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Explicit and Implicit Second Language Training Differentially Affect the Achievement of Native-like Brain Activation Patterns

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It is widely believed that adults cannot learn a foreign language in the same way that children learn a first language. However, recent evidence suggests that adult learners of a foreign language can come to rely on native-like language brain mechanisms. Here, we show that the type of language training crucially impacts this outcome. We used an artificial language paradigm to examine longitudinally whether explicit training (that approximates traditional grammar-focused classroom settings) and implicit training (that approximates immersion settings) differentially affect neural (electrophysiological) and behavioral (performance) measures of syntactic processing. Results showed that performance of explicitly and implicitly trained groups did not differ at either low or high proficiency. In contrast, electrophysiological (ERP) measures revealed striking differences between the groups' neural activity at both proficiency levels in response to syntactic violations. Implicit training yielded an N400 at low proficiency, whereas at high proficiency, it elicited a pattern typical of native speakers: an anterior negativity followed by a P600 accompanied by a late anterior negativity. Explicit training, by contrast, yielded no significant effects at low proficiency and only an anterior positivity followed by a P600 at high proficiency. Although the P600 is reminiscent of native-like processing, this response pattern as a whole is not. Thus, only implicit training led to an electrophysiological signature typical of native speakers. Overall, the results suggest that adult foreign language learners can come to rely on native-like language brain mechanisms, but that the conditions under which the language is learned may be crucial in attaining this goal.
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Explicit and Implicit Second Language Training
Differentially Affect the Achievement of
Native-like Brain Activation Patterns
Kara Morgan-Short
1
*, Karsten Steinhauer
2
*, Cristina Sanz
3
,
and Michael T. Ullman
3
Abstract
It is widely believed that adults cannotlearn a foreign language
in the same way that children learn a first language. However, re-
cent evidence suggests that adult learners of a foreign language
can come to rely on native-like language brain mechanisms. Here,
we show that the type of language training crucially impacts this
outcome. We used an artificial language paradigm to examine
longitudinally whether explicit training (that approximates tradi-
tional grammar-focused classroom settings) and implicit training
(that approximates immersion settings) differentially affect neural
(electrophysiological) and behavioral (performance) measures of
syntactic processing. Results showed that performance of explic-
itly and implicitly trained groups did not differ at either low or
high proficiency. In contrast, electrophysiological (ERP) measures
revealed striking differences between the groupsʼneural activity
at both proficiency levels in response to syntactic violations. Im-
plicit training yielded an N400 at low proficiency, whereas at high
proficiency, it elicited a pattern typical of native speakers: an ante-
rior negativity followed by a P600 accompanied by a late anterior
negativity. Explicit training, by contrast, yielded no significant
effects at low proficiency and only an anterior positivity followed
by a P600 at high proficiency. Although the P600 is reminiscent
of native-like processing, this response pattern as a whole is
not. Thus, only implicit training led to an electrophysiological
signature typical of native speakers. Overall, the results suggest
that adult foreign language learners can come to rely on native-
like language brain mechanisms, but that the conditions under
which the language is learned may be crucial in attaining this
goal.
INTRODUCTION
Learning a language as a child is typically natural and
effortless. Learning a language as an adult, in contrast, is
often fraught with difficultly. Indeed, it is widely believed
that adults are not able to learn a second language (L2)
using the same neurocognitive mechanisms that children
rely on for their first language (L1; Bley-Vroman, 1990;
Lenneberg, 1967). However, recent evidence shows that
even for aspects of language, such as grammar, that are
difficult to learn in L2 (Weber-Fox & Neville, 1996; Newport,
1993), L1-like brain processing may eventually be at-
tained (Gillon Dowens, Vergara, Barber, & Carreiras,
2009; Steinhauer, White, & Drury, 2009; Hahne, Mueller,
& Clahsen, 2006; see below for more details). Yet, a critical
gap in our understanding of adult-learned L2 remains: It is
not yet known whether L1-like brain processing can always
be attained or whether certain factors, such as the input
conditions under which an L2 is learned, crucially constrain
it. Here, we test for the first time whether more explicit in-
put conditions (as in traditional grammar-focused classroom
settings) or more implicit input conditions (as are found in
immersion settings) differentially affect the attainment of na-
tive language brain mechanisms for L2 syntactic processing.
Although neural outcomes of explicit versus implicit L2
training conditions have never been examined, a large
body of behavioral research has addressed this issue (see
Norris & Ortega, 2000). Despite the popular belief that
learning a foreign language as an adult is easier when
one is immersed in the language and imbibes it largely im-
plicitly, behavioral advantages have usually been reported
for explicit rather than implicit trainingwherein explicit
training is defined as training that provides learners with
information about L2 grammar rules or directs them to
search for rules, and implicit training is defined as training
that engages L2 learners with the target language but does
not provide any explicit information or direction to search
for rules (Norris & Ortega, 2000). Although in some stud-
ies, implicit and explicit training lead to similar levels of L2
learning (Sanz & Morgan-Short, 2005; VanPatten & Oikkenon,
1996), we are not aware of any clear empirical evidence
suggesting an advantage for implicit training.
Limitations of this body of behavioral research, however,
have made it difficult to arrive at clear conclusions regard-
ing explicit versus implicit training. Studies typically exam-
ine learning effects of explicit and implicit training on an
L2 that was already learned to low levels of proficiency
1
University of Illinois at Chicago,
2
McGill University, Montreal,
Canada,
3
Georgetown University
*Contributed equally to this work.
© 2012 Massachusetts Institute of Technology Journal of Cognitive Neuroscience 24:4, pp. 933947
(generally in classroom settings). The amount of training
provided is usually quite small (around 1 hr), so participants
remain at lower proficiency levels even after training (Rosa
& Leow, 2004; Sanz & Morgan-Short, 2004; VanPatten &
Oikkenon, 1996). Thus, any advantages of explicit or implicit
training on attaining high proficiency are, surprisingly, still
unknown. Additionally, because learning under implicit
conditions is thought to take longer than under explicit
conditions (Ellis, 2005), such short durations may bias
the results toward an advantage for explicit training (Ellis
et al., 2009; Norris & Ortega, 2000). Moreover, explicit
training conditions often provide more input than implicit
training conditions, in that they provide explicit informa-
tion in addition to the stimuli provided in the implicit train-
ing condition (Rosa & Leow, 2004; VanPatten & Oikkenon,
1996). Thus, neither time-on-task nor the total amount of
input (of different types) is systematically controlled. Fi-
nally, the assessment of L2 in training studies has generally
focused on explicit knowledge (available to conscious aware-
ness; Norris & Ortega, 2000), providing another bias for ex-
plicit training (Ellis et al., 2009; Sanz & Morgan-Short, 2005;
Norris & Ortega, 2000). Thus the reported advantages for
explicit training remain very much in question (Ellis et al.,
2009; Sanz & Morgan-Short, 2005).
We took a different approach to examine this issue. First,
rather than training participants on a natural language to
which they had already been exposed, we trained them on
an artificial language, Brocanto2. Participants were trained
across multiple sessions to actually speak and understand
this language, which refers to pieces and moves of a chess-
like computer game. The grammar of the language fully
complies with grammars of natural languages. However,
the number of rules and vocabulary items is very small. Thus,
the language is learnable to high proficiency in the order of
hours, facilitating longitudinal examination of training effects
from low to high proficiency. Moreover and unlike natural
languages, an artificial language allows a range of factors to
be easily controlled, such as the amount, timing, and type
of exposure ( before as well as during training), and the
(dis)similarity of the language (e.g., phonology and syntax)
to the speakerʼs native language (L1). Importantly, Brocanto2
is a variant of a previously developed artificial language
(Brocanto), which, when learned to high proficiency, shows
L1-like brain patterns (Opitz & Friederici, 2003; Friederici,
Steinhauer, & Pfeifer, 2002). Artificial languages such as
these may thus constitute test tubemodels of natural lan-
guage that allow one to examine issues that would be dif-
ficult, if not impossible, to address in natural language
(Hancock & Bever, 2009; Friederici et al., 2002).
Second, whereas previous L2 research of explicit and
implicit training has been purely behavioral, here we test
neural measures, although we also examine behavioral
(performance) measures. Crucially, although performance
measures can reveal how well an L2 is learned, they cannot
easily tell us how it is learned or processed, that is, what
neurocognitive mechanisms underlie it (Ullman, 2005).
In fact, a particular limitation of performance data is that
similar performance between two conditions or groups
does not necessarily implicate reliance on similar neural
mechanisms. In other cognitive domains, it has been
shown that, although different task demands promote
the use of different brain systems (declarative and pro-
cedural memory), the material may be learned about
equally well by both (Foerde, Knowlton, & Poldrack,
2006;Poldracketal.,2001).Similarly,highL2performance
does not necessarily suggest a dependence on native lan-
guage neurocognitive mechanisms (Ullman, 2005). Yet the
reliance on L1 mechanisms may be an important goal of
L2 learning, because these mechanisms are evidently ex-
tremely well suited to language. Indeed, it is quite plausible
that native-like proficiency might be reliably attained only
with native language neurocognitive mechanisms. Thus,
elucidating the neural as well as performance outcomes
of L2 training seems essential.
ERPs may be the best method for this purpose. ERPs
reflect real-time scalp-recorded electrophysiological brain
activity of cognitive processes that are time-locked to the
presentation of target stimuli. Unlike other neuroimaging
techniques (fMRI and magnetoencephalography), ERP
research has revealed a set of widely studied language-
related activation patterns (ERP components)inL1,
whose characteristics and underlying functions are rela-
tively well understood (see below). These components
thus provide a clear frame of reference for examining the
attainment of native language processing in L2, including in
studies of artificial language (Friederici et al., 2002). Unlike
hemodynamic imaging methods like fMRI, ERPs provide
excellent temporal resolution, allowing one to examine
the time course of processing. Examining ERP along with
behavioral measures improves the likelihood of detecting
effects, in particular because ERPs can be sensitive to ef-
fects that are not found with behavioral measures, includ-
ing in language learning studies (Tokowicz & MacWhinney,
2005; McLaughlin, Osterhout, & Kim, 2004). Finally, unlike
the performance measures in many previous L2 training
studies, which are designed to reveal explicit knowledge,
without any direct measure of the contribution of implicit
knowledge, ERPs have the potential to reveal processes
underlying both types of knowledge.
In L1, different types of processing difficulties elicit dif-
ferent ERP components (Steinhauer & Connolly, 2008).
Difficulties in lexical/semantic processing in L1 (e.g., John
has his coffee with milk and *concrete”—the * indicates a
violation word) elicit central/posterior bilaterally distrib-
uted negativities (N400s) that often peak about 400 msec
poststimulus onset for written words (Kutas & Hillyard,
1980), and tend to be relatively long lasting in spoken lan-
guage (Steinhauer, Alter, & Friederici, 1999; Holcomb &
Neville, 1991). N400s reflect aspects of lexical/semantic
processing and may depend on the declarative memory
brain system (Lau, Phillips, & Poeppel, 2008; Steinhauer
& Connolly, 2008; Ullman, 2001).
Disruptions of rule-governed (morpho)syntactic pro-
cessing in L1, including word order (phrase structure)
934 Journal of Cognitive Neuroscience Volume 24, Number 4
violations (e.g., The man hoped to *meal the enjoy with
friends) often produce three components. First, such
disruptions can, but do not always (Hagoort & Brown,
1999), elicit early (150500 msec) left-to-bilateral anterior
negativities (ANs; Steinhauer & Connolly, 2008; Kaan,
2007; Friederici, Pfeifer, & Hahne, 1993; Neville, Nicol,
Barss, Forster, & Garrett, 1991). Less left-lateralized (more
bilateral) ANs may be associated with lower L1 proficiency
(Pakulak & Neville, 2010). Because these negativities are
not restricted to the left hemisphere, the term ANmay
be more appropriate than the traditional term LANand
will be adopted here. Although (morpho)syntactic viola-
tions do not always elicit ANs (Hagoort & Brown, 1999),
this component is generally found in response to audito-
rily presented word order violations (Steinhauer & Drury,
in press), which are examined here. ANs appear to reflect
aspects of rule-governed automatic structure building
(Steinhauer & Drury, in press; Hasting & Kotz, 2008;
van den Brink & Hagoort, 2004; Friederici & Kotz, 2003;
Hahne & Friederici, 1999) and have been posited to depend
on the procedural memory brain system, which seems to
underlie aspects of grammar (Ullman, 2001, 2004). Second,
(morpho)syntactic disruptions usually elicit late (600 msec)
centro-parietal positivities (P600s; Kaan, Harris, Gibson, &
Holcomb, 2000; Osterhout & Holcomb, 1992), which are
linked to controlled (conscious) processing and structural
reanalysis (Kaan et al., 2000; Hahne & Friederici, 1999).
The biphasic pattern of an AN followed by a P600 may be
characteristic of native speaker processing of (morpho)syn-
tactic violations (Steinhauer & Drury, in press; Hasting &
Kotz, 2008; Steinhauer & Connolly, 2008; van den Brink
& Hagoort, 2004; Friederici et al., 1993). Finally, such vio-
lations can also elicit later (6002000 msec) sustained
ANs (late ANs), which often show bilateral distributions
(Gillon Dowens et al., 2009; Martin-Loeches, Munoz, Casado,
Melcon, & Fernandez-Frias, 2005; Friederici et al., 1993).
Although these late negativities tended not to be discussed
in earlier studies (Hahne & Friederici, 1999; Friederici
et al., 1993), more recent research has commonly reported
them, especially for auditorily presented word order viola-
tions (Steinhauer & Drury, in press; Kotz, 2009; Steinhauer
& Connolly, 2008; Kaan, 2007). Late ANs may reflect
increased working memory demands (Martin-Loeches
et al., 2005). It has also been proposed that the prototypical
ERP response to phrase structure violations consists one
sustained AN (e.g., from about 200 msec to later than
1000 msec) together with a P600, which can temporarily
diminish or eliminate this AN (Steinhauer & Drury, in
press). In summary, the AN, P600, and late AN are all com-
monly elicited in L1 in response to (morpho)syntactic vio-
lations, in particular for auditorily presented word order
violations (Kotz, 2009; Steinhauer & Connolly, 2008; Kaan,
2007).
ERP studies of L2 processing have revealed the follow-
ing. The neurocognition of lexical/semantic processing
does not differ qualitatively between L1 and L2, reliably elic-
iting N400s in both cases, even after minimal L2 exposure,
although in some cases the N400 in L2 is delayed or longer
lasting (Steinhauer et al., 2009; McLaughlin et al., 2004;
Ullman, 2001). In contrast, L2 differs from L1 in aspects
of (morpho)syntactic (grammatical) processing, in particu-
lar at lower levels of exposure and proficiency (Steinhauer
et al., 2009; Ullman, 2001). (Note that proficiency and ex-
posure are generally correlated and are difficult to tease
apart in studies of L2; for simplicity, hereafter we usually
refer only to proficiency levels rather than to both pro-
ficiency and exposure.) At lower levels, ANs are typically
absent, with participants instead showing no negativity at
all (Ojima, Nakata, & Kakigi, 2005; Hahne & Friederici,
2001) or eliciting N400s or N400-like posterior negativities
(Osterhout et al., 2008; Weber-Fox & Neville, 1996). How-
ever, recent studies have reported ANs in higher profi-
ciency L2 (Gillon Dowens et al., 2009; Steinhauer et al.,
2009; Isel, 2007; Hahne et al., 2006; Ojima et al., 2005;
but see Chen, Shu, Liu, Zhao, & Li, 2007). These ANs are
sometimes bilaterally distributed (Isel, 2007), possibly be-
cause of lower L2 proficiency (Steinhauer et al., 2009).
P600s are generally found in L2, particularly at higher
proficiency (Gillon Dowens et al., 2009; Steinhauer et al.,
2009; Osterhout et al., 2008; Hahne et al., 2006; Weber-
Fox & Neville, 1996). In some studies of high proficiency
L2, the AN and P600 are both elicited in response to
(morpho)syntactic violations (Gillon Dowens et al., 2009;
Steinhauer et al., 2009; Hahne et al., 2006). This L1-like
biphasic response has also been found in highly proficient
learners of an artificial language (Friederici et al., 2002).
Finally,lateANshavealsobeenobservedinL2,againmainly
(but not always) at higher proficiency (Chen et al., 2007;
Isel, 2007; but see Ojima et al., 2005), in some cases to-
gether with an ANP600 biphasic response (Gillon Dowens
et al., 2009).
Overall, ERP studies of L2 suggest that, although the
neurocognition of lexical/semantic processing is sim-
ilar in L1 and L2, the neural processes underlying L2
(morpho)syntax depend on the learnerʼs level of profi-
ciency (or exposure). At lower levels, L1 brain processes
(as indexed by ANs and P600s, as well as late ANs) are un-
common or absent. Instead of the automatic structure
building relied on in L1 (indexed by ANs), (morpho)syntax
in lower proficiency L2 may, at least in some circum-
stances, depend on lexical/semantic processes, as reflected
by the N400. In contrast, at higher proficiency levels, the
presence of ANs and P600s, in particular in biphasic re-
sponses, as well as late ANs, suggest that L1 brain process-
ing can in fact be achieved in L2, although the type and
amount of exposure and the level of resulting proficiency
necessary to achieve native-like brain mechanisms remain
unknown.
The study reported here moves beyond the examination
of proficiency in L2. It tests whether the conditions under
which an L2 is learned, in particular explicit versus implicit
training conditions (holding the amount of training time
constant), have distinct effects on neural (ERP) and behav-
ioral (performance) measures of syntactic processing.
Morgan-Short et al. 935
Adult participants learned to understand and speak the
artificial language Brocanto2 in either explicit or implicit
training conditions. ERPs were acquired while participants
judged the acceptability of correct and incorrect (word
order violation) Brocanto2 sentences, first at low exposure
and proficiency and then at high.
METHODS
Participants
We tested 41 right-handed, healthy adults who were not
fluent in any language other than English, based on self-
report. Because the artificial language was structurally sim-
ilar to Romance languages, exposure to any Romance
language was restricted to not more than 3 years of class-
room exposure and 2 weeks of immersion in a Romance
language environment. Participants were randomly assigned
to the explicit or implicit training groups within each gen-
der and were included in the analysis if they reached a low
level of proficiency (see below), completed all tasks, and
did not exhibit a large number of artifacts in their ERP
data. Data from 30 participants (explicit: n=16,7women;
implicit: n= 14, 7 women) were analyzed. The explicit and
implicit groups did not differ (unpaired ttests, ps > .139)
on age (explicit: M= 24.25 years, SD =4.34years;implicit:
M=24.71years,SD = 5.57 years), years of education
(M= 16.25 years, SD =2.82years;M= 16.43 years,
SD = 2.17 years), age of first exposure to any second lan-
guage (M= 12.63 years, SD = 4.72 years; M= 12.64 years,
SD = 4.06 years), or years of exposure to either Romance
languages (M= 1.51 years, SD =1.35years;M= 1.95 years,
SD = 1.30 years) or to any other nonnative language
(M=3.45years,SD =1.71years;M= 4.94 years, SD =
3.46 years). All participants gave written informed consent
and received monetary compensation for their participa-
tion, which was approved by the Georgetown University
Institutional Review Board.
Artificial Language
An artificial language (Brocanto2) rather than a natural
language was examined for several reasons, including our
ability to follow learning longitudinally to high proficiency,
to control for multiple factors such as the amount and type
of exposure, and to avoid various confounds such as simi-
larity to the native language (see above). At the same time,
because Brocanto2 follows universal requirements of nat-
ural languages, is fully productive, is actually spoken and
comprehended, and is based on the artificial language
Brocanto, which shows natural language brain patterns in
both ERP and fMRI (Opitz & Friederici, 2003; Friederici
et al., 2002), the results of this study are likely to generalize
to natural languages.
The lexicon of Brocanto2 consists of a small number of
nonwords with English pronunciation and phonotactics.
It thus avoids phonological L1L2 differences, which are
a common source of difficulty in L2 acquisition. The lan-
guage contains 13 lexical items: 1 article (l-), marked for
gender (masculine li; feminine lu); 2 adjectives (trois-,
neim-), each marked for gender (masculine troise/neime;
feminine troiso/neimo); 4 nouns ( pleck, neep, blom,
vode), two of which are masculine and two feminine
(the nouns are not overtly marked for gender, but their
articles and adjectives must agree with them); 4 verbs
(klin, nim, yab, praz); and 2 adverbs (noyka, zayma).
Note that because Brocanto2 is presented solely audito-
rily, the orthographic representations presented here
are provided solely for the reader. In contrast to English
noun phrases, articles and adjectives in Brocanto2 are
(a) postnominal (i.e., noun[adjective]determiner) and
(b) morphologically marked so as to agree in gender with
the noun to which they refer. Also unlike English, Brocanto2
sentences have a fixed subjectobjectverb word order
and have no morphological features on the verb. Adverbs,
when used, immediately follow the verb. All the grammati-
cal features of Brocanto2 are found in natural languages,
such as Supyire (spoken in Mali), which has subjectobject
verb word order, grammatical gender agreement, and
postnominal adjectives and determiners (Carlson, 1994).
Each of the 1404 possible Brocanto2 sentences is mean-
ingful in that it describes a move of a computer-based
board game, which provided a context for the participants
to use the artificial language; see Table 1 for an example of
Brocanto2 sentence and Figure 1 for an example game
board configuration.
Table 1. Example Correct and Violation Brocanto2 Sentences
Sentence Type Brocanto2 Stimuli
Correct sentence Blom neimo lu neep li praz
Blom-piece square the neep-piece the switch
The square blom-piece switches with the neep-piece.
Word-order violation sentence Blom *nim lu neep li praz
Blom-piece *capture the neep-piece the switch
The *capture blom-piece switches with the neep-piece.
* denotes violation.
936 Journal of Cognitive Neuroscience Volume 24, Number 4
Procedure
The procedure for the current study consisted of training,
practice, and assessment of Brocanto2 over the course of
three experimental sessions (see Figure 2 for a schematic
overview of the entire procedure). At the beginning of the
study, participants responded to a background question-
naire and completed pretraining activities, in which they
were given a brief introduction to the computer-based game
and learned the names of the four game tokens ( pleck,
neep, blom, vode) to 100% accuracy (demonstrated by
naming each token correctly three times). After completing
pretraining, participants were presented with either an ex-
plicit or an implicit aural language training condition. The
training conditions were designed to approximate real-
world language learning settings to maximize the ecological
validity of the training.
The explicit training condition provided input of a type
similar to that found in traditional grammar-focused class-
room settings, where learners are typically provided with
metalinguistic information related to the functions and
rules of aspects of the language, along with a few phrases
and sentences that demonstrate the application of these
rules. Thus, in this condition metalinguistic explanations
were presented along with meaningful examples (see
Appendix A). The metalinguistic explanations were struc-
tured around word categories, that is, around the nouns,
articles, adjectives, verbs and adverbs of Brocanto2. For
each word category, the explanation included information
about its function (e.g., adjectives describe nouns), gram-
matical rules (e.g., adjectives agree with the gender of
the noun that they modify), and word order rules (e.g.,
Figure 2. The experimental
design consisted of three
sessions during which
background questionnaires,
pretraining (learning the rules
of the computer-based game,
and the names of the four game
tokens), explicit and implicit
artificial language training,
practice, and assessments were
administered. Arrows indicate
whether the subsequent
experimental procedure was the
same (downward and inward
pointing arrows) or different
(outward pointing arrows) for
the explicit and implicit training
conditions.
Figure 1. Computer-based game board. Game tokens are represented
by visual symbols, which correspond to nouns in Brocanto2. The tokens
can further be distinguished by their background shapesquare or
roundeach of which corresponds to a Brocanto2 adjective. Players
can move, swap, capture, and release tokens, with each of these actions
corresponding to Brocanto2 verbs, as well as move them either
horizontally or vertically (corresponding to Brocanto2 adverbs).
Morgan-Short et al. 937
adjectives are always placed after the noun that they
modify). Each metalinguistic explanation was accompanied
by one or more meaningful examples (total of 33 examples).
In total, 13.5 min of training was provided in the explicit
condition.
The implicit condition provided the same amount of
training, that is, 13.5 min. This condition was designed to
represent more implicit language learning contexts, such
as immersion settings, in which learners are exposed to a
larger number of meaningful phrases and sentences, but
receive little or no metalinguistic information. Thus the im-
plicit training condition consisted only of meaningful exam-
ples (see Appendix B). Importantly, to match the amount
of total training in the implicit and explicit conditions, the
implicit condition contained not only the 33 meaningful
examples presented in the explicit condition but also 94
additional meaningful examples to match the time of the
metalinguistic explanations in the explicit condition. Cru-
cially, this design allowed us to maintain equivalent training
time between the explicit and implicit training conditions,
which has not been systematically controlled in previous
L2 research (see Introduction; Norris & Ortega, 2000). To
summarize, the explicit and implicit training conditions
were identical in the overall amount of training but dif-
fered in the type of training: metalinguistic explanations
and meaningful examples in the explicit condition versus
only meaningful examples in the implicit condition.
Other potential differences between the conditions
were also eliminated or minimized: both conditions were
computer controlled; both presented Brocanto2 sentences
auditorily, starting with simple phrases and gradually mov-
ing to simple and then complex sentences; and neither
provided English translations. In essence, the study al-
lowed us to contrast explicit and implicit training while
tightly controlling for multiple other variablesa design
that would have been difficult, if not impossible, with a
natural language.
After the initial explicit or implicit training session, par-
ticipants in both groups practiced Brocanto2 in compre-
hension and production blocks, which were designed to
approximate normal language use. Practice was identical
for the two training groups. There were 44 practice blocks,
with 20 trials (sentences and corresponding moves on the
computer game board) in each block. Half of the blocks
consisted of comprehension practice trials, in which par-
ticipants listened to a prerecorded sentence in Brocanto2
and were asked to carry out the stated move on the screen
using the computer mouse. The other half of the blocks
consisted of production practice trials, in which partici-
pants watched a move displayed on the screen and had to
describe it with a single oral sentence in Brocanto2. Com-
prehension and production alternated every two blocks.
For both types of practice, correct/incorrect feedback was
provided; this was identical for the two groups. This is con-
sistent with feedback that occurs in both explicit (e.g., class-
room) and implicit (e.g., immersion) input settings (Lyster
& Mori, 2006; Lyster & Ranta, 1997). Thus, other than the
crucial contrast between explicit and implicit training, all
aspects of the experimental design, including practice,
were identical between the two groups.
Participants continued with comprehension and produc-
tion practice until they reached low proficiency, which was
operationalized as accuracy significantly above chance on
two subsequent comprehension practice blocks. Chance
in each block was calculated as 45% correct, based on
the number of correct moves of the total number of possi-
ble moves. The average score on these two comprehen-
sion practice blocks was indeed above chance for both
the explicitly (M=0.63,SD =0.17)andimplicitly(M=
0.65, SD = 0.19) trained groups (t(28) = 0.232, p=.819).
When participants reached this level, the first ERP test
session was administered (see below). All participants com-
pleted the initial round of training, practice, and low profi-
ciency testing in one day (see Figure 2).
Participants returned for a second round of training fol-
lowed by practice 14 days later (M= 1.53, SD = 1.25).
Training was identical to the first round (same input and
examples). Practice was also the same as in the first round,
although with entirely new sentences, that is, that had not
been presented before. Participants completed all blocks
through block 36 in this round.
Finally, participants returned 15daysafterwards(M=
2.35, SD = 1.41), when they completed the remaining eight
practice blocks. At this point (end of practice), all partici-
pants scored at 80% accuracy or above on comprehension
practice. The average score on the final two comprehen-
sion practice blocks at the end of practice was around 95%
for both groups (explicit: M=0.95,SD = 0.08; implicit:
M=0.94,SD = 0.10; t(28) = 0.783, p= .440), and partici-
pants were considered to be at a high level of proficiency.
Immediately after completing the final practice module,
the second ERP test session was administered.
ERP Assessment
ERP assessment was carried out with 240 Brocanto2
sentences, including 40 sentences with a syntactic word
order violation, and 40 matched correct control sentences
(see Table 1 for examples). Word order violation sen-
tences were created from each of the 40 correct sentences
by replacing a word from one of the five word categories
(e.g., noun, adjective, article, verb, and adverb) with a
word of a different word category that violated the word
order rules of Brocanto2. Thus, the correct and violation
sentences differed only in this target (correct or violation)
word, the onset of which served as the point of compar-
ison for ERP analysis. To avoid confounds with specific
words, word category or sentence position, violations
were equally distributed over (a) the 14 words to the
extent possible, (b) the five word categories, with each
word category being replaced by each of the other word
categories approximately twice (e.g., adjectives were
never replaced by articles because that would not yield
a word order violation and so were replaced by other
938 Journal of Cognitive Neuroscience Volume 24, Number 4
categories three times), and (c) sentence positions to the
extent possible, although violations never occurred on
the first word of the sentence. Note that, for violations
to be equally distributed across word categories, it was
necessary for them to occur in sentence final position in
the case when the violation was created on the adverb.
In all other cases, sentence final violations were avoided.
In summary, this balanced design ensured that across
trials, the violation and control conditions did not differ
with respect to either (i) the critical target words or (ii)
the contexts preceding the target words, thus ruling out
baseline problems as well as lexical confounds that are
typically found in previous ERP work on word order vio-
lations (for a discussion see Steinhauer & Drury, in press).
Additional violation and control sentences examining
grammatical gender agreement and verb argument com-
prised the remaining 160 sentences. As these stimuli were
motivated by somewhat different research questions, they
are reported elsewhere (Morgan-Short, Sanz, Steinhauer,
& Ullman, 2010; Morgan-Short, 2007); because they are
informative to the current study, we also present them
below (see Discussion).
Before ERP recording, participants were given instructions
and a short practice session and were asked to minimize
eye and body movements during sentence presentation.
During ERP data collection, the following presentation se-
quence occurred for each sentence: First, a fixation cross
appeared in the center of the screen simultaneously to
the aural presentation of a Brocanto2 sentence (via ER-4
insert earphones; Etymotic Research, Inc.). The fixation
cross remained for the duration of the sentence. Following
Friederici et al. (2002), words were separated by a 50-msec
interval of silence to establish acoustically identical base-
lines and an absence of coarticulation between words while
allowing for relatively natural-sounding sentences. This
approach to stimulus presentation minimizes prosodic
context effects that potentially contribute to previous ERP
data (Steinhauer & Drury, in press). Following the last
word of each sentence, the fixation cross remained on
the screen for an additional 500 msec, after which time it
was replaced by the prompt Good?Participants had up to
5 seconds to make a judgment about whether the sentence
was good or bad, indicated with the buttons of a computer
mouse (left for good and right for bad). The next sentence
and fixation cross were presented immediately after the re-
sponse. Scalp EEG was continuously recorded in DC mode
at a sampling rate of 500 Hz from 64 electrodes (extended
1020 system) mounted in an elastic cap (Electro-Cap In-
ternational, Inc., Eaton, OH), and analyzed using EEProbe
software (Advanced Neuro Technology, Enschede, the
Netherlands). Scalp electrodes were referenced to the left
mastoid, and impedances were kept below 5 kΩ.Thever-
tical EOG was recorded with two electrodes placed above
and below the right eye, and the horizontal EOG was re-
corded with two electrodes placed on the right and left
canthi. The EEG was amplified by Neuroscan SynAmps
2
amplifiers and filtered on-line with a band-pass filter (DC
to 100 Hz, 24-dB/octave attenuation). Off-line, the EEG
was filtered with a 0.1630 Hz band-pass filter. Data from
all target words free of artifacts greater than 40 μVinthe
EOG and greater than 75 μV in EEG were included in the
analysis.
Statistical Analysis
To compare the groupsʼperformance, participantsʼbehavioral
responses to the on-line judgment task were transformed
to d
0
scores. Differences in the ability to discriminate cor-
rect and violation sentences were examined by submitting
d
0
scores for each participant to a 2 × 2 ANOVA with test ses-
sion (low proficiency, high proficiency) as a repeated factor
and group (explicit, implicit) as a between-subject factor.
For ERP analysis, EEG data time-locked to the onset of
the violation or matched control target word were averaged
for each participant for an array of 24 lateral electrodes
using a 200-msec prestimulus baseline. These electrodes
covered six levels of anterior/posterior distribution: F5,
F3, F4, F6 (anterior-1); FC5, FC3, FC4, FC6 (anterior-2);
C5, C3, C4, C6 (central-1); CT5, CP3, CP4, CT6 (central-2);
P5, P3, P4, P6 (posterior-1); and PO3, OL, OR, PO4
(posterior-2). Within each of these levels, the electrodes
also covered two levels of hemisphere (right, left) and
two levels of laterality (lateral, medial). Additionally, three
midline electrodes (Fz, Cz, Pz) were analyzed. Together,
this array covers the typical scalp distribution of the
language-related ERP components of interest here. Artifact-
free target words were analyzed regardless of whether
participantsʼon-line judgments were correct or not. This
approach, which is common in L2 ERP research (Frenck-
Mestre, Osterhout, McLaughlin, & Foucart, 2008; Chen
et al., 2007; Ojima et al., 2005; Friederici et al., 2002;
Weber-Fox & Neville, 1996), was deemed appropriate
because (a) ERP effects have been found in L2 even when
learners do not accurately judge stimuli (Tokowicz &
MacWhinney, 2005; McLaughlin et al., 2004); (b) the lower
accuracy rates at low proficiency would have resulted in a
lower signal to noise ratio, as compared with high profi-
ciency; and (c) visual inspection of waveforms reflecting tar-
get items to which participants had responded correctly,
and waveforms reflecting all target items (i.e., those used
here) revealed highly similar patterns. Individual ERPs were
entered into separate grand ERP averages for the explicitly
and implicitly trained groups. Time windows were selected
on the basis of previous research and visual inspection of the
grand averages: 150350 msec for early ANs, 350700 msec
for the AN and N400, and 700900 msec for the P600.
Mean amplitudes for each time window were analyzed
using a global ANOVA with the between-subject factor
Group (explicit, implicit); the within-subject factors Test
Session (low proficiency, high proficiency) and Violation
(correct, violation); and the distributional factors Lateral-
ity (lateral, medial), Hemisphere (right, left), and Anterior/
Posterior (anterior-1, anterior-2, central-1, central-2,
posterior-1, posterior-2). When evaluating the Anterior/
Morgan-Short et al. 939
Posterior factor (which included more than one degree of
freedom) the GreenhouseGeisser correction was applied
(corrected pvalues are reported). In all cases, any global
ANOVA that yielded any significant ( p< .05) interaction
including the factor violation was followed up with step-
down ANOVAs to clarify the nature of the interaction.
Similar analyses were also carried out for the midline
electrodes, but without the factors Laterality and Hemi-
sphere. We report significant ( p< .05) violation main
effects and interactions from each global ANOVA as well
as lower-level group-specific or distributional violation
effects revealed by significant step-down analyses. Re-
sults of the midline analysis are reported only when they
revealed effects that were not evidenced in the lateral
analyses.
RESULTS
Behavioral Data
The explicit and implicit groups did not differ in the num-
ber of practice blocks or the amount of practice time
needed to reach low proficiency nor did they differ in
the number of blocks or the amount of time between the
attainment of low proficiency and end of practice ( ps>.6;
over both groups, means of 6.9 blocks and 48.23 min to
reach low proficiency; from low proficiency to end of
practice, means of 37.1 blocks and 161.73 min).
Analysis of participantsʼperformance on the on-line judg-
ment task revealed a main effect of test session [F(1, 28) =
76.18, p< .001], with performance improving between low
and high proficiency, no main effect of group (F<1),anda
significant Test Session × Group interaction [F(1, 28) =
4.67, p= .039], which reflected a larger performance
improvement between low and high proficiency for the
implicit group as compared with the explicit group
although both groups in fact reached high proficiency by
the second test session and the two groups did not differ
significantly in performance at either low or high profi-
ciency (see Figure 3).
ERP Data
Visual inspection of the ERP voltage maps and waveforms
(see Figure 4) suggests an N400 for the implicit group at
low proficiency, but no clear effects for the explicit group.
At high proficiency, the implicit group appears to show an
AN followed by a P600 and a late AN, whereas the explicit
group displays an anterior positivity followed by a P600.
Statistical analysis showed that these effects were reliable.
In the 150350 msec (early AN) time window, the global
ANOVA produced only a Group × Violation interaction
[F(1, 28) = 5.85, p= .022]. Step-down analyses revealed
a main effect of violation for only the implicit group [F(1,
13) = 7.06, p= .020], reflecting a negativity in this training
group over both the low- and high-proficiency test ses-
sions. Inspection of the voltage maps for this time window
for the implicit group (see Figure 4C and D) suggests that
this shared negativity may reflect the emergence of two dif-
ferent effects at low and high proficiency, which become
clearer in the subsequent time window. Indeed, ANOVAs
specific to each test session in the implicit group suggest
that this negativity has a different distribution at low and
high proficiency. At low proficiency, the midline analysis re-
vealed a main effect for Violation [F(1, 13) = 6.03, p=
.029], and the lateral analysis revealed a Violation ×
Laterality × Hemisphere interaction [F(1, 13) = 6.45,
p= .025], for which all follow-ups were not significant.
At high proficiency, the only main effect or interaction to
Figure 3. Mean d
0
scores and
standard errors for the explicitly
trained and implicitly trained
participant groups at low
proficiency and at high
proficiency. Paired ttests on
d
0
scores motivated by a
Group × Test Session interaction
(see Results) indicated that the
two groups did not differ in
their ability to distinguish
correct and violation sentences,
at either low proficiency
(t(28) = 5.61, p=.579)or
high proficiency (t(28) = 1.24,
p=.226).Bothgroups
improved from the first to the
second test session (explicit:
t(28) = 4.80, p< .001;
implicit: t(28) = 7.47, p<.001),
although the improvement was
larger for the implicit than the
explicit group, as indicated by the Group × Test Session interaction. At high proficiency, the d
0
scores of both groups were above 2.5,
which corresponds roughly to a proportion correct of 0.90 (Macmillan & Creelman, 2005), indicating that both groups had reached a high
level of proficiency.
940 Journal of Cognitive Neuroscience Volume 24, Number 4
reach significance was a Violation × Hemisphere interac-
tion [F(1, 13) = 5.90, p= .030], which was driven by a main
effect for violation in the left hemisphere [F(1, 13) = 6.07,
p= .028] but not in the right hemisphere. Although these
differences in distribution were not large enough to elicit any
interactions that included Test Session in the global ANOVA,
they are suggestive of the emergence of different ERP pat-
terns at low and high proficiency in the implicit group.
Figure 4. Voltage maps and waveforms reflecting the difference between correct and violation sentence grand average ERPs. (A) Explicitly trained
learners at low proficiency do not evidence any significant ERP effect. (B) Explicitly trained learners at high proficiency show an anterior positivity
followed by a P600. (C) Implicitly trained learners at low proficiency show a broad, ongoing N400. (D) Implicitly trained learners at high proficiency
show an AN followed by a P600 and a late AN. Voltage map coloration indicates amplitude differences between correct and violation waveforms.
Morgan-Short et al. 941
In the 350700 msec (AN and N400) time window,
the global ANOVA revealed a main effect of Violation [F(1,
28) = 4.54, p= .042]. This negativity was qualified by a
two-way Group × Violation interaction [F(1, 28) = 6.08,
p= .020] and a four-way Group × Test Session × Violation ×
Anterior/Posterior interaction [F(5, 140) = 3.79, p=.048].
Step-down analyses based on the four-way interaction re-
vealed two distinct negative ERP effects, only in the implicit
group: (a) an N400 at low proficiency, present at posterior
electrodes [posterior-2: F(1, 13) = 7.25, p< .019] and (b)
an AN at high proficiency, most prominent at anterior sites,
and extending toward central sites [anterior-1: F(1, 13) =
9.38, p< .009; anterior-2: F(1, 13) = 8.28, p< .013;
central-1: F(1, 13) = 7.84, p< .015; central-2: F(1, 13) =
4.78, p< .048]. Analysis of the midline electrodes pro-
duced an additional finding: The follow-up analyses of
a midline Group × Test Session × Violation × Anterior/
Posterior interaction [F(2, 56) = 4.85, p= .022] revealed
an anterior positivity for the explicit group at high profi-
ciency [Anterior mid: F(1, 15) = 5.81, p<.029].
Thus, for these earlier time windows, the results show
that, at low proficiency, the implicit group elicited an
N400 (Figure 4C), whereas the explicit group elicited no
effects (Figure 4A). At high proficiency, the implicit group
elicited an AN (Figure 4D), whereas the explicit group
exhibited an anterior positivity (Figure 4B).
In the 700900 msec (P600, late AN) time window, we
found a two-way Violation × Anterior/Posterior interaction
[F(5, 140) = 10.69, p= .002], a three-way Test Session ×
Violation × Anterior/Posterior interaction [F(5, 140) =
11.65, p< .001], and a four-way Group × Test Session ×
Violation × Anterior/Posterior interaction [F(5, 140) =
5.04, p= .017]. Step-down analyses based on the four-
way interaction confirmed three distinct ERP components
for the implicit group: an ongoing N400 [central-2: F(1,
13) = 9.81, p= .008] at low proficiency and both a P600
[posterior-1: F(1, 13) = 5.89, p= .031; posterior-2: F(1,
13) = 7.61, p= .016] and a late AN [Anterior 1: F(1, 13) =
5.82, p= .031] at high proficiency. The explicit group, by
contrast, showed neither an N400 nor a late AN, although
a P600 was observed at high proficiency [posterior-1: F(1,
15) = 6.17, p= .025; posterior-2: F(1, 15) = 6.90, p= .019].
In summary, for this later time window, at low pro-
ficiency the implicit group showed an ongoing N400
(Figure 4C), whereas the explicit group displayed no ef-
fects (Figure 4A). At high proficiency, the implicit group
showed both a P600 and a late AN (Figure 4D), whereas
the explicit group showed only a P600 (Figure 4C).
DISCUSSION
We used an artificial language paradigm to examine lon-
gitudinally, at both low and high proficiency, whether
explicit or implicit training conditions differentially affect
neural (ERP) and behavioral (performance) measures of
L2 syntactic processing. Although explicitly and implicitly
trained participants showed statistically indistinguishable
performance at both low and high proficiency, real-time
electrophysiological measures revealed striking differ-
ences between the groupsʼneural activity. Most impor-
tantly, only the implicit training condition showed the
full spectrum of ERP components typically found for L1
syntactic processing.
The finding that performance did not differ between
the two training groups at either low or high proficiency
indicates that the ERP differences cannot be explained by
performance differences. It also suggests that, at least in
this paradigm, both training methods yield comparable
performance outcomes. Nevertheless, the group by test
session interaction on performance indicates that implicit
training may be better than explicit training at realizing
gains toward the attainment of high proficiency. This find-
ing is more in line with the popular view that immersion
is superior to classroom training for reaching high profi-
ciency than are previous behavioral studies that have sug-
gested an advantage for explicit training (see Introduction).
The difference between these results and those of previous
studies may be because of the important methodological
differences between them.
The Type of Training Shapes the Neurocognition
of L2
The fact that the ERP patterns differed between the ex-
plicit and implicit training groups, although performance
between them did not, validates the use of neural mea-
sures and demonstrates that differential L2 training can
produce differences in brain processing that are not re-
flected by behavioral measures.
The specific ERP components found in the implicit
group in each of the two test sessions provide direct in-
sight into the neurocognitive processes that the implicitly
trained L2 learners relied on as they proceeded from low
to high proficiency. The N400 observed at low profi-
ciency supports the view that at early stages of L2 learning,
(morpho)syntactic processing relies, at least in part, on
lexical/semantic mechanisms and declarative memory
(Clahsen & Felser, 2006; Ullman, 2001, 2005) and shows
moreover that this reliance can result from implicit train-
ing. The finding that, at high proficiency, implicit learners
showed an ANP600 biphasic response, as well as a late AN,
provides evidence that implicit training can in fact lead to
L1-like brain processing for syntax: These ERP components
have repeatedly been found in L1, and no additional compo-
nents are generally elicited by native speakers in response
to (morpho)syntactic violations (Steinhauer & Connolly,
2008). More specifically, given the prevailing interpretation
of these components, the findings suggest that, with im-
plicit training, syntactic processing can come to rely on
the same biphasic mechanisms found in L1: rule-governed
automatic structure building, which may involve the en-
gagement of procedural memory, followed by controlled
structural reanalysis, accompanied by an increasing
942 Journal of Cognitive Neuroscience Volume 24, Number 4
demand on working memory. Alternatively, the two ANs
may reflect a single process, which may represent the
maintenance of unintegrated linguistic input in phonologi-
cal working memory (Steinhauer & Drury, in press). Finally,
the findings of an N400 at low proficiency, together with
the ANP600 pattern accompanied by a late AN at high pro-
ficiency, show that the implicitly trained group experi-
enced a qualitative shift in neurocognitive processing
while advancing from low to high proficiency.
The explicit group did not show this pattern. The lack
of any ERP effects at low proficiency suggests that explicit
training does not lead to a systematic and consistent reli-
ance of syntax on either lexical/semantic or L1-like gram-
matical processes at low proficiency nor does their syntax
rely on any other neurocognitive processes that would be
reflected in ERP components. One possible explanation for
this null effect is that explicit training led to increased varia-
bility (e.g., between participants) in the types of explicit
cognitive strategies and/or the timing of ERP components;
such variability could wash out any clear components in
the waveforms, leading to a lack of reliable statistical dif-
ferences. At high proficiency, the data for the explicitly
trained learners are somewhat more revealing. The pres-
ence of a P600 without a preceding AN suggests that,
although explicit training is sufficient to develop the abil-
ity for structural reanalysis that may be under conscious
(explicit) control, it does not reliably lead to the automatic
early syntactic processing that is found in L1 and may
depend on procedural memory. The interpretation of
the absence of a late AN is less clear. It may suggest that
any controlled reanalysis did not depend more on work-
ing memory in the violation than correct condition, pos-
sibly because both conditions required equal engagement
of working memory. Alternatively, if late ANs represent
the continuation of earlier ANs (Steinhauer & Drury, in
press), then the absence of both together is not surpris-
ing. The anterior positivity found at high proficiency for
the explicit group might in part reflect attentional mech-
anisms, which are thought to drive the early fronto-central
positivities that represent the P3a component (Polich,
2007). Indeed, the P3a has been reported in at least
one other ERP violation study of L2 grammar (Mueller,
Oberecker, & Friederici, 2009). Interestingly, studies of
L2 development show that explicit training conditions are
more effective than implicit training conditions in directing
learnersʼattention to L2 forms (Leow & Bowles, 2005; Sanz
& Morgan-Short, 2005). Thus, this positivity might reflect
a reliance on domain-general attentional mechanisms
rather than the syntactic or lexical/semantic processing that
is typical for native speakers and, apparently, for implicit
learners of an L2. This speculative interpretation may
warrant investigation in future studies.
The results reported here can be compared with those
reported by Morgan-Short et al. (2010), which examined
different types of violations in the exact same studythat
is, with the same participants, training, and practice. As men-
tioned above, the participants in the present study were
exposed not only to word order violations but also to
(nounarticle and nounadjective) gender agreement
violations, which are discussed in Morgan-Short et al.
(2010), as well as violations of verb argument structure
(although these have not yet been reported in a published
study, so are not presented here). The gender agreement
violations showed both similarities and differences to the
word order violations. As with the word order violations,
both the explicitly and implicitly trained groups improved
on their ability to judge (both types of ) gender agreement
violations between low and high proficiency but did not
differ in their performance at either proficiency level. For
ERPs, at low proficiency, the implicit group showed N400s
not only for word order violations but also for both types
of agreement violations. The explicit group, by contrast,
showed an N400 only for nounadjective violations but
not for either word order or nounarticle violations. At
high proficiency, however, word order and agreement vio-
lations evidenced quite different patterns. Unlike word
order violations, which yielded an AN and a late AN only
in the implicit group, agreement violations showed the
same effects in both training groups at high proficiency,
with P600s (no ANs) in both groups for nounarticle viola-
tions, and N400s in both groups for nounadjective viola-
tions. Overall the results from the word order and gender
agreement violations suggest the following. First, at low-
proficiency implicit, but not explicit, training seems to reli-
ably lead to N400s and a reliance of grammar on lexical/
semantic processing and declarative memory. Second, at
high proficiency, both the type of linguistic structure and
the type of training appear to influence the nature of pro-
cessing, because for agreement violations, the nature of
the violation (and not the type of training) determined
ERP outcomes, whereas for word order violations, the type
of training was critical. Finally, note that the absence of ANs
for gender agreement, as well as the presence of P600s
for nounarticle agreement and even of N400s for noun
adjective agreement, are broadly consistent with previous
studies of gender agreement violations in L1 (Morgan-
Short et al., 2010). Thus together, the results of the agree-
ment and word order violations suggest that L1-like brain
processing of (morpho)syntax can be achieved by L2
learners and that this achievement depends in some cases
(word order) but not others (gender agreement) on the
type of training.
It is more difficult to compare the findings of the pres-
ent study with other neurocognitive research, because
previous studies have not specifically controlled for or
contrasted explicit and implicit training and do not consis-
tently report the proportion or amounts of such training.
Nevertheless, some studies can provide certain insights.
First, although the finding of N400s for morphosyntactic
processing after a small amount of classroom training
(4 weeks) in French (Osterhout et al., 2008) further supports
the dependence of (morpho)syntax on lexical/semantic
processing and declarative memory at low proficiency, it also
supports the view that explicit training (classroom-based)
Morgan-Short et al. 943
may, in some cases, lead to this outcome. Second, the P600
that was elicited by the high-proficiency explicit learners
is consistent with P600s found after somewhat greater
amounts of classroom training (48 months) in French
(Osterhout et al., 2008). Third, the biphasic ANP600 pat-
tern observed here for implicitly trained learners at high
proficiency is compatible with the results from a study of
word order violations in Brocanto, in which participants
were trained largely (although not completely) implic-
itly, also yielding an ANP600 pattern at high proficiency
(Friederici et al., 2002); low proficiency was not examined.
Additionally, all three studies that reported ANP600
biphasic responses for (morpho)syntactic violations in high-
proficiency late learners of natural languages (see Introduc-
tion) found this response in participants who were living in
an immersion environment (Gillon Dowens et al., 2009;
Steinhauer et al., 2009; Hahne et al., 2006). Finally, the ob-
served shift from an N400 at low proficiency to an ANP600
at high proficiency seems consistent with an fMRI study
reporting an analogous neurocognitive shift (Opitz &
Friederici, 2003). In this study, participants were trained
with visually presented Brocanto sentences (with no asso-
ciated meanings). They showed initial activation in declara-
tive memory structures, which decreased over the course of
training, whereas activation in procedural memory struc-
tures increased. However, like all previous neurocognitive
studies, explicit and implicit inputs were not specifically
controlled for or contrasted, precluding conclusions about
the role of training in these outcomes. The data from the
present study suggest that future neurocognitive studies
of L2 learning should at least clearly report, if not control
for, both the amount and the type of L2 training.
Theoretical Implications
The results from the current study have implications for
neurocognitive theories of L2 and demonstrate that these
theories should take the type of L2 training into account.
The attainment of L1 ERP components by the implicit
group does not appear to be compatible with the view that
adult-learned L2 always relies on entirely different mech-
anisms than L1 and that it is necessary to learn language
during the critical periodto attain native-like brain pro-
cessing (Bley-Vroman, 1989). The N400 displayed by the
implicit group at low proficiency, by contrast, seems incon-
sistent with models hypothesizing that L2 depends on the
same neurocognitive mechanisms as adult L1 (Abutalebi,
2008; Indefrey, 2006). Rather, the data provide further
support for the neurocognitive perspective that L2 gram-
mar, at least in part, depends on lexical/semantic processes
and declarative memory at low proficiency but can come
to rely on native-like grammatical processes and proce-
dural memory at high proficiency (Steinhauer et al.,
2009; Clahsen & Felser, 2006; Ullman, 2001, 2005). Cru-
cially, the data also show that this outcome can interact
with the type of condition under which the language is
learned, with only implicit training leading to these L1
processes, in at least some cases. Thus the data suggest
a refinement of this neurocognitive theory, in that the
proceduralizationof grammar may, in some cases,
benefit from learning under implicit, immersion-like
conditions.
This result is consistent with learning in other cognitive
domains, such as probabilistic classification or rule learn-
ing: both the declarative and procedural memory brain
systems can learn probabilistic patterns and to similar levels
of performance, but only certain training conditions, in par-
ticular those in which explicit knowledge is minimized,
lead to a processing dependence on procedural memory
(Foerde et al., 2006). Also like the present study, such
probabilistic learning has been found to show an early de-
pendence on declarative memory and a later dependence
on procedural memory under learning conditions that pro-
mote learning in the latter system (Poldrack et al., 2001).
These parallel findings further strengthen the dependence
of language on declarative and procedural memory (Ullman,
2001, 2004, 2005) and suggest that investigations of lan-
guage can inform other domains that depend on these
memory systems and vice versa.
Future Directions
The study brings up a number of issues that warrant
examinationinfutureexperiments.First,inthisstudy
and in Morgan-Short et al. (2010), we focused on certain
syntactic structures that have been well studied in ERP re-
search. It remains to be seen whether the neurocognition
of other aspects of syntax or of morphology or phonology
are differentially affected by explicit and implicit training.
Second, whereas in the present study, the L2 (Brocanto2)
differed from the L1 (English) in crucial respects (word
order and gender agreement), future studies may reveal
whether grammatical (dis)similarity between L1 and L2
may interact with the type of training. For example, given
that some research suggests that L1/L2 structural similar-
ity can lead to more native-like ERP patterns in the L2
(Sabourin & Stowe, 2008; Tokowicz & MacWhinney,
2005), it is possible that the type of training might be less
important in such situations, because even explicitly
trained learners might achieve L1-like brain processing.
Third, further studies can tell us whether additional train-
ing or practice, beyond what was examined here, might
lead to different neural or behavioral outcomes. For ex-
ample, the bilaterally distributed AN in the implicit group
might become left-lateralized with further practice and
increased proficiency (indeed, the left lateralization of
the negativity in an earlier time window is consistent with
this possibility). Additionally, it remains possible that
explicit training might eventually lead to native-like neural
processingor not. Fourth, different kinds of explicit or
even implicit training might lead to different outcomes.
For example, perhaps other kinds of explicit training (e.g.,
with feedback containing metalinguistic explanations)
might lead to more native-like brain processing.
944 Journal of Cognitive Neuroscience Volume 24, Number 4
Fifth, future research may elucidate precisely which
aspects of explicit and implicit training led to the observed
results. For example, it is possible that the provision of
additional exemplars in the implicit training condition,
which were provided to match the amount of time dedi-
cated to explicit instruction in the explicit training group,
contributed to the more native-like ERP patterns in the
implicit group. However, both the explicitly and implicitly
trained learners were exposed to an additional 440 sen-
tences during comprehension practice, so in fact the total
number of examplars presented to the explicit group (473 =
33 examplars given to both training groups + 440 compre-
hension practice items) was only 17% lower than the total
number presented to the implicit group (569 = 33 exam-
plars given to both training groups + 96 examplars given
only to the implicit group + 440 comprehension practice
items). Therefore, the difference in training examplars be-
tween the two groups does not seem to be a likely expla-
nation for the observed effects. Alternatively, the provision
of explicit information may actually impede the develop-
ment of native-like processing. Indeed, an analogous effect
has been observed in other cognitive domains, such as se-
quence learning, in which an explicit training condition
actually seems to suppress implicit (procedural) learning
(Fletcher et al., 2005). The examination of this phenom-
enon in language seems warranted.
Sixth, the current study was limited to examining the
effects of explicit and implicit training conditions on L2
performance and processing. It does not speak to
whether learners engaged in explicit or implicit learning
processes or if they acquired explicit or implicit knowl-
edge. Seventh, it remains to be seen whether the results
obtained here generalize to natural languages. The pres-
ent study was designed to maximize this likelihood, be-
cause Brocanto2 follows language universals, is presented
auditorily, is actually spoken and comprehended, is learned
to high levels of proficiency, and shows natural-language-
like brain patterns. However, further studies are needed
to test its generalizability. Thus, like other simplified mod-
els of complex systems in science (e.g., animal models of
human phenomena), using an artificial language allows
us to rapidly and reliably (avoiding confounds) identify
the factors or mechanisms of interest, after which one
can focus on directly testing these already-identified factors
and mechanisms in the slower and more difficult examina-
tion of the full complex system of interest, in this case
natural language.
Conclusion
In summary, in this study learning under an implicit input
condition designed to approximate immersion led to the
full spectrum of native-like brain patterns for aspects of
language processing (ANP600 biphasic pattern, accom-
panied by a late AN), whereas learning under an explicit
input condition designed to approximate traditional class-
room settings did not (P600 only). Thus, the study suggests
that, at least in certain cases, the attainment of L1 neuro-
cognitive mechanisms in second language acquisition
appears to depend not only on the level of proficiency but
also on the conditions under which the L2 was learned.
APPENDIX A
Example Section from Explicit Training Condition
The example section just below provides metalinguistic
information and meaningful examples related to Brocanto2
word order for subjects, objects, and verbs. Note that the
text shown below was presented aurally. During the aural
presentation of examples, corresponding game constella-
tions, which are shown here in bold, were presented
visually on the computer screen.
In Brocanto2, both the subject and the object are
placed before the verb. The subject occurs first and
the object occurs second. Thus, the word order for
Brocanto2 sentences is subjectobjectverb. Now listen
to a few examples.
pleck li vode lu praz
In this example, we first state the subject, pleck li. This
noun is doing the action. Second, we state the object,
vode lu. This noun is receiving the action. Finally, at
the end of the sentence you find the verb, praz. Listen
to the example again.
pleck li vode lu praz
Hereʼs another example:
pleck li blom lu nim
In this example, pleck li is the subject and comes at the
beginning of the sentence. Blom lu is the object and is
placed after the subject. Nim is the verb and is found
after the object. Here are more examples for you to lis-
ten to:
vode lu neep li praz
neep li pleck li yab
blom lu pleck li praz
APPENDIX B
Example Section from Implicit Training Condition
The example section just below provides meaningful ex-
amples related to aspects of Brocanto2 word order. All
examples were aurally presented together with visually
presented corresponding game constellations. Note that
“…” indicates that additional examples were provided.
pleck li vode lu praz
vode lu neep li praz
blom lu pleck li praz
neep li blom lu praz
Morgan-Short et al. 945
vode lu nim
vode lu neep li nim
pleck neime li nim
pleck li blom lu nim
neep li yab
blom lu pleck li yab
blom lu pleck li yab
blom lu yab
Acknowledgments
Support for this project was provided to K. M. S. by a George-
town University Dissertation fellowship, the NIMH under NRSA
F31 MH67407, and the NSF under Doctoral Dissertation Improve-
ment grant 0446836 (formally to M. T. U.); to K. S. by the Canada
Research Chair program and the Canada Foundation for Innova-
tion (project 201876) and by NSERC, Canada (RGPGP 312835);
and to M. T. U. by the NIH under RO1 MH58189 and RO1
HD049347. We thank Angela Friederici for providing materials
and source code for the original version of Brocanto, William Garr
for programming support, and Harriet Wood Bowden for various
contributions.
Author Note: Overall, K. M. S. and K. S. contributed equally to
this study and should both be considered first authors. K. M. S.,
K. S., C. S., and M. T. U. all contributed to the design of the
experiment and the interpretation of the data. K. M. S. devel-
oped the materials together with K. S., and conducted the ex-
periment and analyzed the data. K. S. and M. T. U. supervised
the data analysis. K. M. S. and M. T. U. wrote the manuscript,
which was extensively reviewed by K. S. and C. S.
Reprint requests should be sent to Kara Morgan-Short, Department
of Hispanic and Italian Studies and Department of Psychology,
University of Illinois at Chicago, 1706 University Hall, MC-315,
601 S. Morgan St., Chicago, IL 60607, or via e-mail: karams@uic.
edu or Michael T. Ullman, Department of Neuroscience, George-
town University, Box 571464, Washington, DC 20057-1464, or via
e-mail: michael@georgetown.edu.
REFERENCES
Abutalebi, J. (2008). Neural aspects of second language
representation and language control. Acta Psychologica,
128, 466478.
Bley-Vroman, R. (1989). What is the logical problem of foreign
language learning? In S. M. Gass & J. Schacter (Eds.),
Linguistic perspectives on second language acquisition
(pp. 4168). Cambridge, MA: Cambridge University Press.
Bley-Vroman, R. (1990). The logical problem of foreign
language learning. Linguistic Analysis, 20, 349.
Carlson, R. (1994). A grammar of Supyire. Mouton grammar
library 14. Berlin: Mouton de Gruyter.
Chen, L., Shu, H., Liu, Y., Zhao, J., & Li, P. (2007). ERP
signatures of subjectverb agreement in L2 learning.
Bilingualism: Language and Cognition, 10, 161174.
Clahsen, H., & Felser, C. (2006). Grammatical processing in
language learners. Applied Psycholinguistics, 27, 342.
Ellis, N. C. (2005). At the interface: Dynamic interactions of
explicit and implicit language knowledge. Studies in Second
Language Acquisition, 27, 305352.
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, UK:
Multilingual Matters.
Fletcher, P. C., Zafiris, O., Frith, C. D., Honey, R. A. E., Corlett,
P. R., Zilles, K., et al. (2005). On the benefits of not trying:
Brain activity and connectivity reflecting the interactions
of explicit and implicit sequence learning. Cerebral Cortex,
15, 10021015.
Foerde, K., Knowlton, B. J., & Poldrack, R. A. (2006).
Modulation of competing memory systems by distraction.
Proceedings of the National Academy of Sciences, U.S.A.,
103, 1177811783.
Frenck-Mestre, C., Osterhout, L., McLaughlin, J., & Foucart, A.
(2008). The effect of phonological realization of inflectional
morphology on verbal agreement in French: Evidence
from ERPs. Acta Psychologica, 128, 528536.
Friederici, A. D., & Kotz, S. A. (2003). The brain basis of
syntactic processes: Functional imaging and lesion studies.
Neuroimage, 20(Suppl. 1), S8S17.
Friederici, A. D., Pfeifer, E., & Hahne, A. (1993). Event-related
brain potentials during natural speech processing: Effects
of semantic, morphological and syntactic violations.
Cognitive Brain Research, 1, 183192.
Friederici, A. D., Steinhauer, K., & Pfeifer, E. (2002).
Brain signatures of artificial language processing:
Evidence challenging the critical period hypothesis.
Proceedings of the National Academy of Sciences, U.S.A.,
99, 529534.
Gillon Dowens, M., Vergara, M., Barber, H., & Carreiras, M.
(2009). Morphosyntactic processing in late second-language
learners. Journal of Cognitive Neuroscience, 22,
18701887.
Hagoort, P., & Brown, C. M. (1999). Gender electrified: ERP
evidence on the syntactic nature of gender processing.
Journal of Psycholinguist Research, 28, 715728.
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, 194205.
Hahne, A., & Friederici, A. D. (2001). Processing a second
language: Late learnersʼcomprehension mechanisms as
revealed by event-related brain potentials. Bilingualism:
Language and Cognition, 4, 123141.
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.
Journal of Cognitive Neuroscience, 18, 121134.
Hancock, R., & Bever, T. G. (2009). The study of syntactic cycles
as an experimental science. In E. van Gelderen (Ed.), Cyclical
change (pp. 303319). Amsterdam: John Benjamins
Publishing Company.
Hasting, A. S., & Kotz, S. A. (2008). Speeding up syntax: On
the relative timing and automaticity of local phrase structure
and morphosyntactic processing as reflected in event-related
brain potentials. Journal of Cognitive Neuroscience, 20,
12071219.
Holcomb, P. J., & Neville, H. J. (1991). Natural speech
processing: An analysis using event-related brain potentials.
Psychobiology, 19, 286300.
Indefrey, P. (2006). A meta-analysis of hemodynamic studies
on first and second language processing: Which suggested
differences can we trust and what do they mean?
Language Learning, 56(Suppl. 1), 279304.
Isel, F. (2007). Syntactic and referential processes in second-
language learners: Event-related brain potential evidence.
NeuroReport, 18, 18851889.
Kaan, E. (2007). Event-related potentials and language
processing: A brief overview. Language and Linguistics
Compass, 1, 571591.
946 Journal of Cognitive Neuroscience Volume 24, Number 4
Kaan, E., Harris, A., Gibson, E., & Holcomb, P. (2000). The P600
as an index of syntactic integration difficulty. Language and
Cognitive Processes, 15, 159201.
Kotz, S. A. (2009). A critical review of ERP and fMRI evidence on
L2 syntactic processing. Brain and Language, 109, 6874.
Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences:
Brain potentials reflect semantic incongruity. Science, 207,
203205.
Lau, E. F., Phillips, C., & Poeppel, D. (2008). A cortical network
for semantics: (De)constructing the N400. Nature Reviews
Neuroscience, 9, 920933.
Lenneberg, E. H. (1967). Biological foundations of language.
New York: Wiley.
Leow, R. P., & Bowles, M. A. (2005). Attention and awareness
in SLA. In C. Sanz (Ed.), Mind and context in adult second
language acquisition: Methods, theory, and practice
(pp. 179203). Washington, DC: Georgetown University Press.
Lyster, R., & Mori, H. (2006). Interactional feedback and
instructional counterbalance. Studies in Second Language
Acquisition, 28, 269300.
Lyster, R., & Ranta, L. (1997). Corrective feedback and learner
uptake: Negotiation of form in communicative classrooms.
Studies in Second Language Acquisition, 19, 3766.
Macmillan, N. A., & Creelman, C. D. (2005). Detection theory:
A userʼs guide (2nd ed.). Mahwah, NJ: Lawrence Earlbaum
Associates, Inc.
Martin-Loeches, M., Munoz, F., Casado, P., Melcon, A., &
Fernandez-Frias, C. (2005). Are the anterior negativities
to grammatical violations indexing working memory?
Psychophysiology, 42, 508519.
McLaughlin, J., Osterhout, L., & Kim, A. (2004). Neural
correlates of second-language word learning: Minimal
instruction produces rapid change. Nature Neuroscience,
7, 703704.
Morgan-Short, K. (2007). A neurolinguistic investigation of
late-learned second language knowledge: The effects of
explicit and implicit conditions. Unpublished dissertation,
Georgetown University, Washington, DC.
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, 154193.
Mueller, J. L., Oberecker, R., & Friederici, A. D. (2009). Syntactic
learning by mere exposure: An ERP study in adult learners.
BMC Neuroscience, 10, 89.
Neville, H. J., Nicol, J. L., Barss, A., Forster, K. I., & Garrett, M. F.
(1991). Syntactically based sentence processing classes:
Evidence from event-related brain potentials. Journal of
Cognitive Neuroscience, 3, 151165.
Newport, E. L. (1993). Maturational constraints on language
learning. In P. Bloom (Ed.), Language acquisition
(pp. 543560). Cambridge, MA: MIT Press.
Norris, J. M., & Ortega, L. (2000). Effectiveness of L2 instruction:
A research synthesis and quantitative meta-analysis.
Language Learning, 50, 417528.
Ojima, S., Nakata, H., & Kakigi, R. (2005). An ERP study on second
language learning after childhood: Effects of proficiency.
Journal of Cognitive Neuroscience, 17, 12121228.
Opitz, B., & Friederici, A. D. (2003). Interactions of the
hippocampal system and the prefrontal cortex in learning
language-like rules. Neuroimage, 19, 17301737.
Osterhout, L., & Holcomb, P. J. (1992). Event-related brain
potentials elicited by syntactic anomaly. Journal of Memory
and Language, 31, 785806.
Osterhout, L., Poliakov, A., Inoue, K., McLaughlin, J., Valentine, G.,
Pitkanen, I., et al. (2008). Second-language learning and
changes in the brain. Journal of Neurolinguistics, 21,
509521.
Pakulak, E., & Neville, H. J. (2010). Proficiency differences in
syntactic processing of monolingual native speakers indexed
by event-related potentials. Journal of Cognitive
Neuroscience, 22, 27282744.
Poldrack, R., Clark, J., Pare-Blagoev, E. J., Shohamy, D., Creso
Moyano, J., Meyers, C., et al. (2001). Interactive memory
systems in the human brain. Nature, 414, 546550.
Polich, J. (2007). Updating P300: An integrative theory of P3a
and P3b. Clinical Neurophysiology, 118, 21282148.
Rosa, E. M., & Leow, R. P. (2004). Computerized task-based
exposure, explicitness, type of feedback, and Spanish L2
development. Modern Language Journal, 88, 192216.
Sabourin, L., & Stowe, L. A. (2008). Second language
processing: When are first and second languages processed
similarly? Second Language Research, 24, 397430.
Sanz, C., & Morgan-Short, K. (2004). Positive evidence versus
explicit rule presentation and explicit negative feedback:
A computer-assisted study. Language Learning, 54, 3578.
Sanz, C., & Morgan-Short, K. (2005). Explicitness in pedagogical
interventions: Input, practice, and feedback. In C. Sanz (Ed.),
Mind and context in adult second language acquisition:
Methods, theory, and practice (pp. 234263). Washington,
DC: Georgetown University Press.
Steinhauer, K., Alter, K., & Friederici, A. D. (1999). Brain
potentials indicate immediate use of prosodic cues in
natural speech processing. Nature Neuroscience, 2,
191196.
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
(pp. 91103). Amsterdam: Elsevier.
Steinhauer, K., & Drury, J. E. (in press). On the early left-anterior
negativity (ELAN) in syntax studies. Brain and Language.
Steinhauer, K., White, E. J., & Drury, J. E. (2009). Temporal
dynamics of late second language acquisition: Evidence from
event-related brain potentials. Second Language Research,
25, 1341.
Tokowicz, N., & MacWhinney, B. (2005). Implicit and explicit
measures of sensitivity to violations in second language
grammar: An event-related potential investigation. Studies
in Second Language Acquisition, 27, 173204.
Ullman, M. T. (2001). The neural basis of lexicon and grammar
in first and second language: The declarative/procedural
model. Bilingualism: Language and Cognition, 4, 105122.
Ullman, M. T. (2004). Contributions of memory circuits to
language: The declarative/procedural model. Cognition,
92, 231270.
Ullman, M. T. (2005). A cognitive neuroscience perspective
on second language acquisition: The declarative/procedural
model. In C. Sanz (Ed.), Mind and context in adult second
language acquisition: Methods, theory and practice
(pp. 141178). Washington, DC: Georgetown University Press.
van den Brink, D., & Hagoort, P. (2004). The influence of
semantic and syntactic context constraints on lexical
selection and integration in spoken-word comprehension as
revealed by ERPs. Journal of Cognitive Neuroscience, 16,
10681084.
VanPatten, B., & Oikkenon, S. (1996). Explanation versus
structured input in processing instruction. Studies in
Second Language Acquisition, 18, 495510.
Weber-Fox, C. M., & Neville, H. J. (1996). Maturational
constraints on functional specializations for language
processing: ERP and behavioral evidence in bilingual
speakers. Journal of Cognitive Neuroscience, 8, 231256.
Morgan-Short et al. 947
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... Specifically, over time ( and with increasing proficiency in L2, concepts and meanings of L2 items can also be accessed independently of L1 through the gradual emergence of direct, fast links between L2 and the conceptual store (Kroll & Tokowicz, 2005). Such conceptually-mediated acquisition of lexical items in L2 may be fostered by experiencing an immersion situation, where the learner is surrounded by L2 speakers and often acquires new words implicitly from the immediate context (Ellis, 1994;Morgan-Short et al., 2011), e.g., during extended stays abroad. This does not mean that the direct lexical links from L2 to L1 disappear and word-to-word-translation is abandoned. ...
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Speakers of multiple languages must store the respective lexical items efficiently to enable correct access. Importantly, all items must be linked to semantic information and world knowledge. One prominent model of the mental lexicon of late bilinguals is the Revised Hierarchical Model ( Kroll & Stewart, 1994 ), which postulates bidirectional but asymmetrical connections between separate stores for L1 (native language) and L2 (second/foreign language), and a shared conceptual store. Using German native speakers with advanced English proficiency, Experiment 1 largely confirmed model predictions regarding different preferred mental routes and processing times depending on translation direction. Moreover, the original design was extended by including abstract stimuli and picture naming in L2. A series of additional measures, such as proficiency and age of acquisition, served to specify the language experience of the participants and made it possible to compare the results with a group of non-native speakers of German (Experiment 2). Interestingly, the results suggest that the model also applies to two or more non-native languages, potentially influenced by the experimental and environmental language context.
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A central question in second language (L2) acquisition is whether L2 processing can become nativelike. The Shallow Structure Hypothesis (SSH) posits morphological processing in L2 is qualitatively different from that of L1, whereas the Declarative/Procedural (D/P) Model suggests naturalistic exposure (NE) to L2 can lead to nativelike processing. This study investigated whether L2 morphological processing can become nativelike by examining L1-Persian learners of L2-English with classroom exposure (CE) and those with NE. Using a cross-modal priming task, we presented trimorphemic English words under three different priming conditions: constituent (rewash → rewashable), nonconstituent (washable → rewashable), and unrelated (notify → rewashable). The NE group exhibited stronger priming in the constituent than in the nonconstituent condition, while the CE group showed equal priming in both conditions. The CE group’s performance aligns with the SSH, whereas the NE group demonstrated nativelike processing, supporting the D/P Model and highlighting the importance of NE.
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There is evidence that learning a second language (L2) can shift cognition toward that predicted for the L2 and that this effect might vary with L2 proficiency, age of acquisition, length of immersion, etc. Here we explore the previously neglected variable of language instructional conditions. Participants categorized motion events in a triads-matching task after being trained on two novel linguistic labels highlighting (in)transitivity through one of three instructional conditions. Participants who learned the relevant knowledge under a meaning-focused instructional condition (memorizing meanings of exemplar sentences) showed a higher likelihood of categorizing based on motion (in)transitivity immediately after training than a control group; those who learned under a required rule search instructional condition showed this effect only after additional practice; while those who learned through another type of form-focused instructional condition (direct metalinguistic explanation) did not show this effect even after such practice. These differences were obtained despite the fact that the three groups were matched on awareness of the target system at the level of understanding and near-perfect performance on a grammaticality judgment task. The findings are discussed in terms of the depth of processing in instructed SLA and models of language–cognition interactions.
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Mastering prosody is a different task for adults learning a second language and infants acquiring their first. While prosody crucially aids the process of L1 acquisition, for adult L2 learners it is often considerably challenging. Is it because of an age-related decline of the language-learning ability or because of unfavourable learning conditions? We investigated whether adults can auditorily sensitise to the prosody of a novel language, and whether such sensitisation is affected by orthographic input. After 5 minutes of exposure to Māori, Czech listeners could reliably recognize this language in a post-test using low-pass filtered clips of Māori and Malay. Recognition accuracy was lower for participants exposed to the novel-language speech along with deep-orthography transcriptions or orthography with unfamiliar characters. Adults can thus attune to novel-language prosody, but orthography hampers this ability. Language-learning theories and applications may need to reconsider the consequences of providing orthographic input to beginning second-language learners.
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The implicit/explicit distinction is central to our understanding of the nature of L2 acquisition. This book begins with an account of how this distinction applies to L2 learning, knowledge and instruction. It then reports a series of studies describing the development of a battery of tests providing relatively discrete measurements of L2 explicit/implicit knowledge. These tests were then utilized to examine a number of key issues in SLA - the learning difficulty of different grammatical structures, the role of L2 implicit/explicit knowledge in language proficiency, the relationship between learning experiences and learners language knowledge profiles, the metalinguistic knowledge of teacher trainees and the effects of different types of form-focused instruction on L2 acquisition. The book concludes with a consideration of how the tests can be further developed and applied in the study of L2 acquisition. © 2009 Rod Ellis, Shawn Loewen, Catherine Elder, Rosemary Erlam, Jenefer Philp and Hayo Reinders. All rights reserved.
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This chapter has presented a concise overview of the theoretical and methodological issues surrounding the roles of attention and awareness in adult L2 behavior and learning and provided a brief report of empirical studies that have employed verbal reports to investigate their roles in L2 development in the L2 classroom. The overall findings appear to indicate facilitative effects of attention and awareness on adult L2 learners' subsequent processing, intake, and learning of targeted L2 forms or structures embedded in the L2 data, providing empirical support for Schmidt's noticing hypothesis and the facilitative role of awareness in SLA. In addition, this chapter has stressed that the role awareness plays in adult L2 development needs to be investigated further, although researchers should be aware of the methodological concerns inherent in both the operationalization and measurement of this slippery construct. While current research findings are indeed promising, more robust research designs are clearly needed to address the issue of L2 development premised on the role of attention or awareness, given the wide variety of variables that can potentially impact learners' processes while interacting with L2 data. The findings can only improve our understanding of the processes involved in language learning. © 2005 by The Georgetown University Press. All rights reserved.
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Linguistic Cycles are ever present in language change and involve a phrase or word that gradually disappears and is replaced by a new linguistic item. The most well-known cycles involve negatives, where an initial single negative, such as not, is reinforced by another negative, such as no thing , and subjects, where full pronouns are reanalyzed as endings on the verb. This book presents new data and insights on the well-known cyclical changes as well as on less well-known ones, such as the preposition, auxiliary, copula, modal, and complementation cycles. Part I covers the negative cycle with chapters looking in great detail at the steps that are typical in this cycle. Part II focuses on pronouns, auxiliaries, and the left periphery. Part III includes work on modals, prepositions, and complementation. The book ends with a psycholinguistic chapter. This book brings together linguists from a variety of theoretical frameworks and contributes to new directions in work on language change.
Book
The coming of language occurs at about the same age in every healthy child throughout the world, strongly supporting the concept that genetically determined processes of maturation, rather than environmental influences, underlie capacity for speech and verbal understanding. Dr. Lenneberg points out the implications of this concept for the therapeutic and educational approach to children with hearing or speech deficits.
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The DP perspective constitutes a novel alternative to previously proposed explanatory hypotheses of SLA. It leads to an array of specific predictions that are largely generated by our independent knowledge of the two memory systems and are directly testable using a range of widely used behavioral and neurocognitive methods. The predictions allow the model to be directly compared against alternative accounts and provide the means for it to be both falsified and further specified. Thus the DP model may provide a useful paradigm for the study of SLA. © 2005 by The Georgetown University Press. All rights reserved.
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In two experiments, event-related brain potentials were collected as subjects listened to spoken sentences. In the first, all words were presented as connected (natural) speech. In the second, there was a 750-msec interstimulus interval (ISI) separating each of the words. Three types of senten-ending words were used: best completions (contextually meaningful), unrelated anomalies (contextually meaningless), and related anomalies (contextually meaningless but related to the best completion). In both experiments, large N400s were found for the unrelated and related anomalies, relative to those found for the best-completion final words, although the effect was earlier and more prolonged for unrelated anomalies. The auditory N400 effect onset earlier in the natural-speech experiment than it did in either the 750-msec ISI experiment or previous visual studies.
Chapter
This chapter has attempted to shed some light on a key issue in language acquisition. While children acquire one, two, or even more languages through mere exposure to them, SLA research shows that the L2 competence of adult learners seems to benefit from metalinguistic information, that is, from being given information regarding how the language works. Exposure to explicit evidence seems to speed up the process of language acquisition and to further the level of ultimate attainment. The overwhelming majority of experimental studies comparing groups under implicit and explicit conditions show an advantage for the latter. However, there are several reasons for which caution is necessary when drawing conclusions from studies on the effects of explicit instruction. First, considering that many studies offered only short treatments and assessments tapping explicit knowledge (Norris and Ortega, 2000), it is probable that implicit groups were at a disadvantage. Moreover, studies showing positive effects are generally limited to specific aspects of the language-mostly syntax and some morphology- And to specific forms and rules. It has been posited that explicit instruction might affect the acquisition of simple rules but that its effects might not be as evident for more complex forms (de Graaff, 1997; DeKeyser, 1995; Robinson, 1996). A continuum leading from simple to complex structures and forms based on numerous interrelated criteria would no doubt make this distinction more precise. Finally, the power of rule presentation and explicit feedback is moderated by the presence or absence of practice and by the type of practice, that is, whether it is task-essential. A distinction needs to be made in future research between treatments that compare the effects of explicit information in combination with exposure to target forms or practice that is not task-essential and those that compare the effects of explicit information in combination with task-essential practice. Results from VanPatten and Oikkenon (1996), Benati (2004), and Wong (2004) show that learners change their processing strategies when practice decoding structured input requires it, but not when they are only told how to decode the input. Similarly, explicit information, when provided before practice, during practice, or both, did not lead to beneficial effects (Sanz and Morgan-Short, 2004). Therefore, further research should isolate and investigate practice, an integral part of pedagogical design almost ignored until recently. Swain's (1995, 1998) work on output practice, along with research on input-decoding practice carried out by VanPatten (1994, 2002, 2004) and VanPatten and Oikkenon (1996), and Rosa and Leow's (2004a, 2004b) work on task-essential practice, are fruitful paths to follow. Further research is also necessary to understand the underlying mechanisms responsible for making pedagogical interventions more efficient. It might be that they help allocate attention (recall the focus-on-form literature), narrow the number of hypotheses to be formulated by the learner (Swain, 1995, 1998), tune weights in neural networks (McDonald, 1989), or consolidate memory traces (MacWhinney, 1997). The implicit-explicit distinction is too limited a dichotomy; this chapter has argued for a continuum along which to classify input, practice, processes, and knowledge. A change in constructs and definitions will necessarily be correlated with much-needed improvements in the design of treatments and evaluating tools, some of which were suggested a decade ago (Hulstijn and de Graaff, 1994). © 2005 by The Georgetown University Press. All rights reserved.