Transcript L2 phonology

MPhil Seminar:
Evaluating OT
L1/L2 Acquisition and learnability
8-3-2007
Overview

Focus on comparison of leading
theories (RBP : OT)


What each predicts to be possible and
impossible
How these predictions compare to the data
General acquisition effects
 SLA effects

Scientific study of acquisition:
research questions
theory
Top-down:
what does each
theory predict to be
possible and
impossible?
Bottom-up:
what are the central
acquisition phenomena
that any theory must
account for?
data
Is a theory that makes fewer predictions better?
Evaluating theories

Grammar/Theory 1
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Lexicon: {a}
Rules:
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a optionally  b
Grammar/Theory 2
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Venn diagram of outputs for grammar 1 and grammar 2
Lexicon: {a}
Rules:


a
b
c
a optionally  b
b optionally  c
Loci of predictive difference
box = range of conceivable
outputs for any theory
Some basic learning issues

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All existing OT learning models (except some flawed nascent work
by Tesar) require being handed correct URs, SRs, and CON.
How can an OT grammar be learned, given the large number of
constraint rankings that can produce the limited data set to which
a language learner is exposed? Does UG provide a default ranking
of constraints?
Prince and Smolensky 203: since constraints can’t be inferred from
surface data (because they’re violable), they must be part of UG
Sherrard 1997 and McMahon 2000: in OT 10 (untied) constraints
produce 36 million possible grammars, posing a learning problem,
whereas rules simply state observable generalisations.
McMahon and Calabrese: the first step in learning the grammar is
observing alternations/generalizations. DP stops there, but OT
requires an extra step of going from there to a constraint ranking.
Why is this necessary?
Typical OT presentation of the
learning task

http://www.ling.rochester.edu/people/cross/acquisition-of-ot.pdf
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Prince and Smolensky 1993: All constraints are universal, occurring in
all languages.
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Languages only vary with respect to their lexicons (input forms) and the
rankings of the constraints.
Implication: If constraints are innate, you don't have to learn them.
Constraints that have to be learned anyway
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morpheme constraints
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Leftmost(um): Place the morpheme –um- in the leftmost position possible.
(P&S 1993).
clitic alignment constraints, à la Anderson 1994.
parameterisable constraints

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RhythmicType=Iamb/Trochee: Feet are iambic/trochaic. (P&S 1993)
conjoined constraints?
 Any two simple constraints can be conjoined to form a complex constraint. Are all
possible conjunctions of simple constraints universal? Can you conjoin a
previously conjoined constraint?
Hale and Reiss 1998

Fact: child language production differs from comprehension
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Children understand [khæt] but produce [ta]
Smolensky 1996: Optimality Theory (OT) can generate this
Hale and Reiss: no it can’t, and it shouldn’t
(1) The nature of child phonology
a. The Strong Identity Hypothesis, which holds that child phonology is governed
by the same principles as adult phonology [H/R]
b. The view that child phonology is fundamentally distinct from adult phonology—
that it licenses processes unattested in adult language, that it depends on a
series of developmental stages, and so on [S]
(2) The nature of the evidence
a. Deviations from target forms—in children’s as well as adults’ grammars—are to
be attributed to performance effects, including nonlinguistic cognitive and
motor processing. [H/R]
b. Many deviations from target forms are the result of ‘‘child phonology’’ (i.e., the
child’s phonological competence)—grammatical effects for which the target
language provides no evidence. [S]
OT learning algorithms
M >> F
GLA
CDA
Smolensky’s scheme
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initial state of grammar: M >> F
production: OT grammar selects most harmonic
output for a given input
comprehension: OT grammar selects most harmonic
input for an observed output
correction algorithm: when the comprehension
evaluation is less harmonic (i.e. there are more
asterisks) than the production evaluation, relevant
constraints are demoted to make the comprehension
parse more harmonic than the production parse
Smolensky’s scheme
Problems with S identified by H/R

can’t account for surface neutralization, e.g. German /rad/, /rat/  [rat]
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Asudeh 2001:7 “OT grammars seem to be non-trivially non-reversible, whether
they use interpretive parsing or not. This is in conflict with the reversibility
prized by other constraint-based architectures and is tantamount to having
separate grammars for production and comprehension, which is conceptually
undesirable.”
it isn’t possible to have a steady-state grammar of Smolensky’s type,
because the correction algorithm will always kick in immediately and
rerank the constraints
can’t account for Captain Hazelwood’s drunken speech (misarticulation of
liquids, final devoicing, deaffrication), which in OT requires reranking of
constraints (see next slide on performance)
S has severe problems with chain shifts and opacity effects.
S can’t account for higher accuracy in direct imitation:
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i. improved performance under concentration
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S predicts simply better pronunciation of [s] for [š]
ii. parroting
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S would have to require instantaneous ranking of Cree constraints!
competence vs. performance
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Johnson, Pisoni, and Bernacki (1990) report on the intoxicated speech of
the captain of the Exxon Valdez around the time of the accident at Prince
William Sound, Alaska. (669)
Observed effects:
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Performance problem, not different grammar
Cf. mimicking Cree sentences doesn’t mean that one has acquired Cree
grammar (671)
attempts at producing adult [phεn] pen collected from a 15-month-old
child in a 30-minute period (Ferguson 1986):

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misarticulation of /r/ and /l/
final devoicing
Deaffrication
[mãə], [v], [dεdn], [hI], [mbõ], [HI], [tHtHtH], [bah],
[dhau], [buã]
Kids who can’t speak yet but can accurately identify subject and object of
sentence, distinguish phonemes, etc. (Hirsh-Pasek and Golinkoff 1991)
Hale and Reiss’s C-P model
Some problems with OT acquisition
theories identified by Menn 2000
mushy and non-phonemicized early outputs
 rules that are no longer constraint-based
 U-shaped developmental curves
 the need to differentiate ‘input’ in the sense
of ‘the adult form the child is trying to
approximate’ from ‘input’ in the sense of
‘abstract strings of concatenated morphemes
to be parsed’
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The Gradual Learning Algorithm
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Boersma and Hayes 2001
“The GLA is cast within a stochastic version of OT that places constraints on a
numeric scale (Boersma 1998).
Each time the grammar is used to evaluate Input-Output pairings, the ranking
values are perturbed slightly (by ‘evaluation noise’) before converting the values to
a ranking order. This produces variation between the rankings of constraints whose
values are close to one another.
In the version proposed by Boersma and Hayes (2001), the values of all constraints
preferring the winner are raised, and the values of those preferring the loser are
lowered, all by an equal amount termed the plasticity.
“The result is that the model is much closer to a standard stochastic grammar than
would at first appear; phonological templates which are abundantly instantiated in
the training set end up being highly favored by the grammar, and those which are
poorly instantiated end up disfavored. Thus, the rankings of constraints closely
track the frequency values that would be assigned to the same constraints in some
stochastic grammars.” (Pierrehumbert 2003)
Problems: GLA
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non-convergence (Pater 2005)—see later slide
Pierrehumbert 2003:225-6
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requires constraints like *ks to get morphophonemic rules like velar softening
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in fact, one needs to postulate a universal set of constraints pairing each phoneme with each
other phoneme (i.e. *ks, *kt, *kp…)
“the price of folding unnatural morphophonological correspondences into the phonological
grammar is splitting the correspondences into unrelated constraint pairs, which are ranked
separately.”
“In the GLA model, all constraints are at the same level and all constraint rankings are
trained on the same data set. Thus, the connection drawn above between the
granularity of constraints and the size of the effective training set does not appear to
be available.”
“Phonetics, in common with many other physical processes, provides examples of
skewed [e.g. gamma] distributions relating to physical nonlinearities and
saturations…In contrast, distributions arising from repeated independent decisions (as
in coin-flipping, or a forced choice experiment) tend to be Gaussian. Since the
assumption of Gaussian distributions is critical to the mathematical tractability of
[GLA], the existence of non-Gaussian distributions appears to be problematic.”
“The GLA model also does not distinguish effects relating to type frequency from
effects relating to surface, or token, frequency.”
“It also provides no way to downweight the grammatical impact of extremely frequent
words, as Bybee (2001) and Bailey and Hahn (2001) show to be necessary.”
The Constraint Demotion Algorithm

Tesar and Smolensky 1993 version, as
reported by Crosswhite and Keer
Problems: CDA
learning of unnatural patterns
 can’t deal with variation (Tesar and
Smolensky 1998, Boersma and Hayes 2001)

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see later slide
Learning unnatural patterns
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CDA predicts that natural patterns will be
easier to learn than unnatural ones, because
they require fewer departures from UG
ranking.
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Support from Pater’s 2005 study?
On the other hand, if learning involves
formulating rules and their difficulty is
computed on the basis of their formal
structure, then unnatural patterns should be
equally learnable.

Supported in studies by Pycha et al. 2003, Morrison
2005, and Seidl & Buckley (forthcoming)
Variation is a problem for
constraint demotion algorithms
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“inconsistent data, such as variation in the
ambient language, causes RCD to choke”
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McCarthy 2002:204-5
Cf. Hayes 2000
Intra-individual variability in L2 production
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Tropf 1987:174—multiple renditions of German nicht ‘not’ in
a one-hour session with a Spanish speaker learning German:
IS 31, I 25, ISt 4, Is 4, Ik 4, Et 2, IC 2,
ICt 1, IZ 1
Similar findings in L1 acq
Hayes’ alternative (strictness bands) predicts that only
adjacent constraints can be involved in optionality

Problem: Pierrehumbert’s hovacity data
Non-convergence for GLA in complex
credit scenarios (Pater 2005)

“The GLA will succeed on the learning problem presented in the last
section if it is run in “demotion only” mode, in which it only decreases the
values of loser preferring constraints, and does not change the value of
winner preferring constraints. However, as Boersma (1997, 1998) shows,
this version of the GLA fails to converge in cases involving variation.”
Variation in CDA

from Pater 2005
Second Language Acquisition
OT on SLA
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“We assume that second language
acquisition involves creation of a new
grammar, using the same resources as
first language acquisition (though other
cognitive strategies may be used as well).
One major difference, however, is that the
initial state of second language
acquisition is the final state of first
language acquisition” (Pater and Tessier
2005)
Predictions of canonical OT
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M/F-based learning
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Consistency
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Consistent cross-linguistic treatment of a given phenomenon, e.g. resolution
of theta
same constraints as characterize natural languages, so *D/_# will always be
dealt with via devoicing, etc.
No levels
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No opacity, derived environment, or avoidance effects that don’t appear in L1
or L2
wouldn’t make sense to spontaneously invoke constraint conjunction, Null
Parse, sympathy constraints, etc.
no level-based effects, since there are no levels
unnatural processes will not be imported into L2, because they are
morphologically conditioned (according to Lombardi, Steriade, etc.)
Markedness
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IL effects will result from either hidden UG rankings in L1, or from
intermediate degrees of constraint demotion/promotion; NOT from reversion
to UG rankings when already superseded by L1 rankings
markedness-based changes will conform to universal markedness hierarchy
neutralizations will be in direction of unmarked member of opposition
Natural/unmarked patterns will be easier to learn
Some central SLP phenomena
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Contra M/F-based learning:
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Contra Consistency:
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Optionality and variation
Convention vs. automaticity
Final devoicing
Contra No levels:
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Nonderived environment blocking
Opacity
Avoidance
Level-based interference
Unnatural interference
Contra Markedness:
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Cases where IL phenomenon  NL, TL
Unnatural patterns not harder to learn
Nonderived Environment Blocking
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Eckman and Iverson 1995 et seqq.:
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Kiparsky and Menn 1987:47—derived environment effect in acq of Greek
Polish devoicing and raising with loanwords
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snop (not *snup) but pagoda → pagut, toga → tuk
Standard OT treatment of NDEB: constraint conjunction (Łubowicz 1999)
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Suppression of s-palatalization in Korean acq of English
Suppression of spirantization in Spanish acq of English
Smolensky: only postulate CC as warranted by PLD
DEC is problem for:
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OT claim that grammars only differ in constraint ranking
OT’s rejection of generalizations/rules—DEC in SLA is clearly a generalization kicking in, not a
constraint conjunction spontaneously appearing
Opacity in SLP
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Counterfeeding chain shift substitution
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Cho and Lee 2001, Idsardi 2002 on opacity in Korean acq of English
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same is found in L1 (Dinnsen, O’Connor, and Gierut 2001)
opaque substitution = contrast maintenance + ordering, not
sympathy, turbidity, targeted constraints, etc.
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Smolensky: only postulate constraint conjunction as warranted by PLD
Idsardi 2002: “this spontaneous chain shift…does not reflect
properties of their original L1 grammar, the target L2 grammar, or
of Universal Grammar…only by employing persistent rules can we
correctly create the conditions for chain-shift; persistence of
constraints and constraint rankings into the L2 does not correctly
induce the chain-shifting behavior”
Counterbleeding repairs
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sin → sjin + thin → sin
Weinberger 1987:412—Mandarin learners of English who apply final
epenthesis before final C-cluster simplification, e.g. <and> 
[aenә]
Counterfeeding and counterbleeding in toy L2 acq
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already discussed earlier in course
Avoidance
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speakers sometimes avoid complex L2 configurations
even if their L1 has them
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Celce-Murcia 1977:
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child learning English and French simultaneously avoided
words containing fricatives in one language by using the word
from the other language, e.g. couteau for knife
Well-documented in L1 phonological acquisition and
disorders also
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Laufer and Eliasson 37, Jordens 1977, Kellerman 1977, 1978,
1986
cleft palate speech, lisp…
Standard OT treatment of avoidance: Null Parse
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Wrongly predicts phonologically empty output, rather than
contentful output that crashes
see Orgun and Sprouse 1999, Nevins and Vaux 2004 for
further discussion and exemplification
Ambiguity and animal wug tests
Consistency
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Lombardi 2003: repair of L2 {θ, ð} predictable from structure of
L1 system
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/s, z/: Japanese, France French, German…
/t, d/: Russian, Quebecois, Hungarian, Sinhalese…
Cf. Ritchie 1968, Nemser 1971, Hancin-Bhatt 1994, Weinberger 1997)
Actual facts: intra-lingual/individual variation in L1 and L2 acq
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multiple L1 substitutes for unfamiliar/difficult L2 sound (Hammarberg 1990)
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Cf. L1:
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Polish replacement of θ, ð by [s, z] [t, d] or [f, v] (Gussmann 1984:31)
Japanese, German, and Turkish speakers vary in what they hear θ, ð as (HancinBhatt 1994)
Calabrese uses f/v, whereas other Italians use t/d (Flege, Munro, and MacKay 1996)
(cf. Cockney vs. New York; Archibald 1998:102)
Unschooled French speakers use [t] (Berger 1951); beginners use [f], intermediate
learners use [t] (Wenk 1979); Quebecois use [t], France uses [s] (Archibald
1998:102)
Korean variation between tense [s'] and tense [t'] (think = ssink ~ ttink) (Oh 2002)
Austrian learners of English vary between f ~ s ~ š ~ dental s ~ “lenisized” θ
(Wieden 1997:232)
English L1 acq θ  [f] in stages IV-VII; ð  [d] or [v] in stages V-VII (Wenk 1979,
Grunwell 1982)
θ  [f] ~ [s], e.g. Susie’s think  sink ~ fink (Vihman and Greenlee 1987)
Conclusion: ambiguity resolved by arbitrary choice (convention)
Dealing with coda [voice]
 Overview
 IL
final devoicing as TETU?
 L1 vs. L2 strategies for dealing with
coda [voice]
 Sources and mechanisms of devoicing
Devoicing as TETU?

From Uffmann 2004:

2 guiding principles:
1.
2.
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Ranking for lgs that don’t allow codas
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Faith >> *Coda/voi, *Coda
Demotion of *Coda below Faith based on TL evidence

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*Coda/voi, *Coda >> Faith
Ranking for lgs that allow contrastive voicing in codas
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Initial state = L1 ranking
L2 learners may also assume default M >> F
*Coda/voi >> Faith >> *Coda
[NB requires ignoring evidence for voiced codas]
To get Hungarian-English phenomenon (IL devoicing despite
both NL and TL having voice contrast in codas), Uffmann
proposes L2 learners assume default M >> F until they
receive evidence to the contrary

Problem: contravenes OT assumption that L2 learners start with
L1 ranking (Pater 2005, etc.)
L1 vs. L2 strategies for coda [voice]
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L1: claimed to only use devoicing
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The “too many solutions problem” (Lombardi 1995, Kager
1999, Steriade 2001, McCarthy 2002…)
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Lombardi: MaxOns, *Lar, MaxVoi
Steriade: P-map
[McCarthy 2002: targeted constraints can get deletion repair,
and therefore shouldn’t be part of OT]
Kiparsky 2004: blocking also used (Konni, Meccan Arabic)
L2: epenthesis and deletion attested
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Deletion
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Epenthesis
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Chinese: Anderson 1983, Xu 2004
Brazilian Portuguese: Major 1987; Korean: Kang 2003, Iverson
and Lee 2004; Vietnamese: Nguyen and Ingram 2004; Chinese:
Eckman 1981, Xu 2004
Also found in L1 acq (Major 2001)
More problems with OT analysis of final
devoicing
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L1 prediction doesn’t fall out nicely
from constraint inventory; requires
conspiracy of several constraints
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*Coda/voi-less system (Lombardi 2000,
Beckman 2004) misses key articulatory
motivation by avoiding (cf. Smith et al.
2005)
Lombardi’s scheme should hold for NC as
well, but doesn’t (Myers 2002)
Lombardi can’t get word-initial
neutralization, like in English and Lac
Simon
L1 prediction falsified by L2 data
Not clear why devoicing should be
easier to learn than contrast
L2 devoicing may not be emergent
UG effect:
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English has devoicing (Haggard 1978,
Ladefoged 1982, Pierrehumbert and Talkin
1992, Smith 1997)
May be articulatory phonetic problem
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“L2 speakers…may not have developed
adequate voicing control abilities” (Smith et
al. 2005)
English-Hungarian case requires reversion
to UG ranking, not hidden rankings
Levels

Only low-level automatic L1 rules can cause
interference (Linell 1979, Rubach 1980, 1984,
Broselow 1984, Singh and Ford 1987, Eckman and
Iverson 1995)


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Morphologically-conditioned processes do not cause
interference
Problem 1: incoherent in monostratal model
Problem 2: interference from processes treated as
morphologically-conditioned in OT (McCarthy 1997,
Steriade 2001, Picard 2002, Lombardi 2003)

r-insertion in RP pronunciation of L2 French, German, Spanish
(Wells 1982)

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J’étais déjà[r] ici, ich bin ja[r] fertig, viva[r] España
n-insertion in Korean (H. Kim 1999)

[lukεnñuәsεlph] ‘look at yourself’
Cases where IL phenomenon  NL, TL

Cases where hidden rankings aren’t involved
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Idsardi’s 2002 spontaneous chain shift
Hungarian-English final devoicing (?)
Japanese antepenultimate mora accentuation (?)
Hungarian gemination after stressed vowel (?)
Cases where novel marked configurations are produced,
violating markedness hierarchy

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Russian č
Odd neutralizations in L1 acq:
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Child with dental click for all coronal continuants (Bedore et al. 1994)
Child with ingressives for all postvocalic stops (Gierut and Champion
2000)
4;8 subject, Jason, produces [pfw] “to represent nearly all word-initial
liquid clusters, as well as initial labial fricatives” (Edwards 1996:153)
Amahl’s word-initial engma and voiceless sonorants (Smith 1973:4)
child with s in onset, x in coda (Gierut 1993)
L2: Yuchun’s production of [ü] for [i] in English UG, phonology, etc.

NB she doesn’t just do it after j
Conclusions

Classic OT predictions disconfirmed by acquisition data
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final devoicing, cluster simplification…
Not well captured in OT; SLA requires reference to UG system
overridden by L1 and to phenomena not in L1/L2/UG-CON
Conversely, language allows a broader range of possibilities than is
countenanced in OT


M/F-based learning: NDEB, opacity, avoidance
Consistency: variation, convention
No levels: Level-based interference, Unnatural interference
Markedness: Cases where IL phenomenon  NL, TL; Unnatural patterns not harder to learn
Universal markedness/UG plays a role in SLA


contra
contra
contra
contra
strategies to deal with devoicing, unnatural rules…
Acquisition is generalization-driven and potentially variable


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supported by DEC effects, conventionalized segmental
substitutions, which V to delete in hiatus, deneutralization…
Cf. Kiparsky and Menn’s 1977 model, which involves active
hypothesis formation and testing on the part of the child, and
Fey and Gandour 1979, vs. Stampe’s and OT’s, which are
closer to behaviorist stimulus-response
For variation, cf. Macken and Ferguson’s flexible learning
model that allows for variation, contrary to earlier
deterministic models that had predictable L1L2 transfer
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