects of proficiency. Journal of Cognitive Neuroscience, 17(8), 1212–1228. DOI: 10.1162/0898929055002436

Ortega, L. (2000). Understanding syntactic complexity: The measurement of change in the syn-tax of instructed L2 Spanish. Unpublished doctoral dissertation, University of Hawai’i at Manoa.

2nd proofs

PAGE P r o o f s



Ortega, L., Iwashita, N., Rabie, S., & Norris, J.M. (in preparation). A multilanguage comparison of measures of sytnactic complexity.

Osterhout, L., & Holcomb, P.J. (1992). Event-related brain potentials elicited by syntactic anom-aly. Journal of Memory and Language, 31, 785–806.

DOI: 10.1016/0749-596X(92)90039-ZOsterhout, L., McLaughlin, J., Pitkänen, I., Frenck-Mestre, C., & Molinaro, N. (2006). Novice

learners, longitudinal designs, and event-related potentials: A means for exploring the neurocognition of second language processing. Language Learning, 56(Suppl 1), 199–230. DOI: 10.1111/j.1467-9922.2006.00361.x

Pachella, R.G. (1973). The interpretation of reaction time in information processing research. DTIC Document. Ann Arbor, MI: University of Michigan.

Philp, J., & Tognini, R. (2009). Language acquisition in foreign language contexts and the differential benefits of interaction. International Review of Applied Linguistics in Language Teaching (IRAL), 47(3–4): 245–266.

Posner, M.I., & Boies, S.J. (1971). Components of attention. Psychological Review, 78(5), 391–408. DOI: 10.1037/h0031333

Ratcliff, R. (1993). Methods for dealing with reaction time outliers. Psychological bulletin, 114(3), 510. DOI: 10.1037/0033-2909.114.3.510

Reber, A.S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6, 855–863. DOI: 10.1016/S0022-5371(67)80149-X

Reber, A.S. (1976). Implicit learning of synthetic languages: The role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2(1), 88–94.

DOI: 10.1037/0278-7393.2.1.88Roberts, L. (2012). Review article: Psycholinguistic techniques and resources in second language

acquisition research. Second Language Research, 28(1), 113–127. DOI: 10.1177/0267658311418416Robinson, P. (1997). Generalizability and automaticity of second language learning under

implicit, incidental, enhanced, and instructed conditions. Studies in Second Language Acquisition, 19, 223–247. DOI: 10.1017/S0272263197002052

Robinson, P.J., & Ha, M.A. (1993). Instance theory and second language rule learning under explicit conditions. Studies in Second Language Acquisition, 15(04), 413–438.

DOI: 10.1017/S0272263100012365Rossi, S., Gugler, M.F., Friederici, A.D., & Hahne, A. (2006). The impact of proficiency on syn-

tactic second-language processing of German and Italian: Evidence from event-related potentials. Journal of Cognitive Neuroscience, 18(12), 2030–2048.

DOI: 10.1162/jocn.2006.18.12.2030Roth, D.A. E., Kishon-Rabin, L., Hildesheimer, M., & Karni, A. (2005). A latent consolidation

phase in auditory identification learning: Time in the awake state is sufficient. Learning & Memory, 12(2), 159–164. DOI: 10.1101/87505

Sabourin, L., & Stowe, L.A. (2008). Second language processing: when are first and second languages processed similarly? Second Language Research, 24(3), 397–430.

DOI: 10.1177/0267658308090186Sanz, C. (1996). The problem of variation in SLA theory and research. In J. E. Alatis, C.A.

Straehle, M. Ronkin, & B. Gallenberger (Eds.), Linguistics, Language Acquisition, and Language Variation: Current Trends and Future Prospects (pp. 236–51). Washington DC: Georgetown University Press.

2nd proofs

PAGE P r o o f s



Sanz, C. (1997). Experimental tasks in SLA research: Amount of production, modality, memory, and production processes. In W. R. Glass & A. T. Pérez-Lerroux (Eds.), Contemporary per-spectives on the acquisition of Spanish (Vol. 2; pp. 41–56). Somerville, MA: Cascadilla.

Sanz, C., Lin, H.J., Lado, B., Bowden, H.W., & Stafford, C.A. (2009). Concurrent verbalizations, pedagogical conditions, and reactivity: Two CALL studies. Language Learning, 59(1), 33–71. DOI: 10.1111/j.1467-9922.2009.00500.x

Schiffrin, R.M., & Schneider, W. (1977). Controlled and automatic human information pro-cessing, II: Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. DOI: 10.1037/0033-295X.84.2.127

Schmidt, R. (1992). Awareness and second language acquisition. Annual Review of Applied Linguistics, 13(1), 206–226. DOI: 10.1017/S0267190500002476

Schmidt, R.W. (1990). The role of consciousness in second language learning. Applied Linguis-tics, 112, 17–46.

Segalowitz, N. (2003). Automaticity and second languages. In C.J. Doughty & M.H. Long (Eds.), The handbook of second language acquisition (pp. 382–408). Malden, MA: Blackwell.

DOI: 10.1002/9780470756492.ch13Segalowitz, N., & Freed, B.F. (2004). Context, contact, and cognition in oral fluency acquisition.

Studies in Second Language Acquisition, 26(2), 173–199. DOI: 10.1017/S0272263104262027Segalowitz, N.S., & Segalowitz, S.J. (1993). Skilled performance, practice, and the differentiation

of speed-up from automatization effects: Evidence from second language word recogni-tion. Applied Psycholinguistics, 14, 369–369. DOI: 10.1017/S0142716400010845

Segalowitz, S.J., Segalowitz, N.S., & Wood, A.G. (1998). Assessing the development of automa-ticity in second language word recognition. Applied Psycholinguistics, 19, 53–68.

DOI: 10.1017/S0142716400010572Serafini, E.J. (2013). Exploring relationships between cognitive capacities, second language (L2)

motivation, and development of implicit/explicit L2 knowledge over time by proficiency level. Unpublished doctoral dissertation. Georgetown University.

Spada, N., & Tomita, Y. (2010). Interactions between type of instruction and type of language feature: A meta-analysis. Language Learning, 60(2), 263–308.

DOI: 10.1111/j.1467-9922.2010.00562.xStafford, C.A., Bowden, H.W., & Sanz, C. (2011). Optimizing explicit instruction: Matters of

explicitness, practice, and cue learning. Language Learning (Early View).Steinhauer, K., & Connolly, J.F. (2008). Event-related potentials in the study of language. In

B. Stemmer & H. A. Whitaker (Eds.), Handbook of the neuroscience of language. Oxford: Elsevier.

Sunderman, G., & Kroll, J.F. (2009). When study abroad fails to deliver: The internal resources threshold effect. Applied Psycholinguistics, 30, 79–99. DOI: 10.1017/S0142716408090048

Tokowicz, N., & MacWhinney, B. (2005). Implicit and explicit measures of sensitivity to vio-lations in second language grammar: An event-related potential investigation. Studies in Second Language Acquisition, 27(2), 173–204. DOI: 10.1017/S0272263105050102

van den Brink, D., & Hagoort, P. (2004). The influence of semantic and syntactic context con-straints on lexical selection and integration in spoken-word comprehension as revealed by ERPs. Journal of Cognitive Neuroscience, 16(6), 1068–1084.

DOI: 10.1162/0898929041502670VanPatten, B. (2004). Input processing in SLA. In B. VanPatten (Ed.), Processing instruction:

Theory, research, and commentary. Mahwah, NJ: Lawrence Erlbaum Associates.

2nd proofs

PAGE P r o o f s



VanPatten, B. (2005). Processing instruction. In C. Sanz (Ed.), Mind and context in adult second language acquisition: Methods, theory, and practice (pp. 267–281). Washington, DC: Georgetown University Press.

Weber-Fox, C., & Neville, H. (2001). Sensitive periods differentiate processing of open- and closed-class words: an ERP study of bilinguals. J Speech Lang Hear Res, 44(6), 1338–1353. DOI: 10.1044/1092-4388(2001/104)

White, L. (1991). Adverb placement in second language acquisition: Some effects of positive and negative evidence in the classroom. Second Language Research, 7(2), 133–161.

DOI: 10.1177/026765839100700205Williams, J.N. (2005). Learning without awareness. Studies in Second Language Acquisition, 27,

269–304. DOI: 10.1017/S0272263105050138

Documents

Sanz, C. & Grey, S. (2015). Effects of conditions on L2 development: Moving beyond accuracy. In P. Rebuschat (Ed.), Explicit and implicit pedagogical conditions (pp. 301-324). Amsterdam: