Psycholinguistics II
Session #1
Last Semester
• Abstraction
–
–
–
–
Categories in speech perception
Neural network models and symbolic representations
Lexical access
Language acquisition and learnability
This Semester
• Architecture
– Task-specific segregation of mechanisms
– Information-specific segregation of mechanisms
Jackendoff (2002), Foundations of Language
Sound Interface
Meaning Interface
Surface structure
Semantics
Syntax
Phonology
Deep structure
Jackendoff (2002)
• Standard architecture
– “begins with phrase structure composition and lexical insertion,
and branches outward to phonology and semantics”
– “quite at odds with the logical directionality of processing, where
speech perception has to get from sounds to meaning, and speech
production has to get from meanings to sounds”
Sound Interface
Meaning Interface
Surface structure
Deep structure
Jackendoff (2002)
• Parallel architecture
– logically non-directional: one can start with any piece of structure
in any component and pass along logical pathways provided by the
constraints to construct a coherent larger structure around it […]
Because the grammar is logically non-directional, it is not
inherently biased toward either perception or production – unlike
the syntactocentric architecture, which is inherently biased against
both.”
– “possible to describe the logic of processing in terms Semantics
isomorphic to the rule types in the parallel grammar”
Syntax
Phonology
• Why might we want this…?
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)
Chomsky & Lasnik (1993)
• “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.’” (p.
18)
Competence
• Metaphor
• Real system
– Derivations
– Constraints
– Feeding relations among syntax, semantics, phonology
The Standard View
Standard View
324
697+
?
arithmetic
217 x 32 = ?
Standard View
specialized algorithm
324
697+
?
arithmetic
specialized algorithm
217 x 32 = ?
Standard View
specialized algorithm
specialized algorithm
324
697+
?
arithmetic
217 x 32 = ?
?
something deeper
Standard View
specialized algorithm
speaking
language
specialized algorithm
understanding
grammatical
knowledge,
competence
recursive characterization of
well-formed expressions
Standard View
specialized algorithm
speaking
language
specialized algorithm
understanding
grammatical
knowledge,
competence
recursive characterization of
well-formed expressions
precise
but ill-adapted to
real-time operation
Standard View
specialized algorithm
speaking
language
specialized algorithm
understanding
grammatical
knowledge,
competence
recursive characterization of
well-formed expressions
well-adapted to
real-time operation
but maybe inaccurate
Origins of Standard View
Townsend & Bever (2001, ch. 2)
• “Linguists made a firm point of insisting that, at most, a
grammar was a model of competence - that is, what the
speaker knows. This was contrasted with effects of
performance, actual systems of language behaviors such as
speaking and understanding. Part of the motive for this
distinction was the observation that sentences can be
intuitively ‘grammatical’ while being difficult to
understand, and conversely.”
Townsend & Bever (2001, ch. 2)
• “…Despite this distinction the syntactic model had great
appeal as a model of the processes we carry out when we
talk and listen. It was tempting to postulate that the theory
of what we know is a theory of what we do, thus answering
two questions simultaneously.
1. What do we know when we know a language?
2. What do we do when we use what we know?
Townsend & Bever (2001, ch. 2)
• “…It was assumed that this knowledge is linked to
behavior in such a way that every syntactic operation
corresponds to a psychological process. The hypothesis
linking language behavior and knowledge was that they are
identical.
Townsend & Bever (2001, ch. 2)
• “…It was assumed that this knowledge is linked to
behavior in such a way that every syntactic operation
corresponds to a psychological process. The hypothesis
linking language behavior and knowledge was that they are
identical.”
Note:
(i) No indication of how psychological processes realized
(ii) No commitment on what syntactic operations count
(iii) No claim that syntactic processes are the only processes
in language comprehension.
Miller & Chomsky (1963)
• ‘The psychological plausibility of a transformational model of the
language user would be strengthened, of course, if it could be shown
that our performance on tasks requiring an appreciation of the structure
of transformed sentences is some function of the nature, number and
complexity of the grammatical transformations involved.’ (Miller &
Chomsky 1963: p. 481)
Miller (1962)
1. Mary hit Mark.
2. Mary did not hit Mark.
3. Mark was hit by Mary.
4. Did Mary hit Mark?
5. Mark was not hit by Mary.
6. Didn’t Mary hit Mark?
7. Was Mark hit by Mary?
8. Wasn’t Mark hit by Mary?
K(ernel)
N
P
Q
NP
NQ
PQ
PNQ
Miller (1962)
Transformational
Cube
Townsend & Bever (2001, ch. 2)
• “The initial results were breathtaking. The amount of time it takes to
produce a sentence, given another variant of it, is a function of the
distance between them on the sentence cube. (Miller & McKean
1964).”
“…It is hard to convey how exciting these developments were. It
appeared that there was to be a continuing direct connection between
linguistic and psychological research. […] The golden age had
arrived.”
Derivational Theory of Complexity
• Miller & McKean (1964): Matching sentences with the same meaning
or ‘kernel’
• Joe warned the old woman.
The old woman was warned by Joe.
• Joe warned the old woman.
Joe didn’t warn the old woman.
• Joe warned the old woman.
The old woman wasn’t warned by Joe.
K
P
K
N
K
PN
1.65s
1.40s
3.12s
McMahon (1963)
a.
b.
c.
d.
i. seven precedes thirteen
K (true)
ii. thirteen precedes seven
K (false)
i. thirteen is preceded by seven
P (true)
ii. seven is preceded by thirteen
P (false)
i. thirteen does not precede seven N (true)
ii. seven does not precede thirteen N (false)
i. seven is not preceded by thirteen PN (true)
ii. thirteen is not preceded by seven PN (false)
Easy Transformations
• Passive
– The first shot the tired soldier the mosquito bit fired missed.
– The first shot fired by the tired soldier bitten by the mosquito missed.
• Heavy NP Shift
– I gave a complete set of the annotated works of H.H. Munro to Felix.
– I gave to Felix a complete set of the annotated works of H.H. Munro.
• Full Passives
– Fido was kissed (by Tom).
• Adjectives
– The {red house/house which is red} is on fire.
Failure of DTC?
• Any DTC-like prediction is contingent on a particular
theory of grammar, which may be wrong
• It’s not surprising that transformations are not the only
contributor to perceptual complexity
– memory demands, may increase or decrease
– ambiguity, where grammar does not help
– difficulty of access
Townsend & Bever (2001, ch. 2)
• “Alas, it soon became clear that either the linking
hypothesis was wrong, or the grammar was wrong, or
both.”
Townsend & Bever (2001, ch. 2)
• “The moral of this experience is clear. Cognitive science
made progress by separating the question of what people
understand and say from how they understand and say it.
The straightforward attempt to use the grammatical model
directly as a processing model failed. The question of what
humans know about language is not only distinct from how
children learn it, it is distinct from how adults use it.”
Received Wisdom (ca. 1974)
• Surface structures - YES
• Deep structures - MAYBE
• Transformations - NO
• Real-time operations - ROUGH-AND-READY
e.g. NP V (NP) --> subj. verb object
Implication
• If the standard view is correct, then there is little hope of a
testable, unified model
• Very difficult to make precise predictions about the nature
& timing of linguistic computations
• But there were good arguments for the standard,
fractionated architecture…
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Grammars, 1960s Style
A Simple Derivation
S
(starting axiom)
S
A Simple Derivation
S
1. S
(starting axiom)
 NP VP
S
NP
VP
A Simple Derivation
S
(starting axiom)
1. S  NP VP
2. VP  V NP
S
NP
VP
V
NP
A Simple Derivation
S
(starting axiom)
1. S  NP VP
2. VP  V NP
3. NP  D N
S
NP
VP
V
NP
D
N
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
S
NP
VP
Bill V
NP
D
N
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
S
NP
VP
Bill V
hit D
NP
N
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
S
NP
VP
Bill V
NP
hit D
the
N
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
S
NP
VP
Bill V
NP
hit D
N
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
S
NP
VP
Bill V
NP
hit D
N
the
ball
Reverse the derivation...
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
Bill
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill
hit
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
hit
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
hit
the
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
hit D
the
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
hit D
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
hit D
N
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
Bill V
NP
hit D
N
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
NP
VP
Bill V
NP
hit D
N
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
S
NP
VP
Bill V
NP
hit D
N
the
ball
A Simple Derivation
S
1. S
2. VP
3. NP
4. N
5. V
6. D
7. N
(starting axiom)
 NP VP
 V NP
DN
 Bill
 hit
 the
 ball
S
NP
VP
Bill V
NP
hit D
N
the
ball
Transformations
wh-movement
--> 2
X
1
wh-NP
2
Y
3
1
0
3
Transformations
VP-ellipsis
-->
X
1
VP1
2
Y
3
VP2
4
Z
5
1
2
3
0
5
condition: VP1 = VP2
Difficulties
• How to build structure incrementally in rightbranching structures
• How to recognize output of transformations that
create nulls
Summary
• Running the grammar ‘backwards’ is not so
straightforward - problems of indeterminacy and
incrementality
• Disappointment in empirical tests of Derivational
Theory of Complexity
• Unable to account for processing of local
ambiguities
Standard View
specialized algorithm
speaking
language
specialized algorithm
understanding
grammatical
knowledge,
competence
recursive characterization of
well-formed expressions
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Accuracy of Real-time Systems
• Common view
– Grammaticality judgments are slow
– Real-time analysis is rough-and-ready
• Narrowing gap between real-time systems and grammar
presupposes grammatical accuracy of real-time systems
–
–
–
–
Constraints on movement
Binding principles
Argument structure
etc.
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Parser-Specific Mechanisms
• How is structural uncertainty handled in parsing?
– Ambiguity
The teacher sent…
The boy knew the answer…
– Indeterminacy
What do you think that John…
Parser-Specific Mechanisms
• Classic Argument: existence of ambiguity-resolution mechanisms
implies existence of separate parsing device
• Two Ways to question this
– ambiguity-resolution principles = economy principles
– there are no ambiguity resolution principles per se, only grammatical
constraints (and other lexical, semantic stuff, …), e.g. Aoshima et al,,
2003 on Active Filler Strategy
• Challenges
– how generally can structural ambiguity effects be reduced to independent
principles?
– where does the as soon as possible requirement of parsing come from?
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Townsend & Bever (2001, ch. 2)
• “Linguists made a firm point of insisting that, at most, a
grammar was a model of competence - that is, what the
speaker knows. This was contrasted with effects of
performance, actual systems of language behaviors such as
speaking and understanding. Part of the motive for this
distinction was the observation that sentences can be
intuitively ‘grammatical’ while being difficult to
understand, and conversely.”
Grammaticality ≠ Parsability
• “It is straightforward enough to show that sentence parsing and
grammaticality judgments are different. There are sentences which are
easy to parse but ungrammatical (e.g. that-trace effects), and there are
sentences which are extremely difficult to parse, but which may be
judged grammatical given appropriate time for reflection (e.g. multiply
center embedded sentences). This classic argument shows that parsing
and grammar are not identical, but it tells us very little about just how
much they have in common.”
(Phillips, 1995)
Grammaticality ≠ Parsability
• Ungrammatical yet Comprehensible
– *The boys likes cookies
– *The man donated the museum a painting
– *Who do you think that left?
• Solution: accommodation, ‘mal-rules’
• The examples don’t point to an independent system
– violations are noticed immediately
– violations are ‘diagnosed’ rather well
Grammaticality ≠ Parsability
• Grammatical yet Incomprehensible
– # The man the woman the dog bit chased watched a movie.
– # The horse raced past the barn fell.
Grammaticality ≠ Parsability
• Most ERP syntax literature focuses on speed of ungrammaticality
detection - many violations detected very quickly
• But some judgments are slow
– Why did you think that John fixed the drain?
– * Why did you remember that John fixed the drain?
– Who did everyone meet?
– Who met everyone?
Grammaticality ≠ Parsability
• Syntactic Illusions:
‘More people have been to Russia than I have.’
(Montalbetti, 1984)
• Challenges
–
–
–
–
Provide an explicit account of grammaticality judgment
Make timing predictions that map onto electrophysiology
Explain ‘syntactic illusions’
Flesh out the account of ‘slow judgments’
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Parsing ≠ Production
• Classic Argument: parsing & production look quite different
– different building blocks (cf. clause-sized templates in production)
– differentially affected in language disorders, double dissociations (e.g., classical
conduction aphasia)
– different kinds of breakdown in normal population (e.g., garden paths, slips of the
tongue)
– different tasks!
• But…
–
–
–
–
–
similar arguments hold for lexicon
evidence for incrementality in production (e.g., Ferreira, 1996)
single system can encounter different ‘bottlenecks’ in different tasks
how is imitation (etc.) possible?
challenge: show that this is feasible
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusion
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Derivational Theory of Complexity
• Miller & McKean (1964): Matching sentences with the same meaning
or ‘kernel’
• Joe warned the old woman.
The old woman was warned by Joe.
• Joe warned the old woman.
Joe didn’t warn the old woman.
• Joe warned the old woman.
The old woman wasn’t warned by Joe.
K
P
K
N
K
PN
1.65s
1.40s
3.12s
McMahon (1963)
a.
b.
c.
d.
i. seven precedes thirteen
K (true)
ii. thirteen precedes seven
K (false)
i. thirteen is preceded by seven
P (true)
ii. seven is preceded by thirteen
P (false)
i. thirteen does not precede seven N (true)
ii. seven does not precede thirteen N (false)
i. seven is not preceded by thirteen PN (true)
ii. thirteen is not preceded by seven PN (false)
Easy Transformations
• Passive
– The first shot the tired soldier the mosquito bit fired missed.
– The first shot fired by the tired soldier bitten by the mosquito missed.
• Heavy NP Shift
– I gave a complete set of the annotated works of H.H. Munro to Felix.
– I gave to Felix a complete set of the annotated works of H.H. Munro.
• Full Passives
– Fido was kissed (by Tom).
• Adjectives
– The {red house/house which is red} is on fire.
Failure of DTC?
• Any DTC-like prediction is contingent on a particular
theory of grammar, which may be wrong
• It’s not surprising that transformations are not the only
contributor to perceptual complexity
– memory demands, may increase or decrease
– ambiguity, where grammar does not help
– difficulty of access
Motivations for Separation
1.
2.
3.
4.
5.
6.
7.
Real-time grammar
Accuracy of parsing
Parser-specific mechanisms
Grammaticality ≠ Parsability
Parsing ≠ Production
‘Derivational Theory of Complexity’
Competence/Performance Confusions
(see Fodor, Bever & Garrett, 1974; Levelt, 1974;
Fillenbaum, 1971)
Competence & Performance
• Different kinds of formal systems: ‘Competence systems’
and ‘Performance systems’
• The difference between what a system can generate given
unbounded resources, and what it can generate given
bounded resources
• The difference between a cognitive system and its behavior
Competence & Performance
•
•
•
•
Mechanism vs. behavior
Declarative vs. procedural specification of system
Separation of system with or without resource limitations
Time-dependent vs. time-independent specification
Competence & Performance
(1) It’s impossible to deny the distinction between cognitive states and
actions, the distinction between knowledge and its deployment.
(2) How to distinguish ungrammatical-but-comprehensible examples (e.g.
John speaks fluently English) from hard-to-parse examples.
(3) How to distinguish garden-path sentences (e.g. The horse raced past
the barn fell) from ungrammatical sentences.
(4) How to distinguish complexity overload sentences (e.g. The cat the
dog the rat chased saw fled) from ungrammatical sentences.
Jackendoff (2002), Foundations of Language
Sound Interface
Meaning Interface
Surface structure
Semantics
Syntax
Phonology
Deep structure
Architectural Issues
• Task-specific segregation of mechanisms
– Parser
– Producer
– Grammar
• Information-specific segregation of mechanisms
–
–
–
–
–
Phonology
Lexicon
Syntax
Semantics
World knowledge
Architectural Issues
• Task-specific segregation of mechanisms
– Parser
– Producer
– Grammar
• Information-specific segregation of mechanisms
–
–
–
–
–
Phonology
Lexicon
Syntax
Semantics
World knowledge
Cross-talk among levels?
‘Modularity of Mind’
Jackendoff (2002), Foundations of Language
Sound Interface
Meaning Interface
Surface structure
Semantics
Syntax
Phonology
Deep structure
Does Jackendoff’s argument work?
• Is real-time computation grammatically accurate?
• Is transformational grammar incompatible with
incremental structure building?
• What does it take to solve the incrementality problem?
• Are parsing and production mirror images of one another?
Speed
Measures of Speed
• Measures of speed of processing
–
–
–
–
Speech-Shadowing:
Eye-tracking
Speed-accuracy Tradeoff (SAT)
Event-Related Potentials (ERPs)
Marslen-Wilson 1975
Speech shadowing involves on-line repetition of a speaker…
Speech shadowing involves on-line repetition of a speaker…
250-1000ms
Shadowing latency
The new peace terms have been announced…
surrender of …
They call for the unconditional universe of …
already of …
normal
semantic
syntactic
Marslen-Wilson 1975
Eye-movements
(Sereno & Rayner, 2003)
Head-mounted Eye-Tracking
Sussman & Sedivy (2003)
(Sussman & Sedivy, 2003)
Speed-Accuracy Tradeoff (SAT)
McElree & Griffith (1995)
- grammatical
- subcategorization
- thematic
- syntactic
Event-Related Potentials (ERPs)
John
is
laughing.
s1
s2
s3
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
N400
• Developing understanding of
N400 is informative
I drink my coffee with cream and sugar
I drink my coffee with cream and socks
• Response to ‘violations’
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)
P600
he mows
he *mow
(Slide from Kaan (2001)
Left Anterior Negativity
(LAN)
P600
he mows
he *mow
(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 gefüttert
The baby was fed
 Das Baby wurde im gefüttert
The 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?