PSY 369: Psycholinguistics

PSY 369: Psycholinguistics

Representing language

Some of the big questions

“the horse raced past the barn”

ProductionHow do we turn our thoughts into a spoken or written output?

Some of the big questions

Comprehension

“the horse raced past the barn”

How do we understand language that we hear/see?

Some of the big questions Comprehension Production Representation

How do we store linguistic information? How do we retrieve that information?

Lexicon

SemanticAnalysis

SyntacticAnalysis

WordRecognition

Letter/phonemeRecognition

FormulatorGrammatical EncodingPhonological Encoding

Articulator

ConceptualizerThought

Lexicon

SemanticAnalysis

SyntacticAnalysis

WordRecognition

Letter/phonemeRecognition

FormulatorGrammatical EncodingPhonological Encoding

Articulator

Conceptualizer

Thought

weeks 6-8weeks 9&10

This week

Storing linguistic information Tale of the tape:

High capacity: 40,000 – 60,000 words Fast: Recognition in as little as 200ms (often before word

ends) How do we search that many, that fast!? – suggests that there is a high

amount of organization

Excellent reading: Words in the Mind, Aitchison (1987, 2003)

Or something much more complex

“The world’s largest data bank of examples in context is dwarfed by the collection we all carry around subconsciously in our heads.”

E. Lenneberg (1967)

Storing linguistic information Interesting questions:

How are words stored? What are they made up of? How are words related to each other? How do we use them?

Some vocabulary Mental lexicon The representation of words in long term memory Lexical Access: How do we activate (retrieve) words and their the

meanings (and other properties)?

Theoretical Metaphors: Access vs. retrieval

Retrieval - getting information from the representation

Activate - finding the representation

Often used interchangeably, but sometimes a distinction is made

Here it is

Theoretical Metaphors: Access vs. retrieval

Retrieval - getting information from the representation

Activate - finding the representation

Often used interchangeably, but sometimes a distinction is made

Open it up and see what’s inside

Lexical primitives Word primitives

Morpheme primitives Economical - fewer representations Slow retrieval - some assembly required

Decomposition during comprehension Composition during production

Need a lot of representations Fast retrieval

horse horses barn barns

horse -s barn

Lexical primitives Lexical Decision task (e.g., Taft, 1981)

See a string of letters As fast as you can determine if it is a real

English word or not “yes” if it is “no” if it isn’t

Typically speed and accuracy are the dependent measures

table

vanue

daughter

tasp

cofef

hunter

Lexical primitives Lexical Decision task

tablevanuedaughtertaspcofefhunter

YesNoYesNoNoYes

Lexical primitives Lexical Decision task

daughter

hunter

Lexical primitives Lexical Decision task

daughter

hunter

Pseudo-suffixed

Multimorphemic

daught

hunt -er

-er

Takes longer

This evidence supports the morphemes as primitives view

Lexical primitives May depend on other factors

What kind of morpheme Inflectional (e.g., singular/plural, past/present tense) Derivational (e.g., drink --> drinkable, infect --> disinfect)

Frequency of usage High frequency multimorphemic (in particular if derivational

morphology) may get represented as a single unit e.g., impossible vs. imperceptible

Compound words Semantically transparent

Buttonhole Semantically opaque

butterfly

Lexical organization How are the lexical representations organized?

Alphabetically? Initial phoneme? Semantic categories? Grammatical class? Something more flexible, depending on your

needs?

Lexical organization Factors that affect organization

Phonology Frequency Imageability, concreteness, abstractness Grammatical class Semantics

Lexical organization Phonology

Words that sound alike may be stored “close together”

What word means to formally renounce the throne?

abdicate

Brown and McNeill (1966) Tip of the tongue phenomenon (TOT)

Look at what words they think of but aren’t righte.g, “abstract,” “abide,” “truncate”

Lexical organization

Letters at Word beginning

Word end

10

2 3 3 2 1

20

30

40

50

1

% o

f mat

ches

Similar-meaning words

Similar-sounding words

More likely to approximate target words with similar sounding words than similar meanings

The “Bathtub Effect” - Sounds at the beginnings and ends of words are remembered best (Aitchison, 2003)

Phonology Words that sound alike may be stored “close together”

Brown and McNeill (1966) Tip of the tongue phenomenon (TOT)

Lexical organization Frequency

Typically the more common a word, the faster (and more accurately) it is named and recognized

Typical interpretation: easier to retrieve (or activate)

However, Balota and Chumbley (1984) Frequency effects depend on task

Lexcial decision - big effect Naming - small effect Category verifcation - no effect

A canary is a bird. T/F

Lexical organization Imageability, concreteness, abstractness

UmbrellaLanternFreedomAppleKnowledgeEvil

Try to imagine each word

Lexical organization Imageability, concreteness, abstractness

UmbrellaLanternFreedomAppleKnowledgeEvil

Try to imagine each word

How do you imagine these?

Lexical organization Imageability, concreteness, abstractness

UmbrellaLanternFreedomAppleKnowledgeEvil

More easily remembered

More easily accessed

Lexical organization Grammatical class

Grammatical class constraint on substitution errors

“she was my strongest propeller” (proponent)“the nation’s dictator has been exposed” (deposed)

Word association tasks Associate is typically of same grammatical class

Lexical organization Grammatical class

Open class words Content words (nouns, verbs, adjectives, adverbs)

Closed class words Function words (determiners, prepositions, …)

Lexical organization Semantics

Free associations (see the “cat” demo in earlier lecture) Most associates are semantically related (rather than

phonologically for example) Semantic Priming task

For the following letter strings, decide whether it is or is not an English word

tasp

nurse

doctor

fract

slithest

shoes

doctor

Lexical organization Semantic Priming task

nurse

shoes

Responded to fasterRelated

Unrelated

“Priming effect”

doctor

doctor

Lexical organization Semantics

Words that are related in meaning are linked together

Semantic networks

Lexical organization Another possibility is that there are multiple levels of

representation, with different organizations at each level

Sound based representations

Meaning based representations

Grammatical based representations

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 aroundbreathes

Lexical entry

SemanticFeatures

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

Semantic features attached to the lexical entries

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

has finscan swimhas gills

has featherscan flyhas wings

Bird Fish

Representation permits cognitive economy Reduce redundancy of semantic features

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Fishhas finscan swimhas gills

Birdhas featherscan flyhas wings

Canary can sing

is yellow

Ostrichhas long legsis fastcan’t fly

Local level features may contradict higher level features

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

An apple is a fruit

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

An robin has wings

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

A robin is a bird

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

A robin is an animal

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

A dog has teeth

Collins and Quillian (1969) Testing the model

Semantic verification task An A is a B True/False

A fish has gills

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) Testing the model

Sentence Verification timeRobins eat worms 1310 msecsRobins have feathers 1380 msecsRobins have skin 1470 msecs

A category size effect: The higher the location of B, the longer the

reaction time (“A is a B” or “A has a B”) Participants do an intersection search

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins eat worms

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins eat worms

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins have feathers

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins have feathers

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins have skin

Collins and Quillian (1969)

Animal has skincan move aroundbreathes

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Robins have skin

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 aroundbreathes

Fishhas finscan swimhas gills

Birdhas featherscan flyhas wings

Robin eats worms

has a red breast

Ostrichhas long legsis fastcan’t fly

Semantic Networks 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 Collins & Loftus (1975)

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

Semantic Networksstreet

carbus

vehicle

red

Fire engine

truck

roses

blue

orange

flowers

fire

house

applepear

tulipsfruit

Semantic Networks Retrieval of information

Spreading activation Limited amount of activation to spread Verification times depend on closeness of

two concepts in a network

Semantic Networks

Fire engine

truck bus

vehiclecar

red

house

fire

applepear

fruitroses

flowers

tulips

blue

orange

street

Semantic Networks

Fire engine

truck bus

vehiclecar

red

house

fire

applepear

fruitroses

flowers

tulips

blue

orange

street

Semantic Networks

Fire engine

truck bus

vehiclecar

red

house

fire

applepear

fruitroses

flowers

tulips

blue

orange

street

Semantic Networks

Fire engine

truck bus

vehiclecar

red

house

fire

applepear

fruitroses

flowers

tulips

blue

orange

street

Semantic Networks

Fire engine

truck bus

vehiclecar

red

house

fire

applepear

fruitroses

flowers

tulips

blue

orange

street

Semantic Networks Advantages of Collins and Loftus

model Recognizes diversity of information in a

semantic network Captures complexity of our semantic

representation Consistent with results from priming

studies

Bock and Levelt (1994)

SHEEP GOAT

Sheep Goat

/gout// ip/

€

∫

€

∫ i p g ou t

N

wool milk animalConcepts

• with semantic features

Lemmas

• grammtical features

Lexemes

• morphemes and sounds

Phonemesgrow

th

gives gives Is

anIs a

n

categorycategory

Lexical access How do we retrieve the linguistic

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

information from the lexicon? Models of lexical retrieval

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 contextListen 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

Lexical ambiguity Hogaboam and Pefetti (1975)

Words can have multiple interpretations The role of frequency of meaning

Task, is the last word ambiguous? The jealous husband read the letter (dominant

meaning) The antique typewriter was missing a letter

(subordinate meaning) Participants are faster on the second sentence.

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 modelEn

tries

in o

rder

of

Dec

reas

ing

freq

uenc

yVisual input

cat

Auditory input

/kat/

Access codes

Pointers

mat cat mouseMental lexicon

Cohort model Specifically for auditory word recognition

Speakers can recognize a word very rapidly Usually within 200-250 msec

Recognition point (uniqueness point) - point at which a word is unambiguously different from other words and can be recognized

Three stages of word recognition1) activate a set of possible candidates2) narrow the search to one candidate3) integrate single candidate into semantic and syntactic

context

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

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

…soapspinachpsychologistspinspitsunspank…

spinachspinspitspank…

spinachspinspit…

spin

time

Reminders Don’t forget to complete homework 4 for

Feb 9 (Tues) And Quiz 3 (Chapters 5, 6, & 7) by

11AM Thurs (Feb 4) Exam 1 Feb 11 (Thurs)