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Grammatical Illusions: Encoding and navigating linguistic
structures in real time
Parts #1 & #2 Background and
Unbounded Dependencies
Colin Phillips Dept of Linguistics
Cogn. Neurosci. of Language Lab University of Maryland
From Cells to Syntax
Perceptual Illusions
Illusory Comparative
“More people have been to Russia than I have.”
(Montalbetti 1984, Townsend & Bever 2001, Wellwood et al. 2009)
Outline of Talks 1. Introduction
o grammars o illusions & non-illusions o memory access
2. Unbounded dependencies (wh-mvt, relativization, etc.) o ‘active’ processes o on-line constraint application o standard experimental approaches
3. Anaphora o prospective vs. retrospective processes
4. Agreement 5. Semantic constraints
o Negative Polarity o Comparatives o Thematic role binding
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http://www.ling.umd.edu/colin
1. Some general questions
A. What is knowledge of grammar? B. Motivation for testing on-line constraint application
C. Selective Fallibility/Grammatical Illusions D. Memory and access mechanisms
A. What is grammatical knowledge?
Chomsky (1965) • “Linguistic theory is concerned primarily with an ideal speaker-listener
[…] who knows its language perfectly, and is unaffected by such grammatically irrelevant conditions as memory limitations, distractions, shifts of attention, and interest, and errors (random or characteristic) in applying his knowledge of language in actual performance. […]
We thus make a fundamental distinction between competence (the speaker-hearer’s knowledge of his language) and performance (the actual use of language in concrete situations). Only under the idealization set forth in the preceding paragraph is performance a direct reflection of competence.” (pp. 3-4)
• “When we say that a sentence has a certain derivation with respect to a particular generative grammar, we say nothing about how the speaker or hearer might proceed, in some practical or efficient way, to construct such a derivation. These questions belong to the theory of language use - the theory of performance.” (p. 9)
• Says what knowledge is not … but little about what gr. knowledge is
Standard View
324 697+ ?
217 x 32 = ?
arithmetic
Standard View
324 697+ ?
217 x 32 = ?
specialized algorithm specialized algorithm
‘number���sense’?
something deeper
arithmetic
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Standard View
specialized algorithm specialized algorithm
recursive characterization of���well-formed expressions
speaking understanding
grammatical���knowledge,���competence language
What is a (mental) grammar? • Mentalistic: grammars describe ‘knowledge of language’
They are task-independent
• But how to interpret the mental status of specific grammatical mechanisms?
• A. Literalist view: mechanisms/derivations are real, one among multiple systems used by speakers (Townsend & Bever, 2001)
• B. Formalist view (of derivational theories): all representations in grammatical derivation are constructed at some point; derivational order is a formal relation, not a timing relation
• C. Extensionalist view: grammatical descriptions simply meet the goal of predicting (un)acceptable sentences
• My view: grammars are real-time computational systems (Phillips & Lewis, in press)
Implementation (In)dependence • An abstract system is implementation independent if the same
system can be realized in different ways by multiple lower-level implementations
• Arithmetical operations, e.g., 48 ÷ 8 = 6
Can be implemented in digital computer, abacus, human brain, etc.
• An abstraction is implementation dependent if it is only ever realized in one way at a less abstract level
• Applied to generative grammars: are there multiple ways of generating the same representation in different tasks?
(Phillips & Lewis, in press)
Implementation (In)dependence • Generative grammars typically assumed to be implementation
independent – but there is very little evidence for this (… and very little investigation of the issue, to be honest)
• Sentence representations consistently constructed in same order, regardless of task
(one cannot construct sentences ‘backwards’)
• When sentence comprehension fails, repair appears to simply reprocess the same sentence in the original order
(Phillips & Lewis, in press)
B. Why study on-line constraint application?
Real-time status of grammatical constraints
• In theoretical linguistics, the existence of richly articulated grammatical constraints are used to justify richly articulated representations
• The same reasoning applies when looking at what speakers do in real time: on-line application of grammatical constraints implicates: (i) existence of suitably rich representations (ii) ability to use those representations successfully and quickly
• … much harder to get facts about on-line constraint application
• … and some people think it’s not terribly relevant
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“It has sometimes been argued that linguistic theory must meet the empirical condition that it account for the ease and rapidity of parsing. But parsing does not, in fact, have these properties. […] In general, it is not the case that language is readily usable or ‘designed for use.’” (Chomsky & Lasnik, 1993, p. 18)
“…the language comprehension system creates representations that are ‘good enough’ (GE) given the task that the comprehender needs to perform. GE representations contrast with ones that are detailed, complete, and accurate with respect to the input.” (Ferreira & Patson, 2007, p. 71) “we understand everything twice” (Townsend & Bever, 2001)
Noam Chomsky, MIT
Howard Lasnik, Maryland
Fernanda Ferreira, S. Carolina
Tom Bever, Arizona
Two Views • Real-time processes only indirectly related to grammatical knowledge
predicts on-line insensitivity to many grammatical constraints
(Psycholinguistics: Townsend & Bever 2001; Ferreira & Patson 2007; Lewis et al. 2006; Theoretical linguistics: standard position since Chomsky 1965)
• Real-time processes are all there is
predicts on-line grammatical “infallibility”
(Grammatical models: Kempen & Harbusch 2002; Phillips 2004; Cann et al. 2005 Psycholinguistics: implicit in most ambiguity resolution models since 1970s)
C. Selective Fallibility & Grammatical Illusions
A quick tour
On-line Interpretation of Backwards Anaphora
Kazanina, Lau, Lieberman, Yoshida, & Phillips (2007, J. Mem. Lang.)
Ellen Lau Harvard Med Sch./Maryland
Nina Kazanina U. of Bristol, Psych.
Pronoun Interpretation • A pronoun may precede its antecedent…
– While hei was reading the book, Johni ate an apple.
• But not always…
– *Hei ate the apple while Johni was reading the book.
• Binding Condition C
– A pronoun may not c-command its antecedent
(i.e., the antecedent can’t occur in the scope of the pronoun)
A Constraint on Interpretation
S
NP VP
V NP
John
ate the apple
S’
S
while S
NP VP
Comp
he
was reading the book
While he was reading the book, John ate the apple
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A Constraint on Interpretation S
NP VP
V NP
he
ate the apple
S’ VP
while S
NP VP
Comp
John
was reading the book
He ate the apple while John was reading the book
Pronoun Interpretation • A pronoun may precede its antecedent…
– While hei was reading the book, Johni ate an apple.
• But not always…
– *Hei ate the apple while Johni was reading the book.
• Binding Condition C
– A pronoun may not c-command its antecedent
(i.e., the antecedent can’t occur in the scope of the pronoun)
Can antecedent search ignore NPs in structurally inappropriate positions?
----- -- --- ------- --- ----- ---- --- -- ------!
Self Paced Reading
While -- --- ------- --- ----- ---- --- -- ------!
Self Paced Reading
----- he --- ------- --- ----- ---- --- -- ------!
Self Paced Reading
----- -- was ------- --- ----- ---- --- -- ------!
Self Paced Reading
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----- -- --- reading --- ----- ---- --- -- ------!
Self Paced Reading
----- -- --- ------- the ----- ---- --- -- ------!
Self Paced Reading
----- -- --- ------- --- book, ---- --- -- ------!
Self Paced Reading
----- -- --- ------- --- ----- John --- -- ------!
Self Paced Reading
----- -- --- ------- --- ----- ---- ate -- ------!
Self Paced Reading
----- -- --- ------- --- ----- ---- --- an ------!
Self Paced Reading
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----- -- --- ------- --- ----- ---- --- -- apple.!
Self Paced Reading Immediate Constraint Application
While she was taking classes full-time, Jessica was working two jobs to pay the bills. While she was taking classes full-time, Russell was working two jobs to pay the bills.
While she … Jessica …���
Russell …
Self-Paced Reading, Gender Mismatch Paradigm
(Kazanina et al., 2007)
Immediate Constraint Application
While she was taking classes full-time, Jessica was working two jobs to pay the bills. While she was taking classes full-time, Russell was working two jobs to pay the bills.
She was taking classes full-time while Jessica was working two jobs to pay the bills. She was taking classes full-time while Russell was working two jobs to pay the bills.
While she …
She …
Jessica …���
Russell …
while Jessica …���
while Russell …
Self-Paced Reading, Gender Mismatch Paradigm
(Kazanina et al., 2007)
-60
-40
-20
0
20
40
60
80
100
120
because lastsemester
while-cd SHE wastaking
classes while-ab NAME wasworking
full-time to…
Resi
dual
Rea
ding
Tim
es
nonPrC GM
nonPrc GMM
PrC GM
PrC GMM
Results
GME at the 2nd NP in non-PrC pair
while while Jessica
Russell
(Kazanina et al., 2007)
-60
-40
-20
0
20
40
60
80
100
120
because lastsemester
while-cd SHE wastaking
classes while-ab NAME wasworking
full-time to…
Resi
dual
Rea
ding
Tim
es
nonPrC GM
nonPrc GMM
PrC GM
PrC GMM
Results
GME at the 2nd NP in non-PrC pair NO GME at the 2nd NP in PrC pair
Condition C – immediate
while while Jessica
Russell
(Kazanina et al., 2007)
Principle C • Gender mismatch effect reflects search for antecedent for a pronoun
No gender mismatch effect in Principle C conditions – constraint is immediate
• Contrast is robust across structural environments in English
– Experiment 2: It seemed worrisome to {him, his family} that John/Ruth … – Experiment 3: {He, His managers} chatted amiably with some fans while
the talented young quarterback …
• Similar contrasts across languages
– Japanese: backwards anaphora in surface vs. scrambled word orders (Aoshima, Yoshida, & Phillips, 2009, Syntax)
– Russian: backwards anaphora with variation in conjunctions & aspect (Kazanina & Phillips, 2010, QJEP)
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Illusory NPI Licensing “The bills that no democratic senators supported will ever become law.”
“Ein Mann, der kein Bart hatte, war jemals glücklich.” a man who no beard has was ever happy
(Vasishth et al., 2008; Xiang, Dillon, & Phillips, 2009)
Illusory Agreement “The key to the cells unsurprisingly were rusty …”
“The musicians who the reviewer praise so highly …”
(Bock & Miller 1991; Pearlmutter et al. 1999; Deevy et al. 1998; Staub 2009; Wagers, Lau, & Phillips 2009; Eberhard et al. 2005)
Illusory Comparative
“More people have been to Russia than I have.”
(Montalbetti 1984, Townsend & Bever 2001, Wellwood et al. 2009)
I’m not going to solely blame all of man’s activities on changes in climate
I’m not one to attribute every activity of man to climate change
9/30/08
10/02/08
Kim & Osterhout 2005, J. Mem. Lang.
__ The hearty meal was devoured … … The hearty meal was devouring …
“Thema'c P600s”
For breakfast the boys would only eat toast and jam. For breakfast the eggs would only eat toast and jam.
Kuperberg et al., 2003, Cogn. Br. Res.; see also Kolk et al. 2003, Hoeks et al. 2004 Reviews: Kolk 2006; Kuperberg 2007; Bornkessel-‐Schlesewsky & Schlesewsky 2008
P600
Evidence for independent semantic composition?
D. Memory and access mechanisms
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Success & Failure • Successful on-line constraint application requires:
(i) structured representations in memory (ii) mechanisms that can use those structures to access relevant material
• Therefore, failure of on-line constraint application could reflect:
(i) representations (ii) access mechanisms
Is there a green square?
Is there a green square? Is there a green square?
Is there a green square? Is there a green square?
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Is there a green square? Is there a green square?
Is there a green square? Is there a green square?
Is there a green square?
Visual Search Mechanisms
Feature search is (i) fast, set-size invariant (ii) susceptible to interference and “illusory conjunction”
Conjunction search is slow, serial
(Treisman & Gelade 1980 etc.; but cf. McElree & Carrasco, 1999)
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Two ways to search structures
• Ordered search path through a structure (as in a standard address-based memory architecture)
– Easy to implement structure-sensitive search – Predicts timing advantage for local material – Requires complete and accurate encoding of structure
• Parallel search in content-addressable memory (cf. McElree 2000; Lewis & Vasishth, 2006; Gordon & Hendrick 2004)
– Efficient retrieval of feature-matching items – Risk of retrieving structurally inappropriate items – Does not require full encoding of hierarchy in memory – Not clear how to use relational notions in retrieval (e.g., c-command)
[+wh]
0 0 0 0 0 0 1
Two ways to search structures
• Ordered search path through a structure (as in a standard address-based memory architecture)
– Easy to implement structure-sensitive search – Predicts timing advantage for local material – Requires complete and accurate encoding of structure
• Parallel search in content-addressable memory (cf. McElree 2000; Lewis & Vasishth, 2006; Gordon & Hendrick 2004)
– Efficient retrieval of feature-matching items – Risk of retrieving structurally inappropriate items – Does not require full encoding of hierarchy in memory – Not clear how to use relational notions in retrieval (e.g., c-command)
Evidence for Parallel Search • #1: Non-structural interference effects
– Retrieval of feature-matching but structurally inappropriate items
Negative Polarity Items (Drenhaus et al. 2005) Agreement (Wagers et al. 2009; Badecker & Lewis 2008)
• #2: Timing non-effects
– Processing dynamics identical across all dependency lengths
Wh-movement (McElree et al. 2003) Ellipsis (Martin & McElree 2008)
• Stronger claim (Lewis, McElree, …)
– Parallel cue-based retrieval is pervasive, even exclusive – Sentences encoded as ‘chunks’ in memory – No encoding of order
McElree et al. 2003
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Speed-Accuracy Tradeoff (SAT) Possible Outcomes
Fig. 3 presents hypothetical SAT functions illustrat-ing how different SAT timecourse patterns can dis-criminate between alternative retrieval processes.Consider first the expected result that interpolating morematerial between the filler and gap position decreases theaccuracy of responding. Recall that this could be be-cause there is a lower probability that a representationof the filler is available when the verb is processed and/or
because there is a higher probability of misanalyzingmaterial up to and including the final verb. If additionalmaterial decreases only the overall accuracy of re-sponding, the corresponding functions will differ in as-ymptotic level alone. Panel A depicts two hypotheticalconditions that differ in this manner.
The pre-asymptotic portion of the SAT functionmeasures processing speed or dynamics, jointly specifiedby the intercept of the function (when accuracy departsfrom chance, d 0 ¼ 0) and the rate at which accuracygrows from intercept to asymptote. The intercept mea-sures the minimum time needed to form an interpreta-tion that would serve to discriminate acceptable fromunacceptable forms. The rate of the SAT function re-flects either the rate of continuous information accrual ifprocessing is continuous or the distribution of finishingtimes if processing is discrete or quantal (Dosher, 1976,1979, 1981, 1982, 1984; Meyer, Irwin, Osman, & Kou-nois, 1988). In either case, differences in intercept or rateimplicate underlying differences in the speed of pro-cessing. This situation is depicted in Panel B of Fig. 3,where the functions are associated with different inter-cepts and rates of rise to a common asymptote.
If access to the filler!s representation requires a searchprocess when the matrix verb is encountered, then theSAT intercept and/or rate of will systematically slow asmore material is interpolated between the filler and gap.Rate or intercept differences can arise from factors otherthan retrieval speed; for example, they might arise fromdifferences in the inherent complexity of computing
Fig. 2. Sample trial sequence illustrating the speed-accuracy tradeoff (SAT) variant of the acceptability judgment task.
Fig. 3. Hypothetical SAT functions illustrating two conditionsthat differ by SAT asymptote only (A) or by SAT intercept andrate (B).
B. McElree et al. / Journal of Memory and Language 48 (2003) 67–91 73
Asymptotic difference Reflects the strength of the representation or the likelihood of completing a parse/process.
Rate/intercept difference Reflects the speed of processing: how quickly information accumulates continuously, or the differences in an underlying discrete finishing time distribution.
Evidence from memory dynamics
ACCESSING SEMANTIC AND PHONOLOGICAL INFORMATION 179
position functions within each judgment. After identifyingthe best fit for each judgment, all 15 conditions (3 judg-ments ! 5 serial positions) were fit simultaneously inorder to isolate differences among the three judgments. Inall cases, the fits were performed on individual subjectdata. Fits of the average (over subjects) data were used tosummarize consistent patterns across subjects.
To quantify the impact of serial position on the retrievalfunctions for each judgment, sets of competitive fits were
performed that systematically varied the three parametersof Equation 1. These fits ranged from a null model, in whichthe 5 serial position functions were fit with a single ! (as-ymptote), " (rate), and # (intercept) parameter, to a fullysaturated 15-parameter model, in which each serial posi-tion was allotted a unique !, ", and # parameter. This analy-sis yielded a clear and consistent pattern across the threejudgments. In each case, the best fit to the data was a 5!-1"-2# model. With one exception (noted below), models
Figure 3. Average d $ accuracy (symbols) as a function of processing time for item(A), rhyme (B), and synonym (C) judgments. Smooth curves in each panel show thebest fits of Equation 1 with (the average) parameters listed in Table 1.
talk – yard – boat – store -‐ tales
Probe recognition – SAT response-signal task Wickelgren, et al., 1980, McElree & Dosher, 1989
FAST SLOOOOOWWWW
Bi-partite architecture of memory
Passive
Stringent limitations on the scope of information that can be concurrently processed
Broadbent 1958; Wickelgren et al., 1980; Garavan, 1998; Cowan, 2001; McElree, 2006; Verhaegen & Basak, 2007; Jonides et al., 2008
Active Passive “Focal attention” 1-item capacity
McElree & Griffith (1995)
Thematic *… alarm books
Subcategorization *… agree books
Category *… rarely books
McElree & Griffith (1995)
Detection of thematic violations delayed relative to category and subcategorization violations
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Distance affects asymptote, but not temporal dynamics Interpretation: whole sentence accessed in parallel – no serial search Concern: wh-constructions are not a good test of distance effects
Part #2
Long-distance Dependencies Basic Paradigms and Generalizations
How filler-gap relations are built
Generalization 1
Wh-movement is Local
Long-distance Wh-Questions
Few people think that anybody realizes that Englishmen cook wonderful dinners
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Long-distance Wh-Questions
Few people think that anybody realizes that Englishmen cook what
Long-distance Wh-Questions
What do few people think that anybody realizes that Englishmen cook gap
✓
Island Constraints
What do Few people believe anybody who claims that Englishmen cook what
Island Constraints
What do Few people believe anybody who claims that Englishmen cook what
Relative Clause
Island Constraints
What do few people believe anybody who claims that Englishmen cook gap
Relative Clause
Island Constraints
What do few people believe anybody who claims that Englishmen cook gap
✘
Relative Clause
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Island Constraints * Who did the candidate read a book that praised ___?
[relative clause island] * Who did the candidate read The Times’ article about ___?
[complex NP island] * Who did the candidate wonder whether the press would denounce ___?
[wh-island] * Why did you remember that the senator supported the bill ___?
[factive island] * Who did the fact that the candidate supported ___ upset voters in Florida?
[subject island] * Who did the candidate raise two million dollars by talking to ___?
[adjunct island]
Standard Conclusion: wh-movement must be local
Generalization 2a
Longer is ‘harder’
(Phillips, Kazanina, & Abada, 2005)
Length Matters
Generalization 2b
‘Active’ Gap Finding
Active Gap Creation
My brother wanted to know who
(Stowe 1986, Crain & Fodor 1985) �
Active Gap Creation
My brother wanted to know who Ruth
(Stowe 1986, Crain & Fodor 1985) �
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Active Gap Creation
My brother wanted to know who Ruth will
(Stowe 1986, Crain & Fodor 1985) �
Active Gap Creation
My brother wanted to know who Ruth will bring
(Stowe 1986, Crain & Fodor 1985) �
Active Gap Creation
My brother wanted to know who Ruth will bring gap
(Stowe 1986, Crain & Fodor 1985) �
Active Gap Creation
My brother wanted to know who Ruth will bring us
(Stowe 1986, Crain & Fodor 1985) �
Active Gap Creation
My brother wanted to know who Ruth will bring us
(Stowe 1986, Crain & Fodor 1985) �
home to at Christmas. �
Readers slow down upon encountering an NP where a gap was expected.�
My brother wanted to know if Ruth will bring us home to Mom at Christmas. �
Slowdown
970 ms
755 ms
Stowe results • My brother wanted to know…
…if Ruth will bring us home to Mom at Christmas. …who __ will bring us home to Mom at Christmas. …who Ruth will bring __ home to Mom at Christmas. …who Ruth will bring us home to __ at Christmas.
• Ruth us Mom IF 661 755 755 Wh-S -- 801 812 Wh-O 680 -- 833 Wh-P 689 970 --
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‘Active’ gap finding • Highly robust generalization
– Filled gap effects (Crain & Fodor 1985; Stowe 1986; et seq.) – Plausibility manipulations (Boland et al. 1990; Traxler &
Pickering 1996) – Cross-modal priming (Nicol & Swinney 1989) – Eye-movements in visual world paradigm (Sussman & Sedivy
2003) – Electrophysiological indices (Garnsey et al. 1989; Kaan et al.
2000; Phillips et al. 2005 etc.)
• Wh-dependency formation occurs at least as soon as an appropriate verb is encountered
… but this does not bear on issues of transformational vs. non-transformational approaches
Head-mounted eye-tracker
Traxler & Pickering 1996 • Plausibility manipulation - eye-tracking
– That’s the {pistol/garage} with which the heartless killer shot the hapless man yesterday afternoon.
– That’s the {garage/pistol} in which the heartless killer shot the hapless man yesterday afternoon.
Head-mounted Eye-Tracking
Sussman & Sedivy (2003)
shoe
spider
Jody
milk
Y/N: “Did Jody squash the spider with her shoe?”
WH: “What did Jody squash the spider with __?”
WH
Y/N verb
Sussman & Sedivy, 2003, LCP
Visual world measure of comple_ng filler-‐gap dependencies
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Scenario with multiple events
Q: Can you tell me…
Wh: … what Emily was ea_ng the cake with ___ ? YN: … if Emily was ea_ng the cake with the fork?
1. eat
2. wash
Omaki, Trock, Wagers, Lidz & Phillips, CUNY 2009
Steps for Interpretation
“… what Emily was ea_ng the cake with __?”
1. Recognize the verb; lexical retrieval
2. Integrate wh-‐filler into the structure; build seman_c representa_on for VP “EAT WHAT”
3. Recall events from story (“It is the cake that was eaten.”)
4. Program and execute eye movement
Fixations on Cake
*
Verb Onset
WH
YN
“Can you tell me what/if Emily was ea_ng …”
400 (+200) ms
Omaki, Trock, Wagers, Lidz & Phillips, CUNY 2009
Electrophysiology of Sentence Comprehension
• Semantic anomaly
N400
I drink my coffee with cream and sugar I drink my coffee with cream and socks
Kutas & Hillyard (1980)
N400
ERP Sentence Processing
• Developing understanding of N400 is informa_ve
• Response to normal sentences
N400"
Fully CongruentMost new drugs are tested on"white lab rats."
Van Petten & Kutas (1991)"
Amy bought the napkins that the café manager diligently folded in the booth."Amy bought the napkins that the café manager diligently baked in the booth."
(Yeung et al., 2004)"
MEG counterpart of N400 atsuccessive word positions in sentence comprehension."
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Lau, Phillips, & Poeppel, Nat. Revs. Neurosci. 2008 Ellen Lau, Tufts/MGH
The N400 probably reflects lexical access rather than combinatorial semantic processing
(Garnsey, Tanenhaus, & Chapman, 1989)
N400 as measure of filler-gap dependency completion.
… called the customer … … called the article …
… which customer … called … … which article … called …
Morpho-Syntactic violations
Every Monday he mows the lawn. Every Monday he *mow the lawn.
The plane brought us to paradise. The plane brought *we to paradise. (Coulson et al., 1998)
(Slide from Kaan (2001)
he mows he *mow
P600
(Slide from Kaan (2001)
he mows he *mow
P600
Left Anterior Negativity (LAN)
(Slide from Kaan (2001) (Kaan, Harris, Gibson, & Holcomb, 2000)"
Long-Distance Dependencies
WH "Emily wondered who the performer in the concert had imitated "for the audienceʼs amusement.
Control "Emily wondered whether the performer in the concert had imitated "a pop star for the audienceʼs amusement."
P600 reflects normal structure-building processes."
“P600 amplitude is an index of syntactic integration difficulty.”"
P600 amplitude should covary with integration difficulty."
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Long-Distance Dependencies a. Short control The actress wished that the producers knew that the witty host would tell the jokes during the party. b. Short WH The actress wished that the producers knew which jokes the witty host would tell __ during the party.
c. Long control The producers knew that the actress wished that the witty host would tell the jokes during the party. d. Long WH The producers knew which jokes the actress wished that the witty host would tell __ during the party.
(Phillips, Kazanina & Abada, Cog. Br. Res., 2005)"
Embedded Verb
The actress wished that the producers knew that the witty host would tell …"The actress wished that the producers knew which jokes the witty host would tell…"The producers knew that the actress wished that the witty host would tell …"The producers knew which jokes the actress wished that the witty host would tell…"
Embedded Verb
The actress wished that the producers knew that the witty host would tell …"The actress wished that the producers knew which jokes the witty host would tell…"The producers knew that the actress wished that the witty host would tell …"The producers knew which jokes the actress wished that the witty host would tell…"
Embedded Verb
The actress wished that the producers knew that the witty host would tell …"The actress wished that the producers knew which jokes the witty host would tell…"The producers knew that the actress wished that the witty host would tell …"The producers knew which jokes the actress wished that the witty host would tell…"
Interim Summary • ‘Active’ formation of filler-gap dependencies
– Diverse measures converge on similar generalization
– Most measures give timing information - some just pinpoint ‘difficulty’, others show more specific measures
– ‘Active’ mechanism need not be specific to wh-movement
Can timing evidence resolve theoretical disputes?
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Competing Theories
What do Englishmen cook gap/trace/copy
What do Englishmen cook Direct Association ���HPSG/GPSG ���Categorial Grammar���Dependency Grammar���etc.
Indirect Association ���Transformational Grammar���(--> Projection Principle)
Competing Theories
What do Englishmen cook gap/trace/copy
What do Englishmen cook Direct Association ���HPSG/GPSG ���Categorial Grammar���Dependency Grammar���etc.
Indirect Association ���Transformational Grammar���(--> Projection Principle)
Attempts to distinguish between these theories ���using evidence from language processing…
1. English Filled-Gap Effect
My brother wanted to know who Ruth will bring us home to at Christmas
My brother wanted to know if Ruth will bring us home to Mom at Christmas
(Stowe 1986)
1. English Filled-Gap Effect
My brother wanted to know who Ruth will bring us home to at Christmas
My brother wanted to know if Ruth will bring us home to Mom at Christmas
(Stowe 1986)
Surprise at pronoun following verb is ���compatible with both theories!
Cross-modal Priming
Cross-Modal Priming
The guests drank vodka, sherry and port at the reception
(Swinney 1979, Seidenberg et al. 1979)
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Cross-Modal Priming
The guests drank vodka, sherry and port at the reception
WINE
SHIP
(Swinney 1979, Seidenberg et al. 1979)
Cross-Modal Priming
The guests drank vodka, sherry and port at the reception
WINE
SHIP
(Swinney 1979, Seidenberg et al. 1979)
Cross-Modal Priming
The guests drank vodka, sherry and port at the reception
WINE
SHIP
(Swinney 1979, Seidenberg et al. 1979)
Cross-Modal Priming
The guests drank vodka, sherry and port at the reception
WINE
SHIP
(Swinney 1979, Seidenberg et al. 1979)
2. Trace Reactivation Studies
Which boy did the old man from Osaka meet at the station?
(e.g. Nicol & Swinney 1989, Bever & McElree 1988, MacDonald 1989)
2. Trace Reactivation Studies
Which boy did the old man from Osaka meet at the station?
boy
girl
boy
girl
faster decision
same speed
(e.g. Nicol & Swinney 1989, Bever & McElree 1988, MacDonald 1989)
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2. Trace Reactivation Studies
Which boy did the old man from Osaka meet at the station?
boy
girl
boy
girl
faster decision
same speed
(e.g. Nicol & Swinney 1989, Bever & McElree 1988, MacDonald 1989)
Both theories can account for reactivation at or after the verb!
Pickering & Barry 1991
3. Verb Position vs. Trace Position
(Pickering & Barry 1991)
give NP PP
3. Verb Position vs. Trace Position
(Pickering & Barry 1991)
give NP PP
To which child did the teacher give [a long speech about the importance of honesty] ___?
3. Verb Position vs. Trace Position
(Pickering & Barry 1991)
give NP PP
To which child did the teacher give [a long speech about the importance of honesty] ___?
Various diagnostics indicate that the dependency���is formed at the verb, not at the trace position.
3. Verb Position vs. Trace Position
(Pickering & Barry 1991)
give NP PP
To which child did the teacher give [a long speech about the importance of honesty] ___?
Various diagnostics indicate that the dependency���is formed at the verb, not at the trace position.
Still compatible with both theories!
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WH
CP
C IP
VP
NP
V
…
WH
CP
C IP
VP
NP
V
…
Direct Association Gap-based Approach
gap
Effects at Verb Position
#1
#1
#2
Timing evidence as theoretical arbitration
…works when theories make timing predictions
see Phillips & Wagers 2007 (Oxford Hbk of Psycholing.) On course readings page
Pre-Verbal Gap Effects
• Suggestion… Perhaps the two theories could be distinguished by effects of dependency formation at argument positions that precede the verb of a clause.
• Rationale Filled-gap effect expected at pre-verbal position only under indirect association/gap-based theory.
Motivations
What is driving gap creation?
Locality in Japanese Wh-Questions
Aoshima, Phillips & Weinberg. J. Mem. Lang. 51, 23-54 (2004)
Japanese Wh-Question Formation
Japanese wh-phrases are canonically in-situ.
Japanese uses question particles (Q-particles) to mark questions. �Yes/No Question
-nom book-acc read-Q
Wh-Question
‘What did Mary read?’
‘Did Mary read the book?’
Mary-ga hon-o yonda-no.
Mary-ga nani-o yonda-no. -nom what-acc read-Q
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Japanese Wh-Question Formation Position of Q-particle indicates the scope of wh-questions.
Direct Wh-question
‘What did Tom say that Mary read?’
Japanese Wh-Question Formation Position of Q-particle indicates the scope of wh-questions.
Direct Wh-question Tom-wa [Mary-ga nani-o yonda-to] itta-no? -top -nom what-acc read-DeclC said-Q ‘What did Tom say that Mary read?’
Japanese Wh-Question Formation Position of Q-particle indicates the scope of wh-questions.
Indirect Wh-question
‘Tom said what Mary read.’
Direct Wh-question Tom-wa [Mary-ga nani-o yonda-to] itta-no? -top -nom what-acc read-DeclC said-Q ‘What did Tom say that Mary read?’
Japanese Wh-Question Formation Position of Q-particle indicates the scope of wh-questions.
Indirect Wh-question Tom-wa [Mary-ga nani-o yonda-ka] itta. -top Mary-nom what-acc read-Q said
Japanese: Types of wh-questions depend on the positions of the question particle.
‘Tom said what Mary read.’
Direct Wh-question Tom-wa [Mary-ga nani-o yonda-to] itta-no? -top -nom what-acc read-DeclC said-Q ‘What did Tom say that Mary read?’
Processing Japanese Wh-Questions
Tom-wa
-top
Processing Japanese Wh-Questions
Tom-wa [Mary-ga
-top -nom
This nominative NP indicates the start of the embedded clause.
9/15/10
26
Processing Japanese Wh-Questions
Tom-wa [Mary-ga nani-wo
Due to verb-final property, wh-scope is temporarily ambiguous prior to the verb.
-top -nom what-acc
Processing Japanese Wh-Questions
Tom-wa [Mary-ga nani-wo
-top -nom what-acc read
yonda-to (Declarative)
yonda-ka (Q-Particle)
Due to verb-final property, wh-scope is temporarily ambiguous prior to a verb.
Experiment: Processing In-situ Wh-Question
Tom-wa [Mary-ga nani-wo yonda-to (Declarative)
yonda-ka (Q-Particle)
Miyamoto and Takahashi (2003); Aoshima, Phillips and Weinberg (2004)
Experiment: Self-paced reading task �
----- --- --- ---- ---- --- --------
先生は --- --- ---- ---- --- --------
teacher-top
Experiment: Self-paced reading task
----- 生徒が --- ---- ---- --- --------
student-nom
Experiment: Self-paced reading task
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----- --- 教室で ---- ---- --- --------
classroom-at
Experiment: Self-paced reading task
----- --- --- だれに ---- ---- --- --------
who-dat
Experiment: Self-paced reading task
----- --- --- ---- まんがを ---- --------
comics-acc
Experiment: Self-paced reading task
----- --- --- ---- ---- あげたか --------
gave-Q
Experiment: Self-paced reading task
----- --- --- ---- ---- --- 知っています。
knows
Experiment: Self-paced reading task Experiment: Processing Japanese Wh-Question
Readers slow down when they see a declarative complementizer –to ‘that’ at the first verb.
……….. Mary-ga nani-wo yonda-to (Declarative)
yonda-ka (Q-Particle)
Miyamoto and Takahashi (2003); Aoshima, Phillips and Weinberg (2004)
Slowdown 889 ms
787 ms
9/15/10
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Parallelism between English and Japanese
Tom-top Mary-nom what-acc read-Q … .
My brother wanted to know who Ruth will bring gap ….
English speakers want to interpret the thematic role of wh-phrase.
Japanese speakers want to interpret the wh-scope of wh-phrase.
Parallelism between English and Japanese:
Native speakers of English and Japanese both show a reliable locality bias in processing wh-questions.
Who-dat
who-dat
Scrambled wh-question
In-situ wh-question
Tom-top [Mary-nom book-acc gave-Q] said.
Tom-top [Mary-nom book-acc gave-Q] said.
who-dat
‘Tom asked who Mary gave a book to.’
‘Tom asked who Mary gave a book to.’
Long-distance Wh-scrambling formation
Who-dat Tom-top [Mary-nom book-acc gave-Q] said. gap
Long-distance Wh-scrambling formation
Fronted wh-phrases – Wh-gap dependencies must be formed, just like English wh-questions.
Ambiguous structures
Who-dat Tom-top [Mary-nom book-acc
Who-dat Tom-top [Mary-nom book-acc
Ambiguous structures
Verb-final property provides temporarily ambiguous structures.
read-Q] said.
read-that] said-Q.
Ambiguous structures
Who-dat Tom-top [Mary-nom book-acc
read-that] said-Q.
gap
Verb-final property provides temporarily ambiguous structures.
‘To whom did Tom say that Mary read the book?’
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Who-dat Tom-top [Mary-nom book-acc
Ambiguous structures
Verb-final property provides temporarily ambiguous structures.
read-Q] said. gap
‘Tom said who Mary read the book to.’
Who-dat Tom-top [Mary-nom book-acc
Experiment
read-Q]
read-that]
Readers slow down when they see a declarative complementizer –to ‘that’ at the first verb.
Slowdown
Aoshima, Phillips and Weinberg (2004)
835 ms
723 ms
Long-distance scrambling is preferred!
English Filled Gap Effect
who
Ruth
will
bring
us
My brother wanted to know
home to at Christmas Slowdown
Readers slow down upon encountering an NP where a gap was expected.
(Stowe 1986, Crain & Fodor 1985)
Japanese Filled-Gap Effect
Position of the unexpected NP is before the verb.
Second NP-dat is unexpected if the first NP-dat has already been interpreted in embedded clause.
WH-dat
NP-top
CP
gap
NP-nom
Verb
VP
NP-dat
Slowdown
Verb
Japanese Filled-Gap Effect
WH-dat
NP-top
CP
NP-nom VP
WH-nom
NP-dat
CP
NP-nom
Verb
VP
NP-dat
target control
gap
Verb NP-dat
Slowdown
Verb Verb
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Low-tech Replication • Pencil-and-paper sentence fragment completion
– Wh-dat NP-nom NP-nom …
• Fragments with dative wh-phrases were completed with embedded gap in 61% of trials
Implications • Japanese speakers show preference to interpret fronted wh-
phrase as long-distance scrambled
• This does follow naturally in terms of local satisfaction of grammatical constraints
• … but it does not correspond to the notion of locality that syntactic theory typically aims to capture
• Syntactic locality does not reduce to ambiguity resolution constraints
Traces (again)
WH
CP
C IP
VP
NP
V
…
WH
CP
C IP
VP
NP
V
…
Direct Association Gap-based Approach
gap
Effects at Verb Position
#1
#1
#2
Traces (again) • Does pre-verbal dependency formation implicate
gaps/traces?
– Yes! If direct association to verb requires presence of verb
– No! If verb position is built in advance of overt verb
9/15/10
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Constraints on Unbounded Dependencies
Stowe 1986 • Experiment 1
My brother wanted to know …
…if Ruth will bring us home to Mom at Christmas …who Ruth will bring us home to at Christmas
• Experiment 2
The teacher asked …
…if [the silly story about Greg’s older brother] was supposed to mean anything. …what [the silly story about Greg’s older brother] was supposed to mean.
Stowe 1986 • The teacher asked …
…if [the silly story about Greg’s older brother]… …what [the silly story about Greg’s older brother]…
…if the team laughed about Greg’s older brother… …what the team laughed about Greg’s older brother…
• the silly story about Greg’s if-S 611 677 752 750 798 wh-S 616 698 760 880 800 if-V 613 735 754 678 782 wh-V 608 698 736 755 1063
Traxler & Pickering 1996 • Plausibility manipulation, subject islands
– WAITING FOR A PUBLISHING CONTRACT The big city was a fascinating subject for the new book.
– We like the book that the author wrote unceasingly and with great dedication about while waiting for a contract.
– We like the city that the author wrote unceasingly and with great dedication about while waiting for a contract.
– We like the book that the author who wrote unceasingly and with great dedication saw while waiting for a contract.
– We like the city that the author who wrote unceasingly and with great dedication saw while waiting for a contract.
Islands Non-Islands
The Real-Time Status of Island Constraints
Phillips (2006), Language, 82, 795-823
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Island Constraints
What do few people believe anybody who claims that Englishmen cook gap
✘
Relative Clause
Island Constraints * Who did the candidate read a book that praised ___?
[relative clause island] * Who did the candidate read The Times’ article about ___?
[complex NP island] * Who did the candidate wonder whether the press would denounce ___?
[wh-island] * Why did you remember that the senator supported the bill ___?
[factive island] * Who did the fact that the candidate supported ___ upset voters in Florida?
[subject island] * Who did the candidate raise two million dollars by talking to ___?
[adjunct island]
Standard Conclusion: wh-movement must be local
(Phillips, Kazanina, & Abada, 2005)
Length Matters
Active Gap Creation
My brother wanted to know who Ruth will bring us
(Stowe 1986, Crain & Fodor 1985) �
home to at Christmas. �
Readers slow down upon encountering an NP where a gap was expected.�
My brother wanted to know if Ruth will bring us home to Mom at Christmas. �
Slowdown
970 ms
755 ms
‘Active’ gap finding • Highly robust generalization
– Filled gap effects (Crain & Fodor 1985; Stowe 1986; et seq.) – Plausibility manipulations (Boland et al. 1990; Traxler &
Pickering 1996) – Cross-modal priming (Nicol & Swinney 1989) – Eye-movements in visual world paradigm (Sussman & Sedivy
2003) – Electrophysiological indices (Garnsey et al. 1989; Kaan et al.
2000; Phillips et al. 2005 etc.)
• Wh-dependency formation occurs at least as soon as an appropriate verb is encountered
… but this does not bear on issues of transformational vs. non-transformational approaches
A Common Inference • Grammatical generalization: wh-dependencies are local • Parsing generalization(s): local wh-dependencies are easier/
preferred • ERGO… perhaps the grammatical generalization derives from
the parsing generalization (Fodor 1978; Berwick & Weinberg 1984; Deane 1991; Pritchett 1991; Kluender & Kutas 1993; Hawkins 1999; Sag et al. 2005; Maratsos & Kowalsky 2005 – Variant I: locality constraints are nevertheless grammaticized – Variant II: locality constraints in grammar are epiphenomenal
Fodor Weinberg Berwick Kluender Hawkins Sag Maratsos
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Real-time Status of Island Constraints
• Are island constraints respected in real-time syntactic computation?
• Many studies - conflicting results (various techniques, various island-types, etc.)
• …but, it is not even true of the grammar that it disallows long-distance dependencies that cross islands…
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
Parasitic Gaps
which people did the proposal to expand the school ultimately overburdened the teachers.
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
Generalization (Subject Island Constraint) No long-distance dependencies across subject boundaries
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Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
Generalization (informal) Violations can be rescued by subsequent well-formed gaps.
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
which school did the proposal that expanded the school ultimately overburdened the teachers.
Updated Generalization (informal) A subclass of violations can be rescued by subsequent gaps.
Grammaticality Ratings
1
1.5
2
2.5
3
3.5
4
Good Bad Both
Gap Type
Acc
ep
tab
ilit
y R
ati
ng
INFFIN
Parasitic Gaps
which school did the proposal to expand the school ultimately overburdened the teachers.
which school did the proposal that expanded the school ultimately overburdened the teachers.
which students…
which students…
implausible at ‘expand’���plausible at ‘overburden’
plausible at ‘expand’���plausible at ‘overburden’
Materials a) The school superintendent learned which schools the proposal to
expand drastically and innovatively upon the current curriculum would overburden during the following semester. [INF, Plaus]
b) The school superintendent learned which high school students the proposal to expand … [INF, Implaus]
c) The school superintendent learned which schools the proposal that expanded … [FIN, Plaus]
d) The school superintendent learned which high school students the proposal that expanded … [Fin, Implaus]
Materials
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35
… which schools/students the proposal to expand …
*
… which schools/students the proposal that expanded …
n.s
Implications - Previous Findings
• This experiment showed violation of one type of island, and non-violation of another type of island: same task, same participants
• Suggests that variability in previous results cannot just be attributed to methodological artifacts
• Incrementality and accuracy preserved
• Can variability in previous results be due to choice of islands tested, and to possibility of parasitic gaps?
Implications - ‘Parsing Accounts’
• Repeated attempts to reduced movement constraints to artifacts of ‘processing constraints’ (working memory, etc.)
• The existence of parasitic gaps shows that it’s not true that dependencies that cross islands are always impossible.
• If subject parasitic gaps were only marginally acceptable, or were processed non-incrementally, this would be compatible with ‘parsing accounts’ of islands
• But since parasitic gaps are constructed immediately, this is more problematic for ‘processing accounts’ of islands
Implications - ‘Parsing Accounts’
which school did the proposal to expand the school ultimately overburdened the teachers.
which school did the proposal to expand the school ultimately overburdened the teachers.
Any ‘processing based’ account of why this is bad…
…will fail to explain why the first gap can be created here…
(cf. Deane, 1991; Pritchett, 1991)
Therefore… • The notion that long-distance dependencies cannot cross
islands is an over-simplification
• The parser appears to be well aware of this
• Creates a challenge for attempts to ‘explain away’ island phenomena as artifacts of processing
• Further evidence that a good deal of what we know about language is deployed immediately in language processing
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Islands and Resource Limitations
Jon Sprouse (UC Irvine)
Matt Wagers (UC Santa Cruz)
Sprouse, Wagers, & Phillips, submitted
The logic of resource limitation theories
an even greater processing disruption
processing load 1
processing load 2 + + = limited
resources
John thought that you knew who the president would pardon __ ?
John knew who you thought that the president would pardon __ ?
processing load 1: Longer wh-dependencies are harder to process than shorter dependencies (LENGTH)
Crucially, longer dependencies lead to lower acceptability
There are many potential reasons for this: memory encoding, or maintenance, or retrieval...
The logic of resource limitation theories
an even greater processing disruption
processing load 1
processing load 2 + + = limited
resources
processing load 2: Island structures are harder to process than non- island structures (STRUCTURE)
Who __ thinks [CP that John bought a car ]? Who __ wonders [CP whether John bought a car ]?
Crucially, island structures without island violations lead to lower acceptability
There are many potential reasons for this: referential processing, syntactic complexity, semantic complexity...
(Kluender and Kutas 1993b, Sprouse 2007)
Does Capacity Affect Island Acceptability?
Participants:
N = 144 Age Range: 18-25 Mean Age: 21.13 (1.67)
Expected mean: 4 (1) (Cowan 2000)
Actual mean: 4.17 (.98)
To avoid rehearsal: Repeat the word the while listening To avoid mnemonics: Repeat the task 10x with the same 8 words in a
different order
p < .001
high low
Basic Idea: if island effects reflect capacity constraints, then varying capacity should modulate island judgments.
Verbal memory span test (Cowan 2000 for review) Listen to 8 words spoken at .5s intervals Recall as many as possible in the correct order
5.38 n=36
2.98 n=36
Adjunct Island - comparisons 1. Baseline: Who __ thinks that John forgot his briefcase at the office? 2. Wh-only: What do you think that John forgot __ at the office? 3. Island-only: Who __ laughs if John forgets his briefcase at the office? 4. Both: What do you laugh if John forgets __ at the office?
None of the paired comparisons were significant: the low and high groups rated these sentences equally, and both show the same interaction.
Paired comparisons: low versus high
p<.44 p<.35
p<.52 p<.92
1
3
4
2
NP Island - comparisons 1. Baseline: Who __ claimed that John bought a car? 2. Wh-only: What did you claim that John bought __? 3. Island-only: Who __ made the claim that John bought a car? 4. Both: What did you make the claim that John bought__?
None of the paired comparisons were significant: the low and high groups rated these sentences equally, and both show the same interaction.
Paired comparisons: low versus high
p<.18 p<.38
p<.50 p<.23
1
3
4
2
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1
2 3
4
It looks like the high capacity group rates grammatical structures that involve additional processing resources as less acceptable
Paired comparisons: low versus high
p<.51 p<.01
p<.004 p<.88
This looks like calibration of the scale: structures that require additional processing are pushed lower to better equalize the scale distribution
But there is no effect for island violations, and the super-additive interaction survives this calibration
Whether Island - comparisons 1. Baseline: Who __ thinks that John bought a car? 2. Wh-only: What did you think that John bought __? 3. Island-only: Who __ wonders whether John bought a car? 4. Both: What did you wonder whether John bought __?
1 2
3
4
It looks like the high capacity group rates grammatical structures that involve additional processing resources as less acceptable
Paired comparisons: low versus high
p<.31 p<.004
p<.003 p<.51
This looks like calibration of the scale: structures that require additional processing are pushed lower to better equalize the scale distribution
But there is no effect for island violations, and the super-additive interaction survives this calibration
Subject Island - comparisons 1. What do you think the speech interrupted __ ? 2. What do you think __ interrupted the TV show? 3. What do you think the speech by the president interrupted the TV show about __? 4. Who do you think the speech by __ interrupted the TV show about surgery?
Expt 2
MagEst n=173
2 memory tasks: serial recall n-back
Early Warning Signals for Japanese Islands
Masaya Yoshida Sachiko Aoshima
Colin Phillips
John-ga …
John-nom …
(Mazuka & Itoh 1995)
John-ga Mary-ni …
John-nom Mary-dat …
(Mazuka & Itoh 1995)
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John-ga Mary-ni ringo-o …
John-nom Mary-dat apple-acc …
(Mazuka & Itoh 1995)
John-ga Mary-ni ringo-o tabeta …
John-nom Mary-dat apple-acc ate …
(Mazuka & Itoh 1995)
John-ga Mary-ni ringo-o tabeta inu-o ageta
John-nom Mary-dat apple-acc ate dog-acc gave
(Mazuka & Itoh 1995)
John-ga Mary-ni [[ti ringo-o tabeta] inu-oi] ageta
John-nom Mary-dat [apple-acc ate dog-acc] gave ‘John gave Mary the dog that ate the apple
(Mazuka & Itoh 1995)
Japanese Relative Clauses • Notorious garden paths arise because relative
clauses are head final in Japanese.
• But: overt movement/scrambling in Japanese is subject to (roughly) the same island constraints as English
Time-course of gap creation
Gap-creation takes place before the verb is processed. Structures are built incrementally.
Gap is posited in the most deeply embedded clause.
Embedded clause could be an island (e.g. relative clause)
How could island violations ever be avoided in real-time computation?
What evidence could allow a speaker to learn about avoiding islands?
NP-subj
Verb CP
gap
NP-subj
Verb
VP
WH-dat
gap
9/15/10
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Early Warning • Japanese numeral classifiers
– san-satsu hon 3-cl book
– san-nin gakusei 3-cl students
• Numeral classifiers and Relative Clauses
– John-ga san-satsu-no [RC … ] hon-o yonda John-nom 3-cl [RC … ] book-acc read
Early Warning • Japanese numeral classifiers
– san-satsu hon 3-cl book
– san-nin gakusei 3-cl students
• Numeral classifiers and Relative Clauses
– John-ga san-satsu-no [RC gakusei-ga … ] hon-o yonda John-nom 3-cl [RC student-nom… ] book-acc read
Early Warning • Can numeral classifiers be used to detect relative
clauses?
– John-ga san-nin-no gakusei-ga … John-nom 3-clhuman student-nom …
– John-ga san-satsu-no gakusei-ga … John-nom 3-clbooks etc. student-nom …
Early Warning • Can numeral classifiers be used to detect relative
clauses?
– John-ga [san-nin-no gakusei-ga … John-nom [3-clhuman student-nom …
– John-ga san-satsu-no [gakusei-ga … John-nom 3-clbooks etc. [student-nom …
complement���clause
relative���clause
Early Warning • Can numeral classifiers be used to detect relative clauses?
– John-ga [san-nin-no gakusei-ga … John-nom [3-clhuman student-nom …
– John-ga san-satsu-no [gakusei-ga … John-nom 3-clbooks etc. [student-nom …
• Experiment #1: sentence fragment completion (n = 64) rel. clause other
classifier match 1 566 classifier mismatch 483 91
complement���clause
relative���clause
Early Warning • Can numeral classifiers be used to detect relative clauses?
– John-ga san-nin-no [gakusei-ga … V] NP-o … V John-nom 3-clhuman [student-nom …
– John-ga san-satsu-no [gakusei-ga … V] NP-o … V John-nom 3-clbooks etc. [student-nom …
• Experiment #2: reading-times for relative clauses (n = 32)
– are relative clauses processed more easily following a mismatching classifier-noun sequence?
classifier match
classifier���mismatch
9/15/10
40
550
650
750
850
950
1050
1150
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
(ms.)
GNC MathingGNC Mismatching
Early Warning
mismatch"
RC verb + head"
Direct signals of relative clause processed more easily in���classifier-mismatch (‘indicator’) condition.
Early Warning • Experiment #3: Filled-gap Effect and Relative Clauses (n = 80)
– WH-DAT John-ga san-nin-no [gakusei-ga … NP-DAT John-nom 3-clhuman [student-nom …
– WH-DAT John-ga san-satsu-no [gakusei-ga … NP-DAT John-nom 3-clbooks etc. [student-nom …
GNC Mismatching Conditions
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500550600650700750800850900950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MatchingNonScr/GNC Matching
Matching���Classifier
Mismatching���Classifier
GNC Mismatching Conditions
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500550600650700750800850900950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MatchingNonScr/GNC Matching
NP-nom ±match
Matching���Classifier
Mismatching���Classifier
GNC Mismatching Conditions
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500550600650700750800850900950
1000
1 2 3 4 5 6 7 8 9 10 11 12Regions
Mea
n RT
s (m
s.)
Scr/GNC MatchingNonScr/GNC Matching
NP-nom ±match
Matching���Classifier
Mismatching���Classifier
NP-dat
Filled-gap���Effect
Early Warning • Yes - Japanese speakers can use numeral classifiers
to
– pre-emptively construct relative clauses – avoid island constraint violations
9/15/10
41
Next to anaphora …
Overflow …
Argument Structure
remind V NP
V NP IP
(Boland et al. 1995)
Argument Structure
Samuel asked whether Mark reminded them to watch the child.
Which child did Mark remind them to watch ___?
Which movie did Mark remind them to watch ___?
remind V NP
V NP IP
(Boland et al. 1995)
Argument Structure
Samuel asked whether Mark reminded them to watch the child.
Which child did Mark remind them to watch ___?
Which movie did Mark remind them to watch ___?
remind V NP
V NP IP
(Boland et al. 1995)
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Argument Structure
Samuel asked whether Mark reminded them to watch the child.
Which child did Mark remind them to watch ___?
Which movie did Mark remind them to watch ___?
remind V NP
V NP IP
(Boland et al. 1995)
Boland et al., 1995 1a. Which client did the salesman visit while in the city? b. Which prize did the salesman visit while in the city? 2a. Which child did your brother remind to watch the show? b. Which movie did your brother remind to watch the show?
Sentence Matching • HOUSE
HOUSE • HSEUO
HSEUO
• HOUSE HORSE
• HSEUO HSERO
(Freedman & Forster 1985)
Sentence Matching • DOGS GROWL
DOGS GROWL
• GROWL DOGS GROWL DOGS
(Freedman & Forster 1985)
Sentence Matching • Specificity constraint violations
– Who did the duchess sell a portrait of? – *Who did the duchess sell Turner’s portrait of?
• Other violations
– Mary were writing a letter to her husband. – Where does bears usually hibernate?
– The baby ate his cereal up all. – Lesley’s parents are chemical engineers both.
(Freedman & Forster 1985)