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PSY 369: Psycholinguistics. Language Comprehension: Sentence comprehension. Language perception. Word recognition. Syntactic analysis. Semantic & pragmatic analysis. Input. c. dog. a. cat. cap. S. t. wolf. The cat chased the rat. VP. NP. tree. V. NP. /k/. yarn. cat.

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psy 369 psycholinguistics

PSY 369: Psycholinguistics

Language Comprehension:

Sentence comprehension

overview of comprehension

Language

perception

Word

recognition

Syntactic

analysis

Semantic &

pragmatic

analysis

Input

c

dog

a

cat

cap

S

t

wolf

The cat chased

the rat.

VP

NP

tree

V

NP

/k/

yarn

cat

the

chased

the

rat

/ae/

cat

claw

/t/

fur

hat

Overview of comprehension
eye movements in reading
Eye-movements in reading
  • One of the most common measures used in sentence comprehension research is measuring Eye-movements

Clothes make the man. Naked people have little or no influence on society.

Clothes make the man. Naked people have little or no influence on society.

eye movements in reading1
Eye-movements in reading
  • One of the most common measures used in sentence comprehension research is measuring Eye-movements

Clothes make the man. Naked people have little or no influence on society.

Clothes make the man. Naked people have little or no influence on society.

the human eye
The Human Eye
  • At its center is the fovea, a pit that is most sensitive to light and is responsible for our sharp central vision.
  • The central retina is cone-dominated and the peripheral retina is rod-dominated.
eye movements in reading2
Eye-movements in reading
  • Limitations of the visual field
    • 130 degrees vertically, 180 degrees horizontally (including peripheral vision
    • Perceptual span for reading: 7-12 spaces

Clothes make the man. Naked people have little or no influence on society.

measuring eye movements
Measuring Eye Movements

Purkinje Eye Tracker

  • Laser is aimed at the eye.
  • Laser light is reflected by cornea and lens
  • Pattern of reflected light is received by an array of light-sensitive elements.
  • Very precise
  • Also measures pupil accomodation
  • No head movements
measuring eye movements1
Measuring Eye Movements

Video-Based Systems

  • Infrared camera directed at eye
  • Image processing hardware determines pupil position and size (and possibly corneal reflection)
  • Good spatial precision (0.5 degrees) for head-mounted systems
  • Good temporal resolution (up to 500 Hz) possible
eye movements
Eye Movements
  • Within the visual field, eye movements serve two major functions
    • Saccades to Fixations – Position target objects of interest on the fovea
    • Tracking – Keep fixated objects on the fovea despite movements of the object or head
fixations
Fixations
  • The eye is (almost) still – perceptions are gathered during fixations
  • The most important of eye “movements”
    • 90% of the time the eye is fixated
    • duration: 150ms - 600ms
saccades
Saccades
  • Saccades are used to move the fovea to the next object/region of interest.
    • Connect fixations
    • Duration 10ms - 120ms
      • Very fast (up to 700 degrees/second)
    • No visual perception during saccades
      • Vision is suppressed
      • Evidence that some cognitive processing may also be suppressed during eye-movements (Irwin, 1998)

Video examples: 1 | 2 | 3 | 4

saccades1
Saccades

Move to here

saccades2
Saccades

Move to here

saccades4
Saccades
  • Saccades are used to move the fovea to the next object/region of interest.
    • Connect fixations
    • Duration 10ms - 120ms
      • Very fast (up to 700 degrees/second)
    • No visual perception during saccades
      • Vision is suppressed
  • Ballistic movements (pre-programmed)
  • About 150,000 saccades per day
smooth pursuit
Smooth Pursuit
  • Smooth movement of the eyes for visually tracking a moving object
  • Cannot be performed in static scenes (fixation/saccade behavior instead)
smooth pursuit versus saccades
Saccades

Jerky

No correction

Up to 700 degrees/sec

Background is not blurred (saccadic suppression)

Smooth pursuit

Smooth and continuous

Constantly corrected by visual feedback

Up to 100 degrees/sec

Background is blurred

Smooth Pursuit versus Saccades
eye movements in reading3
Eye-movements in reading
  • Eye-movements in reading are saccadic rather than smooth

Clothes make the man. Naked people have little or no influence on society.

Video examples: 1 | 2 | 3 | 4

slide22

dog

The

man

hit

the

with

the

leash.

S

NP

det

N

The

man

slide23

dog

The

man

hit

the

with

the

leash.

S

NP

VP

V

det

N

The

man

hit

slide24

dog

The

man

hit

the

with

the

leash.

S

NP

VP

V

NP

NP

det

N

det

N

The

man

hit

the

dog

slide25

PP

with

the

leash

dog

The

man

hit

the

with

the

leash.

S

NP

VP

V

NP

NP

Modifier

det

N

det

N

The

man

hit

the

dog

slide26

PP

with

the

leash

dog

The

man

hit

the

with

the

leash.

S

NP

VP

V

NP

Instrument

NP

det

N

det

N

The

man

hit

the

dog

slide27

dog

The

man

hit

the

with

the

leash.

  • How do we know which structure to build?
parsing
Parsing
  • The syntactic analyser or “parser”
    • Main task: To construct a syntactic structure from the words of the sentence as they arrive
    • Main research question: how does the parser “make decisions” about what structure to build?
different approaches
Different approaches
  • Immediacy Principle: access the meaning/syntax of the word and fit it into the syntactic structure
    • Serial Analysis (Modular): Build just one based on syntactic information and continue to try to add to it as long as this is still possible
    • Interactive Analysis: Use multiple levels (both syntax and semantics) of information to build the “best” structure
different approaches1
Different approaches
  • Immediacy Principle: access the meaning/syntax of the word and fit it into the syntactic structure
    • Serial Analysis (Modular): Build just one based on syntactic information and continue to try to add to it as long as this is still possible
    • Interactive Analysis: Use multiple levels (both syntax and semantics) of information to build the “best” structure
  • Parallel Analysis: Build both alternative structures at the same time
  • Minimal Commitment: Stop building - and wait until later material clarifies which analysis is the correct one.
sentence comprehension1
Sentence Comprehension
  • A vast amount of research focuses on: Garden path sentences
    • A garden path sentence invites the listener to consider one possible parse, and then at the end forces him to abandon this parse in favor of another.
real headlines
Real Headlines
  • Juvenile Court to Try Shooting Defendant
  • Red tape holds up new bridge
  • Miners Refuse to Work after Death
  • Retired priest may marry Springsteen
  • Local High School Dropouts Cut in Half
  • Panda Mating Fails; Veterinarian Takes Over
  • Kids Make Nutritious Snacks
  • Squad Helps Dog Bite Victim
  • Hospitals are Sued by 7 Foot Doctors
sentence comprehension2

S

NP

VP

The horse

Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.
sentence comprehension3
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.

S

NP

VP

V

The horse

raced

sentence comprehension4
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.

S

NP

VP

V

PP

P

NP

The horse

raced

past

sentence comprehension5
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.

S

NP

VP

V

PP

P

NP

The horse

raced

past

the barn

sentence comprehension6
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.

S

NP

VP

V

PP

P

NP

The horse

raced

past

the barn

fell

sentence comprehension7
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.
  • raced is initially treated as a past tense verb

S

NP

VP

V

PP

P

NP

The horse

raced

past

the barn

sentence comprehension8
Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.
  • raced is initially treated as a past tense verb
  • This analysis fails when the verb fell is encountered

S

NP

VP

V

PP

P

NP

The horse

raced

past

the barn

fell

sentence comprehension9

S

VP

NP

V

NP

RR

PP

V

P

NP

The horse

raced

past

the barn

fell

Sentence Comprehension
  • Garden path sentences
    • The horse raced past the barn fell.
  • raced is initially treated as a past tense verb
  • This analysis fails when the verb fell is encountered
  • raced can be re-analyzed as a past participle.

S

NP

VP

V

PP

P

NP

The horse

raced

past

the barn

fell

a serial model
A serial model
  • Formulated by Lyn Frazier (1978, 1987)
    • Build trees using syntactic cues:
      • phrase structure rules
      • plus two parsing principles
        • Minimal Attachment
        • Late Closure
    • Go back and revise the syntax if later semantic information suggests things were wrong
a serial model1
A serial model
  • Minimal Attachment
    • Prefer the interpretation that is accompanied by the simplest structure.
      • simplest = fewest branchings (tree metaphor!)
      • Count the number of nodes = branching points

The girl hit the man with the umbrella.

slide44

Minimal attachment

S

8 Nodes

NP

VP

the girl

V

NP

Preferred

S

hit

NP

PP

NP

VP

the man

P

NP

the girl

V

NP

PP

with

the umbrella

hit

the man

P

NP

with

the umbrella

9 nodes

The girl hit the man with the umbrella.

a serial model2
A serial model
  • Late Closure
    • Incorporate incoming material into the phrase or clause currently being processed.

OR

    • Associate incoming material with the most recent material possible.

She said he tickled her yesterday

slide46

Parsing Preferences .. late closure

S

Preferred

S

np

vp

np

vp

she

v

S\'

adv

she

v

S\'

said

np

vp

yesterday

said

np

vp

he

v

np

he

v

np

adv

tickled

her

tickled

her

yesterday

(Both have 10

nodes, so use LC

not MA)

She said he tickled her yesterday

minimal attachment

Modular prediction

Interactive prediction

Minimal attachment
  • Garden path sentences

(Rayner & Frazier, ‘83)

The spy saw the cop with a telescope.

minimal attach

Build this structure first

non-minimal attach

Build this structure first

minimal attachment1

Modular prediction

Lexical/semantic information rules this one out

Interactive prediction

Minimal attachment
  • Garden path sentences

(Rayner & Frazier, ‘83)

The spy saw the cop with a revolver.

minimal attach

Build this structure first

non-minimal attach

Build this structure first

slide49

S

S

NP

VP

NP

the spy

V

NP

VP

S’

S’

the spy

saw

NP

PP

V

PP

NP

the cop

P

NP

saw

P

NP

with

the revolver

but the cop didn’t see him

the cop

but the cop didn’t see him

with

the revolver

MA

Non-MA

The spy saw the cop with the binoculars..

The spy saw the cop with the revolver …

(Rayner & Frazier, ‘83)

<- takes longer to read

interactive models

evidence typically gets examined, but can’t do the examining

Interactive Models
  • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence
  • Trueswell et al (1994)
  • The evidence examined by the lawyer …
  • The defendant examined by the lawyer…
interactive models1
Interactive Models
  • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence
  • Trueswell et al (1994)
  • The evidence examined by the lawyer …
  • The defendant examined by the lawyer …

A defendant can be examined but can also do examining.

semantic expectations
Semantic expectations
  • Taraban & McCelland (1988)
    • Expectation
  • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence
  • The couple admired the house with a friendbut knew that it was over-priced.
  • The couple admired the house with a gardenbut knew that it was over-priced.
semantic expectations1
Semantic expectations
  • Taraban & McCelland, 1988
  • The couple admired the house with a friendbut knew that it was over-priced.
  • The couple admired the house with a gardenbut knew that it was over-priced.

The Non-MA structure may be favoured

what about spoken sentences
What about spoken sentences?
  • All of the previous research focused on reading, what about parsing of speech?
    • Methodological limits – ear analog of eye-movements not well developed
      • Auditory moving window
      • Reading while listening
      • Looking at a scene while listening
    • Some research on use of intonation
intonation as a cue
Intonation as a cue

A: I’d like to fly to Davenport, Iowa on TWA.

B: TWA doesn’t fly there ...

B1: They fly to Des Moines.

B2: They fly to Des Moines.

chunking or phrasing
Chunking, or “phrasing”

A1: I met Mary and Elena’s mother at the mall yesterday.

A2: I met Mary and Elena’s mother at the mall yesterday.

phrasing can disambiguate
Phrasing can disambiguate

Mary & Elena’s mother

mall

I met Mary and Elena’s mother at the mall yesterday

One intonation phrase with relatively flat overall pitch range.

phrasing can disambiguate1
Phrasing can disambiguate

Elena’s mother

mall

Mary

I met Mary and Elena’s mother at the mall yesterday

Separate phrases, with expanded pitch movements.

summing up
Summing up
  • Is ambiguity resolution a problem in real life?
    • Yes (Try to think of a sentence that isn’t partially ambiguous)
  • Many factors might influence the process of making sense of a string of words. (e.g. syntax, semantics, context, intonation, co-occurrence of words, frequency of usage, …)
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