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PSY 369: Psycholinguistics
Mental representations II
Oops! Homework #1: question 2d Should be “bank” not “word”
Name this picture
“dog”
“animal”
“Labrador”
“bird”
“wolf”
“plant”
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
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 around
breathes
has finscan swim
has gills
has featherscan fly
has wingsBird Fish
Representation permits cognitive economy Reduce redundancy of semantic features
Collins and Quillian (1969)
Animal has skincan move around
breathes
Fishhas finscan swim
has gillsBird
has featherscan fly
has wings
Canary can sing
is yellow
Ostrichhas long legsis fast
can’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 around
breathes
Bird
has featherscan fly
has wings
Robin eats worms
has a red breast
Robins eat worms
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
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
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
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
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
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 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 Networks
street
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
give
s gives Is a
n
Is an
categorycategory
Summary slide here
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