PSY 369: Psycholinguistics

Representing languagePart II: Semantic Networks & Lexical Access

Announcements I added another Homework option

I am aiming to have a number of options, from which you have to do a subset

You’ll have to do a total of 4 of them over the semester, can do more than four and I’ll take the 4 highest grades

2.1, 2.2, 2.3 – all article related to today’s topic. Assignment is to pick one, read it and summarize it. For this option, can only do 1 of these three.

Semantic Networks Semantic Networks

Words can be represented as an interconnected network of sense relations

Each word is a particular node Connections among nodes represent semantic

relationships

Collins and Quillian (1969)

Animal has skincan move around

breathes

has finscan swim

has gills

has featherscan fly

has wingsBird Fish

Representation permits cognitive economy Reduce redundancy of semantic features

SemanticFeatures

Lexical entry

Collins and Quillian Hierarchical Network model Lexical entries stored in a hierarchy

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

Use time on verification tasks to map out the structure of the lexicon.

An apple has teeth

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins eat worms Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins eat worms Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins have feathers Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins have feathers Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins have skin Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move around

breathes

Bird

has featherscan fly

has wings

Robin eats worms

has a red breast

Robins have skin Testing the model

Sentence Verification time

Robins eat worms 1310 msecsRobins have feathers 1380

msecsRobins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969) Problems with the model

Effect may be due to frequency of association

“A robin breathes” is less frequent than “A robin eats worms”

Assumption that all lexical entries at the same level are equal

The Typicality Effect A whale is a fish vs. A horse is a fish Which is a more typical bird? Ostrich or Robin.

Collins and Quillian (1969)

Animal has skincan move around

breathes

Fishhas finscan swim

has gillsBird

has featherscan fly

has wings

Robin eats worms

has a red breast

Ostrichhas long legsis fast

can’t flyVerification times: “a robin is a bird” faster than “an ostrich is a bird”

Robin and Ostrich occupy the same relationship with bird.

Semantic Networks Alternative account: store feature information with

most “prototypical” instance (Eleanor Rosch, 1975)

chaircouc

h

tabledesk

1) chair1) sofa2) couch3) table::12) desk13) bed::42) TV54)

refrigerator

bed

TV

refrigerator

Rate on a scale of 1 to 7 if these are good examples of category: Furniture

Semantic Networks Alternative account: store feature

information with most “prototypical” instance (Eleanor Rosch, 1975)

Prototypes: Some members of a category are better instances of

the category than others Fruit: apple vs. pomegranate

What makes a prototype? More central semantic features

What type of dog is a prototypical dog? What are the features of it?

We are faster at retrieving prototypes of a category than other members of the category

Spreading Activation Models

street

carbus

vehicle

red

Fire engine

truck

roses

blue

orange

flowers

fire

house

applepear

tulips

fruit

Words represented in lexicon as a network of relationships

Organization is a web of interconnected nodes in which connections can represent:

categorical relations degree of association typicality

Collins & Loftus (1975)

Spreading Activation Models

street

carbus

vehicle

red

Fire engine

truck

roses

blue

orange

flowers

fire

house

applepear

tulips

fruit

Retrieval of information Spreading activation Limited amount of

activation to spread Verification times

depend on closeness of two concepts in a network

Collins & Loftus (1975)

Spreading Activation Models Advantages of Collins and Loftus

model Recognizes diversity of information in a

semantic network Captures complexity of our semantic

representation (at least some of it)

Consistent with results from priming studies

Spreading Activation Models More recent spreading activation models

Probably the dominant class of models currently used Typically have multiple levels of representations

Lexical access Up until this point we’ve focused on

structure of lexicon But the evidence is all inferred from usage

Speech errors, priming studies, verification, lexical decision

While structure is important, so are the processes that may be involved in activating and retrieval the information

We’ve seen this already a little with intersection searches and spreading activation

Retrieval

Activate

Lexical access How do we retrieve the linguistic

information from Long-term memory? What factors are involved in accessing

(activating and/or retrieving?) information from the lexicon?

Models of lexical access

Retrieval

Activate

Recognizing a word

Recognizing a word

catdogcapwolftreeyarncat

clawfurhat

Search for a match

cat

Input

Recognizing a word

cat

dogcapwolftreeyarncat

clawfurhat

Search for a match

cat

Input

Recognizing a word

cat

dogcapwolftreeyarncat

clawfurhat

Search for a match Select word

cat

Retrieve lexical

information

CatnounAnimal, pet,Meows, furry,Purrs, etc.

cat

Input

Lexical access Factors affecting lexical access

Frequency Semantic priming Role of prior context Phonological structure Morphological structure Lexical ambiguity

Word frequency

GambastyaReveryVoitleChardWefeCratilyDecoyPuldowRaflot

MulvowGovernorBlessTugletyGareReliefRuftilyHistoryPindle

Lexical Decision Task:

OrioleVulubleChaltAwrySignetTraveCrockCrypticEwe

DevelopGardotBusyEffortGarvolaMatchSardPleasantCoin

Word frequency

GambastyaReveryVoitleChardWefeCratilyDecoyPuldowRaflot

MulvowGovernorBlessTugletyGareReliefRuftilyHistoryPindle

Lexical Decision Task:

Lexical Decision is dependent on word frequency

OrioleVulubleChaltAwrySignetTraveCrockCrypticEwe

DevelopGardotBusyEffortGarvolaMatchSardPleasantCoin

Low frequency High(er) frequency

Word frequency

The kite fell on the dog

Eyemovement studies:

Word frequency

The kite fell on the dog

Eyemovement studies:

Word frequency

The kite fell on the dog

Eyemovement studies:

Word frequency

The kite fell on the dog

Eyemovement studies: Subjects spend about 80

msecs longer fixating on low-frequency words than high-frequency words

Semantic priming Meyer & Schvaneveldt (1971)

Lexical Decision TaskPrime Target TimeNurse Butter 940 msecsBread Butter 855 msecs

Evidence that associative relations influence lexical access

Role of prior context

Listen to short paragraph. At some point during theParagraph a string of letters will appear on the screen. Decide if it is an English word or not. Say ‘yes’ or ‘no’ as quickly as you can.

Role of prior context

ant

Role of prior context Swinney (1979)

Hear: “Rumor had it that, for years, the government building has been plagued with problems. The man was not surprised when he found several spiders, roaches and other bugs in the corner of his room.”

Lexical Decision taskContext related: antContext inappropriate: spyContext unrelated sew

Results and conclusions Within 400 msecs of hearing "bugs", both ant and

spy are primed After 700 msecs, only ant is primed

Morphological structure Snodgrass and Jarvell (1972)

Do we strip off the prefixes and suffixes of a word for lexical access?

Lexical Decision Task: Response times greater for affixed words than

words without affixes Evidence suggests that there is a stage where

prefixes are stripped.

Models of lexical access Serial comparison models

Search model (Forster, 1976, 1979, 1987, 1989) Parallel comparison models

Logogen model (Morton, 1969) Cohort model (Marslen-Wilson, 1987, 1990)

Logogen model (Morton 1969)Auditory stimuli

Visual stimuli

Auditory analysis

Visual analysis

Logogen system

Outputbuffer

Context system

Responses

Available Responses

Semantic Attributes

Logogen model

The lexical entry for each word comes with a logogen

The lexical entry only becomes available once the logogen ‘fires’

When does a logogen fire? When you read/hear the word

Think of a logogen as being like a ‘strength-o-meter’ at a fairground

When the bell rings, the logogen has ‘fired’

‘cat’[kæt]

• What makes the logogen fire?

– seeing/hearing the word

• What happens once the logogen has fired?

– access to lexical entry!

– High frequency words have a lower threshold for firing

–e.g., cat vs. cot

‘cat’[kæt]

• So how does this help us to explain the frequency effect?

‘cot’[kot]

Low freq takes longer

• Spreading activation from doctor lowers the threshold for nurse to fire

– So nurse take less time to fire

‘nurse’[nə:s]

‘doctor’[doktə]

nurse

doctor

Spreading activation network

doctor nurse

Search modelE

ntri

es in

ord

er o

f

Dec

reas

ing

freq

uenc

yVisual input

cat

Auditory input

/kat/

Access codes

Pointers

mat cat mouseMental lexicon

Cohort model Three stages of word recognition

1) Activate a set of possible candidates

2) Narrow the search to one candidate Recognition point (uniqueness point) - point at which a

word is unambiguously different from other words and can be recognized

3) Integrate single candidate into semantic and syntactic context

Specifically for auditory word recognition Speakers can recognize a word very rapidly

Usually within 200-250 msec

Cohort model Prior context: “I took the car for a …”

/s/ /sp/ /spi/ /spin/

…soapspinachpsychologistspinspitsunspank…

spinachspinspitspank…

spinachspinspit…

spin

time

Comparing the models Each model can account for major findings (e.g.,

frequency, semantic priming, context), but they do so in different ways. Search model is serial and bottom-up Logogen is parallel and interactive (information

flows up and down) Cohort is bottom-up but parallel initially, but then

interactive at a later stage