Incrementality in Comprehension Speed and Accuracy.
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Transcript of Incrementality in Comprehension Speed and Accuracy.
Incrementality in Comprehension
Speed and Accuracy
Speed
Measures of Speed
• Measures of speed of processing– Speech-Shadowing:
– Eye-tracking
– Speed-accuracy Tradeoff (SAT)
– Event-Related Potentials (ERPs)
Speed-Accuracy Tradeoff (SAT)
McElree & Griffith (1995)
- grammatical- subcategorization- thematic- syntactic
Event-Related Potentials (ERPs)
s1 s2 s3
John is laughing.
Event-Related Potentials
• Event-Related Potentials (ERPs) are derived from the electroencephalogram (EEG) by averaging signals that are time-locked to a specific event.
• Scalp voltages provide millisecond-accuracy, promise detailed timing information about syntactic computation, plus information about amplitude and scalp topography
ERP Sentence Processing
• Developing understanding of N400 is informative
• Response to ‘violations’
N400
I drink my coffee with cream and sugarI drink my coffee with cream and socks
Kutas & Hillyard (1980)
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 mowshe *mow
P600
(Slide from Kaan (2001)
he mowshe *mow
P600
Left Anterior Negativity (LAN)
(Slide from Kaan (2001)
Neville et al., 1991
The scientist criticized a proof of the theorem.
The scientist criticized Max’s of proof the theorem.
500ms/word
500ms/word
Hahne & Friederici, 1999
Das Baby wurde gefüttertThe baby was fed
Das Baby wurde im gefüttertThe baby was in-the fed
Hahne & Friederici, 1999
ELAN
How Fast?
• Various types of evidence for processes above the word level within 200-400 msec (conservatively) of the start of a word.
• How are we able to compute so quickly?
• What is it that is computed so quickly?– Rough-and-ready structural analysis?
– Richer syntactic analysis?
Long-distance DependenciesBasic Paradigms
When are gaps posited?
Parsing wh-constructions: evidence for on-line gap location
Laurie Stowe (1986)
English Filled Gap Effect
who
My brother wanted to know
Crain & Fodor 1985, Stowe 1986
English Filled Gap Effect
who
Ruth
My brother wanted to know
Crain & Fodor 1985, Stowe 1986
English Filled Gap Effect
who
Ruth
will
My brother wanted to know
Crain & Fodor 1985, Stowe 1986
English Filled Gap Effect
who
Ruth
will
bring gap
My brother wanted to know
Crain & Fodor 1985, Stowe 1986
English Filled Gap Effect
who
Ruth
will
bring
us
My brother wanted to know
home to atChristmas
Slowdown
Crain & Fodor 1985, Stowe 1986
Readers slow down upon encountering an NPwhere a gap was expected, relative to a controlstructure, in which no gap was expected.
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 MomIF 661 755 755Wh-S -- 801 812Wh-O 680 -- 833Wh-P 689 970 --
Crain & Fodor 1985
• Filled-Gap Paradigm
– Who had the little girl expected us to sing those stupid French songs for __ at Christmas.
– The little girl had expected us to sing those stupid French songs for Cheryl at Christmas.
Garnsey et al. 1989
• ERP recordings, plausibility manipulation
The businessman knew which article the secretary called __ at home.The businessman knew which customer the secretary called __ at home.
N400 at called.
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)
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?
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.
ERPs and Long-DistanceSyntactic Dependencies
Colin PhillipsNina KazaninaShani Abada
(Kaan, Harris, Gibson, & Holcomb, 2000)
Kaan et al. (2000)
WH Emily wondered who the performer in the concert had imitated for the audience’s amusement.
Control Emily wondered whether the performer in the concert had imitated a pop star for the audience’s amusement.
P600 reflects normal structure-building processes.
“P600 amplitude is an index of syntactic integration difficulty.”
P600 amplitude should covary with integration difficulty.
Experiment Design
a. Short controlThe actress wished that the producers knew that the witty host would tell the jokes during the party.b. Short WHThe actress wished that the producers knew which jokes the witty host would tell __ during the party.
c. Long controlThe producers knew that the actress wished that the witty host would tell the jokes during the party.d. Long WHThe producers knew which jokes the actress wished that the witty host would tell __ during the party.
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…
Sussman & Sedivy (2003)
(Sussman & Sedivy, 2003)
Where to look for gaps
‘Active’ Gap Creation
Filler vs. Gap-Driven Parsing
• Fodor 1978
– Gap-driven: construct a wh-dependency only once a ‘doubtless’ gap has been identified
– Filler-driven: construct a wh-dependency once a filler and a possible gap position have been identified
‘Active Filler Strategy’
• Active Filler Strategy (Frazier & Clifton, 1989: 95)
When a filler has been identified, rank the option of assigning it to a gap over all other options.
• Active Filler Strategy (Clifton & Frazier, 1989: 292)
When a filler of category XP has been identified in a nonargument position, such as COMP [complementizer], rank the option of assigning its corresponding gap to the sentence over the option of identifying a lexical phrase of category XP
Subject Gaps
• Most evidence from English involves complement positions of verbs
• Subject gaps in German & Dutch (e.g. Frazier, 1987)– Karl hielp de mijnwerkers die de boswachter vonden.
K helped the mineworkers who the forester found.pl
– Karl hielp de mijnwerkers die de boswachter vond.K helped the mineworkers who the forester found.sg.
Analyses of mean raw word-by-word reading time revealed no significant difference in reading time for 'Susan' between (2a) and (2b) but a significant difference between (2c) and (2d). The longer reading time for 'Susan' in (2c)was highly localised in that neither in the four-word adjunct region before,nor at the verb after, 'Susan' was there a significant reading time difference between (2c) and (2d). This highly localised effect will be taken as afilled-gap effect in the subject position after alternative explanations in terms of the frequency of use and/or markedness of the sentence structures involved, the noun phrase accessibility hierarchy and semantic/thematic processing have been considered and dismissed. Implications of the subject filled-gap effect for the debate between gap-based and gap-free accounts of sentence processing and for processing theories which claim to predict Stowe's original null finding will be discussed.
Examples:
(1) a. My brother wanted to know who Ruth will bring us home to at Christmas. b. My brother wanted to know if Ruth will bring us home to Mom at Christmas.
(2) a. That is the book which Susan asked her students not to quote from. b. That is the book from which Susan asked her students not to quote. c.That is the book which, for no apparent reason, Susan asked her students not to quote from. d. That is the book from which, for no apparent reason, Susan asked her students not to quote.
(Ming-Wei Lee, 2003)
Types of Dependencies
Gaps?
Competing Theories
What do Englishmen cook gap/trace/copy
What do Englishmen cook
Direct AssociationHPSG/GPSGCategorial GrammarDependency Grammaretc.
Indirect AssociationTransformational Grammar(--> Projection Principle)
Competing Theories
What do Englishmen cook gap/trace/copy
What do Englishmen cook
Direct AssociationHPSG/GPSGCategorial GrammarDependency Grammaretc.
Indirect AssociationTransformational Grammar(--> Projection Principle)
Attempts to distinguish between these theoriesusing 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 iscompatible with both theories!
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)
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 dependencyis 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 dependencyis formed at the verb, not at the trace position.
Still compatible with both theories!
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
Pre-Verbal Gap Effects
• The two theories could be distinguished by effects of dependency formation associated with argument positions that precede the verb of a clause.
• Filled-gap effect expected at pre-verbal position only under indirect association/gap-based theory.
Updates
Questions arising last week…
How General is Active Gap Creation?
Lee (2004)
• Subject filled gap effect
– That is the laboratory which (on two different occasions) Irene used a courier to deliver samples to __.
– That is the laboratory to which (on two different occasions) Irene used a courier to deliver samples __.
Intermediate 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…
Argument Structure & Gap Creation
Stowe et al. (1991)
• Manipulating subcategorization frequency of verb
– The teacher wondered which book the students read quietly about.
– The teacher wondered which song the students read quietly about.
– The teacher wondered which patient the orderly hurried quickly towards.
– The teacher wondered which bed the orderly hurried quickly towards.
Pickering & Traxler (2003)
• Mean PP completion rate - 0.78– That’s the cat that the dog worried compulsively about __ after going to
the vet because of an injury.
– That’s the car that the dog worried compulsively about __ after going to the vet because of an injury.
• Mean PP completion rate - 0.12– That’s the general that the soldier killed enthusiastically for __ during the
war in Korea.
– That’s the country that the soldier killed enthusiastically for __ during the war in Korea.
Grodner, Gibson, & Tunstall (2002)
• Trueswell et al., 1994
– The defendant examined by the lawyer turned out to be unreliable.
– The evidence examined by the lawyer turned out to be unreliable.
• Grodner et al., 2002
– The witness who the evidence {that was} examined by the lawyer implicated seemed to be very nervous.
– The witness thought that the evidence {that was} examined by the lawyer implicated his next door neighbor.
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?
Motivations
What is driving gap creation?
Two approaches for processing wh-phrases
Strategy-driven Approach:
Active Filler StrategyWhen a wh-phrase has been identified, rank the option of assigning it to a gap above all other options.
(Crain & Fodor 1985, Frazier & Clifton 1989, among others)
Principle-based Approach
Online interpretation of wh-phrases is driven by independently motivated grammatical requirements, e.g. thematic role assignment.
(Gibson 1991, Pritchett 1992, among others)
Two approaches for processing wh-phrases: head-initial languages
Strategy-based
gap
WH
CP
C IP
VP
NP
V
…
the first possible gap position = complement of the first verb
Grammatical principle-based
gap
WH
CP
C IP
VP
NP
V
…
the first possible gap position = complement of the first verb
Two approaches for processing wh-phrases: head-final languages
Strategy-based Grammatical principle-based
WH
C
CP
VP
IP
NP …
WH
C
V
CP
VP
IP
NP …
gap
gap V
CP
NPVP
The first opportunity to satisfy thematic requirements occurs at the embedded clause.
…
V
the first possible gap position
CP
Long-distance Wh-scrambling
Japanese wh-phrases are canonically in-situ, but they can be fronted by means of scrambling.
Dare-ni Taro-wa [Jiro-ga t atta-ka] itta.
Who-dat Taro-top Jiro-nom met-Q said
‘Taro said who Jiro met.’
Typing Mismatch Effects
Edson MiyamotoShoichi Takahashi
Question FormationJapanese uses question particles (Q-particles) to mark questions.
John-nom the book-acc read.John-nom the book-acc read-Q [yes/no question]
Sally-top John-nom what-acc read-declC said-Q [root question]‘What did Sally say that John read?’
Sally-top John-nom what-acc read-Q said [embedded question]‘Sally said what John read.’
Q-Particles
…John-ga hon-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
…John-nom book-acc read
Q-Particles
…John-ga hon-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
Normally, a Q-particle is unexpected relative to the high frequency declarative marker.
…John-nom book-acc read
Q-Particles
…John-ga hon-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
…John-ga nani-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
Normally, a Q-particle is unexpected relative to the high frequency declarative marker.
…John-nom what-acc read
…John-nom book-acc read
Q-Particles
…John-ga hon-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
…John-ga nani-o
yonda-to (Declarative)
yonda-ka (Q-Particle)
Normally, a Q-particle is unexpected relative to the high frequency declarative marker.
In a clause in which a wh-phrase is interpreted, the expectations are reversed.
…John-nom what-acc read
…John-nom book-acc read
Design & Procedure
• 2 x 2 factorial design• 4 lists were created by distributing 24 items in a Latin
Square design• 48 filler sentences• Comprehension questions: matching a subject with a
predicate• Self-paced reading task, Moving Window display• 48 native speakers of Japanese
Self-paced reading task
----- --- --- ---- ---- --- ------ -------
Self-paced reading task
どの子供に --- --- ---- ---- --- ------ -------
Self-paced reading task
----- 叔母は --- ---- ---- --- ------ -------
Self-paced reading task
----- --- 母親が ---- ---- --- ------ -------
Self-paced reading task
----- --- --- ケーキを ---- --- ------ -------
Self-paced reading task
----- --- --- ---- 焼いたと --- ------ -------
Self-paced reading task
----- --- --- ---- ---- 台所で ------ -------
Self-paced reading task
----- --- --- ---- ---- --- お手伝いさんに -------
Self-paced reading task
----- --- --- ---- ---- --- ------ 知らせましたか。
Experiment 1: Results In-situ Condition
b. <INSIT+DECLC>
NP-top [NP-nom Wh-dat NP-acc V-DeclC] … Verb-Q
d. <INSIT+Q>
NP-top [NP-nom Wh-dat NP-acc V-Q] … Verb
In-situ
600
700
800
900
1000
1100
1 2 3 4 5 6 7 8
Region
Reading Time
DeclC
QP
F1 (1, 47) = 5.5, p <.01 F2 (1, 18) = 2.8, p = 0.09
V-DeclC/Q
Miyamoto & Takahashi’s observation is replicated.
Experiment 1: Results Scrambled Condition
a. <SCRAM+DECLC>
Wh-dat NP-top [NP-nom NP-acc V-DeclC] … Verb-Q
c. <SCRAM+Q>
Wh-dat NP-top [NP-nom NP-acc V-Q ] … Verb.
Scrambled
600
700
800
900
1000
1100
1 2 3 4 5 6 7 8
Region
Reading Time
DeclC
QP
F1 (1, 47) = 6.1, p <.01F2 (1, 18) = 5.6, p <.01
V-DeclC/Q
Readers also exhibit Typing Mismatch effect in Scrambled Condition.
Results: Scrambled Condition
• Readers create a gap position
in the embedded clause.
• Wh-phrase is associated with the first verb that readers encounter.
• This finding is expected under the grammatical principle-based approach.
NP-top
Verb
CP
gap
NP-nom
Verb
VP
WH-dat
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
VerbNP-dat
Slowdown
Verb 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
VerbNP-dat
Slowdown
Verb 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
VerbNP-dat
Slowdown
Verb Verb
Kamide & Mitchell 1999
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
VerbNP-dat
Slowdown
Verb Verb
Japanese readers exhibit Filled Gap effect. Confirms that theyinterpret a sentence-initial wh-phrase in the embedded clause,before reaching the embedded verb (Region 7).
Filled Gap
600
700
800
900
1000
1100
1200
1 2 3 4 5 6 7 8
filled
non-filled
F1 (1, 33) = 11.9, p <.01F2 (1, 19) = 6.4, p <.05
NP-dat
Comprehension accuracy: 86.3%
Verb
Sentence Completion Task
• Sentence fragments
– which man-DAT boy-NOM woman-NOM …
• In spontaneous completions, wh-phrase treated as long-distance scrambled 61% of the time
– Evidence:#1: Q-particles on embedded verb#2: Ditransitive embedded verb
How could this happen?
Structure building in Japanese
John-ga Mary-ni atta. -nom -dat met
John-ga Mary-ni atta
John-ga Mary-ni
• Incremental structure-building
N’
NP
attaV
NP Det N’
Det John-no [Case: Gen]
[Case: Gen]
attaV
[Case: Gen, Left]
• Feature-based left-corner parsing algorithm Schneider (1999)
John-no [Case: Gen]
John-ga Mary-ni atta. -nom -dat met
John-ga
• Predicted head is projected.
Mary-ni
ExistingStructure
IncomingMaterial
[ T ]
[ T ]
Mary-ni
[Case: Dat]
[Case: Dat]
• Each word (head) has a bundle of features.
[Case: Nom, Left]
[Case: Dat, Left]
John-ga
[ T ]
[ T ]
Mary-ni
[Case: Dat]
[Case: Dat]
[ T ]
[Case: Dat]
[Case: Nom]
Schneider (1999)
ExistingStructure
IncomingMaterial
John-ga [T]
[T]
[T][Case: Dat]
Mary-ni[Case: Dat]
John-ga T'
TP
Tatta
Mary-ni attaatta
John-ga Mary-ni atta. -nom -dat met
• Subsumption relation: predicted heads are replaced by licensing heads. Schneider (1999)
T
atta T[Case: Dat, left]
Parsing steps
When new material arrives, …
• Step 1: Replace the leftmost predicted head. ELSE
• Step 2: Merge the new material. ELSE
• Step 3: Build a new predicted head for the new material. [and return to Step 1.] ELSE
Parsing steps
When new material arrives, …
• Step 1: Replace the leftmost predicted head. ELSE
• Step 2: Merge the new material. ELSE
• Step 3: Build a new predicted head for the new material. [and return to Step 1.] ELSE
Insertion principle:Insert new structure to the left of all predicted
heads.
ExistingStructure
IncomingMaterial
C
[ X ]
[ X ]A
[ X ][ Y ]
[ Y ]B[ X ]A
[ X ][ Y ]
[ Y ]B
[ X ]
[ Y ]C
Insertion principle:Insert new structure to the left of all predicted
heads.
ExistingStructure
IncomingMaterial
C
[ X ]
[ X ]A
[ X ][ Y ]
[ Y ]B [ X ]A
[ X ]
[ Y ]
[ Y ]B
[ X ]
C
[ X ]
Incorrect word order!
Insertion principle:Insert new structure to the left of all predicted
heads.
ExistingStructure
IncomingMaterial
C
[ X ]
[ X ]A
[ X ][ Y ]
[ Y ]B [ X ]A
[ X ][ Y ]
[ Y ]B
[ X ]
C [ X ]
[Y] would never be confirmed!
Insertion principle:Insert new structure to the left of all predicted
heads.
ExistingStructure
IncomingMaterial
C
[ X ]
[ X ]A
[ X ][ Y ]
[ Y ]B[ X ]A
[ X ][ Y ]
[ Y ]B
[ X ]
[ Y ]C
Parsing a non-canonical word order
Dare-ni John-gaWho-dat John-nom
Mary-ga tdare-ni atta-ka sitteiru.
Mary-nom met-Q knows‘John knows whom Mary met.’
[Case: Nom, Left]
[T]
John-ga[Case: Nom] John-ga
IncomingMaterial
ExistingStructure
dare-ni
[T]
?
Dare-ni John-ga ...
who-dat John-nom … a. Build a predicted structure whose head is a possible licenser.
b. Add a feature on the potential scrambler, *[Category, T, Right]
If the current element is a potential scrambler,
dare-ni
John-ga[Case: Nom]
[T]
[T]John-ga
dare-ni
[T]
[Case: Dat]
[dare-ni]
[T]
[Case: Dat]
dare-ni [Case: Dat]
[dare-ni] [Case: Dat]
*[Category, T, Right]
[Case: Nom, Left]
[T]
John-ga [T]
Parsing steps
When new material arrives, …
• Step 1: Replace the leftmost predicted head. ELSE
• Step 2: Merge the new material. ELSE
• Step 3: Build a new predicted head for the new material. [and return to Step 1.] ELSE
• Step 4: Create a scrambling structure.
This is when unforced reanalysis of gap-creation occurs.
Mary-ga
dare-ni
John-ga
[T]
[T]
[T]
[T][Case: Dat]
[dare-ni] [Case: Dat]
Dare-ni John-ga Mary-ga …
who-dat John-nom Mary-nom …
The model can predict this reanalysis without any additional assumptions.
Insertion principle: Insert new material to the left of all predicted heads.
…
Unforced reanalysis
Mary-ga
dare-ni
John-ga
[T]
[T]
[T]
[T]
[Case: Dat]
[dare-ni] [Case: Dat]
…
Mary-ga
dare-ni
John-ga
[T]
[T]
[T]
[T][Case: Dat]
[dare-ni] [Case: Dat]
…
NP-subj
NP-subj
Ditransitive Verb
NP-dat
gap
Reanalysis as a last resort operation
Japanese readers prefer to interpret the dative NP as a matrix argument, preserving the initial attachment.
Kamide & Mitchell (1999)
Ditransitive Verb Transitive Verb Slowdown
John-ga kodomo-ni Mary-ga …
John-nom child-dat Mary-nom
ExistingStructure
IncomingMaterial
Mary-ga[Case: Nom]
[T]
[T]
[Case: Dat]
[Case: Dat]kodomo-ni
John-ga
[T][T]
[T]John-ga
[T]
[Case: Dat, Acc]
kodomo-ni
[T]
[T]
Mary-ga
[C]
[C]
[Case: Dat, Acc]
[Case: Dat, Acc]
Mary-ga
…
Parsing attachment and reanalysis
The same insertion principle is applied to both cases.
[T]
[T]
[Case: Dat]
[Case: Dat]kodomo-ni
John-ga
[T]
[T]
[T]John-ga
dare-ni
[T]
[Case: Dat]
[dare-ni]
[T]
[Case: Dat]
Mary-ga Mary-ga
Reanalysis is allowed. Reanalysis is avoided.
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
More on the Importance of Dependency Completion
Frazier, Clifton & Randall 1983
• Null elements that could be trace or PRO
– The mayor is the crook who the police chief wanted __ to leave town.
– The mayor is the crook who the police chief wanted __ to leave town with __.
– The mayor is the crook who the police chief tried __ to leave town with __.
– The mayor is the crook who the police chief forced __ to leave town.
• Claim: PRO is preferred in first empty position, regardless of verb subcategorization. [claims for unambiguous cases challenged by Boland et al., 1990.]
Constraints on Gap Positions
Sentence Matching
• HOUSEHOUSE
• HSEUOHSEUO
• HOUSEHORSE
• HSEUOHSERO
(Freedman & Forster 1985)
Sentence Matching
• DOGS GROWLDOGS GROWL
• GROWL DOGSGROWL 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)
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’sif-S 611 677 752 750 798wh-S 616 698 760 880 800if-V 613 735 754 678 782wh-V 608 698 736 755 1063
Traxler & Pickering 1996
• Plausibility manipulation, subject islands
– WAITING FOR A PUBLISHING CONTRACTThe 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.
IslandsNon-Islands
The Real-Time Status of Island Constraints
Colin PhillipsBeth Rabbin
Leticia PablosKaia Wong
Island Constraints
What do few people believe anybody who claims that Englishmen cook gap
Relative Clause
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
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
Acceptability Rating
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]
Experiment 3 - Infinitive
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0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Region
Residual Reading Time (ms)
INF, ImplausINF, Plaus
… which schools/students the proposal to expand …
*
Experiment 3 - Finite
-50
-40
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-20
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0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Region
Residual Reading Time (ms)
FIN, ImplausFIN, Plaus
… which schools/students the proposal that expanded …
n.s
Implications - Previous Findings
• This experiment (and another that I did not present here) 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
Early Warning Signalsfor Japanese Islands
Masaya YoshidaSachiko Aoshima
Colin Phillips
John-ga …
John-nom …
(Mazuka & Itoh 1995)
John-ga Mary-ni …
John-nom Mary-dat …
(Mazuka & Itoh 1995)
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
VerbCP
gap
NP-subj
Verb
VP
WH-dat
gap
Early Warning
• Japanese numeral classifiers
– san-satsu hon3-cl book
– san-nin gakusei3-cl students
• Numeral classifiers and Relative Clauses
– John-ga san-satsu-no [RC … ] hon-o yondaJohn-nom 3-cl [RC … ] book-acc read
Early Warning
• Japanese numeral classifiers
– san-satsu hon3-cl book
– san-nin gakusei3-cl students
• Numeral classifiers and Relative Clauses
– John-ga san-satsu-no [RC gakusei-ga … ] hon-o yondaJohn-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 …
complementclause
relativeclause
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
complementclause
relativeclause
Early Warning
• Can numeral classifiers be used to detect relative clauses?
– John-ga san-nin-no [gakusei-ga … V] NP-o … VJohn-nom 3-clhuman [student-nom …
– John-ga san-satsu-no [gakusei-ga … V] NP-o … VJohn-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?
classifiermatch
classifiermismatch
550
650
750
850
950
1050
1150
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RT (ms.)
GNC MathingGNC Mismatching
Early Warning
mismatch
RC verb + head
Direct signals of relative clause processed more easily inclassifier-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 12
Regions
Mean RTs (ms.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RTs (ms.)
Scr/GNC MatchingNonScr/GNC Matching
MatchingClassifier
MismatchingClassifier
GNC Mismatching Conditions
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RTs (ms.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RTs (ms.)
Scr/GNC MatchingNonScr/GNC Matching
NP-nom ±match
MatchingClassifier
MismatchingClassifier
GNC Mismatching Conditions
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RTs (ms.)
Scr/GNC MismatchingNonScr/GNC Mismatching
GNC Matching Conditions
500
550
600
650
700
750
800
850
900
950
1000
1 2 3 4 5 6 7 8 9 10 11 12
Regions
Mean RTs (ms.)
Scr/GNC MatchingNonScr/GNC Matching
NP-nom ±match
MatchingClassifier
MismatchingClassifier
NP-dat
Filled-gapEffect
Early Warning
• Yes - Japanese speakers can use numeral classifiers to
– pre-emptively construct relative clauses
– avoid island constraint violations
Coreference Relations
Parallel Issues
• Do constraints on binding restrict the search for antecedents for pronouns/anaphors? [cf. Island constraints]
• Is there a binding analog of active gap creation? [not relevant for forward anaphora]
– John thinks Bill is suspicious of him.
– While he was washing the dishes, John was watching TV.
Principle B-as-initial-filter
• Nicol (1988), Nicol & Swinney (1989): cross-modal priming study in which subjects had to make a lexical decision to a visually presented word while listening to sentences
– The boxer told the skier that the doctor for the team would blame him for the recent injury.
punch – facilitationslope – facilitationnurse - no effect
Principle A-as-initial-filter
• Nicol (1988), Nicol & Swinney (1989): cross-modal priming study in which subjects had to make a lexical decision to a visually presented word while listening to sentences
– The boxer told the skier that the doctor for the team would blame himself for the recent injury.
punch – no effectslope – no effectnurse - facilitation
Nicol 1993
• All visual dual-task priming
– The boxer said that the skier in the hospital had blamed himself for the recent injury.
– The boxer said that the skier in the hospital had blamed him for the recent injury.
– The boxer talked to the skier in the hospital and blamed him for the recent injury.
– The boxer talked to the skier in the hospital and blamed himself for the recent injury.
• Results of control: BT-incompatible
Principle B-as-initial-filter
• Clifton, Kennison & Albrecht (1997): self-paced reading task. The supervisor(s) is a binding-accessible antecedent for his in (c-d) (but there is a number-match only in (d)), but not for him in (a-b).
a) The supervisors paid him yesterday to finish typing the manuscript.b) The supervisor paid him yesterday to finish typing the manuscript.
c) The supervisors paid his assistant yesterday to finish typing the manuscript.d) The supervisor paid his assistant yesterday to finish typing the manuscript.
• A number mismatch/match effect found in (c) vs. (d), but not in (a) vs. (b) => support for PrB as initial filter hypothesis
fast
slow
Principle A-as-a-late-filter
• Badecker & Straub (2002)
a) Jane thought that Bill owed himself another opportunity to solve the problem.
b) John thought that Bill owed himself another opportunity to solve the problem.
• The two conditions are different only in the gender of the inaccessible antecedent of himself; yet reading times at the two words following himself were faster in (a) than in (b) => binding constraints did not immediately rule out binding-inaccessible positions from the consideration.
Runner et al. 2002
• Head-mounted eye-tracking
– “Look at Ken. Have Ken touch Harry’s picture of {him|himself}
– Him: almost all looks to correct picture
– Himself: ~25% of looks to incorrect picture
Sturt 2003Experiment 1
Accessible-mismatch/Inaccessible-mismatch
Jonathan was pretty worried at the City Hospital.
He remembered that the surgeon had pricked herself with a
used syringe needle. There should be an investigation soon.
Accessible-mismatch/Inaccessible-match
Jennifer was pretty worried at the City Hospital.
She remembered that the surgeon had pricked herself with a
used syringe needle. There should be an investigation soon.
Experiment 1- Early processing: first-pass at reflexive region
Experiment 1- Later processing: Second-pass at pre-final region
Experiment 1- Later processing: second pass RT at reflexive region
Sturt 2003
Sturt 2003Experiment 2
Accessible-mismatch/Inaccessible-match
Jonathan was pretty worried at the City Hospital.
The surgeon [RC who treated Jonathan] had pricked herself with a used syringe needle. There should be an investigation soon.
Accessible-mismatch/Inaccessible-mismatch
Jennifer was pretty worried at the City Hospital.
The surgeon [RC who treated Jennifer] had pricked herself with a used syringe needle. There should be an investigation soon.
What does Pronoun Reactivate?
• Love & Swinney (1995)
– Jeff had read about problems with savings and loan institutions, so he went to the bank to ask about the safety that it provided with respect to Cd investments.
Backward Anaphora
Japanese
which of his children (DAT) the man (NOM) …
which of his children (NOM) the man (DAT) …
Japanese pronouns and their antecedents
Verb
the man-nom
NP-dat
which of his children
which of his children (DAT) the man (NOM) …
Japanese pronouns and their antecedents
Verb
the man-nom
NP-dat
which of his children
NP-dat
which of his children
which of his children (DAT) the man (NOM) …
Japanese pronouns and their antecedents
Verb
the man-nom
NP-dat
which of his children
NP-dat
which of his children
Verb
NP-nom
which of his children
which of his children (DAT) the man (NOM) …
the man-dat
which of his children (NOM) the man (DAT) …
** ??
which of his children (DAT) the man (NOM) …
which of his children (NOM) the man (DAT) …
Gender Mismatch
the woman
the woman
Gender Mismatch paradigm: Carreiras et al. (1996); Osterhout et al. (1997); Sturt (2003)
which of his children (DAT) the man (NOM) …
which of his children (NOM) the man (DAT) …
Gender Mismatch
the woman
the woman
Gender Mismatch paradigm: Carreiras et al. (1996); Osterhout et al. (1997); Sturt (2003)
Conditionsa. Scrambled - Gender Mismatch
Adverb / [his / which NP]-dat / Adverb / NP FEMALE-nom / Adverb / NP-acc /
verb-Q / NPMALE-top / verb
b. Scrambled - Gender Match
Adverb / [his / which NP]-dat / Adverb / NP MALE-nom / Adverb / NP-acc /
verb-Q / NPFEMALE-top / verb
c. Non-scrambled - Gender Mismatch
Adverb / [his / which NP]-nom / Adverb / NP FEMALE-dat / Adverb / NP-acc /
verb-Q / NPMALE-top / verb
d. Non-scrambled - Gender Match
Adverb / [his / which NP]-nom / Adverb / NP MALE-dat / Adverb / NP-acc /
verb-Q / NPMALE-top / verb.
Examples
a. 台所で 彼の どの子供に 朝食後 叔母が 急いで お弁当を 渡したか 父親は 覚えていた。
b. 台所で 彼の どの子供に 朝食後 叔父が 急いで お弁当を 渡したか 叔母は 覚えていた。
c. 台所で 彼の どの子供が 朝食後 叔母に 急いで お弁当を 渡したか 父親は 覚えていた。
d. 台所で 彼の どの子供が 朝食後 叔父に 急いで お弁当を 渡したか 父親は 覚えていた。
Design & Procedure
• 2 x 2 factorial design• 4 lists were created by distributing 24 items in a Latin
Square design• 56 filler sentences• Comprehension questions: matching a subject with a
predicate• Self-paced reading task, Moving Window display• 40 native speakers of Japanese
Results: Scrambled conditions
Slowdown at mismatching NP is observed.
500
600
700
800
900
1000
1100
1 2 3 4 5 6 7 8 9 10
Region
Scrambled, match
Scrambled, mismatch
F1(1, 39) = 8.6, p<.01;F2(1,23)=7.4, p<.01
± Match
his/her
Results: Non-scrambled conditions
Slowdown at mismatching NP only when NP is possible antecedent.
500
600
700
800
900
1000
1100
1 2 3 4 5 6 7 8 9 10
Region
Unscrambled, match
Unscrambled, mismatch
Fs<1± Match
his/her
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, Lau, Lieberman, Phillips, & Yoshida, 2004)
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, Lau, Lieberman, Phillips, & Yoshida, 2004)
-60
-40
-20
0
20
40
60
80
100
120
because lastsemester
while-cd SHE wastaking
classes while-ab NAME wasworking
full-time to…
Residual Reading Times
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., 2004)
-60
-40
-20
0
20
40
60
80
100
120
because lastsemester
while-cd SHE wastaking
classes while-ab NAME wasworking
full-time to…
Residual Reading Times
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 pairCondition C – immediate
while while Jessica
Russell
(Kazanina et al., 2004)