Speech Percep (Gervain & Mehler 2010 Annu Rev Psychol)

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Speech Perception andLanguage Acquisitionin the First Year of Life

Judit Gervain1 and Jacques Mehler2

1Department of Psychology, University of British Columbia, Vancouver British ColumbiV6T 1Z4, Canada

2Neuroscience Sector, Scuola Internazionale Superiore di Studi Avanzati,Trieste 31014, Italy; email: [email protected]

Annu. Rev. Psychol. 2010. 61:191218

First published online as a Review in Advance onSeptember 28, 2009

TheAnnual Review of Psychologyis online atpsych.annualreviews.org

This articles doi:10.1146/annurev.psych.093008.100408

Copyright c2010 by Annual Reviews.All rights reserved

0066-4308/10/0110-0191$20.00

Key Words

infancy, learning mechanisms, phonological bootstrapping, evolutionof language

Abstract

During the first year of life, infants pass important milestones in language development. We review some of the experimental evidence con

cerning these milestones in the domains of speech perception, phonological development, word learning, morphosyntactic acquisition, and

bilingualism, emphasizing their interactions. We discuss them in th

context of their biological underpinnings, introducing the most recenadvances not only in language development, but also in neighboring

areas such as genetics and the comparative research on animal communication systems. We argue for a theory of language acquisition that in

tegrates behavioral, cognitive, neural, and evolutionary considerationand proposes to unify previously opposing theoretical stances, such a

statistical learning, rule-based nativist accounts, and perceptual learnintheories.

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Contents

INTRODUCTION .. . . . . . . . . . . . . . . . . 192THEORETICAL APPROACHES . . . . 193

Nativist Approachesto Language Acquisition. . . . . . . . . 193

Perceptual Primitives

in Language Acquisition. . . . . . . . . 195Statistical Approaches

to Language Acquisition. . . . . . . . . 195EVOLUTIONARY ORIGINS AND

BIOLOGICAL FOUNDATIONS:APES, BIRDS, AND HUMANS . . . 196

Genetic Studies of SpeechProduction and Language . . . . . . . 197

Similarities Between Birdsongand Human Speech . . . . . . . . . . . . . 197

Does a New Computational

Component Cause theEmergence of Languagein the Human Brain?. . . . . . . . . . . . 199

THE EARLY SENSITIVITY TO

SPEECH AND SUBSEQUENTPHONOLOGICAL

DEVELOPMENT .. . . . . . . . . . . . . . . 200Acquisition of the Native

P h o n o l o g y . . . . . . . . . . . . . . . . . . . . . . 2 0 1The Early Sensitivity to Rhythm and

Its Potential BootstrappingR o l e . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 0

THE WORD SEGMENTATIONPROBLEM: LEARNING

MECHANISMS AND

PERCEPTUAL PRIMITIVES . . . . 20Statistically Based Word

S eg m en tation . . . . . . . . . . . . . . . . . . . 2 0

Perceptual and Linguistic

Constraints on StatisticalLearning . . . . . . . . . . . . . . . . . . . . . . . 20

Language-Specific Cuesto Segmentation . . . . . . . . . . . . . . . . 20

The Interaction of Statistical andLanguage-Specific Cues......... 20

Early Form-Meaning Associations . . 20

BROAD LEXICAL CATEGORIES:FUNCTORS AND CONTENTWORDS . . . . . . . . . . . . . . . . . . . . . . . . . . 20

WORD ORDER AND OTHERTYPOLOGICAL

DIFFERENCES . . . . . . . . . . . . . . . . . . 20

THE NEURAL CORRELATESOF LANGUAGE IN YOUNG

INFANTS . . . . . . . . . . . . . . . . . . . . . . . . . 21CONCLUSION . . . . . . . . . . . . . . . . . . . . . 21

INTRODUCTION

The emergence of language has intrigued sci-entists and the general public alike, but it was

only in the second half of the twentieth century

that a systematic empirical investigation of lan-guage acquisition began. This work was greatly

inspired by thesuggestion that theenvironmentis mainly a trigger rather than a tutor for lan-

guage acquisition, at least during the first yearsof life (Chomsky 1959). Consequently, to ex-

plain the uniquely human capacity of language,scholars proposed innate acquisition mecha-

nisms, specific to language (Chomsky 1959).A few years later, research into the biologi-

cal foundations of language was expanded, giv-ing a better grasp of the innate dispositions

for language acquisition (Lenneberg 1967).contrast, other researchers suggested that cl

sical learning mechanisms, ones that humashare with other animals, may be suffici

to acquire language (Elman 1996, Tomase2000). Under this view, the human specific

of language arises from quantitative rather thqualitative differences between the species.

Some of these theoretical questions m

be resolved by studying preverbal infants,particular newborns, as this allows us to detmine how much of our language acquisiti

abilities are due to dispositions detecta

much before the surroundings have shapour cognitive apparatus. Therefore, our revi

mostly focuses on the development of langua

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and its underlying mechanisms during the firstyear of life. This choice is also justified by the

growing body of research and recent advances

in understanding how different mechanisms,such as statistical and distributional learning,

rule extraction, as well as perceptual andmemory constraints, work together during

language development.Our review discusses landmarks in language

acquisition as well as their biological underpin-nings. We focus on studies that connect brain,

mind, and behavior. We believe that buildingbridges between these different levels is the way

of the future and that the next decades will seethesuccessofsuchintegrativemethodologyand

theory building.

In the review, we first describe the differ-ent theoretical approaches to language acqui-

sition. We then review the increasingly impor-tant and fast-growing body of literature on the

biological foundations of human language, fo-cusing mostly on genetic and evolutionary as-

pects. Then we review the empirical evidencethat has accumulated over the past decades in

support of the theories and approaches intro-duced. We discuss the findings following the

levels of organization in language from phonol-ogy through word segmentation and lexical ac-

quisition to grammar. Finally, we consider some

of the novel empirical findings that relate to theneural basis of language acquisition and pro-

cessing in newborns and young infants. Build-ing on these empirical findings, we argue for

an integrative theory of language acquisition,proposing that rule learning, perceptual boot-

strapping, and statistical learning all contributeto different levels of language acquisition, and

that the most interesting objective is to under-stand their interactions andthe division of labor

among them.

THEORETICAL APPROACHES

Language acquisition came to the forefront

of cognitive and developmental research whenNoam Chomsky (1957, 1959) pointed out

that acquiring language poses a serious learn-ing problem. Infants never receive explicit

information about the structure of the gram-mar that generated the utterances they are

exposed to. In the absence of structural infor-

mation, the finite data set that infants receive asinput is compatible with an infinite number of

underlying rules or grammarsa challenge tolearning known in philosophy and mathematics

as the induction problem.The most important theoretical approaches

to language acquisition in the past 50 years haveinvestigated this logical problem, proposing so-

lutions to it or denying its existence.

Nativist Approachesto Language Acquisition

Language cannot be learned exclusively fromthe input, yet young infants seem to acquire

it with remarkable ease. Therefore, Chomsky(1959) argued that the acquisition process has

to be guided by innate knowledge. This log-ical argument gave rise to a nativist theoret-

ical approach to language acquisition as wellas a large body of related empirical research

(for a representative summary, see Guasti2002).This view capitalizes on the observation that al-

though they are superficially different from oneanother, languages of the world share a large

number of structural characteristics; for exam-

ple, they all use lexical categories like functors(small grammatical words, such as he, it, on, of,

this) and content words (e.g., nouns and verbsthat carry lexical meaning, such as flower, ta-

ble, run, sing). Under the nativist view, the uni-versal features of language design are part of

our species biological endowment and are en-coded in the language faculty as innate princi-

ples. By contrast, aspects of language structurethat vary (e.g., the relative order of verbs and

objects or whether a language allows pronom-inal subjects to be dropped) are assumed to be

encoded by parameters, i.e., mental switches

that implement all the universal options [e.g.,a verb-object (VO) order and an OV order; li-

censing pronoun-drop or not].This account assumed that infants are able

to detect and extract abstract regularities fromthe input. Indeed, Marcus et al. (1999) showed

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that 7-month-old infants are able to learn ab-stract, algebraic generalizations. In their study,

infants were familiarizedwith an artificialgram-

mar encoding an identity-based regularity (e.g.,ABB:wo fe fe). In the test phase, babies showed

longer looking times for items that were in-consistent with the grammar of familiarization

(e.g., ABA) than for items that were consistentwith it, indicating that they extracted the un-

derlying regularity.Under the principles and parameters view,

language acquisition is mediated by settingthe parameters to the values that characterize

the native language. For instance, an English-learning infant will have to set the word-order

parameter to VO, e.g., eat an apple, and the pro-

drop parameter to negative, e.g., It is raining,but not Is raining, while a Japanese infant will

set both parameters to the opposite value, e.g.,

ringo-wo taberu apple.accusative eat eat an ap-

ple and futte iru raining is (it) is raining.However, parameters are defined over abstract

linguistic entities such as verbs, nouns, and pro-nouns, so the infant still faces the problem of

linking these abstract mental representationsto actual physical entities in the speech signal

(Pinker 1984).One solution proposed to the linking prob-

lem is the use of bootstrapping mechanisms.

These are heuristic learning mechanisms thatexploit the universal correlations that exist be-

tween perceptually available, surface charac-teristics of a language and its abstract mor-

phosyntactic properties. Three types of surfacecues have been proposed to act as triggers for

bootstrapping.One approach (e.g., Pinker 1984) suggests

that the relevant cue is of semantic/conceptualnature. By understanding the general mean-

ing of some simple sentences and by knowingthe meaning of some words, typically nouns,

the infant can construct syntactic trees, given

configurational universals, such as the phrasestructure suggested by generative grammar or

other linguistic theories, which are believed tobe part of the innate language faculty. From

these trees, the child can derive the syntac-tic rules of her mother tongue, which in turn

help her parse and understand more compsentences.

A second approach (e.g., Gleitman Landau 1994) claims that the already acqui

pieces of syntactic knowledge help bootst

the rest of syntax. The initial (productive) licon of the child contains a large number

nouns. This allows the infant to track the potion of nouns within sentences. With this inf

mation, infants can learn the type andargumstructure of verbs. In English, for instance,

transitive verbs have one noun (phrase) (Npreceding them, transitive action verbs h

one NP preceding and one following thementalverbshaveoneNPprecedingtheman

clause following them, andso forth. Thus, upencountering a sentence containing an ini

NP and a final NP with a verb between the

the verb can be categorized as transitive.It is important to note that these two

proaches build on already acquired linguisknowledge. But how are these initial pieces

quired?Athirdapproach,theoneweareexploing here,suggests thatmorphosyntacticprop

ties are signaled by their acoustic/phonologicorrelates (Mehler et al. 2004; Morgan

Demuth 1996; Nespor et al. 1996, 2008). Morgan & Demuth (1996, p. 2) put it: [T]h

accounts propose that information availablespeech may contain clues to certain fundam

tal syntactic distinctions [. . .]. This approa

unlike the other two, assumes no prior lguistic knowledge on the part of the learn

and thus may explain the earliest acquisitioNouns and verbs, for instance, are abstr

lexical categories. However, in English, nouoften bear stress on the first syllable (reco

N: ) and verbs on the last (recV: ) (Cutler & Carter 1987, Da

& Kelly 1997). The stress pattern, then, can

as a cue to the two categories. Although thispecific to English, there seem to be phonolo

ical and prosodic cues that might signal synttic properties universally (Mehler et al. 20

Morgan & Demuth 1996; Nespor et al. 192008). An important focus of our review, the

fore, is not only to characterize howinfants pceive and learn about the acoustic, phone

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and phonological aspects of language, but alsoto explore how these might bootstrap the be-

ginnings of morphosyntax during the first year

of life.

Perceptual Primitivesin Language Acquisition

How the acoustic and phonological aspects ofspeech are related to underlying structure hasreceived increasing attention recently. Accord-

ing to a recent proposal by Endress et al. (2009),language might recruit previously existing per-

ceptual mechanisms or primitives and usetheir outputs to feed abstract linguistic com-

putations. In the perception and memory lit-erature, for instance, it has long been known

that sequence edges are particularly salient po-sitions, facilitatingperception, learning, and re-

call of elements in those positions (see En-dress et al. 2009 for a summary). This, the au-thors argue, might be related to why languages

show a universal preference for word-initialand word-final morphosyntactic processes as

opposed to word-internal ones; e.g., prefixingand suffixing are common among languages,

whereas infixing is very rare. Indeed, Endresset al. (2005) have recently demonstrated that

adult learners perform well in an artificial gram-marlearning task if the regularity that they need

to learn (identical adjacent repetition of sylla-bles) is at the edge of a syllable sequence, but

they fail if the same regularity appears sequence

internally.Similarly, as Endress et al. (2007) have

demonstrated, identical repetitions are per-ceived automatically as salient Gestalts by adult

learners in artificial grammar paradigms. Whenparticipants task was to learn a sequence of

three tones where the second and third toneswere identical, they succeeded. But they failed

when the tone sequences implemented an or-dinal regularity, for example, a high tone fol-

lowed by a low tone followed by a middle tone.Repetitions or identity appears to be a spe-

cial input configuration that is more readily

perceived than are other relations of the samemathematical complexity, for example, ordinal

relations.

In the following sections, we review howsome perceptual primitives, for example, the

detection of repetitions (Endress et al. 2005,2007; Gervain et al. 2008a), edge salience

(Endress et al. 2005, 2007), or prosodic group-

ing principles (Nespor et al. 2008), might helpbootstrap the acquisition of morphosyntactic

structure.

Statistical Approachesto Language Acquisition

Although the above described nativist position

has been very influential in the past 50 years, thelong tradition of empiricist approaches to lan-

guage acquisition has re-emerged in the pasttwo decades. These empiricist positions take

different forms, from statistical learning ap-proaches to connectionism (Elman et al. 1996);

what they share, though, is a belief that no in-nate language-specific knowledge is requiredto explain language acquisition. Rather, lan-

guage development is a piecemeal learning pro-cessthatrelieson general-purposemechanisms,

typically statistical in nature, shared by mostperceptual and cognitive domains. No innate

mental contents specific to language such aslexical categories, principles, or parameters are

assumed.These statistical learning approaches gained

new momentum in the language-acquisition lit-erature when Saffran et al. (1996) demonstrated

that very young infants are able to use statistical

information containedin speech andto then usesuch information to segment continuousspeech

into its constituent words. These initial resultshavegivenrisetoalargebodyofresearch,partly

reviewed in The Word Segmentation Problemsection below, investigating the role, scope, and

limitations of statistical learning in language ac-quisition.

These statistical accounts have also beencombined with social learning theory. In

Tomasellos (2000) account, infants begin bylearning frequently occurring sequences in the

input (e.g., Where is the toy? Where is the cup?

This is a ball. This is a dog.). As a secondstep, infants discover similarities among these

memorized sequences and extract semiabstract



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constructions or templates with a memorizedcomponent and one variable element (Whereis the ? This is a .). In these templates, the

variable elements are not variables in a mathe-matical sense, as their scope might be limited

to an arbitrary set of elements, for example,the members of the family, animals, or cars.

Abstract, adult-like linguistic knowledge is be-lieved to emerge only later, as young children

generalize further, using the semiabstract tem-plates. In Tomasellos (2000) view, infants and

young children are aided by their social learningabilities during the stepwise abstraction pro-

cess. They understand and construct the mean-ing of utterances not solely on the basis of the

semantics of the linguistic constituents in the

utterances addressed to them, but also by in-ferring the possible meaning from the speakers

intention, which even very young infants havebeen shown to have access to (Csibra & Gergely

2009, Gergely & Csibra 2003, Onishi &Baillargeon 2005).

Our review takes an integrative stance, em-phasizing that innate language-specific, percep-

tual, andstatistical mechanisms areallnecessaryfor language acquisition. What needs to be ex-

plored is their respective roles and the interac-tions between them.

EVOLUTIONARY ORIGINS ANDBIOLOGICAL FOUNDATIONS:

APES, BIRDS, AND HUMANS

The nativist position on language acquisitiongrounded language in human biology. The ini-

tial investigations focused on the neurobiologyof language, citing critical period effects, lan-

guage acquisition in congenitally blind and deafchildren, neurally based language pathologies,

etc. (see Lenneberg 1967 for a classical formu-

lation). More recently, in an attempt to inves-tigate the most fundamental questions about

language, numerous papers have explored itsevolution. In parallel, studies of nonhuman ani-

mals are proceeding in the hope of determiningwhether human abilities have arisen in the hu-

man mind as a patchwork of different precursorsystems that were present in ancestral species.

This line of research is of particular revance for language acquisition because it rai

convergent theoretical questions about inna

genetically endowed language abilities. If a pdisposition for language is innate in huma

it became part of our genetic heritage duing evolution. Therefore, research into nonh

man species cognitive andcommunicative abities complements studies of early infancy. Su

comparative research also sheds light on thesue of language specificity. If humans and no

human animals share cognitive and/or leaing abilities, these cannot be language spec

since only our species has language. Howevthey may have been precursors bringing h

mans closer to language.Research comparing human (infant) l

guage acquisition and nonhuman cogniti

perceptual, and learning abilities usually taone of two routes. Traditionally, humans ca

bilities were compared to those of their closevolutionary relatives, primates. Indeed, co

parative studies between infants and primahave shown that the latter are also capable

statistical learning (Newport et al. 2004), laguage discrimination on the basis of rhyth

(Ramus et al. 2000), and categorical phoneperception (Morse et al. 1987), among oth

abilities. More recently, birdsong has been plored as a possible analogy for human la

guage. This may, at first, appear surprisi

since songbirds are not closely related to hmans. However, vocal communication, like h

man language, plays an important role in sonbirds cognitive as well as social developme

which is not the case for nonhuman primatSongbirds sophisticated vocalization syst

thus allows us to investigate not only learnand cognitive abilities underlying language

an abstract system, but also the mechanisinvolved in vocalization, i.e., the relationsh

between perception and production. In adtion, birdsong is highly complex, which allo

a better comparison with human language th

structurally simpler primate calls do. To quPrather et al. (2009), all songbirds studied

date [. . .] learn their song notes by imitationfeature of human speech that is otherwise r

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among animals [. . .]. Swamp sparrows songscomprise repeated groups of 25 notes, which

are composed of short pure-tonal frequency

sweeps, with note categories differing primar-ily in duration, bandwidth and rate of change in

frequency.Below we show the relevance of birdsong

as a comparative model of speech, if notnecessarily of language. Investigating birdsong

from this perspective gives us the opportu-nity to dissociate evolutionary ancestry from

adaptive pressures. Phylogenetically differentvocal communication systems might have

developed similar mechanisms not becauseof common ancestry, but as a response to

similar environmental and adaptive pressures.

Comparing human language to birdsongmakes it possible to explore the components

of human language that are the result of selec-tion and those that arose through hereditary

endowment.

Genetic Studies of Speech Productionand Language

Mutations in FOXP2 cause speech, morpho-

logical, andin all likelihood, other language dis-

orders (Gopnik & Crago 1991, Haesler et al.2007, Marler & Peters 1981). Haesler et al.

(2007) began to study whether birds also pos-sess behaviors and neural structures related to

FOXP2 mutations after patients suffering fromspeech dyspraxia were found to have functional

abnormalities related to high levels of FOXP2in the striatum and basal ganglia. They rea-

soned that if birds also had problems relatedto elevated levels of FOXP2, then it would be

possible to use birds as a model to understandwhether the genetic underpinnings of speech

were similar to those of birdsongs. The au-

thors used zebra finchesbecause they learn theirsongs by imitating adult tutors and because

they change songs seasonally. Haesler et al.(2007) noticed that the expression of FOXP2

tends to increase in Area X when zebra fincheslearn to sing. The levels of FOXP2 decrease

before the birds begin to learn their songs.The authors experimentally lowered the level

of FOXP2 in Area X during song learningand found that the experimental birds with de-

creased levels of FOXP2 sing in atypical waysas compared with controls. This study sug-

gests that songbirdshave mechanisms for learn-

ing their songs that are reminiscent of humanslearning to speak and are susceptible to mu-

tations in FOXP2. Since these findings, sev-eral other experiments have enriched our un-

derstanding of the expression of the geneticendowment and learning abilities (e.g., Miller

et al. 2008).

Similarities Between Birdsongand Human Speech

The similarities of some mechanisms observedin songbirds and humans are indeed quite

striking. Birdsong and human speech mightuse similar brain mechanisms: Auditory brain

areas responsible for perception and motor ar-eas responsible for production might be closely

linked in both systems with single neuronsresponding to both perceived and produced

vocalizations. For humans, the motor theory ofspeech, linking perception and production, was

proposed decades ago (Liberman et al. 1967).More recently, Prather et al. (2008) identified

similar mechanisms in swamp sparrows. The

brain area HVC (high vocal center) of maleswamp sparrows is engaged during song pro-

duction, song perception, and learning of songsfrom tutors. Prather et al. (2008) investigated

whether HVC neurons display both types ofactivity by recording from this area in freely be-

having male swamp sparrows during presenta-tion as well as production of songs. The authors

found that some HVC neurons were active dur-ing singingandlistening, which, as the authors

demonstrated, was due to a motor estimation ofauditory feedback. To confirm that this activity

is indeed motor in nature and not simply due to

auditory feedback as the bird perceives its ownsong, the authors played different distracting

songs to birds while they were singing, soauditory feedback was disrupted. Increased

neural activity was observed despite this ma-nipulation. This, as the authors suggest, bears



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resemblance to the motor theory of speechperception (Liberman et al. 1967) as well as to

the mirror neuron system in the frontal cortex

of monkeys (Gallese et al. 1996, Rizzolatti et al.2001).

Birdsong has been suggested as a potentialanalog for speech and/or language due to its

complex structure. In birdsong, just like in hu-man language, the origin of this structural com-

plexity, whether it is genetically determined orlearned, is an exciting question. Feher et al.

(2009) have askedwhether species-typical songscan be created de novo in zebra finches, much

like language can emerge in groups of linguis-tic isolates in the span of a few generations

(Senghas et al. 2004). Feher et al. (2009) stud-

ied juvenile birds, raised in isolation. Songs thatare usually observed in isolated (ISO) birds are

less structured, noisier, and contain high-pitchupsweeps,making it possible to quantify thedif-

ferences observed between the wild-type (WT)and ISO type of songs. Each juvenile bird was

trained by a particular ISO tutor in a sound-proof cage. A number of isolated birds served

as individual tutors to teach juveniles who hadbeen deprived of prior exposure. Pupils of the

first generation become tutors for other juve-nile isolates, an operation that went on until the

fourth generation was reached. Changes were

observed in each successive training stage fromthefirsttothefourthgeneration.Thedatashow

that the WTand ISO songs differ in their spec-tral features andduration of the acoustic state of

songs, but across generations there is a progres-sion from the ISO toward the WT song prop-

erties. The authors claim that song culture isthe result of an extended developmental pro-

cess, a multigenerational phenotype partly ge-netically encoded in a founding population and

partly in environmental variables, but takingmultiple generations to emerge. These find-

ings bear strong resemblance to language emer-

gence de novo in that more structured and morespecies-typical song and language emerge as a

result of the acquisition/learning process, sug-gesting that impoverished input is sufficient to

trigger the genetically encoded mechanisms re-sponsible for song/speech.

The above reviewed evidence indicates tsimilarities between birdsong and speech ex

at the level of neural mechanisms as well asterms of the underlying genetic bases. Bu

birdsong a good model for the core prope

of human language, namely its structural coplexity? Gardner et al. (2005) looked at canar

(Serinus canaria), which produce hierarchicaorganized songs. Songs consist of syllable

which, when repeated, form a phrase. Suphrases appear in young canaries after 60 d

when they are raised typically, that is, in a poulation of singing adults. It is known that de

ened juveniles produce the species-specific erarchical organization, although syllables a

phrases are impoverished. Gardner et al. (20exposed isolated juveniles to synthesized son

that were ungrammatical because they iplemented a random walk through the s

lable space. Initially, the production of the i

lates seemed congruent with the random wexposure. Upon transition to adulthood, ho

ever, normal syllables became recognizable aprimitive phrasing started to emerge. At

end of the learning process, juveniles producstandard syllables, and species-typical phras

was clearly noticeable. The authors concludthat imitation and innate song constraints

separate processes that can be segregatedtime: freedom in youth, rules in adulthood.

Gentner et al. (2000) and Prather et (2009) further examined song organization a

perception in birds, focusing on categorical p

ception (for a discussion of categorical perction in humans, see The Early Sensitivity

Speech and Subsequent Phonological Devopment section). In the latter study, the

thorssystematically manipulatednote duratia learned aspect of swan sparrow song, a

found that sensorimotor neurons showed a cegorical response to gradually varying note d

ration. This neural response coincided wcategory boundaries observed behaviorally

the animals. Furthermore, sparrows comfrom song dialects exhibiting different ca

gorical boundaries responded according to

boundaries of their own species, indicating tboundaries were indeed learned.

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In sum, it appears that birdsong and humanspeech are comparable in terms of their under-

lying neuralmechanisms, thepresence of innate

guiding principles as well as some of their orga-nizational properties. This, of course, does not

imply that birdsong is equivalent to human lan-guage in terms of its productivity and structural

complexity. Nor does it mean that songbirdscognitive abilities are more similar to those

of humans than are the cognitive abilities ofprimate species. Comparisons with birdsong

provide us with an optimal testing ground toexplore the genetic ancestry as well as the adap-

tive pressures that have shaped human languageduring the evolution of our species.

These similarities notwithstanding, human

language appears to have a unique productivityand computational power not paralleled in any

other species. Where do these features origi-nate? After reviewing the abilities and mech-

anisms shared by humans and other animals,we turn to those that might be unique to our

species.

Does a New ComputationalComponent Cause the Emergence ofLanguage in the Human Brain?

In an influential paper, Hauser et al. (2002)

proposed that enquiries into language evolu-tion should be incorporated into theories of

language. They suggested that it may be con-venient to distinguish between two aspects of

the human language faculty: the language fac-ulty in the broad sense (FLB) and the language

faculty in the narrow sense (FLN). Their pro-posalisthattheFLBiscomposedofvariousele-

ments such as sensory motor systems, memorysystems, social abilities, and so forth, whereas

the FLN comprises a very limited number of

computational components or a single com-putational component, which the authors view

as quite likely to have been sufficient for theemergence of language. A similar conclusion

has been drawn by other researchers with re-spect to mathematical abilities. The human

species is unique in its capacity to create rev-olutionary cultural inventions such as writing

and mathematics, which dramatically enhanceits native competence. From a neurobiologi-

cal standpoint, such inventions are too recentfor natural selection to have dedicated spe-

cific brain mechanisms to them. It has there-

fore been suggested that they co-opt or recy-cle evolutionarily older circuits with a related

function [. . .], thus enriching (without neces-sarily replacing) their domain of use (Knops

et al. 2009, p. 1538).This way of presenting the theoretical

framework proposes that many components(use of the vocal tract, categorical perception,

etc.) are present in other animals. For a de-tailed discussion of which phonological abilities

might be found in nonhuman species, see Yip(2006). The computational abilities required

to acquire the syntax of the native language,

by contrast, are unique to humans. Hauseret al. (2002) framed their paper as a quest for

the crucial evolutionary step that allowed ourspecies to acquire the complex syntax of human

languages.In a follow-up experimental paper, Fitch

& Hauser (2004) and Saffran et al. (2008)proposed that recursion, responsible for

discrete infinity, might be the one and uniquecomponent of FLN. This proposal generated

a great number of experiments and theoreticaldebates seeking to support or infirm the

conjecture (Bahlman et al. 2006, Fitch et al.

2005, Friederici et al. 2006, Hauser et al. 2002,Hochmann et al. 2008, Pinker & Jackendoff

2005). Fitch & Hauser (2004) based theirstudies on the complexity of grammars that

Chomsky (1957 and subsequent work) pro-posed. Chomsky made the claim that human

languages are best characterized as context-free or phrase-structure grammars (PSG), not

as computationally more limited finite-stategrammars (FSG). Fitch & Hauser (2004) report

an experiment investigating whether humansand monkeys are similar in their abilities to

learn a FSG and a PSG from the simple pre-

sentation of items derived from the grammars.The authors used two artificial grammars.

The FSG had items conforming to structure(AB)n with n 3, the PSG to structure AnBn



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with n 3. The authors habituated humansand cotton-top tamarin monkeys to either of

these items. As and Bs were consonant-vowel

syllables, with a female voice pronouncing theA syllables and a male the B syllables. In the

test phase, humans had to rate new items ascongruent or incongruent with the grammar

they had learned, whereas monkeys weretested with a head-turn procedure to estimate

whether the underlying grammar had been ex-tracted. Humans behaved as if they had learned

both grammars and monkeys as if they had thecapacity to extract only the FSG grammar.

Later, Gentner et al. (2006) studied Euro-pean starlings and challenged the notion that

only humans can learn PSG. They used the

same kinds of grammars as had Fitch & Hauser(2004), except that As and Bs corresponded to

twospecificcategoriesofsoundsthesebirdsuse.Before being tested, birds were trained with

a protracted operant-conditioning schedule, aprocedure that Fitch & Hauser (2004) did not

use with the cotton-top tamarins. After thisextended training phase, starlings learned the

PSG.Perruchet & Rey (2005) criticized Fitch &

Hauser (2004) on different grounds, arguingthat in Fitch & Hausers (2004) study, humans

did not actually need to establish nonadjacent

dependencies to succeed and cannot thereforebe assumed to have extracted the underlying

structure of the An Bn items. Indeed, the distri-butional properties and/or the rhythmic prop-

erties of Fitch & Hausers (2004) material of-fer a better explanation of how humans pro-

cessedtheAnBn items. Indeed, Hochmann et al.(2008) showed that human participants in the

test did not dismiss A2B3 or A3B2 as incongru-ent with the grammar AnBn. Moreover, when

interrogated at the end of the experiment, those

few participants who did dismiss such items re-ported that they explicitly counted the num-

ber of As and Bs and only accepted sequenceswith equal numbers. Despite these empirical is-

sues, the theoretical proposal made by Hauseret al. (2002) remains highly interesting and in-

vites further research.

We follow this brief review of the evotionary aspects of human language and anim

abilities with a detailed discussion of young

fants speech and language-processing capaties to provide an empirical basis for the eva

ation of the theoretical and evolutionary claiintroduced so far.

THE EARLY SENSITIVITY TOSPEECH AND SUBSEQUENTPHONOLOGICALDEVELOPMENT

Newborn infants show surprising speeprocessing abilities from birth. They pre

forward-going speech and primate vocali

tions over acoustically matched nonspeesounds or backward speech (Dehae

Lambertz et al. 2002; Pena et al. 20Vouloumanos & Werker 2004, 2007), th

mothers voice over other femalevoices (Mehet al. 1978), and their native language over u

familiar languages (Mehler et al. 1988, Moet al. 1993). These early language discrim

nation abilities might represent some formimprinting to the properties of the native la

guage upon the first encounter immediatelyter birth, or alternatively the result of exposu

to the maternal language in utero. Newbo

can make most of the phonemic distinctioattested in the worlds languages (Dehae

Lambertz & Dehaene 1994, Eimas et al. 19Werker & Tees 1984b), and they are able to d

tinguish languagesthey have never heard befon the basis of their rhythmical characterist

(Mehler et al. 1988; Nazzi et al. 1998; Ramet al. 1999, 2000). Newborns are also able

detect theacoustic cues that signalword bounaries (Christophe et al. 1994), discrimin

words with different patterns of lexical str(Sansavini et al. 1997), and distinguish funct

words (e.g., it, this, in, of, these, some) fr

content words (e.g., baby, table, eat, slowly, hapon the basis of their different acoustic char

teristics (Shi et al. 1999). These early, innabilities lay the foundations for later langu

learning.

200 Gervain Mehler


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Acquisition of the Native Phonology

One of the most fundamental and at the sametime most surprising perceptual abilities of

newborns is that they are able to discriminatemost sound contrasts used in the worlds

languages. In other words, they are born ascitizens of the world, ready to learn any

natural language. Just like adults, newbornsperceive these sounds categorically (Eimas

et al. 1971, Liberman et al. 1957), perceivingacoustic variation from within a phoneme

boundary as the same sound and the same

acoustic variation spanning adult phonemeboundaries as being different sounds.

During the first year of life, as a result ofexposure to the native language, this initial

universal discrimination narrows down to thephonemes, that is, minimal meaningful differ-

ences (e.g., pin versus bin), of the native lan-guage (Werker & Tees 1984a). Discrimination

of most nonnative contrasts is lost (Werker &Tees 1984a), whereas it is maintained or even

enhanced for native contrasts (Kuhl et al. 2006).English, for instance, only has a dental /d/

sound, whereas Hindi discriminates between

a retroflex /D/ and a dental /d/. Newbornsand young infants born into English-speaking

environments readily discriminate the Hindisounds. But after eight months of exposure to

English, where the two categories are not dis-tinguished,English-learninginfants start losing

the discrimination (Werker & Tees 1984a). In-deed, English-speaking adults find it very hard

to discriminate this contrast. Hindi infants andadults,as a result of exposure to Hindi, maintain

it.What learning mechanism might account

for this learning-by-forgetting (Mehler 1974,

Mehler & Dupoux 1994) or perceptual attune-ment (Scott et al. 2007) process? It has been

suggested that native phonological categoriesmight be established through a distributional

learning mechanism (Maye et al. 2002). In alanguage like English, where there is only one

/d/ sound, most actual realizations that infantsencounter will cluster around a prototypical

/d/ pronunciation, so the distribution ofEnglish /d/ sounds will have a mode around

the most typical acoustic parameters for /d/.On the other hand, in Hindi, where there are

two /d/ sounds, the same acoustic space will

show a bimodal distribution, as there will bemany instances around the typical /D/ sound

as well as around the typical /d/ sound. As aresult, in English, infants will be exposed to a

unimodal distribution, and in Hindi, a bimodalone. It has been shown that infants are sensitive

to this statistical distribution, and they createa single phoneme category when exposed to a

unimodal distribution, whereas they establishtwo categories if the distribution in the input is

bimodal (Maye et al. 2002). In their study, Mayeand colleagues (2002) used the /da/-/ta/ contin-

uum, where the two syllables are distinguished

by the onset of voicing (voice onset time, orVOT). Since /d/ is a voiced consonant, in /da/,

voicing starts at 0 msec, that is, immediately atthe onset of the syllable, whereas in /ta/, the

consonant is voiceless; thus, voicing starts onlyat the onset of the vowel. By delaying VOT

incrementally, a continuum was created from/da/ with VOT at 0 msec through six syllables

with VOT at 20 msec, 40 msec, etc., to /ta/ withVOT at 140 msec. One group of 6- to 8-month-

old infants, the unimodal group, was exposed toa frequency distribution along this continuum

where syllables in the middle (instances 4 and 5with VOT 60 msec and 80 msec, respectively)

had the highest frequency of occurrence. A sec-

ond group, the bimodal group, was exposed toa distribution where tokens closer to the end

points (with VOT 20 msec and 120 msec) werethe most frequent ones. When tested on the

discrimination of the end points of the contin-uum, /da/ and /ta/, the bimodal group showed

better discrimination than the unimodal group(Maye et al. 2002).

These results suggest that infants have theability to track the frequency of sound tokens

in the input and might use this informationto tune into native phonemic categories (Best

& McRoberts 2003, Kuhl 2004, Maye et al.

2002).



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The Early Sensitivity to Rhythm andIts Potential Bootstrapping Role

The previous sections have illustrated the chal-lenge of acquiring ones native language. How-

ever, some infants successfully acquire not onlyone, but two or more languages at the same

time. How do these infants discriminate be-

tween their languages?Linguists have long recognized that lan-

guagesdiffer perceptibly in their sound patternsand,inparticular,intheirrhythm(Abercrombie

1967, James 1940, Ladefoged 1993, Pike 1945).Initially, these differences were describedas cat-

egorical and were derived from the isochronyprinciple, that is, as a function of the linguis-

tic unit that has a constant duration in a givenlanguage. According to this view, languages fall

into one of three rhythmic classes. In stress-

timed languages such as English, Dutch, orPolish, theisochronous unit is thetime betweentwo subsequent stressed syllables. For exam-

ple, in the sentence Pronunciation is important

in English, the duration of time between thestressed syllables (in bold) is roughly the same.

In syllable-timed languages, such as Spanishor Italian, the unit of isochrony is the sylla-

ble, that is, syllables are roughly of equal du-ration. For instance, in tavolo table (Italian),

no vowel is reduced, so all syllables are of the

same length. In mora-timed languages, suchas Japanese or Tamil, the isochronous unit isthe mora. The mora is the measure of syllable

weight [light/short syllables such as a (the in-definite article) consist of one mora; heavy/long

syllables such asseeconsist of two morae].

These differences in rhythm are intuitiveand easy to perceive for adults. If infants have

the same sensitivity to linguistic rhythm, itmight help them discriminate their languages,

at least when those are from different rhythmi-

cal classes. Such an early sensitivity was indeedobserved by Mehler et al. (1988), who showedthat newborns were able to discriminate their

future native language from a rhythmicallydifferent language, even if both were low-

pass filtered, suppressing phoneme identity.This initial finding, suggesting that language

discrimination relies upon suprasegmenrhythmical cues, was extended by Nazzi et

(1998), showing that rhythmical differenwere sufficient for discrimination; familiar

with the languages was not necessary. Th

authors found that French newborns readdiscriminated between low-pass filtered utt

ances in English and Japanese, two languathey had never heard before.

These results established that rhythm miserve as an initial cue to language discrim

nation. However, the exact acoustic featucorresponding to the subjective percept

rhythm were still unknown. The isochroprinciple proved incorrect, as empirical inve

gations obtained no isochrony for the relevunits (Dauer 1983), and several langua

were found that showed characteristics

both stress-timed and syllable-timed rhyth(Nespor 1990). Rhythmicity thus appeared

be a gradient rather than a categorical prope(Nespor 1990). Building on these observatio

Ramus et al. (1999) proposed an operatiodefinition for rhythm and rhythmical classifi

tion as a function of three acoustic paramete(a) %V, the proportion of vowels/vocalic sp

relative to the total length of an utteran(b) V, the variability in the length of voc

spaces, and (c) C, the variability in the lenof consonant clusters. The authors measur

these parameters in naturalistic recordinof speech in eight languages (e.g., Engli

Dutch, French, Italian, Japanese) and fou

that languages clustered into groups similarthe traditional rhythmical classes when plot

in two-dimensional spaces defined by any tof the three acoustic parameters. This defi

tion recreated the traditional classification aaccounted for languages previously found

be ambiguous (Nespor 1990) with respectclassification or currently undergoing chan

because continuous rather than categormeasures were used. It is important to n

that work by Grabe & Low (2002), also usia computational definition of rhythm, fai

to recreate the traditional rhythmic class

However, as subsequent work by Ramus (20suggests, there were important methodologi

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differences between Ramus et al.s (1999) andGrabe & Lows (2002) studies, which might

account for the different findings. Grabe &

Low (2002) analyzed speech from one speakerper language, whereas Ramus et al. (1999)

recorded four speakers for each language, thusobtaining a measure that matched the general

pattern of languages more closely than did theidiosyncrasies of individual speakers.

The classification in terms of %V, V, andC suggested that it wasnt specific segmental

identity that defined rhythm, but rather therelative length and variability of vocalic and

consonantal spaces. Ramus & Mehler (1999)and Ramus et al. (1999) tested this prediction in

a series of experiments in which they replaced

individual vowels by /a/ and individual conso-nants by /s/. Utterances resynthesized this way

suppressed phonemic and consequently lexicalidentity, but preserved the proportion of vowels

and consonants in the signal. Adults as wellas newborns were able to discriminate ut-

terances from two rhythmically differentlanguages when this resynthesis was applied.

However, they failed when both vowelsand consonants were transformed into /a/,

suppressing the difference between them.These results clearly established that the three

parameters relating to the ratio of vowels and

consonants in the speech signal were necessaryand sufficient acoustic cues for rhythm-based

language discrimination at birth. The dis-crimination of rhythmically similar languages

emerges at around age 4 months; it has beenhypothesized to rely on more subtle cues, such

as phoneme identity or phonotactics (Bosch &Sebastian-Galles 2001, Ramon-Casas et al.

2009).In addition to language discrimination, lin-

guistic rhythm might also serve as a boot-strapping cue for morphosyntax. Languages

belonging to different rhythmic classes also

show different morphosyntactic properties. Forinstance, mora-timed languages, that is, lan-

guages with a high value for %V, such asJapanese, tend to have simple syllabic struc-

ture, agglutinating morphology, and object-verb (OV) word order, whereas languages with

lower %V values, such as English or Polish,typically have complex syllable structure, in-

flecting morphology, and VO word order(Fenk-Oczlon & Fenk 2005). Given these cor-

relations, Mehler et al. (2004) have proposed

that rhythm might act as a bootstrap for gen-eral morphosyntactic type. The proposal hasnt

been tested empirically, but it is of potential im-portance because it links a robust acoustic cue,

detected even by neonates, to the most generaland most abstract morphosyntactic properties.

THE WORD SEGMENTATIONPROBLEM: LEARNING

MECHANISMS ANDPERCEPTUAL PRIMITIVES

Parallel to the task of breaking the syntac-

tic code of their native language, infants alsoneed to start building a lexicon. According to

an increasingly widespread view (see Swingley2009 for a review), lexical acquisition starts as

early as the second half of the first year oflife, when infants begin to segment potential

word forms out of the continuous speech streamthey hear. These forms are believed not yet

to be reliably associated with meaning; never-theless, they play a significant role not only in

building the lexicon, but also in morphosyntac-

tic acquisition. In other words, lexical acquisi-tion starts much before infants utter their first

words.Learning word forms is a challenging task

since speech is continuous: Most word bound-aries are not marked by pauses, and words

typically do not occur in isolation. Yet thesensitivity to potential word forms appears as

early as birth. Newborns are able to discrim-inate identical phoneme sequences that only

differ in that some span a word boundary,whereas others dont (e.g., panorama typiqueversus mathematicien, respectively; Christophe

et al. 1994). This result provides a good ex-ample of infants early sensitivity to perceptual

Gestalts like edges and to prosodic structure ingeneral. In addition, newborns are also able to

discriminate word forms with different patternsof lexical stress (Sansavini et al. 1997).



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These early sensitivities notwithstanding,extracting and storing a relatively large num-

ber of word forms from speech starts only

at about age 6 to 8 months. Several mecha-nisms have been proposed to account for this

feat. Statistical learning has been proposed asa general-purpose, potentially universal mech-

anism, which might be operational early on,whereas language-specific mechanisms, which

require some familiarity with the native lan-guage, such as tracking allophonic variation,

phonotactics, or stress patterns, are suggestedto emerge somewhat later (Swingley 2005).

Statistically Based Word Segmentation

Proponents of structural linguistics (Harris1955) and information theory (Shannon 1948)

have long recognized that the statistical infor-mation encoded in language provides cues to its

constituent units (e.g., morphemes and words)and structural patterns. Some words are much

morefrequent,thatis,moreprobable,thanoth-ers in absolute terms (e.g., this,it, in, are, dog,

time) or in a given context (e.g., chipsafterfish

and. . .;doorisat the beginning of a sentence).

Building on these observations, Hayes &Clark (1970) tested whether adult participants

can use statistical information to extract words

from a continuous stream of sine-wave speechanalogs and found successful segmentation.

Later, Saffran et al. (1996) showed that 8-month-old infants could use statistical infor-

mation, more specifically transition probabil-ities (TPs; i.e., the probability with which one

syllable predicts the next or the previous one),to segment a continuous stream of syllables,

where syllables within a word predicted one an-other with a probability of 1.00, while syllables

spanning word boundaries had TPs of 0.33. In-fants could use dips in TPs to identify word

boundaries.

Statistical learning has been shown to be arobust, domain-general, age-independent, and

not specifically human ability. It operates overspeech sounds as well as tones (Kudo et al.

2006) andvisual stimuli (Fiser & Aslin 2002a,b).It is performed by newborns (Teinonen et al.

2009), infants at 8 and 13 months (Saffran et1996), and adults (Pena et al. 2002). Mo

over, nonhuman species, such as tamarin mokeys (Hauser et al. 2001) and rats (Toro

Trobalon 2005), are also able to learn statisti

information.

Perceptual and Linguistic Constrainon Statistical Learning

Saffran et al.s (1996) results shed new light

the well-known fact that humans are powerstatistical learners. But how is statistical lea

ing used in language acquisition? A recent of studies suggests that statistics are not us

across the board for learning language. Rath

they are recruited for specific learning taskin particular, word segmentation and lexi

acquisitiontriggered by cues in the speesignal, and their application is limited by l

guistic constraints.Inspired by the fact that both morpholo

and syntax make use of constructions with dtant dependencies, Pena et al. (2002), Ne

port & Aslin (2004), and Newport et al. (200asked the question whether transition proba

ities between nonadjacent items can be learnPena et al. (2002) found that adults readily s

mented out trisyllabic words from an art

cial language when they were defined by hiTPs between the first and the last syllab

(A X C). However, subjects failed to generize the pattern to novel X items unless (su

liminal) segmentation cues were inserted inthe stream to facilitate the original segmen

tion task, allowing participants to better procthe regularity (Pena et al. 2002). These resu

suggest that cues in the signal, for examppauses, act as triggers for different processi

mechanisms, for example, statistics versus rgeneralization.

A second and related issue that arises is

nature of the units or representations to whstatistical computations apply. Bonatti et

(2005) observed that adults readily segmover nonadjacent consonants, but not o

nonadjacent vowels. This finding was furthconfirmed by Toro et al. (2008), who devis

204 Gervain Mehler


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a series of artificial grammar experiments toshow that consonants and vowels serve as

preferential input to different kinds of learning

mechanisms. They found that participantsperformed well when their task was to do

statistical computations over consonants orrule-learning over vowels (the rule to be

learned was a repetition-based generalization).But their performance dropped to chance in

the opposite case, i.e., statistical computationsover vowels and rule-learning over consonants.

Taken together, these studies indicate that notall linguistic representations are equally suit-

able for statistical learning. Consonants seemto be the primary target, while vowels are pref-

erentially recruited for rule learning.1 These

findings converge with certain observationsin linguistics (Nespor et al. 2003) suggesting

that consonants and vowels have differentlinguistic functions. Consonants are believed

to be responsible for encoding the lexicon; e.g.,consonantal stems carry the semantic contents

of lexical items in Semitic languages. Bycontrast, vowels are claimed to signal morpho-

logical form and syntactic function, e.g., Ablautphenomena in Germanic languages,sing,sang,

sung. These studies provide further evidencethat statistical computations are selectively trig-

gered and constrained by cues in the input, and

their primary function is lexical segmentation.However, the use of statistics for segmen-

tation and word-form learning might not beuniversal. In some languages, such as Chinese

or infant-directed English, most words aremonosyllabic, rendering statistical com-

putations vacuous (Yang 2004, Yang &Gambell 2004). Morphologically complex

languages, such as Hungarian (haz-a-i-nk-bol

1

It needs to be noted that Newport & Aslin (2004)foundsuc-cessful statistical segmentation for vowels as well as conso-nants. However, they used an artificial speech stream that al-lowedimmediate repetitions of the same word frame, makingthe statistical patterns highly salient, whereas Bonatti et al.s(2005) and Toro et al.s (2008) stream had no immediate rep-etitions. It seems, then, that vowels might also be used forstatistical computations under special conditions, such as theinformationally highly redundant stream used by Newport& Aslin (2004).

house.possessive.plural.1stpl.from from ourhouses) and Turkish, might pose the opposite

problem, as it is not clear what unit would

be segmented out: complex word forms orindividual stems and suffixes.

Taken together, these studies indicate thatstatistical segmentation alone is not sufficient

to solve the task of extracting word forms fromcontinuous speech. Other cues, taking into ac-

count the morphophonological properties ofindividual languages, are needed to comple-

ment statistical computations.

Language-Specific Cuesto Segmentation

Although words are not separated by clearpauses in continuous speech, there are some

acoustic and phonological features that corre-late reliably enough with word boundaries to

allow successful segmentation in most cases. Atleast three such cues have been identified in

the literature, mostly on the basis of English:word-level stress patterns, phonotactic regular-

ities, and allophonic variation.Many languages assign word-level stress to

a specific position within words; for example,Hungarian, has strictly word-initial stress. But

even in languages where stress is not fixed but

is lexically determined for each word, there arepredominant patterns that can serve as heuris-

tic cues. In English, word-level stress is lex-ically determined, but most bisyllabic nouns

follow a strong-weak, that is, trochaic pattern(e.g., doctor, infant) Thus, segmenting speech

at strong syllables is a potentially useful heuris-tic known as the metrical segmentation strategy

(Cutler 1994, Cutler & Carter 1987). Indeed,Jusczyk et al. (1999) found that 7.5-month-

old English-exposed infants show a trochaicbias, treating heavy syllables as word-initial

(doctor, candle). Importantly, the bias required

words to be multisyllabic. Heavy monosylla-bles (dock, can) were not recognized ( Jusczyk

et al. 1999), but trisyllabic words with initialstress (strong-weak-weak) were treated as fa-

miliar, whereas weak-strong-weak and weak-weak-strong patterns were not (Curtin et al.



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2001). Importantly, the metrical segmentationstrategy is a heuristic tool, since some English

bisyllables are not trochaic, but iambic (e.g.,

guitar). In these cases, the strategy predicts ini-tial missegmentation. This was confirmed em-

pirically: 7.5-month-oldswho readily recognizetrochaic words in continuous passages failed

to show similar recognition for iambs ( Jusczyket al. 1999).

Legal and illegal phoneme distributions,that is, phonotactics, also provide information

about word boundaries. In English, the se-quence /br/ is frequent word initially, but it is

rare word internally. Therefore, it is a good can-didate for a potential word onset. Conversely,

words frequently end in /nt/, which is there-

fore a possible cue to the end of words. In atask where infants were exposed to CVCCVC

(C, consonant; V, vowel) sequences with word-internally frequent or infrequent CC clusters,

they segmented the sequences into two wordsin the latter case, but not in the former case

(Mattys et al. 1999; Mattys & Jusczyk 2001a,b).Variation in the realization of phonemes,

known as allophony, can also indicate wordboundaries. In English, for instance, aspirated

stop consonants appear at the onsets of stressedsyllables, whereas their unaspirated allophones

appear elsewhere (Church 1987). At 9 months

of age, infants are able to posit word bound-aries based on allophonic (e.g., night rates ver-

sus nitrates) and distributional cues, and at10.5 months, allophonic cues alone are suffi-

cient for successful segmentation ( Jusczyk et al.1999).

The Interaction of Statistical andLanguage-Specific Cues

The above cues are mostly heuristic in nature

and might lead to missegmentation in less fre-quent or atypical cases. Such missegmentations

can be induced in experimental conditions( Jusczyk et al. 1999) and can also be observed

in young childrens spontaneous production(Slobin 1997). However, infants acquire the

majority of the word forms they know withouterror. This implies that they are using more

than just one cue at a time, since convergcues yield more accurate segmentation.

Several studies have shown that young

fants are indeed capable of using different csimultaneously. When stress and phonot

tic cues provide conflicting information abword boundaries, 9-month-old infants prefe

rely on stress cues (Mattys et al. 1999; MattyJusczyk 2001a,b). When stress and statistical

formation are contrasted, 6-month-olds follthe statistical information (Saffran & Thiess

2003), whereas 8-month-olds use stress cu( Johnson & Jusczyk 2001).This shift indicat

move from universal to more language-specstrategies as infants gain increasing familiar

with their native language.

Artificial grammar learning work with adualso indicates that statistical information a

prosody are both computed in segmentattasks, and prosody is typically used to constr

statistics in linguistically meaningful waysdiscussed above. If, for instance, the contin

ous speech stream is not monotonous as uin Saffran et al.s (1996) original work,

has utterance-like intonational contours ovlaid on it, then participants readily segment s

tistically coherent words inside prosodic cotours, but not spanning two contours (Shu

et al. 2007). Similarly, while participants erneously recognize phantom words in art

cial speech streams, that is, words that ne

occurred in the stream, but their pair-wise slable transitions have high probabilities (e

fekulawas never heard, butfe-kuand ku-lapeared in the stream with high TPs), this fa

recognitioncanbesuppressedifthestreamcotains prosodic cues to word boundaries, such

pauses or word-final lengthening (EndressMehler 2009).

Early Form-Meaning AssociationsAs suggested above, infants start learning woas early as age 6 to 8 months by extracting p

tential word forms from the input using statical as well as phonological cues (see Swing

2009 for a review). In order to develop a lecon,they also need to start matchingthese w

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forms with possible meanings. Learning the fullmeaning of words, especially abstract words or

grammatical functors, requires advanced abili-

ties, such as categorization, understanding ref-erentiality, and solving the induction problem

for meaning (Nazzi & Bertoncini 2003, Quine1960, Waxman & Gelman 2009). We do not

discuss these complex and advanced forms ofword learning here. We only review the earliest

stages of lexical acquisition, when a linguisticlabel gets associated with a perceptually avail-

able, concrete object.These early associations were investigated

by Stager & Werker (1997), who showed thatinfants use their phonological knowledge and

representations differently at different stages of

the word-learning process. At 8 months, beforeword learning en masse begins, infants read-

ily discriminate a minimal pair of word forms,bihand dih, and they are also able to associate

them with two different objects. At 14 months,which is the beginning of the word-learning

stage, infants succeed in the simple phoneticdiscrimination task, but fail to distinguish the

two words when they are used in a labeling con-text, that is, associated with two distinct objects.

They succeed, however, even in this context ifthe words are very distinct, for example,lifand

neem. At 17 months, when word learning is in

full swing, infants succeed again in both tasks.The authors accounted for these results by ar-

guing that phonological knowledge is recruitedfor word learning in different ways at different

developmental stages. When starting to asso-ciate word forms with meanings, infants need

to pay attention to the details of both and estab-lish an association between them. At this early

stage, infants might not attribute more impor-tance to the minimal phonemic difference be-

tweentwo words than to other properties of thewords, such as the speakers gender. Given the

high cognitive demands of the association task,

a minimal phonemic difference might go un-noticed. At later stages, when infants become

experienced word learners, label-object associ-ations become less taxing for the cognitive sys-

tems; thus, even minor differences can be morereadily utilized.

Confirmation for the cognitive load hy-pothesis comes from recent studies that found

successful associations in 14-month-olds withminimally different labels when the cognitive

load of the task was reduced, e.g., by using

words known to the infants (Fennell & Werker2003),by prefamiliarizingthem with theobjects

(Fennell & Werker 2004), by giving them avisual choice between two objects in a test

(Yoshida et al. 2009), or by making the acousticdifference between words more salient (Curtin

et al. 2009) or more relevant for the task(Thiessen 2007).

BROAD LEXICAL CATEGORIES:FUNCTORS AND CONTENT

WORDS

Words in the lexicon are organized into hierar-chical categories. The most general and cross-

linguistically universaldivideis theone betweenclosed-class functors (free or bound), such as

articles, pronouns, and pre- or postpositions,and open-class content words, such as nouns,

verbs, and adjectives. The most important dif-ference between these two broad categories

is functional: Functors signal morphosyntacticstructure (e.g., plurality, tense, and argument

structure), whereas content words carry lexical

meaning. In addition, there are a number of sta-tistical and acoustic/phonological differences

between them. Functors have very high tokenfrequencies. In corpora, they often account for

30% to 50% of the whole input (Gervain et al.2008b, Kucera & Francis 1967). Content words

typically have much lower token frequencies.By contrast, they are acoustically more salient,

as they carry stress, consist of multiple sylla-bles, and have at least one nonreduced vowel

(Morgan et al. 1996).It is well known that young children often

omit functors in their early productions (Guasti

2002), which raised the question of whetherthey are able to perceive and represent func-

tors at all. An early study (Shipley et al. 1969)showed that children whose linguistic produc-

tion was at the telegraphic phase (i.e., containedno function words) nevertheless understood



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instructions better if the instructions them-selves were not telegraphic, but contained func-

tion words as well. Later, Gerken et al. (1990)

established that the omission of functors inearly production stems from a limitation on

production and not on perception or encod-ing. In a series of imitation experiments with

2- to 3-year-old children, they found that chil-dren tend to omit weak, unstressed monosyl-

labic morphemes, typically functors, but notstrong, stressed ones, typically content words,

even if both are nonsense non-English words.Also, they imitate nonexisting content words

with greater ease if they appear in the envi-ronment of real English function words as op-

posed to environments of nonsense function

words. Moreover, children make a distinctionbetween those nonsense functors that follow

the usual consonant patterns of English func-tors andthose that do not. Taken together, these

results indicate that even though young chil-dren produce few functors, they build fairly de-

tailed representations of them, which they canuse in segmenting and labeling the incoming

speech stream. In a later experiment, Gerken& McIntosh (1993) obtained similar results for

sentence comprehension.The above experiments were carried out

with children who already have substantial

knowledge of the grammar of their native lan-guage. But segmentation and labeling cues are

most relevant at the beginning of acquisition tobreak up the input. Indeed, Shi et al. (1999)

asked whether newborns are able to distin-guish functors and content words on the basis

of the phonological differences between them.Their findings indicate that newborn infants

of both English-speaking and non-English-speaking mothers are able to categorically dis-

criminate between English function and con-tent words presented in isolation. By 6 months

of age, infants start to show a preference for

content words (Shi & Werker 2001), and by11 months, they are also able to represent fre-

quent functors in some phonological detail (Shiet al. 2006). They are also able to use functors,

frequent and infrequent ones alike, to segmentout a following content word (Shi et al. 2006).

Hohle & Weissenborn (2003) obtained simresults, showing functor versus content wo

discrimination in 7- to 9-month-old Germinfants exposed to continuous speech.

On the basis of the findings described abo

it is not unreasonable to assume that the fution word versus content word distinction

available to infants very early on, and althoufunctors might not frequently appear in infan

earliest productions, they might be amotheir earliest word form representations, se

ing to bootstrap the early content words cagories, e.g., nouns and verbs. Borrowed fr

the structuralist-generativist linguistic tration, the idea that functors are fundamental

thecategorization of content words hasrecengained empirical support from corpus stud

(Mintz 2002, Redington et al. 1998).

WORD ORDER AND OTHERTYPOLOGICAL DIFFERENCES

The acquisition and production of the fiwords at around the age of one year m

an important milestone in young infants lguage development. Multiword utterances

pear much later, after the second birthd

However, the acquisition of the most basic sytactic properties of the native language, su

as word order, might actually start much elier, during the first year of life, in parallel w

and possibly in relation to early speech perction and word-learning abilities. Indeed, Bro

(1973) has shown that infants get basic woorder right from their first productions, wh

suggests that word order is a property that thhave acquired prior to the production of mu

word utterances.How do infants acquire word order so ear

According to the lexicalist position (Tomase2000), infants and young children initially

not represent word order in an abstract for

Rather, they learnrelatively fixed constructiooften specific to individual lexical items, usua

individual verbs (for example, eat is precedby a noun phrase, the eater, and is follow

by a noun phrase, the eatee). The generativaccount, by contrast, assumes that even you

208 Gervain Mehler


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learners have general and abstract word-orderrepresentations encoding the relative order of

the phrasal head and its complements and spec-

ifiers. For example, in a language with a head-complement, these technical terms are always

spelled with capital initials; in linguistics, itwould be better to follow this conventional or-

der: objects follow verbs, nouns follow prepo-sitions, etc. (e.g.,eat an apple;on the table). One

way to differentiate between these two accountsis to show that infants have some rudimentary

representation of word order prior to the ac-quisition of a sizeable lexicon.

Recent results suggest that such a prelex-ical word-order representation might be cre-

atedearly on using frequencyas a bootstrapping

cue (Gervain et al. 2008b). As discussed above,functors are more frequent than content words.

In addition, their position relative to utteranceboundaries correlates with the general word or-

der of languages (Gervain et al. 2008b, Morganet al. 1996). In Italian, for instance, the gen-

eral word order is VO; therefore, functors thathead a phrase appear phrase initially (for exam-

ple, prepositions:sul tavoloon-the table on thetable). By contrast, in Japanese, functors head-

ingphrases arefinal (for example, postpositions:

Tokyo niTokyo to to Tokyo). In infant-directed

speech corpora in these two languages, the dis-

tribution of frequent words, that is, functors,was exactly the opposite. In Italian, most two-

word phrases at utterance boundaries startedwith a frequent word, that is, functor, whereas

in Japanese, most of these phrases ended in afrequent word. Importantly, 8-month-old in-

fants appear to be sensitive to these distribu-tional differences. When exposed to a struc-

turally ambiguous artificial speech stream inwhich frequent and infrequent nonwords al-

ternated and the beginning and the end ofthe stream was ramped in amplitude to mask

phase information, Japanese infants preferred

to parse the stream into frequent-final units,whereas Italian infants showed longer looking

times for frequent-initial test items (Gervainet al. 2008b). This suggests that prelexical in-

fants show a rudimentary initial representationof word order, at least in terms of the relative

positions of frequent and infrequent words,that is, typically functors and content words.

This finding has been confirmed by recent re-sults ( J. Hochmann, A. Endress, and J. Mehler,

manuscript under review) suggesting that in-

fants do indeed treat frequent words as functorsand infrequent ones as content words. When

infants were given the choice to pair either thefrequent words or the infrequent words with

objects, they chose the infrequent ones as pos-sible labels for naming objects ( J. Hochmann,

A. Endress, and J. Mehler, manuscript underreview).

However, unlike Italian and Japanese, somelanguages do not show a consistent word-order

pattern. German, for example, uses both OVand VO orders within the verb phrase, depend-

ing on the syntactic context. Also, some infantsgrow up with an OV and a VO language simul-

taneously (for example, Japanese and English).

In these cases, frequency alone does not provideenough information about word order, since

both frequent-initial and frequent-final phrasesoccur in the input. This implies that further

cues are necessary to bootstrap word order. Onecue that has been suggested in the literature

is prosody. Nespor et al. (2008) found that thelocation and the acoustic realization of prosodic

prominence correlate with word order bothacross and within languages. Thus, in OV lan-

guages such as Turkish and in phrases with OVorder within mixed languages such as German,

prominence within prosodic phrases is initial,

and it is implemented as a pitch contrast (high-low), whereas in VO languages such as Italian

or French as well as in the VO phrases of mixedlanguages, a durational contrast is utilized,

and prominence is final (short-long). If infantscan use this prosodic cue in conjunction with

frequency, then a more precise and fine-grainedrepresentation of word order can be acquired,

even in cases where the two word orders, OVand VO, occur within a single language.

It has been argued that this grouping,that is, prominence-initial for pitch or inten-

sity contrasts and prominence-final for dura-

tional contrasts, is an auditory bias that ap-plies to speech and nonspeech stimuli alike (the



20/31

iambic-trochaic law; Hayes 1995). More re-cently, some data have been reported suggest-

ing that the grouping principle might emerge

as a result of language experience (Iversen et al.2008; K.A. Yoshida, J.R. Iversen, A.D. Patel, R.

Mazuka, H. Nito, J. Gervain, and F. Werker,manuscript under revision). However, these re-

sults are not conclusive, as other studies havefound no language-related differences (R. Bion,

S. Benavides, and M. Nespor, manuscript un-der review; Hay & Diehl 2007). Irrespective

of whether this bias is independent of lan-guage experience or a result of it, infants might

use it as a cue to word order at a very earlyage.

The hypothesis that even prelexical infants

might possess some simple word order repre-sentations, possibly bootstrapped by frequency

and prosody, received independent confirma-tion from studies using naturalistic stimuli in

German. Weissenborn et al. (1996) found thatGerman infants were sensitive to word order

violations in German subordinate clauses.

THE NEURAL CORRELATES OFLANGUAGE IN YOUNG INFANTS

With the advancement of brain imaging tech-niques, it has become increasingly possible to

pursue the original agenda of the research onthe biological foundations of language with

infant populations. Researchers have startedcharting the brain areas and circuits dedicated

to language and speech perception in newbornsand young infants.

One of the most important findings of thisincreasing body of research is that the newborn

and infant brain shows a functional organiza-tion for language processing that is similar to

thatoftheadultbrain(Dehaene-Lambertzetal.

2002, 2008; Gervain et al. 2008a; Pena et al.2003; Taga & Asakawa 2007). This organiza-

tion appears to be at least partly under geneticcontrol and develops even without experience

with language (e.g., in congenitally deaf indi-viduals; Dehaene-Lambertz et al. 2008).

More specifically, it has been observed that3-month-old infants as well as newborns show

a left-hemisphere advantage when listento speech as compared with reversed spee

and silence (Bortfeld et al. 2009, DehaeLambertz et al. 2002, Pena et al. 2003). T

early left lateralization has been confirmed

ing diffusion tensor imaging, a technique thaable to track white matter fascicles and mye

nation. The left hemisphere showed advancdevelopment in 2-month-old infants (Dub

et al. 2008). Interestingly, those aspects of laguage processing that are usually right later

ized in adults, e.g., the processing of prosoalso appear to be right lateralized in infa

(Homae et al. 2006, 2007).In addition to this general lateralization p

tern, recent results have allowed identificatof the areas involved in language processing

more fine-grained level. Gervain et al. (2008

using near-infrared spectroscopy, have fouthat the newborn brain is able to extract iden

cal, adjacent repetitions of syllables from speestimuli. The repetitions were detected as so

kind of perceptual Gestalt or primitive by tleft (and to a lesser extent by the right) te

poral areas immediately upon exposure. Othe course of the study, the repeated exp

sure to dozens of different stimuli, all instatiating the same underlying regularity (AA

mubaba, penana, etc.), also gave rise toincreased response in the left frontal areas, su

gesting the general pattern has been learn

or extracted from the stimuli. This conntion between the temporal areas, responsi

for auditory processing, and the frontal areinvolved in higher-level learning and memo

has also been documented in a series of stuies by Dehaene-Lambertzand her collaborat

(Dehaene-Lambertz & Baillet 1998; DehaenLambertz & Gliga 2004; Dehaene-Lambe

et al. 2006, 2008).These authors used activatspeed to identify a circuit of areas, from the p

mary auditory cortex through the superior teporal gyrus to the inferior frontal area, wh

respond to speech in a hierarchical, cascadi

fashion, possibly integrating over increasinlarge and/or abstract linguistic units.

These results indicate that brain ornization shows structural and functio

210 Gervain Mehler


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specialization for language from the start. Thisis not to say, though, that language experience

has no role to play. We demonstrated above

how language experience shapes phonologicaland morphosyntactic development during the

first year of life when measured behaviorally.In the past decade, numerous studies emerged

documenting the underlying neural changes(for a recent review, see Kuhl & Rivera-Gaxiola

2008). For instance, Kuhl et al. (2008) foundthat at 7.5 months, better discrimination

abilities for native phonemes, measured usingelectrophysiological techniques, correlate with

the rate of later language development. Thisfinding suggests that behavioral attunement

to the native language is mediated by brain

structures that become specifically responsiveto frequently encountered, i.e., native, linguis-

tic contrasts, which in turn promotes furtherlearning of linguistic distinctions relevant for

the native language and suppresses sensitivityto nonnative contrasts. Word learning also

shows electrophysiological signatures at anearly age. Familiar words evoke responses that

are different in amplitude as well as in scalpdistribution measurements from responses to

unfamiliar words from about 9 months of age(Molfese 1990, Vihman et al. 2007).

Most of these studies were carried out with

infants exposed to just one language. In manylinguistic communities, though, exposure to

multiple languages is the norm. An increas-ing body of research is now attempting to

understand how such an environment affectsphonological discrimination and categorization

(Bosch & Sebastian-Galles 1997, Conboy &Mills 2006, Mehler et al. 2004, Weikum et al.

2007, Werker & Byers-Heinlein 2008).Interestingly, exposure to two languages

from birth seems to affect development inother cognitive domains as well. In a series of

experiments, Kov acs & Mehler (2009a,b) have

explored why bilinguallyraised children,havingto learn twice as much about language as their

monolingual peers, display a speed of acquisi-tion comparable to that of monolingual infants.

In the first study, Kov acs & Mehler (2009a)compared 7-month-old monolingual and

bilingual groups in an eye-tracker task, wherethey had to learn to anticipate where a puppet

would appear on the screen immediately aftera trisyllabic word was heard. Both groups

performed equally well in this task. During the

second phase of the experiment, immediatelyafter the first phase, b

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