Discussion 1: Language Acquisition

Neuron

Review

Brain Mechanisms in Early Language Acquisition

Patricia K. Kuhl1,* 1Institute for Learning & Brain Sciences, University of Washington, Seattle, WA 98195, USA *Correspondence: [email protected] DOI 10.1016/j.neuron.2010.08.038

The last decade has produced an explosion in neuroscience research examining young children’s early pro- cessing of language. Noninvasive, safe functional brain measurements have now been proven feasible for use with children starting at birth. The phonetic level of language is especially accessible to experimental studies that document the innate state and the effect of learning on the brain. The neural signatures of learning at the phonetic level can be documented at a remarkably early point in development. Continuity in linguistic development from infants’ earliest brain responses to phonetic stimuli is reflected in their language and prereading abilities in the second, third, and fifth year of life, a finding with theoretical and clinical impact. There is evidence that early mastery of the phonetic units of language requires learning in a social context. Neuroscience on early language learning is beginning to reveal the multiple brain systems that underlie the human language faculty.

Introduction Neural and behavioral research studies show that exposure to

language in the first year of life influences the brain’s neural

circuitry even before infants speak their first words. What do

we know of the neural architecture underlying infants’ remark-

able capacity for language and the role of experience in shaping

that neural circuitry?

The goal of the review is to explore this topic, focusing on the

data and arguments about infants’ neural responses to the

consonants and vowels that make up words. Infants’ responses

to these basic building blocks of speech—the phonemes used

in the world’s languages—provide an experimentally tractable

window on the roles of nature and nurture in language acquisi-

tion. Comparative studies at the phonetic level have allowed us

to examine the uniqueness of humans’ language processing

abilities. Moreover, infants’ responses to native and nonnative

phonemes have documented the effects of experience as infants

are bathed in a specific language. We are also beginning

to discover how exposure to two languages early in infancy

produces a bilingual brain. We focus here on when and how

infants master the sound structure of their language(s), and the

role of experience in explaining this important developmental

change. As the data attest, infants’ neural commitment to the

elementary units of language begins early, and the review show-

cases the extent to which the tools of modern neuroscience

are advancing our understanding of infants’ uniquely human

capacity for language.

Humans’ capacity for speech and language provoked classic

debates on nature versus nurture by strong proponents of

nativism (Chomsky, 1959) and learning (Skinner, 1957). While

we are far beyond these debates and informed by a great deal

of data about infants, their innate predispositions, and their

incredible abilities to learn once exposed to natural language

(Kuhl, 2009; Saffran et al., 2006), we are still just breaking ground

with regard to the neural mechanisms that underlie language

development (see Friederici and Wartenburger, 2010; Kuhl and

Rivera-Gaxiola, 2008). This decade may represent the dawn of

a golden age with regard to the developmental neuroscience

of language in humans.

Windows to the Young Brain The last decade has produced rapid advances in noninvasive

techniques that examine language processing in young chil-

dren (Figure 1). They include Electroencephalography (EEG)/

Event-related Potentials (ERPs), Magnetoencephalography

(MEG), functional Magnetic Resonance Imaging (fMRI), and

Near-Infrared Spectroscopy (NIRS).

Event-related Potentials (ERPs) have been widely used to

study speech and language processing in infants and young

children (for reviews, see Conboy et al., 2008a; Friederici, 2005;

Kuhl, 2004). ERPs, a part of the EEG, reflect electrical activity

that is time-locked to the presentation of a specific sensory stim-

ulus (for example, syllables or words) or a cognitive process

(recognition of a semantic violation within a sentence or phrase).

By placing sensors on a child’s scalp, the activity of neural net-

works firing in a coordinated and synchronous fashion in open

field configurations can be measured, and voltage changes

occurring as a function of cortical neural activity can be

detected. ERPs provide precise time resolution (milliseconds),

making them well suited for studying the high-speed and tempo-

rally ordered structure of human speech. ERP experiments can

also be carried out in populations who cannot provide overt

responses because of age or cognitive impairment. Spatial reso-

lution of the source of brain activation is, however, limited.

Magnetoencephalography (MEG) is another brain imaging

technique that tracks activity in the brain with exquisite temporal

resolution. The SQUID (superconducting quantum interference

device) sensors located within the MEG helmet measure the

minute magnetic fields associated with electrical currents that

are produced by the brain when it is performing sensory, motor,

or cognitive tasks. MEG allows precise localization of the neural

currents responsible for the sources of the magnetic fields.

Cheour et al. (2004) and Imada et al. (2006) used new head-

tracking methods and MEG to show phonetic discrimination in

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 713

Figure 1. Four Techniques Now Used Extensively with Infants and Young Children to Examine Their Responses to Linguistic Signals (From Kuhl and Rivera-Gaxiola, 2008).

Neuron

Review

newborns and infants in the first year of life. Sophisticated head-

tracking software and hardware enables investigators to correct

for infants’ head movements, and allows the examination of

multiple brain areas as infants listen to speech (Imada et al.,

2006). MEG (as well as EEG) techniques are completely safe

and noiseless.

Magnetic resonance imaging (MRI) can be combined with

MEG and/or EEG, providing static structural/anatomical pictures

of the brain. Structural MRIs show anatomical differences in

brain regions across the lifespan, and have recently been used

to predict second-language phonetic learning in adults (Goles-

tani and Pallier, 2007). Structural MRI measures in young infants

identify the size of various brain structures and these measures

have been shown to be related to language abilities later in child-

hood (Ortiz-Mantilla et al., 2010). When structural MRI images

are superimposed on the physiological activity detected by

MEG or EEG, the spatial localization of brain activities recorded

by these methods can be improved.

714 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

Functional magnetic resonance imaging (fMRI) is a popular

method of neuroimaging in adults because it provides high

spatial-resolution maps of neural activity across the entire brain

(e.g., Gernsbacher and Kaschak, 2003). Unlike EEG and MEG,

fMRI does not directly detect neural activity, but rather the

changes in blood-oxygenation that occur in response to neural

activation. Neural events happen in milliseconds; however, the

blood-oxygenation changes that they induce are spread out

over several seconds, thereby severely limiting fMRI’s temporal

resolution. Few studies have attempted fMRI with infants

because the technique requires infants to be perfectly still, and

because the MRI device produces loud sounds making it neces-

sary to shield infants’ ears. fMRI studies allow precise localiza-

tion of brain activity and a few pioneering studies show remark-

able similarity in the structures responsive to language in infants

and adults (Dehaene-Lambertz et al., 2002, 2006).

Near-Infrared Spectroscopy (NIRS) also measures cerebral

hemodynamic responses in relation to neural activity, but utilizes

Neuron

Review

the absorption of light, which is sensitive to the concentration of

hemoglobin, to measure activation (Aslin and Mehler, 2005).

NIRS measures changes in blood oxy- and deoxy-hemoglobin

concentrations in the brain as well as total blood volume

changes in various regions of the cerebral cortex using near

infrared light. The NIRS system can determine the activity in

specific regions of the brain by continuously monitoring blood

hemoglobin level. Reports have begun to appear on infants in

the first two years of life, testing infant responses to phonemes

as well as longer stretches of speech such as ‘‘motherese’’

and forward versus reversed sentences (Bortfeld et al., 2007;

Homae et al., 2006; Peña et al., 2002; Taga and Asakawa,

2007). As with other hemodynamic techniques such as fMRI,

NIRS typically does not provide good temporal resolution. How-

ever, event-related NIRS paradigms are being developed

(Gratton and Fabiani, 2001). One of the most important potential

uses of the NIRS technique is possible co-registration with other

testing techniques such as EEG and MEG.

Neural Signatures of Early Learning Perception of the phonetic units of speech—the vowels and

consonants that make up words—is one of the most widely

studied linguistic skills in infancy and adulthood. Phonetic

perception and the role of experience in learning is studied in

newborns, during development as infants are exposed to a

particular language, in adults from different cultures, in children

with developmental disabilities, and in nonhuman animals.

Phonetic perception studies provide critical tests of theories of

language development and its evolution. An extensive literature

on developmental speech perception exists and brain measures

are adding substantially to our knowledge of phonetic develop-

ment and learning (see Kuhl, 2004; Kuhl et al., 2008; Werker

and Curtin, 2005).

In the last decade, brain and behavioral studies indicate a very

complex set of interacting brain systems in the initial acquisition

of language, many of which appear to reflect adult language pro-

cessing, even early in infancy (Dehaene-Lambertz et al., 2006).

In adulthood, language is highly modularized, which accounts

for the very specific patterns of language deficits and brain

damage in adult patients following stroke (P.K.K. and A. Dama-

sio, Principles of Neuronal Science V [McGraw Hill], in press,

E.R. Kandel, J.H. Schwartz, T.M. Jessell, S. Siegelbaum, and

J. Hudspeth, eds). Infants, however, must begin life with brain

systems that allow them to acquire any and all languages to

which they are exposed, and can acquire language as either

an auditory-vocal or a visual-manual code, on roughly the

same timetable (Petitto and Marentette, 1991). We are in

a nascent stage of understanding the brain mechanisms under-

lying infants’ early flexibility with regard to the acquisition of

language – their ability to acquire language by eye or by ear,

and acquire one or multiple languages – and also the reduction

in this initial flexibility that occurs with age, which dramatically

decreases our capacity to acquire a new language as adults

(Newport, 1990). The infant brain is exquisitely poised to ‘‘crack

the speech code’’ in a way that the adult brain cannot. Uncover-

ing why this is the case is a very interesting puzzle.

In this review I will also explore a current working hypothesis

and its implications for brain development—that to crack the

speech code requires infants to combine a powerful set of

domain-general computational and cognitive skills with their

equally extraordinary social skills. Thus, the underlying brain

systems must mutually influence one another during develop-

ment. Experience with more than one language, for example,

as in the case of people who are bilingual, is related to increases

in particular cognitive skills, both in adults (Bialystok, 1991) and

in children (Carlson and Meltzoff, 2008). Moreover, social inter-

action appears to be necessary for language acquisition, and

an individual infant’s social behavior can be linked to their

ability to learn new language material (Kuhl et al., 2003; B.T. Con-

boy et al., 2008, ‘‘Joint engagement with language tutors

predicts learning of second-language phonetic stimuli,’’ presen-

tation at the 16th International Conference on Infancy Studies,

Vancouver).

Regarding the social effects, I have suggested that the social

brain—in ways we have yet to understand—‘‘gates’’ the com-

putational mechanisms underlying learning in the domain of

language (Kuhl, 2007). The assertion that social factors gate

language learning explains not only how typically developing

children acquire language, but also why children with autism

exhibit twin deficits in social cognition and language, and

why nonhuman animals with impressive computational abilities

do not acquire language. Moreover, this gating hypothesis

may explain why social factors play a far more significant role

than previously realized in human learning across domains

throughout our lifetimes (Meltzoff et al., 2009). Theories of social

learning have traditionally emphasized the role of social factors

in language acquisition (Bruner, 1983; Vygotsky, 1962; Toma-

sello, 2003a, 2003b). However, these models have emphasized

the development of lexical understanding and the use of others’

communicative intentions to help understand the mapping

between words and objects. The new data indicate that social

interaction ‘‘gates’’ an even more basic aspect of language —

learning of the elementary phonetic units of language — and

this suggests a more fundamental connection between the brain

mechanisms underlying human social understanding and the

origins of language than has previously been hypothesized.

In the next decade, the methods of modern neuroscience will

be used to explore how the integration of brain activity across

specialized brain systems involved in linguistic, social, and

cognitive analyses take place. These approaches, as well as

others described here, will lead us toward a view of language

acquisition in the human child that could be transformational.

The Learning Problem Language learning is a deep puzzle that our theories and

machines struggle to solve but children accomplish with ease.

How do infants discover the sounds and words used in their

particular language(s) when the most sophisticated computers

cannot? What is it about the human mind that allows a young

child, merely one year old, to understand the words that induce

meaning in our collective minds, and to begin to use those words

to convey their innermost thoughts and desires? A child’s

budding ability to express a thought through words is a breath-

taking feat of the human mind.

Research on infants’ phonetic perception in the first year of life

shows how computational, cognitive, and social skills combine

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 715

Figure 2. The Relationship between Age of Acquisition of a Second Language and Language Skill Adapted from Johnson and Newport (1989).

Neuron

Review

to form a very powerful learning mechanism. Interestingly, this

mechanism does not resemble Skinner’s operant conditioning

and reinforcement model of learning, nor Chomsky’s detailed

view of parameter setting. The learning processes that infants

employ when learning from exposure to language are complex

and multi-modal, but also child’s play in that it grows out of

infants’ heightened attention to items and events in the natural

world: the faces, actions, and voices of other people.

Language Exhibits a ‘‘Critical Period’’ for Learning A stage-setting concept for human language learning is the

graph shown in Figure 1, redrawn from a study by Johnson

and Newport on English grammar in native speakers of Korean

learning English as a second language (1989). The graph as

rendered shows a simplified schematic of second language

competence as a function of the age of second language

acquisition.

Figure 2 is surprising from the standpoint of more general

human learning. In the domain of language, infants and young

children are superior learners when compared to adults, in spite

of adults’ cognitive superiority. Language is one of the classic

examples of a ‘‘critical’’ or ‘‘sensitive’’ period in neurobiology

(Bruer, 2008; Johnson and Newport, 1989; Knudsen, 2004;

Kuhl, 2004; Newport et al., 2001).

Scientists are generally in agreement that this learning curve is

representative of data across a wide variety of second-language

learning studies (Bialystok and Hakuta, 1994; Birdsong and

Molis, 2001; Flege et al., 1999; Johnson and Newport, 1989;

Kuhl et al., 2005a, 2008; Mayberry and Lock, 2003; Neville

et al., 1997; Weber-Fox and Neville, 1999; Yeni-Komshian

et al., 2000; though see Birdsong, 1992; White and Genesee,

1996). Moreover, not all aspects of language exhibit the same

temporally defined critical ‘‘windows.’’ The developmental tim-

ing of critical periods for learning phonetic, lexical, and syntactic

levels of language vary, though studies cannot yet document the

precise timing at each individual level. Studies indicate, for

example, that the critical period for phonetic learning occurs

prior to the end of the first year, whereas syntactic learning flour-

716 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

ishes between 18 and 36 months of age. Vocabulary develop-

ment ‘‘explodes’’ at 18 months of age, but does not appear to be

as restricted by age as other aspects of language learning—one

can learn new vocabulary items at any age. One goal of future

research will be to document the ‘‘opening’’ and ‘‘closing’’ of

critical periods for all levels of language and understand how

they overlap and why they differ.

Given widespread agreement on the fact that we do not learn

equally well over the lifespan, theory is currently focused on

attempts to explain the phenomenon. What accounts for adults’

inability to learn a new language with the facility of an infant?

One of the candidate explanations was Lenneberg’s hypoth-

esis that development of the corpus callosum affected language

learning (Lenneberg, 1967; Newport et al., 2001). More recent

hypotheses take a different perspective. Newport raised a

‘‘less is more’’ hypothesis, which suggests that infants’ limited

cognitive capacities actually allow superior learning of the simpli-

fied language spoken to infants (Newport, 1990). Work in my

laboratory led me to advance the concept of neural commitment,

the idea that neural circuitry and overall architecture develops

early in infancy to detect the phonetic and prosodic patterns of

speech (Kuhl, 2004; Zhang et al., 2005, 2009). This architecture

is designed to maximize the efficiency of processing for the

language(s) experienced by the infant. Once established, the

neural architecture arising from French or Tagalog, for example,

impedes learning of new patterns that do not conform. I will

return to the concept of the critical period for language learning,

and the role that computational, cognitive, and social skills may

play in accounting for the relatively poor performance of adults

attempting to learn a second language.

Focal Example: Phoneme Learning The world’s languages contain approximately 600 consonants

and 200 vowels (Ladefoged, 2001). Each language uses a unique

set of about 40 distinct elements, phonemes, which change

the meaning of a word (e.g., from bat to pat in English). But

phonemes are actually groups of non-identical sounds, phonetic

units, which are functionally equivalent in the language. Japa-

nese-learning infants have to group the phonetic units r and l

into a single phonemic category (Japanese r), whereas English-

learning infants must uphold the distinction to separate rake

from lake. Similarly, Spanish learning infants must distinguish

phonetic units critical to Spanish words (bano and pano),

whereas English learning infants must combine them into a sin-

gle category (English b). If infants were exposed only to the

subset of phonetic units that will eventually be used phonemi-

cally to differentiate words in their language, the problem would

be trivial. But infants are exposed to many more phonetic

variants than will be used phonemically, and have to derive the

appropriate groupings used in their specific language. The

baby’s task in the first year of life, therefore, is to make some

progress in figuring out the composition of the 40-odd phonemic

categories in their language(s) before trying to acquire words that

depend on these elementary units.

Learning to produce the sounds that will characterize infants

as speakers of their ‘‘mother tongue’’ is equally challenging,

and is not completely mastered until the age of 8 years (Ferguson

et al., 1992). Yet, by 10 months of age, differences can be

Figure 3. Effects of Age and Experience on Phonetic Discrimination Effects of age on discrimination of the American English /ra-la/ phonetic contrast by American and Japanese infants at 6–8 and 10–12 months of age. Mean percent correct scores are shown with standard errors indicated (adapted from Kuhl et al., 2006).

Neuron

Review

discerned in the babbling of infants raised in different countries

(de Boysson-Bardies, 1993), and in the laboratory, vocal imita-

tion can be elicited by 20 weeks (Kuhl and Meltzoff, 1982). The

speaking patterns we adopt early in life last a lifetime (Flege,

1991). My colleagues and I have suggested that this kind of

indelible learning stems from a linkage between sensory and

motor experience; sensory experience with a specific language

establishes auditory patterns stored in memory that are unique

to that language, and these representations guide infants’

successive motor approximations until a match is achieved

(Kuhl and Meltzoff, 1996). This ability to imitate vocally may

also depend on the brain’s social understanding mechanisms

which form a human mirroring system for seamless social inter-

action (Hari and Kujala, 2009), and we will revisit the impact of

the brain’s social understanding systems later in this review.

What enables the kind of learning we see in infants for speech?

No machine in the world can derive the phonemic inventory of

a language from natural language input (Rabiner and Huang,

1993), though models improve when exposed to ‘‘motherese,’’

the linguistically simplified and acoustically exaggerated speech

that adults universally use when speaking to infants (de Boer

and Kuhl, 2003). The variability in speech input is simply too

enormous; Japanese adults produce both English r- and l- like

sounds, exposing Japanese infants to both sounds (Lotto

et al., 2004; Werker et al., 2007). How do Japanese infants learn

that these two sounds do not distinguish words in their language,

and that these differences should be ignored? Similarly, English

speakers produce Spanish b and p, exposing American infants

to both categories of sound (Abramson and Lisker, 1970). How

do American infants learn that these sounds do not distinguish

words in English? An important discovery in the 1970s was

that infants initially hear all these phonetic differences (Eimas,

1975; Eimas et al., 1971; Lasky et al., 1975; Werker and Lalonde,

1988). What we must explain is how infants learn to group

phonetic units into phonemic categories that make a difference

in their language.

The Timing of Phonetic Learning Another important discovery in the 1980s identified the timing of

a crucial change in infant perception. The transition from an early

Neuron 67, S

universal perceptual ability to distinguish

all the phonetic units of all languages

to a more language specific pattern of

perception occurred very early in devel-

opment—between 6 and 12 months of

age (Werker and Tees, 1984), and initial

work demonstrated that infants’ percep-

tion of nonnative distinctions declines

during the second half of the first year of life (Best and McRo-

berts, 2003; Rivera-Gaxiola et al., 2005; Tsao et al., 2006; Werker

and Tees, 1984). Work in this laboratory also established a new

fact: At the same time that nonnative perception declines, native

language speech perception shows a significant increase. Japa-

nese infants’ discrimination of English r-l declines between 8

and 10 months of age, while at the same time in development,

American infants’ discrimination of the same sounds shows an

increase (Kuhl et al., 2006) (Figure 3).

Phonetic Learning Predicts the Rate of Language Growth We argued that the increase observed in native-language

phonetic perception represented a critical step in initial language

learning and promoted language growth (Kuhl et al., 2006). To

test this hypothesis, we designed a longitudinal study examining

whether a measure of phonetic perception predicted children’s

language skills measured 18 months later. The study demon-

strated that infants’ phonetic discrimination ability at 6 months

of age was significantly correlated with their success in language

learning at 13, 16, and 24 months of age (Tsao et al., 2004).

However, we recognized that in this initial study the association

we observed might be due to infants’ cognitive skills, such as the

ability to perform in the behavioral task, or to sensory abilities

that affected auditory resolution of the differences in formant

frequencies that underlie phonetic distinctions.

To address these issues, we assessed both native and nonna-

tive phonetic discrimination in 7-month-old infants, and used

both a behavioral (Kuhl et al., 2005a) and an event-related poten-

tial measure, the mismatch negativity (MMN), to assess infants’

performance (Kuhl et al., 2008). Using a neural measure removed

potential cognitive effects on performance; the use of both native

and nonnative contrasts addressed the sensory issue, since

better sensory abilities would be expected to improve both

native and nonnative speech discrimination.

The native language neural commitment (NLNC) view sug-

gested that future language measures would be associated

with early performance on both native and nonnative contrasts,

but in opposite directions. The results conformed to this predic-

tion. When both native and nonnative phonetic discrimination

eptember 9, 2010 ª2010 Elsevier Inc. 717

Figure 4. Speech Discrimination Predicts Vocabulary Growth (A) A 7.5-month-old infant wearing an ERP electrocap. (B) Infant ERP waveforms at one sensor location (CZ) for one infant are shown in response to a native (English) and nonnative (Mandarin) phonetic contrast at 7.5 months. The mismatch negativity (MMN) is obtained by subtracting the standard waveform (black) from the deviant wave- form (English, red; Mandarin, blue). This infant’s response suggests that native-language learning has begun because the MMN negativity in response to the native English contrast is consid- erably stronger than that to the nonnative contrast. (C) Hierarchical linear growth modeling of vocabu- lary growth between 14 and 30 months for MMN values of +1 SD and �1 SD on the native contrast at 7.5 months (C, left) and vocabulary growth for MMN values of +1 SD and �1 SD on the nonnative contrast at 7.5 months (C, right) (adapted from Kuhl et al., 2008).

Neuron

Review

was measured in the same infants at 7.5 months of age, better

native language perception predicted significantly higher lan-

guage abilities between 18 and 30 months of age, whereas

better nonnative phonetic perception at the same age predicted

poorer language abilities at the same future points in time (Kuhl

et al., 2005a, 2008). As shown in Figure 4, the ERP measure at

7.5 months of age (Figure 4A) provided an MMN measure of

speech discrimination for both native and nonnative contrasts;

greater negativity of the MMN reflects greater discrimination

(Figure 4B). Hierarchical linear growth modeling of vocabulary

between 14 and 30 months for MMN values of +1SD and �1SD (Figure 4C) revealed that both native and nonnative phonetic

discrimination significantly predict future language, but in oppo-

site directions with better native MMNs predicting advanced

future language development and better nonnative MMNs pre-

dicting less advanced future language development.

The results are explained by NLNC: better native phonetic

discrimination enhances infants’ skills in detecting words and

this vaults them toward language, whereas better nonnative

abilities indicated that infants remained at an earlier phase of

development – sensitive to all phonetic differences. Infants’

ability to learn which phonetic units are relevant in the lan-

guage(s) they are exposed to, while decreasing or inhibiting their

attention to the phonetic units that do not distinguish words in

their language, is the necessary step required to begin the

path toward language. These data led to a theoretical argument

that an implicit learning process commits the brain’s neural

circuitry to the properties of native-language speech, and that

neural commitment has bi-directional effects – it increases

learning for patterns (such as words) that are compatible with

the learned phonetic structure, while decreasing perception of

718 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

a

s

h

la

A A

le

S

ra

p

s

e

p

s

o

p

p

s

th

b

d

s

in

E

fr

n

is

in

in

nonnative patterns that do not match the

learned scheme (Kuhl, 2004).

Recent data indicate very long-term

associations between infants’ phonetic

perception and future language and

reading skills. Our studies show that the

ability to discriminate two simple vowels

t 6 months of age predicts language abilities and pre-reading

kills such as rhyming at the age of 5 years, an association that

olds regardless of socio-economic status and the children’s

nguage skills at 2.5 years of age (Cardillo, 2010).

Computational Solution to Phonetic Learning surprising new form of learning, referred to as ‘‘statistical

arning’’ (Saffran et al., 1996), was discovered in the 1990s.

tatistical learning is computational in nature, and reflects implicit

ther than explicit learning. It relies on the ability to automatically

ick up and learn from the statistical regularities that exist in the

tream of sensory information we process, and strongly influ-

nces both phonetic learning and early word learning.

For example, data show that the developmental change in

honetic perception between the ages of 6 and 12 months is

upported by infants’ sensitivity to the distributional frequencies

f the sounds in the language(s) they hear, and that this affects

erception. To illustrate, adult speakers of English and Japanese

roduce both English r- and l-like sounds, even though English

peakers hear /r/ and /l/ as distinct and Japanese adults hear

em as identical. Japanese infants are therefore exposed to

oth /r/ and /l/ sounds, even though they do not represent

istinct categories in Japanese. The presence of a particular

ound in ambient language, therefore, does not account for

fant learning. However, distributional frequency analyses of

nglish and Japanese show differential patterns of distributional

equency; in English, /r/ and /l/ occur very frequently; in Japa-

ese, the most frequent sound of this type is Japanese /r/ which

related to but distinct from both the English variants. Can

fants learn from this kind of distributional information in speech

put?

Neuron

Review

A variety of studies show that infants’ perception of phonetic

categories is affected by distributional patterns in the sounds

they hear. In one study using very simple stimuli and short-

term exposure in the laboratory, 6- and 8-month-old infants

were exposed for 2 min to 8 sounds that formed a continuum

of sounds from /da/ to /ta/ (Maye et al., 2002; see also Maye

et al., 2008). All infants heard all the stimuli on the continuum,

but experienced different distributional frequencies of the

sounds. A ‘‘bimodal’’ group heard more frequent presentations

of stimuli at the ends of the continuum; a ‘‘unimodal’’ group

heard more frequent presentations of stimuli from the middle

of the continuum. After familiarization, infants in the bimodal

group discriminated the /da/ and /ta/ sounds, whereas those in

the unimodal group did not. Furthermore, while previous studies

show that infants integrate the auditory and visual instantiations

of speech (Kuhl and Meltzoff, 1982; Patterson and Werker,

1999), more recent studies show that infants’ detection of statis-

tical patterns in speech stimuli, like those used by Maye and

her colleagues, is influenced both by the auditory event and

the sight of a face articulating the sounds. When exposed only

to the ambiguous auditory stimuli in the middle of a speech

continuum, infants discriminated the /da-ta/ contrast when

each auditory stimulus was paired with the appropriate face

articulating either /da/ or /ta/; discrimination did not occur if

only one face was used with all auditory stimuli (Teinonen

et al., 2008).

Cross-cultural studies also indicate that infants are sensitive

to the statistical distribution of sounds they hear in natural

language. Infants tested in Sweden and the United States at

6 months of age showed a unique response to vowel sounds

that represent the distributional mean in productions of adults

who speak the language (i.e., ‘‘prototypes’’); this response was

shown only for stimuli infants had been exposed to in natural

language (native-vowel prototypes), not foreign-language vowel

prototypes (Kuhl et al., 1992). Taken as a whole, these studies

indicate infants pick up the distributional frequency patterns in

ambient speech, whether they experience them during short-

term laboratory experiments, or over months in natural environ-

ments, and can learn from them.

Statistical learning also supports word learning. Unlike written

language, spoken language has no reliable markers to indicate

word boundaries in typical phrases. How do infants find words?

New experiments show that, before 8-month-old infants know

the meaning of a single word, they detect likely word candidates

through sensitivity to the transitional probabilities between adja-

cent syllables. In typical words, like in the phrase, ‘‘pretty baby,’’

the transitional probabilities between the two syllables within

a word, such as those between ‘‘pre’’ and ‘‘tty,’’ and between

‘‘ba’’ and ‘‘by,’’ are higher than those between syllables that

cross word boundaries, such and ‘‘tty’’ and ‘‘ba.’’ Infants are

sensitive to these probabilities. When exposed to a 2 min string

of nonsense syllables, with no acoustic breaks or other cues to

word boundaries, they treat syllables that have high transitional

probabilities as ‘‘words’’ (Saffran et al., 1996). Recent findings

show that even sleeping newborns detect this kind of statistical

structure in speech, as shown in studies using event-related

brain potentials (Teinonen et al., 2009). Statistical learning has

been shown in nonhuman animals (Hauser et al., 2001), and in

humans for stimuli outside the realm of speech, operating for

musical and visual patterns in the same way as speech (Fiser

and Aslin, 2002; Kirkham et al., 2002; Saffran et al., 1999).

Thus, a very basic implicit learning mechanism allows infants,

from birth, to detect statistical structure in speech and in other

signals. Infants’ sensitivity to this statistical structure can influ-

ence both phoneme and word learning.

Effects of Social Interaction on Computational Learning As reviewed, infants show robust learning effects in statistical

learning studies when tested in the laboratory with very simple

stimuli (Maye et al., 2002, 2008; Saffran et al., 1996). However,

complex natural language learning may challenge infants in

a way that these experiments do not. Are there constraints on

statistical learning as an explanation for natural language

learning? A series of later studies suggest that this is the case.

Laboratory studies testing infant phonetic and word learning

from exposure to a complex natural language suggest limits

on statistical learning, and provide new information suggesting

that social brain systems are integrally involved, and, in fact,

may be necessary to explain natural language learning.

The new experiments tested infants in the following way: At 9

months of age, the age at which the initial universal pattern of

infant perception has changed to one that is more language-

specific, infants were exposed to a foreign language for the first

time (Kuhl et al., 2003). Nine-month-old American infants

listened to 4 different native speakers of Mandarin during 12

sessions scheduled over 4–5 weeks. The foreign language

‘‘tutors’’ read books and played with toys in sessions that were

unscripted. A control group was also exposed for 12 sessions

but heard only English from native speakers. After infants in

the experimental Mandarin exposure group and the English

control group completed their sessions, all were tested with

a Mandarin phonetic contrast that does not occur in English.

Both behavioral and ERP methods were used. The results indi-

cated that infants had a remarkable ability to learn from the

‘‘live-person’’ sessions – after exposure, they performed signifi-

cantly better on the Mandarin contrast when compared to the

control group that heard only English. In fact, they performed

equivalently to infants of the same age tested in Taiwan who

had been listening to Mandarin for 10 months (Kuhl et al., 2003).

The study revealed that infants can learn from first-time natural

exposure to a foreign language at 9 months, and answered what

was initially the experimental question: can infants learn the

statistical structure of phonemes in a new language given first-

time exposure at 9 months of age? If infants required a long-

term history of listening to that language—as would be the

case if infants needed to build up statistical distributions over

the initial 9 months of life—the answer to our question would

have been no. However, the data clearly showed that infants

are capable of learning at 9 months when exposed to a new

language. Moreover, learning was durable. Infants returned to

the laboratory for their behavioral discrimination tests between

2 and 12 days after the final language exposure session, and

between 8 and 33 days for their ERP measurements. No ‘‘forget-

ting’’ of the Mandarin contrast occurred during the 2 to 33 day

delay.

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 719

Figure 5. Social Interaction Facilitates Foreign Language Learning The need for social interaction in language acqui- sition is shown by foreign-language learning experiments. Nine-month-old infants experienced 12 sessions of Mandarin Chinese through (A) natural interaction with a Chinese speaker (left) or the identical linguistic information delivered via television (right) or audiotape (data not shown). (B) Natural interaction resulted in significant learn- ing of Mandarin phonemes when compared with a control group who participated in interaction using English (left). No learning occurred from tele- vision or audiotaped presentations (middle). Data for age-matched Chinese and American infants learning their native languages are shown for comparison (right) (adapted from Kuhl et al., 2003).

Neuron

Review

We were struck by the fact that infants exposed to Mandarin

were socially very engaged in the language sessions and began

to wonder about the role of social interaction in learning. Would

infants learn if they were exposed to the same information in the

absence of a human being, say, via television or an audiotape?

If statistical learning is sufficient, the television and audio-only

conditions should produce learning. Infants who were exposed

to the same foreign-language material at the same time and at

the same rate, but via standard television or audiotape only,

showed no learning—their performance equaled that of infants

in the control group who had not been exposed to Mandarin at

all (Figure 5).

Thus, the presence of a human being interacting with the infant

during language exposure, while not required for simpler statis-

tical-learning tasks (Maye et al., 2002; Saffran et al., 1996), is crit-

ical for learning in complex natural language-learning situations

in which infants heard an average of 33,000 Mandarin syllables

from a total of four different talkers over a 4–5-week period

(Kuhl et al., 2003).

Explaining the Effect of Social Interaction on Language Learning The impact of social interaction on language learning (Kuhl et al.,

2003) led to the development of the Social Gating Hypothesis

720 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

s

a

1

ti

a

e

a

m

a

w

s

a

s

le

o

in

o

M

li

v

ti

v

a

(Kuhl, 2007). ‘‘Gating’’ suggested that

social interaction creates a vastly dif-

ferent learning situation, one in which

additional factors introduced by a social

context influence learning. Gating could

operate by increasing: (1) attention and/

or arousal, (2) information, (3) a sense of

relationship, and/or (4) activation of brain

mechanisms linking perception and

action.

Attention and arousal affect learning in

a wide variety of domains (Posner, 2004),

and could impact infant learning during

exposure to a new language. Infant atten-

tion, measured in the original studies,

was significantly higher in response to

the live person than to either inanimate

ource (Kuhl et al., 2003). Attention has been shown to play

role in the statistical learning studies as well. ‘‘High-attender’’

0-month-olds, measured as the amount of infant ‘‘looking

me,’’ learned from bimodal stimulus distributions when ‘‘low-

ttenders’’ did not (Yoshida et al., 2006; see also Yoshida

t al., 2010). Heightened attention and arousal could produce

n overall increase in the quantity or quality of the speech infor-

ation that infants encode and remember. Recent data suggest

role for attention in adult second-language phonetic learning as

ell (Guion and Pederson, 2007).

A second hypothesis was raised to explain the effectiveness of

ocial interaction – the live learning situation allowed the infants

nd tutors to interact, and this added contingent and reciprocal

ocial behaviors that increased information that could foster

arning. During live exposure, tutors focused their visual gaze

n pictures in the books or on the toys as they spoke, and the

fants’ gaze tended to follow the speaker’s gaze, as previously

bserved in social learning studies (Baldwin, 1995; Brooks and

eltzoff, 2002). Referential information is present in both the

ve and televised conditions, but it is more difficult to pick up

ia television, and is totally absent during audio-only presenta-

ons. Gaze following is a significant predictor of receptive

ocabulary (Baldwin, 1995; Brooks and Meltzoff, 2005; Mundy

nd Gomes, 1998), and may help infants link the foreign speech

Figure 6. Social Engagement Predicts Foreign Language Learning (A) Nine-month-old infants experienced 12 sessions of Spanish through natural interaction with a Spanish speaker. (B) The neural response to the Spanish phonetic contrast (d-t) and the propor- tion of gaze shifts during Spanish sessions were significantly correlated (from Conboy et al., unpublished data).

Neuron

Review

to the objects they see. When 9-month-old infants follow a tutor’s

line of regard in our foreign-language learning situation, the

tutor’s specific meaningful social cues, such as eye gaze and

pointing to an object of reference, might help infants segment

word-like units from ongoing speech, thus facilitating phonetic

learning of the sounds contained in those words.

If this hypothesis is correct, then the degree to which

infants interact and engage socially with the tutor in the social

language-learning situation should correlate with learning.

In studies testing this hypothesis, 9-month-old infants were

exposed to Spanish (Conboy and Kuhl, 2010), extending the

experiment to a new language. Other changes in method

expanded the tests of language learning to include both Spanish

phonetic learning and Spanish word learning, as well as adding

measures of specific interactions between the tutor and the

infant to examine whether interactive episodes could be related

to learning of either phonemes or words.

The results confirmed Spanish language learning, both of the

phonetic units of the language and the lexical units of the

language (Conboy and Kuhl, 2010). In addition, these studies

answered a key question—does the degree of infants’ social

engagement during the Spanish exposure sessions predict the

degree of language learning as shown by ERP measures of

Spanish phoneme discrimination? Our results (Figure 6) show

that they do (Conboy et al., 2008a). Infants who shifted their

gaze between the tutor’s eyes and newly introduced toys during

the Spanish exposure sessions showed a more negative MMN

(indicating greater neural discrimination) in response to the

Spanish phonetic contrast. Infants who simply gazed at the

tutor or at the toy, showing fewer gaze shifts, produced less

negative MMN responses. The degree of infants’ social engage-

ment during sessions predicted both phonetic and word

learning—infants who were more socially engaged showed

greater learning as reflected by ERP brain measures of both

phonetic and word learning.

Language, Cognition, and Bilingual Language Experience Specific cognitive abilities, particularly the executive control of

attention and the ability to inhibit a pre-potent response (inhibi-

tory control), are associated with exposure to more than one

language. Bilingual adult speakers show enhanced executive

control skills (Bialystok, 1999, 2001; Bialystok and Hakuta,

1994; Wang et al., 2009), a finding that has been extended to

young school-aged bilingual children (Carlson and Meltzoff,

2008). In monolingual infants, the decline in discrimination of

nonnative contrasts (which promotes more rapid growth in

language, see Figure 4C) is associated with enhanced inhibitory

control, suggesting that domain-general cognitive mechanisms

underlying attention may play a role in enhancing performance

on native and suppressing performance on nonnative phonetic

contrasts early in development (Conboy et al., 2008b; Kuhl

et al., 2008). In support of this view, it is noteworthy that in the

Spanish exposure studies, a median split of the post-exposure

MMN phonetic discrimination data revealed that infants showing

greater phonetic learning had higher cognitive control scores

post-exposure. These same infants did not differ in their pre-

exposure cognitive control tests (Conboy, Sommerville, and

P.K.K., unpublished data). Taken as a whole, the data are consis-

tent with the notion that cognitive skills are strongly linked to

phonetic learning at the initial stage of phonetic development

(Kuhl et al., 2008).

The ‘‘Social Brain’’ and Language Learning Mechanisms While attention and the information provided by interaction with

another may help explain social learning effects for language, it is

also possible that social contexts are connected to language

learning through even more fundamental mechanisms. Social

interaction may activate brain mechanisms that invoke a sense

of relationship between the self and other, as well as social

understanding systems that link perception and action (Hari

and Kujala, 2009). Neuroscience research focused on shared

neural systems for perception and action have a long tradition

in speech research (Liberman and Mattingly, 1985), and interest

in ‘‘mirror systems’’ for social cognition have re-invigorated this

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 721

Figure 7. Perception-Action Brain Systems Respond to Speech in Infancy (A) Neuromagnetic signals were recorded in newborns, 6-month-old infants (shown), and 12-month-old infants in the MEG machine while listening to speech and nonspeech auditory signals. (B) Brain activation in response to speech recorded in auditory (B, top row) and motor (B, bottom row) brain regions showed no activation in the motor speech areas in the newborn in response to auditory speech but increasing activity that was temporally synchronized between the auditory and motor brain regions in 6- and 12-month-old infants (from Imada et al., 2006).

Neuron

Review

tradition (Kuhl and Meltzoff, 1996; Meltzoff and Decety, 2003;

Pulvermuller, 2005; Rizzolatti, 2005; Rizzolatti and Craighero,

2004). Might the brain systems that link perception and produc-

tion for speech be engaged when infants experience social inter-

action during language learning?

The effects of Spanish language exposure extend to speech

production, and provide evidence of an early coupling of

sensory-motor learning in speech. The English-learning infants

who were exposed to 12 sessions of Spanish (Conboy and

Kuhl, 2010) showed subsequent changes in their patterns of

vocalization (N. Ward et al., 2009, ‘‘Consequences of short-

term language exposure in infancy on babbling,’’ poster pre-

sented at the 158th meeting of the Acoustical Society of Amer-

ica, San Antonio). When presented with language from a Spanish

speaker (but not from an English speaker), a new pattern of infant

vocalizations was evoked, one that reflected the prosodic

patterns of Spanish, rather than English. This only occurred in

response to Spanish, and only occurred in infants who had

been exposed to Spanish in the laboratory experiment.

Neuroscience studies using speech and imaging techniques

have the capacity to examine whether the brain systems

involved in speech production are activated when infants listen

to speech. Two new infant studies take a first step toward an

answer to this developmental issue. Imada et al. (2006) used

magnetoenchephalography (MEG) to study newborns, 6-month-

old infants, and 12-month-old infants while they listened to

nonspeech, harmonics, and syllables (Figure 7). Dehaene-Lam-

bertz and colleagues (2006) used fMRI to scan 3-month-old

infants while they listened to sentences. Both studies show acti-

vation in brain areas responsible for speech production (the infe-

rior frontal, Broca’s area) in response to auditorally presented

speech. Imada et al. reported synchronized activation in

response to speech in auditory and motor areas at 6 and 12

months, and Dehaene et al. reported activation in motor speech

areas in response to sentences in 3-month-olds. Is activation of

722 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

Broca’s area to the pure perception of speech present at birth?

Newborns tested by Imada et al. (2006) showed no activation

in motor speech areas for any signals, whereas auditory areas

responded robustly to all signals, suggesting the possibility

that perception-action linkages for speech develop by 3 months

of age as infants begin to produce vowel-like sounds.

Using the tools of modern neuroscience, we can now ask how

the brain systems responsible for speech perception and speech

production forge links early in development, and whether these

same brain areas are involved when language is presented

socially, but not when language is presented through a disem-

bodied source such as a television set.

Brain Rhythms, Cognitive Effects, and Language Learning MEG studies will provide an opportunity to examine brain

rhythms associated with broader cognitive abilities during

speech learning. Brain oscillations in various frequency bands

have been associated with cognitive abilities. The induced brain

rhythms have been linked to attention and cognitive effort, and

are of primary interest since MEG studies with adults have shown

that cognitive effort is increased when processing nonnative

speech (Zhang et al., 2005, 2009). In the adult MEG studies,

participants listened to their native- and to nonnative-language

sounds. The results indicated that when listening to native

language, the brain’s activation was more focal, and faster,

than when listening to nonnative-language sounds (Zhang

et al., 2005). In other words, there was greater neural efficiency

for native as opposed to nonnative speech processing. Training

studies show that adults can improve nonnative phonetic per-

ception when training occurs under more social learning condi-

tions, and MEG measures before and after training indicate

that neural efficiency increases after training (Zhang et al.,

2009). Similar patterns of neural inefficiency occur as young chil-

dren learn words. Young children’s event-related brain potential

Neuron

Review

responses are more diffuse and become more focally lateralized

in the left hemisphere’s temporal regions as they develop (Con-

boy et al., 2008a; Durston et al., 2002; Mills et al., 1993, 1997;

Tamm et al., 2002) and studies with young children with autism

show this same pattern – more diffuse activation – when com-

pared to typically developing children of the same age (Coffey-

Corina et al., 2008).

Brain rhythms may be reflective of these same processes in

infants as they learn language. Brain oscillations in four

frequency bands have been associated with cognitive effects:

theta (4–7 Hz), alpha (8–12 Hz), beta (13–30 Hz) and gamma

(30–100 Hz). Resting gamma has been related to early language

and cognitive skills in the first three years (Benasich et al., 2008).

The induced theta rhythm has been linked to attention and cogni-

tive effort, and will be of strong interest to speech researchers.

Power in the theta band increases with memory load in adults

tested in either verbal or nonverbal tasks (Gevins et al., 1997;

Krause et al., 2000) and in 8-month-old infants tested in working

memory tasks (Bell and Wolfe, 2007). Examining brain rhythms in

infants using speech stimuli is now underway using EEG with

high-risk infants (C.R. Percaccio et al., 2010, ‘‘Native and nonna-

tive speech-evoked responses in high-risk infant siblings,’’

abstracts of the International Meeting for Autism Research,

May 2010, Philadelphia) and using MEG with typically devel-

oping infants (A.N. Bosseler et al., 2010, ‘‘Event-related fields

and cortical rhythms to native and nonnative phonetic contrasts

in infants and adults,’’ abstracts of the 17th International Confer-

ence of Biomagnetism), as they listen to native and nonnative

speech. Comparisons between native and nonnative speech

may allow us to examine whether there is increased cognitive

effort associated with processing nonnative language, across

age and populations. We are also testing whether language pre-

sented in a social environment affects brain rhythms in a way that

television and audiotape presentations do not. Neural efficiency

is not observable with behavioral approaches—and one promise

of brain rhythms is that they provide the opportunity to compare

the higher-level processes that likely underlie humans’ neural

plasticity for language early in development in typical children

as well as in children at risk for autism spectrum disorder, and

in adults learning a second language. These kinds of studies

may reveal the cortical dynamics underlying the ‘‘Critical Period’’

for language.

These results underscore the importance of a social interest in

speech early in development in both typical and atypical popula-

tions. An interest in ‘‘motherese,’’ the universal style with which

adults address infants across cultures (Fernald and Simon,

1984; Grieser and Kuhl, 1988) provides a good metric of the

value of a social interest in speech. The acoustic stretching in

motherese, observed across languages, makes phonetic units

more distinct from one another (Burnham et al., 2002; Englund,

2005; Kuhl et al., 1997; Liu et al., 2003, 2007). Mothers who

use the exaggerated phonetic patterns to a greater extent

when talking to their typically developing 2-month-old infants

have infants who show significantly better performance in

phonetic discrimination tasks when tested in the laboratory (Liu

et al., 2003). New data show that the potential benefits of early

motherese extend to the age of 5 years (Liu et al., 2009). Recent

ERP studies indicate that infants’ brain responses to the exag-

gerated patterns of motherese elicit an enhanced N250 as well

as increased neural synchronization at frontal-central-parietal

sites (Zhang et al., personal communication).

It is also noteworthy that children with Autism Spectrum

Disorder (ASD) prefer to listen to non-speech rather than speech,

when given a choice, and this preference is strongly correlated

with the children’s ERP brain responses to speech, as well as

with the severity of their autistic symptoms (Kuhl et al., 2005b).

Early speech measures may therefore provide an early

biomarker of risk for ASD. Neuroscience studies in both typically

developing and children with ASD that examine the coherence

and causality of interaction between social and linguistic brain

systems will provide valuable new theoretical data as well as

potentially improving the early diagnosis and treatment of chil-

dren with autism.

Neurobiological Foundations of Communicative Learning Humansarenot theonlyspeciesinwhichcommunicativelearning

is affected by social interaction (see Fitch et al., 2010, for review).

Young zebra finches need visual interaction with a tutor bird to

learn song in the laboratory (Eales, 1989). A zebra finch will over-

ride its innate preference for conspecific song if a Bengalese finch

foster father feeds it, even when adult zebra finch males can be

heard nearby (Immelmann, 1969). More recent data indicate

that male zebra finches vary their songs across social contexts;

songs produced when singing to females vary from those

produced in isolation, and females prefer these ‘‘directed’’ songs

(Woolley and Doupe, 2008). Moreover, gene expression in high-

level auditory areas is involved in this kind of social context

perception (Woolley and Doupe, 2008). White-crowned spar-

rows, which reject the audiotaped songs of alien species, learn

the same alien songs when a live tutor sings them (Baptista and

Petrinovich, 1986). In barn owls (Brainard and Knudsen, 1998)

and white-crowned sparrows (Baptista and Petrinovich, 1986),

a richer social environment extends the duration of the sensitive

periodforlearning.Socialcontextsalso advancesongproduction

in birds; male cowbirds respond to the social gestures and

displays of females, which affect the rate, quality, and retention

of song elements in their repertoires (West and King, 1988), and

white-crowned sparrow tutors provide acoustic feedback that

affects the repertoires of young birds (Nelson and Marler, 1994).

Studies of the brain systems linking social and auditory-vocal

learning in humans and birds may significantly advance theories

in the near future (Doupe and Kuhl, 2008).

Neural Underpinnings of Cognitive and Social Influences on Language Learning Our current model of neural commitment to language describes

a significant role for cognitive processes such as attention in

language learning (Kuhl et al., 2008). Studies of brain rhythms

in infants and other neuroscience research in the next decade

promise to reveal the intricate relationships between language

and cognitive processes.

Language evolved to address a need for social communica-

tion and evolution may have forged a link between language

and the social brain in humans (Adolphs, 2003; Dunbar, 1998;

Kuhl, 2007; Pulvermuller, 2005). Social interaction appears to

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 723

Neuron

Review

be necessary for language learning in infants (Kuhl et al., 2003),

and an individual infant’s social behavior is linked to their ability

to learn new language material (Conboy et al., 2008a). In fact,

social ‘‘gating’’ may explain why social factors play a far more

significant role than previously realized in human learning across

domains throughout our lifetimes (Meltzoff et al., 2009). If social

factors ‘‘gate’’ computational learning, as proposed, infants

would be protected from meaningless calculations – learning

would be restricted to signals that derive from live humans rather

than other sources (Doupe and Kuhl, 2008; Evans and Marler,

1995; Marler, 1991). Constraints of this kind appear to exist for

infant imitation: when infants hear nonspeech sounds with the

same frequency components as speech, they do not attempt

to imitate them (Kuhl et al., 1991).

Research has begun to appear on the development of the

neural networks in humans that constitute the ‘‘social brain’’

and invoke a sense of relationship between the self and other,

as well as on social understanding systems that link perception

and action (Hari and Kujala, 2009). Neuroscience studies using

speech and imaging techniques are beginning to examine links

between sensory and motor brain systems (Pulvermuller, 2005;

Rizzolatti and Craighero, 2004), and the fact that MEG has now

been demonstrated to be feasible for developmental studies of

speech perception in infants during the first year of life (Imada

et al., 2006) provides exciting opportunities. MEG studies

of brain activation in infants during social versus nonsocial

language experience will allow us to investigate cognitive effects

via brain rhythms and also examine whether social brain

networks are activated differentially under the two conditions.

Many questions remain about the impact of cognitive skills

and social interaction on natural speech and language learning.

As reviewed, new data show the extensive interface between

cognition and language and indicate that whether or not multiple

languages are experienced in infancy affects cognitive brain

systems. The idea that social interaction is integral to language

learning has been raised previously for word learning; however,

previous data and theorizing have not tied early phonetic

learning to social factors. Doing so suggests a more fundamental

connection between the motivation to learn socially and the

mechanisms that enable language learning.

Understanding how language learning, cognition, and social

processing interact in development may ultimately explain the

mechanisms underlying the critical period for language learning.

Furthermore, understanding the mechanism underlying the crit-

ical period may help us develop methods that more effectively

teach second languages to adult learners. Neuroscience studies

over the next decade will lead the way on this theoretical work,

and also advance our understanding of the practical results of

training methods, both for adults learning new languages, and

children with developmental disabilities struggling to learn their

first language. These advances will promote the science of

learning in the domain of language, and potentially, shed light

on human learning mechanisms more generally.

ACKNOWLEDGMENTS

The author and research reported here were supported by a grant from the National Science Foundation’s Science of Learning Program to the University

724 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

of Washington LIFE Center (SBE-0354453), and by grants from the National Institutes of Health (HD37954, HD55782, HD02274, DC04661).

REFERENCES

Abramson, A.S., and Lisker, L. (1970). Discriminability along the voicing continuum: cross-language tests. Proc. Int. Congr. Phon. Sci. 6, 569–573.

Adolphs, R. (2003). Cognitive neuroscience of human social behaviour. Nat. Rev. Neurosci. 4, 165–178.

Aslin, R.N., and Mehler, J. (2005). Near-infrared spectroscopy for functional studies of brain activity in human infants: promise, prospects, and challenges. J. Biomed. Opt. 10, 11009.

Baldwin, D.A. (1995). Understanding the link between joint attention and language. In Joint Attention: Its Origins and Role in Development, C. Moore and P.J. Dunham, eds. (Hillsdale, NJ: Lawrence Erlbaum Associates), pp. 131–158.

Baptista, L.F., and Petrinovich, L. (1986). Song development in the white- crowned sparrow: social factors and sex differences. Anim. Behav. 34, 1359–1371.

Bell, M.A., and Wolfe, C.D. (2007). Changes in brain functioning from infancy to early childhood: evidence from EEG power and coherence during working memory tasks. Dev. Neuropsychol. 31, 21–38.

Benasich, A.A., Gou, Z., Choudhury, N., and Harris, K.D. (2008). Early cognitive and language skills are linked to resting frontal gamma power across the first 3 years. Behav. Brain Res. 195, 215–222.

Best, C.C., and McRoberts, G.W. (2003). Infant perception of non-native consonant contrasts that adults assimilate in different ways. Lang. Speech 46, 183–216.

Bialystok, E. (1991). Language Processing in Bilingual Children (Cambridge University Press).

Bialystok, E. (1999). Cognitive complexity and attentional control in the bilin- gual mind. Child Dev. 70, 636–644.

Bialystok, E. (2001). Bilingualism in Development: Language, Literacy, and Cognition (New York: Cambridge University Press).

Bialystok, E., and Hakuta, K. (1994). Other Words: The Science and Psychology of Second-Language Acquisition (New York: Basic Books).

Birdsong, D. (1992). Ultimate attainment in second language acquisition. Ling. Soc. Am. 68, 706–755.

Birdsong, D., and Molis, M. (2001). On the evidence for maturational constraints in second-language acquisitions. J. Mem. Lang. 44, 235–249.

Bortfeld, H., Wruck, E., and Boas, D.A. (2007). Assessing infants’ cortical response to speech using near-infrared spectroscopy. Neuroimage 34, 407–415.

Brainard, M.S., and Knudsen, E.I. (1998). Sensitive periods for visual calibra- tion of the auditory space map in the barn owl optic tectum. J. Neurosci. 18, 3929–3942.

Brooks, R., and Meltzoff, A.N. (2002). The importance of eyes: how infants interpret adult looking behavior. Dev. Psychol. 38, 958–966.

Brooks, R., and Meltzoff, A.N. (2005). The development of gaze following and its relation to language. Dev. Sci. 8, 535–543.

Bruer, J.T. (2008). Critical periods in second language learning: distinguishing phenomena from explanation. In Brain, Behavior and Learning in Language and Reading Disorders, M. Mody and E. Silliman, eds. (New York: The Guilford Press), pp. 72–96.

Bruner, J. (1983). Child’s Talk: Learning to Use Language (New York: W.W. Norton).

Burnham, D., Kitamura, C., and Vollmer-Conna, U. (2002). What’s new, pussycat? On talking to babies and animals. Science 296, 1435.

Cardillo, G.C. (2010). Predicting the predictors: Individual differences in longi- tudinal relationships between infant phoneme perception, toddler vocabulary,

Neuron

Review

and preschooler language and phonological awareness. Doctoral Disserta- tion, University of Washington.

Carlson, S.M., and Meltzoff, A.N. (2008). Bilingual experience and executive functioning in young children. Dev. Sci. 11, 282–298.

Cheour, M., Imada, T., Taulu, S., Ahonen, A., Salonen, J., and Kuhl, P.K. (2004). Magnetoencephalography is feasible for infant assessment of auditory discrimination. Exp. Neurol. 190 (Suppl 1 ), S44–S51.

Chomsky, N. (1959). Review of Skinner’s Verbal Behavior. Language 35, 26–58.

Coffey-Corina, S., Padden, D., Kuhl, P.K., and Dawson, G. (2008). ERPs to words correlate with behavioral measures in children with Autism Spectrum Disorder. J. Acoust. Soc. Am. 123, 3742–3748.

Conboy, B.T., and Kuhl, P.K. (2010). Impact of second-language experience in infancy: brain measures of first- and second-language speech perception. Dev. Sci., in press. Published online June 28, 2010. 10.1111/j.1467-7687. 2010.00973.x.

Conboy, B.T., Rivera-Gaxiola, M., Silva-Pereyra, J., and Kuhl, P.K. (2008a). Event-related potential studies of early language processing at the phoneme, word, and sentence levels. In Early Language Development: Volume 5. Bridging Brain and Behavior, Trends in Language Acquisition Research, A.D. Friederici and G. Thierry, eds. (Amsterdam, The Netherlands: John Benjamins), pp. 23–64.

Conboy, B.T., Sommerville, J.A., and Kuhl, P.K. (2008b). Cognitive control factors in speech perception at 11 months. Dev. Psychol. 44, 1505–1512.

de Boer, B., and Kuhl, P.K. (2003). Investigating the role of infant-directed speech with a computer model. ARLO 4, 129–134.

de Boysson-Bardies, B. (1993). Ontogeny of language-specific syllabic productions. In Developmental Neurocognition: Speech and Face Processing in the First Year of Life, B. de Boysson-Bardies, S. de Schonen, P. Jusczyk, P. McNeilage, and J. Morton, eds. (Dordrecht, Netherlands: Kluwer), pp. 353–363.

Dehaene-Lambertz, G., Dehaene, S., and Hertz-Pannier, L. (2002). Functional neuroimaging of speech perception in infants. Science 298, 2013–2015.

Dehaene-Lambertz, G., Hertz-Pannier, L., Dubois, J., Mériaux, S., Roche, A., Sigman, M., and Dehaene, S. (2006). Functional organization of perisylvian activation during presentation of sentences in preverbal infants. Proc. Natl. Acad. Sci. USA 103, 14240–14245.

Doupe, A.J., and Kuhl, P.K. (2008). Birdsong and human speech: common themes and mechanisms. In Neuroscience of Birdsong, H.P. Zeigler and P. Marler, eds. (Cambridge, England: Cambridge University Press), pp. 5–31.

Dunbar, R.I.M. (1998). The social brain hypothesis. Evol. Anthropol. 6, 178–190.

Durston, S., Thomas, K.M., Worden, M.S., Yang, Y., and Casey, B.J. (2002). The effect of preceding context on inhibition: an event-related fMRI study. Neuroimage 16, 449–453.

Eales, L.A. (1989). The influence of visual and vocal interaction on song learning in zebra finches. Anim. Behav. 37, 507–508.

Eimas, P.D. (1975). Auditory and phonetic coding of the cues for speech: discrimination of the /r–l/ distinction by young infants. Percept. Psychophys. 18, 341–347.

Eimas, P.D., Siqueland, E.R., Jusczyk, P., and Vigorito, J. (1971). Speech perception in infants. Science 171, 303–306.

Englund, K.T. (2005). Voice onset time in infant directed speech over the first six months. First Lang. 25, 219–234.

Evans, C.S., and Marler, P. (1995). Language and animal communication: parallels and contrasts. In Comparative Approaches to Cognitive Science: Complex Adaptive Systems, H.L. Roitblat and J.-A. Meyer, eds. (Cambridge, MA: MIT Press), pp. 341–382.

Ferguson C.A., Menn L., and Stoel-Gammon C., eds. (1992). Phonological Development: Models, Research, Implications (Timonium, MD: York Press).

Fernald, A., and Simon, T. (1984). Expanded intonation contours in mothers’ speech to newborns. Dev. Psychol. 20, 104–113.

Fiser, J., and Aslin, R.N. (2002). Statistical learning of new visual feature combi- nations by infants. Proc. Natl. Acad. Sci. USA 99, 15822–15826.

Fitch, W.T., Huber, L., and Bugnyar, T. (2010). Social cognition and the evolu- tion of language: constructing cognitive phylogenies. Neuron 65, 795–814.

Flege, J.E. (1991). Age of learning affects the authenticity of voice-onset time (VOT) in stop consonants produced in a second language. J. Acoust. Soc. Am. 89, 395–411.

Flege, J.E., Yeni-Komshian, G.H., and Liu, S. (1999). Age constraints on second-language acquisition. J. Mem. Lang. 41, 78–104.

Friederici, A.D. (2005). Neurophysiological markers of early language acquisi- tion: from syllables to sentences. Trends Cogn. Sci. 9, 481–488.

Friederici, A.D., and Wartenburger, I. (2010). Language and Brain. Wiley Inter- disciplinary Reviews: Cognitive Science 1, 150–159.

Gernsbacher, M.A., and Kaschak, M.P. (2003). Neuroimaging studies of language production and comprehension. Annu. Rev. Psychol. 54, 91–114.

Gevins, A., Smith, M.E., McEvoy, L., and Yu, D. (1997). High-resolution EEG mapping of cortical activation related to working memory: effects of task diffi- culty, type of processing, and practice. Cereb. Cortex 7, 374–385.

Golestani, N., and Pallier, C. (2007). Anatomical correlates of foreign speech sound production. Cereb. Cortex 17, 929–934.

Gratton, G., and Fabiani, M. (2001). Shedding light on brain function: the event- related optical signal. Trends Cogn. Sci. 5, 357–363.

Grieser, D.L., and Kuhl, P.K. (1988). Maternal speech to infants in a tonal language: support for universal prosodic features in motherese. Dev. Psychol. 24, 14–20.

Guion, S.G., and Pederson, E. (2007). Investigating the role of attention in phonetic learning. In Language Experience in Second Language Speech Learning: In Honor of James Emil Flege, O.-S. Bohn and M. Munro, eds. (Amsterdam: John Benjamins), pp. 55–77.

Hari, R., and Kujala, M.V. (2009). Brain basis of human social interaction: from concepts to brain imaging. Physiol. Rev. 89, 453–479.

Hauser, M.D., Newport, E.L., and Aslin, R.N. (2001). Segmentation of the speech stream in a non-human primate: statistical learning in cotton-top tama- rins. Cognition 78, B53–B64.

Homae, F., Watanabe, H., Nakano, T., Asakawa, K., and Taga, G. (2006). The right hemisphere of sleeping infant perceives sentential prosody. Neurosci. Res. 54, 276–280.

Imada, T., Zhang, Y., Cheour, M., Taulu, S., Ahonen, A., and Kuhl, P.K. (2006). Infant speech perception activates Broca’s area: a developmental magneto- encephalography study. Neuroreport 17, 957–962.

Immelmann, K. (1969). Song development in the zebra finch and other estrildid finches. In Bird Vocalizations, R. Hinde, ed. (London: Cambridge University Press), pp. 61–74.

Johnson, J.S., and Newport, E.L. (1989). Critical period effects in second language learning: the influence of maturational state on the acquisition of English as a second language. Cognit. Psychol. 21, 60–99.

Kirkham, N.Z., Slemmer, J.A., and Johnson, S.P. (2002). Visual statistical learning in infancy: evidence for a domain general learning mechanism. Cogni- tion 83, B35–B42.

Knudsen, E.I. (2004). Sensitive periods in the development of the brain and behavior. J. Cogn. Neurosci. 16, 1412–1425.

Krause, C.M., Sillanmäki, L., Koivisto, M., Saarela, C., Häggqvist, A., Laine, M., and Hämäläinen, H. (2000). The effects of memory load on event-related EEG desynchronization and synchronization. Clin. Neurophysiol. 111, 2071–2078.

Kuhl, P.K. (2004). Early language acquisition: cracking the speech code. Nat. Rev. Neurosci. 5, 831–843.

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 725

Neuron

Review

Kuhl, P.K. (2007). Is speech learning ‘gated’ by the social brain? Dev. Sci. 10, 110–120.

Kuhl, P.K. (2009). Early language acquisition: neural substrates and theoretical models. In The Cognitive Neurosciences IV, M.S. Gazzaniga, ed. (Cambridge, MA: MIT Press), pp. 837–854.

Kuhl, P.K., and Meltzoff, A.N. (1982). The bimodal perception of speech in infancy. Science 218, 1138–1141.

Kuhl, P.K., and Meltzoff, A.N. (1996). Infant vocalizations in response to speech: vocal imitation and developmental change. J. Acoust. Soc. Am. 100, 2425–2438.

Kuhl, P.K., and Rivera-Gaxiola, M. (2008). Neural substrates of language acquisition. Annu. Rev. Neurosci. 31, 511–534.

Kuhl, P.K., Williams, K.A., and Meltzoff, A.N. (1991). Cross-modal speech perception in adults and infants using nonspeech auditory stimuli. J. Exp. Psy- chol. Hum. Percept. Perform. 17, 829–840.

Kuhl, P.K., Williams, K.A., Lacerda, F., Stevens, K.N., and Lindblom, B. (1992). Linguistic experience alters phonetic perception in infants by 6 months of age. Science 255, 606–608.

Kuhl, P.K., Andruski, J.E., Chistovich, I.A., Chistovich, L.A., Kozhevnikova, E.V., Ryskina, V.L., Stolyarova, E.I., Sundberg, U., and Lacerda, F. (1997). Cross-language analysis of phonetic units in language addressed to infants. Science 277, 684–686.

Kuhl, P.K., Tsao, F.-M., and Liu, H.-M. (2003). Foreign-language experience in infancy: effects of short-term exposure and social interaction on phonetic learning. Proc. Natl. Acad. Sci. USA 100, 9096–9101.

Kuhl, P.K., Conboy, B.T., Padden, D., Nelson, T., and Pruitt, J. (2005a). Early speech perception and later language development: implications for the ‘crit- ical period.’. Lang. Learn. Dev. 1, 237–264.

Kuhl, P.K., Coffey-Corina, S., Padden, D., and Dawson, G. (2005b). Links between social and linguistic processing of speech in preschool children with autism: behavioral and electrophysiological measures. Dev. Sci. 8, F1–F12.

Kuhl, P.K., Stevens, E., Hayashi, A., Deguchi, T., Kiritani, S., and Iverson, P. (2006). Infants show a facilitation effect for native language phonetic percep- tion between 6 and 12 months. Dev. Sci. 9, F13–F21.

Kuhl, P.K., Conboy, B.T., Coffey-Corina, S., Padden, D., Rivera-Gaxiola, M., and Nelson, T. (2008). Phonetic learning as a pathway to language: new data and native language magnet theory expanded (NLM-e). Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 979–1000.

Ladefoged, P. (2001). Vowels and Consonants: An Introduction to the Sounds of Language (Oxford: Blackwell Publishers).

Lasky, R.E., Syrdal-Lasky, A., and Klein, R.E. (1975). VOT discrimination by four to six and a half month old infants from Spanish environments. J. Exp. Child Psychol. 20, 215–225.

Lenneberg, E. (1967). Biological Foundations of Language (New York: John Wiley and Sons).

Liberman, A.M., and Mattingly, I.G. (1985). The motor theory of speech perception revised. Cognition 21, 1–36.

Liu, H.-M., Kuhl, P.K., and Tsao, F.-M. (2003). An association between mothers’ speech clarity and infants’ speech discrimination skills. Dev. Sci. 6, F1–F10.

Liu, H.-M., Tsao, F.-M., and Kuhl, P.K. (2007). Acoustic analysis of lexical tone in Mandarin infant-directed speech. Dev. Psychol. 43, 912–917.

Liu, H.-M., Tsao, F.-M., and Kuhl, P.K. (2009). Age-related changes in acoustic modifications of Mandarin maternal speech to preverbal infants and five-year- old children: a longitudinal study. J. Child Lang. 36, 909–922.

Lotto, A.J., Sato, M., and Diehl, R. (2004). Mapping the task for the second language learner: the case of Japanese acquisition of /r/ and /l. In From Sound to Sense, J. Slitka, S. Manuel, and M. Matthies, eds. (Cambridge, MA: MIT Press), pp. C181–C186.

726 Neuron 67, September 9, 2010 ª2010 Elsevier Inc.

Marler, P. (1991). The instinct to learn. In The Epigenesis of Mind: Essays on Biology and Cognition, S. Carey and R. Gelman, eds. (Hillsdale, NJ: Lawrence Erlbaum Associates), pp. 37–66.

Mayberry, R.I., and Lock, E. (2003). Age constraints on first versus second language acquisition: evidence for linguistic plasticity and epigenesis. Brain Lang. 87, 369–384.

Maye, J., Werker, J.F., and Gerken, L. (2002). Infant sensitivity to distributional information can affect phonetic discrimination. Cognition 82, B101–B111.

Maye, J., Weiss, D.J., and Aslin, R.N. (2008). Statistical phonetic learning in infants: facilitation and feature generalization. Dev. Sci. 11, 122–134.

Meltzoff, A.N., and Decety, J. (2003). What imitation tells us about social cogni- tion: a rapprochement between developmental psychology and cognitive neuroscience. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358, 491–500.

Meltzoff, A.N., Kuhl, P.K., Movellan, J., and Sejnowski, T. (2009). Foundations for a new science of learning. Science 17, 284–288.

Mills, D., Coffey-Corina, S., and Neville, H. (1993). Language acquisition and cerebral specialization in 20 month old infants. J. Cogn. Neurosci. 5, 317–334.

Mills, D., Coffey-Corina, S., and Neville, H. (1997). Language comprehension and cerebral specialization from 13 to 20 months. Dev. Neuropsychol. 13, 397–445.

Mundy, P., and Gomes, A. (1998). Individual differences in joint attention skill development in the second year. Infant Behav. Dev. 21, 469–482.

Nelson, D.A., and Marler, P. (1994). Selection-based learning in bird song development. Proc. Natl. Acad. Sci. USA 91, 10498–10501.

Neville, H.J., Coffey, S.A., Lawson, D.S., Fischer, A., Emmorey, K., and Bellugi, U. (1997). Neural systems mediating American sign language: effects of sensory experience and age of acquisition. Brain Lang. 57, 285–308.

Newport, E. (1990). Maturational constraints on language learning. Cogn. Sci. 14, 11–28.

Newport, E.L., Bavelier, D., and Neville, H.J. (2001). Critical thinking about critical periods: perspectives on a critical period for language acquisition. In Language, Brain, and Cognitive Development: Essays in Honor of Jacques Mehlter, E. Dupoux, ed. (Cambridge, MA: MIT Press), pp. 481–502.

Ortiz-Mantilla, S., Choe, M.-S., Flax, J., Grant, P.E., and Benasich, A.A. (2010). Associations between the size of the amygdale in infancy and language abili- ties during the preschool years in normally developing children. Neuroimage 49, 2791–2799.

Patterson, M.L., and Werker, J.F. (1999). Matching phonetic information in lips and voice is robust in 4.5-month-old infants. Infant Behav. Dev. 22, 237–247.

Peña, M., Bonatti, L.L., Nespor, M., and Mehler, J. (2002). Signal-driven computations in speech processing. Science 298, 604–607.

Petitto, L.A., and Marentette, P.F. (1991). Babbling in the manual mode: evidence for the ontogeny of language. Science 251, 1493–1496.

Posner M.I., ed. (2004). Cognitive Neuroscience of Attention (New York: Guil- ford Press).

Pulvermuller, F. (2005). Brain mechanisms linking language to action. Nat. Rev. Neurosci. 6, 574–582.

Rabiner, L.R., and Huang, B.H. (1993). Fundamentals of Speech Recognition (Englewood Cliffs, NJ: Prentice Hall).

Rivera-Gaxiola, M., Silva-Pereyra, J., and Kuhl, P.K. (2005). Brain potentials to native and non-native speech contrasts in 7- and 11-month-old American infants. Dev. Sci. 8, 162–172.

Rizzolatti, G. (2005). The mirror neuron system and imitation. In Perspectives on Imitation: From Neuroscience to Social Science – I: Mechanisms of Imita- tion and Imitation in Animals, S. Hurley and N. Chater, eds. (Cambridge, MA: MIT Press), pp. 55–76.

Rizzolatti, G., and Craighero, L. (2004). The mirror-neuron system. Annu. Rev. Neurosci. 27, 169–192.

Neuron

Review

Saffran, J.R., Aslin, R.N., and Newport, E.L. (1996). Statistical learning by 8-month-old infants. Science 274, 1926–1928.

Saffran, J.R., Johnson, E.K., Aslin, R.N., and Newport, E.L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition 70, 27–52.

Saffran, J.R., Werker, J.F., and Werner, L.A. (2006). The infant’s auditory world: hearing, speech, and the beginnings of language. In Handbook of Child Psychology: Volume 2, Cognition, Perception and Language VI, W. Damon and R.M. Lerner, series eds., R. Siegler and D. Kuhn, volume eds. (New York: Wiley), pp. 58–108.

Skinner, B.F. (1957). Verbal Behavior (New York: Appleton-Century-Crofts).

Taga, G., and Asakawa, K. (2007). Selectivity and localization of cortical response to auditory and visual stimulation in awake infants aged 2 to 4 months. Neuroimage 36, 1246–1252.

Tamm, L., Menon, V., and Reiss, A.L. (2002). Maturation of brain function asso- ciated with response inhibition. J. Am. Acad. Child Adolesc. Psychiatry 41, 1231–1238.

Teinonen, T., Aslin, R.N., Alku, P., and Csibra, G. (2008). Visual speech contrib- utes to phonetic learning in 6-month-old infants. Cognition 108, 850–855.

Teinonen, T., Fellman, V., Näätänen, R., Alku, P., and Huotilainen, M. (2009). Statistical language learning in neonates revealed by event-related brain potentials. BMC Neurosci. 10, 21.

Tomasello, M. (2003a). Constructing A Language: A Usage-Based Theory of Language Acquisition (Cambridge, MA: Harvard University Press).

Tomasello, M. (2003b). The key is social cognition. In Language and Thought, D. Gentner and S. Kuczaj, eds. (Cambridge, MA: MIT Press), pp. 47–51.

Tsao, F.-M., Liu, H.-M., and Kuhl, P.K. (2004). Speech perception in infancy predicts language development in the second year of life: a longitudinal study. Child Dev. 75, 1067–1084.

Tsao, F.-M., Liu, H.-M., and Kuhl, P.K. (2006). Perception of native and non- native affricate-fricative contrasts: cross-language tests on adults and infants. J. Acoust. Soc. Am. 120, 2285–2294.

Vygotsky, L.S. (1962). Thought and Language (Cambridge, MA: MIT Press).

Wang, Y., Kuhl, P.K., Chen, C., and Dong, Q. (2009). Sustained and transient language control in the bilingual brain. Neuroimage 47, 414–422.

Weber-Fox, C.M., and Neville, H.J. (1999). Functional neural subsystems are differentially affected by delays in second language immersion: ERP and

behavioral evidence in bilinguals. In Second Language Acquisition and the Critical Period Hypothesis, D. Birdsong, ed. (Mahwah, NJ: Lawerence Erlbaum and Associates, Inc), pp. 23–38.

Werker, J.F., and Curtin, S. (2005). PRIMIR: a developmental framework of infant speech processing. Lang. Learn. Dev. 1, 197–234.

Werker, J.F., and Lalonde, C. (1988). Cross-language speech perception: initial capabilities and developmental change. Dev. Psychol. 24, 672–683.

Werker, J.F., and Tees, R.C. (1984). Cross-language speech perception: evidence for perceptual reorganization during the first year of life. Infant Behav. Dev. 7, 49–63.

Werker, J.F., Pons, F., Dietrich, C., Kajikawa, S., Fais, L., and Amano, S. (2007). Infant-directed speech supports phonetic category learning in English and Japanese. Cognition 103, 147–162.

West, M.J., and King, A.P. (1988). Female visual displays affect the develop- ment of male song in the cowbird. Nature 334, 244–246.

White, L., and Genesee, F. (1996). How native is near-native? The issue of ulti- mate attainment in adult second language acquisition. Second Lang. Res. 12, 233–265.

Woolley, S.C., and Doupe, A.J. (2008). Social context-induced song variation affects female behavior and gene expression. PLoS Biol. 6, e62.

Yeni-Komshian, G.H., Flege, J.E., and Liu, S. (2000). Pronunciation proficiency in the first and second languages of Korean–English bilinguals. Bilingualism: Lang. Cogn. 3, 131–149.

Yoshida, K.A., Pons, F., Cady, J.C., and Werker, J.F. (2006). Distributional learning and attention in phonological development. Paper presented at Inter- national Conference on Infant Studies, Kyoto, Japan, 19–23 June.

Yoshida, K.A., Pons, F., Maye, J., and Werker, J.F. (2010). Distributional phonetic learning at 10 months of age. Infancy 15, 420–433.

Zhang, Y., Kuhl, P.K., Imada, T., Kotani, M., and Tohkura, Y. (2005). Effects of language experience: neural commitment to language-specific auditory patterns. Neuroimage 26, 703–720.

Zhang, Y., Kuhl, P.K., Imada, T., Iverson, P., Pruitt, J., Stevens, E.B., Kawa- katsu, M., Tohkura, Y., and Nemoto, I. (2009). Neural signatures of phonetic learning in adulthood: a magnetoencephalography study. Neuroimage 46, 226–240.

Neuron 67, September 9, 2010 ª2010 Elsevier Inc. 727