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Types of Noun Phrases
Referential vs. Quantified NPs
Inherently referring noun phrases pick out individuals in
the world.
e.g. John, Mary, the President, the department chair
Non-referring noun phrases do not pick out an individual
(or individuals) in the world. We will call these
‘quantified NPs’ or ‘operators’
e.g.
No bear, every boy, all the students, each boy,
who, two rabbits, some teacher, etc.
A Paradigm with a Missing Cell
1)
2)
3)
4)
[No talk show host]i believes that Oprah admires himk
[No talk show host]i believes that Oprah admires himi
[No talk show host]i admires himk
*[No talk show host]i admires himi
The phenomenon is the same for referential NP, e.g.,
‘Geraldo’
Example (4) cannot mean that no talk show host admires
himself; it must mean that no talk show host admires some
other male
Two Accounts of Principle B
1.
2.
3.
4.
Geraldo believes that Oprah admires himk
Geraldoi believes that Oprah admires himi
Geraldoi admires himk
*Geraldoi admires himi
5.
6.
7.
8.
[No talk show host]i believes that Oprah admires himk
[No talk show host]i believes that Oprah admires himi
[No talk show host]i admires himk
*[No talk show host]i admires himi
Chomsky: Principle B applies to pronouns which are
c-commanded by referential NPs and to pronouns
which have operators as antecedents (i.e. 1-8)
Reinhart: Principle B applies only to pronouns which are
c-commanded by an operator (i.e. 5-8). Example
(4) is ruled out by pragmatic Rule I (Info strength)
The Experimental Findings
Chien and Wexler (1990 Language Acquisition)
Methodology: Picture-judgment task
Results: First, children ‘appear’ to violate Principle B in
sentences like ‘Mama Bear is touching her’, allowing the
prohibited meaning, i.e. Mama Bear is touching herself
about 50% of the time
However, the same children do not violate Principle B
when the antecedent of the pronoun is a quantified NP
such as ‘every bear’. E.g., Every bear is touching her is
accepted only 15% of the time in a situation where every bear
touches herself, but no bear touches another salient female
who is depicted in the picture (say Goldilocks).
Examples of Pictures Used.
The reflexives
make sure
children allow
this kind of
interpretation
Main Question:
can children
reject the
picture on the
right -->
Main Question:
can children
reject the
picture on the
right -->
G1: under 4yrs
G2: 4-5yrs
G3: 5-6yrs
G4: 6-7yrs
A: Adults
Look at G3
for largest
difference
Summary of Chien and Wexler’s Findings
In general, the youngest children didn’t perform well. It
may be that the task is too hard for them, at least for
some of the test sentences.
Putting the youngest children (Groups 1 and 2) aside,
however, children do pretty well with reflexives. They
reject the mismatch sentence/picture pairs at high rates.
Children also perform well with pronouns with a
quantified NP as antecedent by 5-6 years-old (Group 3),
but these children still accept coreference for pronouns
with a referential NP as antecedent.
Picture Tasks
Experiments using pictures can give us an idea about
what’s going on in children’s grammars, although these
experiments tend to underestimate children’s knowledge,
as compared to experiments using the Truth Value
Judgment task
However, large numbers of subjects can be run, because
the experiment is quick to carry out, though children don’t
enjoy it.
So, how would the results of Chien and Wexler’s
experiment stand up -- if we switch to a TVJ task?
A Target Picture
What Isn’t Shown in a Picture?
A story with ‘events’ taking place
in real time. The target sentence
is a possible outcome
which satisfies the condition of
plausible dissent
but events took a different turn,
so the actual outcome
makes the sentence false
Plausible denial is also unmet for QNPs
Not every bear is touching her -- in fact, none of them are.
What Isn’t Shown in a Picture?
Possible Outcome:
Mama Bear touches Goldilocks
Events take a turn such that
Mama Bear doesn’t touch
Goldilocks, but touches herself
The TVJ task
Background:
Every troll dried so-and-so
Assertion:
Every troll dried a salient
female character: Ariel
Possible Outcome:
Every troll dried Ariel.
Actual Outcomes:
Only one troll dried Ariel
Every troll dried herself
NB: there are other Background/Assertion pairs
Test Sentence “Every troll dried her”
The characters are introduced
Theme : A Swimming Story
The 3 trolls are planning to go to the beach.
They take towels, & a watermelon -- for a picnic
The Plot Unfolds
The Trolls are swimming in the deep blue sea,
when who along comes Ariel the Mermaid.
The Plot Unfolds
The trolls invite Ariel to share the watermelon
with them -- once they are all dried off. Ariel’s
hair & tail are very wet.
Potential Antecedent for the Pronoun
Ariel accepts, but needs to dry off. “Can you trolls
help. My hair is very wet, and so is my tail.”
The Possible Outcome
“Sure, we can help. We’ll go get our towels.”
Possible Outcome
Troll 1: “I have a big towel. I’ll dry your hair,
and then I’ll get dry too. I’m still wet.
Possible Outcome
Troll 1: ”Here you go Ariel. Now your hair is dry.”
Condition of Falsification
Troll 2: “Your tail is still wet. Oh look. I only
brought a small towel. It’s not big enough to dry
us both. And I’m wet too.”
Possible Outcome becomes Untenable
Troll 3: “I can’t help either. Your wet tail will
soak my little towel, and I need it to dry off.”
The Actual Outcome: & Reminder
Troll 1: “Now I feel better” <troll dries self>
Troll 2: “Me too.”
Troll 3: “I’m getting all dry too.”
The Story Ends
Let’s have watermelon together now.
(The towels are still next to the Trolls.)
The Linguistic Antecedent
“That was a story about Ariel and some girl
trolls. And I know one thing that happened.
Every troll dried her.”
The Truth Value Judgment
Child: “No.”
Kermit: “No? What really happened?”
Child: “Only one troll dried her.”
Does the Truth Value Judgment Task Help?
Yes and No.
No: Using the Truth Value Judgment Task, children
distinguish between pronouns with quantified NP
antecedents and referential NP antecedents, permitting
local coreference in with referential NPs as antecedents.
Yes: The pattern is obtained for children at least one year
younger.
Thornton and Wexler (1999)
Subjects: 19 children aged 4;0 to 5;1 (mean age = 4;8)
Every reindeer brushed him
Bert brushed him
08% acceptance
58% acceptance
Parameter Setting:
Null Subjects
Null Subjects
• Child English
– Eat cookie.
• Hyams (1986)
– English children have an Italian setting of null-subject parameter
– Trigger for change: expletive subjects
• Valian (1991)
– Usage of English children is different from Italian children
(proportion)
• Wang (1992)
– Usage of English children is different from Chinese children (null
objects)
Parameter Setting:
Complex Predicates
(Snyder 2001)
Complex Predicates
English Spanish
a.
b.
c.
d.
e.
John painted the house red
Mary picked the book up
Fred made Jeff leave
Fred saw Jeff leave
Alice sent Sue the letter
resultative
verb-particle
make-causative
perceptual report
double object










f.
g.
Alice sent the letter to Sue
Bob put the book on the table
dative
put-locative




N-N Compounding
• English: frog man (various meanings)
French: homme grenouille (fixed meaning)
• English: banana cup
wine glass
party chair
blood lady
Compounding Parameter
American Sign Language
Austrooasiatic (Khmer)
Finno-Ugric
Germanic (German, English)
Japanese-Korean
Sino-Tibetan (Mandarin)
Tai (Thai)
Basque
Afroasiatic (Arabic, Hebrew)
Austronesian (Javanese)
Bantu (Lingala)
Romance (French, Spanish)
Slavic (Russian, Serbo-Croatian)
Resultatives













Productive N-N Compounds













Compounding Parameter
American Sign Language
Austrooasiatic (Khmer)
Finno-Ugric
Germanic (German, English)
Japanese-Korean
Sino-Tibetan (Mandarin)
Tai (Thai)
Basque
Afroasiatic (Arabic, Hebrew)
Austronesian (Javanese)
Bantu (Lingala)
Romance (French, Spanish)
Slavic (Russian, Serbo-Croatian)
Resultatives













Productive N-N Compounds











 correlation is not
bidirectional

Developmental Evidence
• Complex predicate properties argued to appear as a group in English
children’s spontaneous speech (Stromswold & Snyder 1997)
• Appearance of N-N compounding is good predictor of appearance of
verb particle constructions and other complex predicate constructions even after partialing out contributions of
–
–
–
–
Age of reaching MLU 2.5
Production of lexical N-N compounds
Production of adjective-noun combinations
Correlations are remarkably good
Language Change:
Learning about Verbs
Classes of Verbs
• Verbs with syntax like pour
manner-of-motion
– dribble, drip, spill, shake, spin, spew, slop, etc.
• Verbs with syntax like fill
change-of-state
– cover, decorate, bandage, blanket, soak,
drench, adorn, etc.
• Verbs with syntax like load
manner-of-motion
& change-of-state
– stuff, cram, jam, spray, sow, heap, spread, rub,
dab, plaster, etc.
Learning Syntax from Semantics
VP
Manner-of
-motion
V
NP
PP
figure ground
VP
Change-of
-state
V
SEMANTICS
Linking Rules
Figure
Frame
Ground
Frame
NP
PP
ground figure
SYNTAX
• Assumption: linking generalizations are universal
• Shared by competing accounts of learning verb syntax &
semantics
Evidence
• Semantics --> Syntax (Gropen et al., 1991, etc.)
– Teach child meaning of novel verb, e.g. this is moaking
– Elicit sentences using that verb
• Syntax --> Semantics (Naigles et al. 1992; Gleitman et al.)
– Show child multiple scenes, use syntax to draw attention
– Adults: verb-guessing task, show effects of scenes, syntax, semantics.
Language Change
Theoretical Approaches
Language Change
• How to take input and reach a new grammar
– Error signal
• Failure to analyze input
• Failure to predict input
– Cues
• Syntactic
• Non-syntactic
• How do any of these deal with problem of overgeneralization?
Triggers
Gibson & Wexler (1994)
• Triggering Learning Algorithm
– Learner starts with random set of parameter values
– For each sentence, attempts to parse sentence using current settings
– If parse fails using current settings, change one parameter value
and attempt re-parsing
– If re-parsing succeeds, change grammar to new parameter setting
+ B -
+
A
-
Si
Gibson & Wexler (1994)
• Triggering Learning Algorithm
– Learner starts with random set of parameter values
– For each sentence, attempts to parse sentence using current settings
– If parse fails using current settings, change one parameter value
and attempt re-parsing
– If re-parsing succeeds, change grammar to new parameter setting
+ B -
+
A
-
Si
Single Value Constraint
Greediness Constraint
Gibson & Wexler (1994)
• For an extremely simple 2-parameter space, the learning
task is easy - any starting point, any destination
• Triggers do not really exist in this model
VO
SVO
VOS
OV
SOV
OVS
SV VS
Gibson & Wexler (1994)
• Extending the space to 3-parameters
– There are non-adjacent grammars
– There are local maxima, where current grammar and all neighbors
fail
VO
SVO
VOS
OV
SOV
OVS
SV VS
+V2
-V2
Gibson & Wexler (1994)
• Extending the space to 3-parameters
– There are non-adjacent grammars
– There are local maxima, where current grammar and all neighbors
fail
VO
SVO
VOS
OV
SOV
OVS
SV VS

+V2
-V2
String:
Adv S V O
Gibson & Wexler (1994)
• Extending the space to 3-parameters
– There are non-adjacent grammars
– There are local maxima, where current grammar and all neighbors
fail
– All local maxima involve impossibility of retracting a +V2
hypothesis
VO
SVO
VOS
OV
SOV
OVS
SV VS

+V2
-V2
String:
Adv S V O
Gibson & Wexler (1994)
• Solutions to local maxima problem
– #1: Initial state is
V2:
default to [-V2]
S:
unset
O:
unset
– #2: Extrinsic ordering
VO
SVO
VOS
OV
SOV
OVS
SV VS

+V2
-V2
String:
Adv S V O
Fodor (1998)
• Unambiguous Triggers
– Local maxima in TLA result from the use of ‘ambiguous triggers’
– If learning only occurs based on unambiguous triggers, local
maxima should be avoided
• Difficulties
– How to identify unambiguous triggers?
– Unambiguous trigger can only be parsed by a grammar that
includes value Pi of parameter P, and by no grammars that include
value Pj.
– A parameter space with 20 binary parameters implies 220 parses for
any sentence.
Fodor (1998)
• Ambiguous Trigger
– SVO can be analyzed by at least 5 of the 8 grammars in G&W’s
parameter space



VO
SVO
VOS
OV
SOV
OVS

+V2
-V2
Fodor (1998)
• Structural Triggers Learner (STL)
– Parameters are treelets
– Learner attempts to parse input sentences using supergrammar, that
contains treelets for all values of all unset parameters, e.g., 40 treelets for
20 unset binary parameters.
– Algorithm
• #1: Adopt a parameter value/trigger structure if and only if it occurs as a part
of every complete well-formed phrase marker assigned to an input sentence by
the parser using the supergrammar.
• #2: Adopt a parameter value/trigger structure if and only if it occurs as a part
of a unique complete well-formed phrase marker assigned to the input by the
parser using the supergrammar.
Fodor (1998)
• Structural Triggers Learner
– If a sentence is structurally ambiguous, it is taken to be
uninformative (slightly wasteful, but conservative)
– Unable to take advantage of collectively unambiguous sets of
sentences, e.g. SVO and OVS, which entail [+V2]
– Still unclear (to me) how it manages its parsing task
Competition Model
• 2 grammars - start with even strength, both get credit for
success, one gets punished for failure
• Each grammar is chosen for parsing/production as a
function of its current strength
• Must be that increasing Pi for one grammar decreases Pj
for other grammars
• Is it the case that the presence of some punishment will
guarantee that a grammar will, over time, always fail to
survive?
Competition Model
• Upon the presence of an input datum s, the child
– Selects a grammar Gi with the probability pi.
– Analyzes s with Gi.
– Updates competition
• If successful, reward Gi by increasing pi.
• Otherwise, punish Gi by decreasing pi.
• This implies that change only occurs when a selected
grammar succeeds or fails
Competition Model
• Linear reward-penalty scheme
– Given an input sentence s, the learner selects a grammar Gi with
probability pi.
– If Gi --> s then
• p’i = pi + (1 - pi)
• p’j = (1 - )pj
– If Gi -/-> s then
• p’i = (1 - )pi
• p’j = /(N - 1) + (1 - )pj
From Grammars to Parameters
• Number of Grammars problem
– Space with n parameters implies at least 2n grammars (e.g. 240 is
~1 trillion)
– Only one grammar is used at a time, so implies very slow
convergence
• Competition among Parameter Values
– How does this work?
– Naïve Parameter Learning model (NPL) may reward incorrect
parameter values as hitchhikers, or punish correct parameter values
as accomplices.
Empirical Predictions
• Hypothesis
Time to settle upon target grammar is a function of the
frequency of sentences that punish the competitor
grammars
• First-pass assumptions
– Learning rate set low, so many occurrences needed to lead to
decisive changes
– Similar amount of input needed to eliminate all competitors
Empirical Predictions
• ±wh-movement
– Any occurrence of overt wh-movement punishes a [-wh-mvt]
grammar
– Wh-questions are highly frequent in input to English-speaking
children (~30% estimate!)
– [±wh-mvt] parameter should be set very early
– This applies to clear-cut contrast between English and Chinese
• French: [+wh-movement] and lots of wh-in-situ
• Japanese: [-wh-movement] plus scrambling
Empirical Predictions
• Verb-raising
– Reported to be set accurately in speech of French children (Pierce,
1992)
French: Two Verb Positions
a. Il ne voit pas le canard
he sees not the duck
b. *Il ne pas voit le canard
he not sees the duck
c. *Il veut ne voir pas le canard
he wants to.see not the duck
d. Il veut ne pas voir le canard
he wants not to.see the duck
French: Two Verb Positions
a. Il ne voit pas le canard
he sees not the duck
b. *Il ne pas voit le canard
he not sees the duck
c. *Il veut ne voir pas le canard
he wants to.see not the duck
d. Il veut ne pas voir le canard
he wants not to.see the duck
agreeing (i.e. finite)
forms precede pas
non-agreeing (i.e.
infinitive) forms
follow pas
French Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
finite infinitive
127
119
• Just like adults
(Pierce, 1992)
French Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
V-neg
122
neg-V
124
• Just like adults
(Pierce, 1992)
French Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
finite infinitive
V-neg 121
neg-V 6
1
118
• Just like adults
(Pierce, 1992)
Empirical Predictions
• Verb-raising
– Reported to be set accurately in speech of French children (Pierce,
1992)
– Crucial evidence
… verb neg/adv …
– Estimated frequency in adult speech: ~7%
– This frequency set as an operational definition of sufficiently
frequent for early mastery (early 2’s)
Empirical Predictions
• Verb-second
– Classic argument: V2 is mastered very early by German/Dutch
speaking children (Poeppel & Wexler, 1993, Hageman, 1995)
– Yang’s challenges
• Crucial input is infrequent
• Claims of early mastery are exaggerated
Two Verb Positions
a. Ich sah den Mann
I saw the man
b. Den Mann sah ich
the man saw I
c. Ich will [den Mann sehen]
I want the man to.see[inf]
d. Den Mann will ich [sehen]
the man want I to.see[inf]
Two Verb Positions
a. Ich sah den Mann
I saw the man
b. Den Mann sah ich
the man saw I
agreeing verbs (i.e.
finite verbs) appear in
second position
c. Ich will [den Mann sehen]
I want the man to.see[inf]
d. Den Mann will ich [sehen]
the man want I to.see[inf]
non-agreeing verbs (i.e.
infinitive verbs) appear
in final position
German Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
finite infinitive
208
43
• Just like adults
Andreas, age 2;2
(Poeppel & Wexler, 1993)
German Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
V-2
203
V-final
48
• Just like adults
Andreas, age 2;2
(Poeppel & Wexler, 1993)
German Children’s Speech
• Verb forms: correct or
default (infinitive)
• Verb position changes
with verb form
finite infinitive
V-2
197
V-final 11
6
37
• Just like adults
Andreas, age 2;2
(Poeppel & Wexler, 1993)
Empirical Predictions
• Cross-language word orders
–
–
–
–
–
Dutch:
Hebrew:
English:
Irish:
Hixkaryana:
SVO, XVSO, OVS
SVO, XVSO, VSO
SVO, XSVO
VSO, XVSO
OVS, XOVS
– Order of elimination
• Frequent SVO input quickly eliminates #4 and #5
• Relatively frequent XVSO input eliminates #3
• OVS is needed to eliminate #2 - only ~1.3% of input
Empirical Predictions
• But what about the classic findings by Poeppel & Wexler
(etc.)?
– They show mastery of V-raising, not mastery of V-2
– Yang argues that early Dutch shows lots of V-1 sentences, due to
the presence of a Hebrew grammar (based on Hein corpus)
e.g. week ik niet [know I not]
Empirical Predictions
• Early Argument Drop
– Resuscitates idea that early argument omission in English is due to
mis-set parameter
– Overt expletive subjects (‘there’) ~1.2% frequency in input
Null Subjects
• Child English
– Eat cookie.
• Hyams (1986)
– English children have an Italian setting of null-subject parameter
– Trigger for change: expletive subjects
• Valian (1991)
– Usage of English children is different from Italian children
(proportion)
• Wang (1992)
– Usage of English children is different from Chinese children (null
objects)
Empirical Predictions
• Early Argument Drop
– Resuscitates idea that early argument omission in English is due to
mis-set parameter
– Wang et al. (1992) argument about Chinese was based on
mismatch in absolute frequencies between Chinese & English
learners
– Yang: if incorrect grammar is used probabilistically, then absolute
frequency match not expected - rather, ratios should match
– Ratio of null-subjects and null-objects is similar in Chinese and
English learners
– Like Chinese, English learners do not produce wh-obj pro V?
Empirical Predictions
• Null Subject Parameter Setting
– Italian environment
• English [- null subject] setting killed off early, due to presence of
large amount of contradictory input
• Italian children should exhibit an adultlike profile very early
– English environment
• Italian [+ null subject] setting killed off more slowly, since
contradictory input is much rarer (expletive subjects)
• The fact that null subjects are rare in the input seems to play no role
Tuesday 12/9
Poverty of the Stimulus
• Structure-dependent auxiliary fronting
– Is [the man who is sleeping] __ going to make it to class?
– *Is [the man who __ sleeping] is going to make it to class?
• Pullum: relevant positive examples exist (in the Wall Street
Journal)
• Yang: even if they do exist, they’re not frequent enough to
account for mastery by children age 3;2 in Crain &
Nakayama’s experiments (1987)
Other Parameters
• Noun compounding/complex predicates
– English
• Novel N-N compounds punish Romance-style grammars
• Simple data can lead to elimination of competitors
– Spanish
• Lack of productive N-N compounds is irrelevant
• Lack of complex predicate constructions is irrelevant
• How can the English (superset) grammar be excluded?
• Subset problem
Subset Problem
• Subset problem is serious: all grammars are assumed to be
present from the start
– How can the survivor model avoid the subset problem?
Li
Lj
Other Parameters
• P-stranding Parameter
– English
• Who are you talking with?
• Positive examples of P-stranding punish non P-stranding
grammars
– Spanish
• ***Quien hablas con?
• Non-occurrence of P-stranding does not punish P-stranding
grammars
• Could anything be learned from the consistent use of piedpiping, or from the absence of P-stranding?
Other Parameters
• Locative verbs & Verb compounding
– John poured the water into the glass.
*John poured the glass with water.
– (*)John filled the water into the glass. <-- ok if [+V compounding]
John filled the glass with water.
– English
• Absence of V-compounding is irrelevant
• Simple examples above do not punish Korean grammar (superset)
• Korean grammar may be punished by more liberal properties
elsewhere, e.g. pile the table with books.
– Korean
• Occurrence of ground verbs in figure frame punishes English grammar
• Occurrence of V-compounds punishes English grammar
Other Parameters
• “Or as PPI” Parameter
– John didn’t eat apples or oranges
– English
• Neither…nor reading punishes Japanese grammar
– Japanese
• Examples of Japanese reading punish English???
Other Parameters
• Classic Subjacency Parameter (Rizzi, 1982)
– English: *What do you know whether John likes ___?
Italian: ok
Analysis: Bounding nodes are (i) NP, (ii) CP (It.)/IP (Eng.)
– English
• Input regarding subjacency is consistent with Italian grammar
– Italian
• If wh-island violations occur, this punishes English grammar
• Worry: production processes in English give rise to non-trivial
numbers of wh-island violations.
Triggers
• Triggers
– Unambiguous triggers - if they exist - do not have the role of
identifying the winner as much as punishing the losers
– Distributed triggers - target grammar is identified by conjunction
of two different properties, neither of which is sufficient on its own
• Difficult for Fodor and for Gibson/Wexler - no memory
• Survivor model: also no memory, but distributed trigger works by
never punishing the winner, but separately punishing the losers
Questions about Survivor Model
• How to address the Subset Problem
• Better empirical evidence for multiple grammars
Role of Probabilistic Information
• Probabilistic information has limited role
– It is used to predict the time-course of hypothesis evaluation
– It contributes to the ultimate likelihood of success in only a very
limited sense
– It does not contribute to the generation of hypotheses - these are
provided by a pre-given parameter space
– By gradually accumulating evidence for or against hypotheses, the
model becomes somewhat robust to noise
• One-grammar-at-a-time models
– Negative evidence (i.e. parse failure) has drastic effect
– Hard to track degree of confidence in a given hypothesis
– Therefore hard to protect against fragility
Statistics as Evidence or as Hypothesis
• Phonological learning
– Age 0-1: developmental change reflects tracking of surface
distribution of sounds?
– Age 1-2: [current frontier] developmental change involves
abstraction over a growing lexicon, leading to more efficient
representations
• Neural Networks etc.
– Statistical generalizations are the hypotheses
• Lexical Access
– Context guides selection among multiple lexical candidates
• Syntactic Learning
– Survivor model: statistics tracks accumuulation of evidence for
pre-given hypotheses
Three Benchmarks
• Complexity
• Consistency
• Causality
Complexity
• Most statistical models have little to say about the kinds of
complex linguistic phenomena that linguists spend their
time worrying about
[Models that focus on percentage success in parsing
naturally occurring corpora often don’t even evaluate
success on specific constructions]
Consistency 1: Condition C
• Condition C selectively excludes cases of backwards
anaphora
– Ok
– *
While he was reading the book, Pooh ate an apple.
He ate an apple while Pooh was reading the book.
• Evidence for the constraint is probably obscure in the input
data, but it’s quite robust in adult speakers
120
nonPrC GM
100
nonPrc GMM
Residual Reading Times
80
PrC GM
PrC GMM
60
40
20
0
-20
-40
-60
because
while
last
while-cd
semester
SHE
was
taking
GME at the 2nd NP in non-PrC
pair
classes
while
while-ab
Jessica
NAME
Russell
was
working
full-time
to…
120
nonPrC GM
100
nonPrc GMM
Residual Reading Times
80
PrC GM
PrC GMM
60
40
20
0
-20
-40
-60
because
while
last
while-cd
semester
SHE
was
taking
GME at the 2nd NP in non-PrC
pair
NO GME at the 2nd NP in PrC
classes
while
while-ab
Jessica
NAME
Russell
was
working
full-time
to…
Principle C –
EARLY filter
Backward Anaphora in Russian
ADULTS:
Eng
Rus
PrC:
Hei ate an apple while Johni was reading the book.
Oni s'el yabloko, poka Ivani
chital
knigu.


While:
While hei was reading the book Johni ate an apple.
Poka oni chital
knigu, Ivani s'el yabloko.


Russian 3-year-olds
Rejection of coreference reading
100%
% No-responses
80%
75%
60%
Russian 3-year-olds
40%
20%
12%
0%
Principle C
while-sent
(Mann-Whitney
U-test, p<0.01)
Consistency 2: Argument Structure
• Example from cross-language variation in argument
structure of locative verbs
• Despite variation across-languages in simple VP structures,
cross-language uniformity emerges in rarer constructions
– Adjectival passives
– Korean serialization
– Etc.
Causality 1: Japanese
Embedded gap-creation
 A gap is posited in the most
deeply embedded clause.
WH-dat
NP-subj
gap
CP
Verb
NP-subj VP
gap
Verb
 This is surely very low in
frequency in Japanese
corpora
Causality 2: Hindi
Nevins, Phillips, Poeppel (in
progress, b)
Hindi: Syntactic vs. Semantic
Split-Ergative Case System:
man-erg book-ø read-pst
ERP study of tense-marking violations
(a)
man-erg book-ø read-fut
violates syntactic prediction
(b)
last year man-ø arrive-fut
violates semantic prediction
To be continued…