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 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 skin can move around breathes Lexical entry Semantic Features 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 skin can move around breathes has fins can swim has gills has feathers can fly has wings Bird Fish Representation permits cognitive economy Reduce redundancy of semantic features

Collins and Quillian (1969) Animal has skin can move around breathes Fish has fins can swim has gills Bird has feathers can fly has wings Canary can sing is yellow Ostrich has long legs is 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 SentenceVerification time Robins eat worms 1310 msecs Robins have feathers 1380 msecs Robins 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms

Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms

Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers

Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers

Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have skin

Collins and Quillian (1969) Animal has skin can move around breathes Bird has feathers can 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 skin can move around breathes Fish has fins can swim has gills Bird has feathers can fly has wings Robin eats worms has a red breast Ostrich has long legs is 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 car bus vehicle red Fire engine truck roses blue orange flowers fire house apple pear tulips fruit

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 truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

Semantic Networks Fire engine truckbusvehiclecar red housefire apple pear fruit roses flowers tulips blue orangestreet

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) SHEEPGOAT SheepGoat /gout/ / ip/ ipgout N woolmilkanimal Concepts with semantic features Lemmas grammtical features Lexemes morphemes and sounds Phonemes growth gives Is an category

Summary slide here