psy 369: psycholinguistics
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PSY 369: Psycholinguistics. Representing language. How do we turn our thoughts into a spoken or written output?. Some of the big questions. Production. “the horse raced past the barn”. How do we understand language that we hear/see?. Some of the big questions. Comprehension. - PowerPoint PPT PresentationTRANSCRIPT
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)