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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
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