PSY 369: Psycholinguistics

Language Comprehension:Semantic networks

Overview of comprehension

The cat chasedthe rat.

Input

catdogcapwolftreeyarncat

clawfurhat

Wordrecognition

Language perception

ca

t

/k//ae/

/t/

Syntacticanalysis

cat

S

VP

ratthe

NP

chased

Vthe

NP

Semantic &pragmaticanalysis

Different approaches Immediacy Principle: access the meaning/syntax of the word and

fit it into a syntactic structure Serial Analysis (Modular): Build just one based on syntactic information and

continue to try to add to it as long as this is still possible

Interactive Analysis: Use multiple levels (both syntax and semantics) of information to build the “best” structure

Minimal attachment Garden path sentences (Rayner & Frazier, 1983)

S

NP

the spyVP

V

sawNP

the cop

PP

P

with

NP

the revolver

S’

but the cop didn’t see him

S

NP

the spyVP

V

saw

NP

NP

the cop

PP

P

with

NP

the revolver

S’

but the cop didn’t see him

MA Non-MA

The spy saw the cop with the binoculars.. The spy saw the cop with the revolver

Conclusion: participants didn’t use semantic information initially, built the wrong structure and had to reanalyze. Supports a serial model.

<- takes longer to read

Interactive Models

The evidence (that was) examined by the lawyer … The defendant (that was) examined by the lawyer…

Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence (e.g. overriding purely syntactic principles like Minimal Attachment)

Trueswell et al (1994). Local semantic feature like Animacy

Taraban & McCelland (1988). Expectation The couple admired the house with a friend but

knew that it was over-priced. The couple admired the house with a garden but

knew that it was over-priced.

What about spoken sentences?

All of the previous research focused on reading, what about parsing of speech?

Methodological limits – ear analog of eye-movements not well developed

Auditory moving window Reading while listening Looking at a scene while listening

Summing up Is ambiguity resolution a problem in real life?

Yes (Try to think of a sentence that isn’t partially

ambiguous) Many factors might influence the process of

making sense of a string of words. (e.g. syntax, semantics, context, intonation, co-occurrence of words, frequency of usage, …)

Semantics Two levels of analysis (and two traditions of

psycholinguistic research) Word level (lexical semantics, chapter 11)

What is meaning? How do words relate to meaning? How do we store and organize words?

Sentence level (compositional semantics) (chapter 12)

How do we construct higher order meaning? How do word meanings and syntax interact?

Separation of word and meaning Words are not the same as meaning

Words are symbols linked to mental representations of meaning (concepts)

Even if we changed the name of a rose, we would not change the concept of what a rose is

Concepts and words are different things Translation argument – we can translate words between

languages (even if not every word meaning is represented by a single word)

Imperfect mapping - Multiple meanings of words e.g., ball, bank, bear

Elasticity of meaning - Meanings of words can change with context

e.g., newspaper

Semantics Meaning is more than just associations

Write down the first word you think of in response to that word.

CAT

“Dog”, “mouse”, “hat”, “fur”, “meow”, “purr”, “pet”, “curious”, “lion”

You cannot just substitute these words into a sentence frame and have the same meaning.

Frisky is my daughter’s ______. Sometimes you get a related meaning, other times

something very different.

Semantics Referential theory of meaning (Frege, 1892)

Sense (intension) and reference (extension) “The world’s most famous athlete.” “The athlete making the most endorsement income.” 2 distinct senses, 1 reference

Now In the 90’s Over time the senses typically stay the same, while the references may change

2013 Bleacher report

Word and their meanings Semantic Feature Lists

Decomposing words into smaller semantic attributes/primitives

Perhaps there is a set of necessary and sufficient features

Features “father” “mother” “daughter” “son”

Human + + + +

Older + + - -

Female - + + -

Word and their meanings Semantic Feature Lists

“John is a bachelor.” What does bachelor mean?

What if John: is married? is divorced? has lived with the mother of his children for 10 years but they aren’t

married? has lived with his partner Joe for 10 years?

Suggests that there probably is no set of necessary and sufficient features that make up word meaning

(other classic examples “game” “chair”)

Meaning as Prototypes Prototype theory: store feature information with

abstract prototype (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

Meaning as Prototypes Prototype theory: store feature information with

abstract prototype (Eleanor Rosch, 1975) Prototypes:

Some members of a category are better instances of the category than others (prototypicality effect)

Fruit: apple vs. pomegranate What makes a prototype?

Possibly an abstraction of exemplars More central semantic features

What type of dog is a prototypical dog? What are the features of it?

We are faster at retrieving prototypical of a category than other less prototypical members of the category

Meaning as Prototypes The main criticism of the model

The model fails to provide a rich enough representation of conceptual knowledge

How can we think logically if our concepts are so vague? Why do we have concepts which incorporate objects which are clearly

dissimilar, and exclude others which are apparently similar (e.g. mammals)?

How do our concepts manage to be flexible and adaptive, if they are fixed to the similarity structure of the world?

If each of us represents the prototype differently, how can we identify when we have the same concept, as opposed to two different concepts with the same label?

Meaning as Exemplars Instance theory: each concept is represented as

examples of previous experience (e.g., Medin & Schaffer, 1978)

Make comparisons to stored instances Typically have a probabilistic component

Which instance gets retrieved for comparison

dog

Meaning as Theories A development of the prototype idea to include more

structure in the prototype (e.g., Carey, 1985; Keil, 1986)

Concepts provide us with the means to understand our world

A lot of this work came out of concepts of natural kinds They are not just the labels for clusters of similar things They contain causal/explanatory structure, explaining why

things are the way they are Similar to “scientific theories”

They help us to predict and explain the world

Meaning as 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

IS A IS A

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

Testing the model

Sentence Verification time

Robins eat worms 1310 msecs

Robins have feathers 1380 msecs

Robins 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 msecs

Robins have feathers 1380 msecs

Robins 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 msecs

Robins have feathers 1380 msecs

Robins 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 msecs

Robins have feathers 1380 msecs

Robins 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 msecs

Robins have feathers 1380 msecs

Robins 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 msecs

Robins have feathers 1380 msecs

Robins have skin 1470 msecs

Participants do an intersection search

Collins and Quillian (1969) Problems with the model

Difficulty representing some relationships How are “truth”, “justice”, and “law” related?

Effect may be due to frequency of association(organization and conjoint frequency confounded) “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.

Collins and Quillian (1969) Problems with the model

Smith, Shoben & Rips (1974) showed that there are hierarchies where more distant categories can be faster to categorize than closer ones

A chicken is a bird was slower to verify

than A chicken is an animal

Animal

Bird

has featherscan fly

has wings

Chicken lays eggs

clucks

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

Meaning as networks There may be multiple levels of representation, with different

organizations at each level

Sound based representations

Meaning based representations

Grammatical based representations

Today’s focus

Meaning beyond the word Not all meaning resides at the level of the

individual words. Conceptual combinations Sentences

Move to compositional semantics

Conceptual combination How do we combine words and concepts

We can use known concepts to create new ones Noun-Noun combinations

Modifier noun Head noun

“Skunk squirrel”

“Radiator box”

“Helicopter flower”

Conceptual combination How do we combine words and concepts

Relational combination Relation given between head and modifier “squirrel box” a box that contains a squirrel

Property mapping combination Property of modifier attributed to head “skunk squirrel” a squirrel with a white stripe on its back

Hybrid combinations A cross between the head and modifier “helicopter flower” a bird that has parts of helicopters and

parts of flowers

Conceptual combination How do we combine words and concepts

Instance theory has problems Modification? (brown apple) Separate Prototypes? (big wooden spoon)

But sometimes the combination has a prototypical feature that is not typical of either noun individually (pet birds live in cages, but neither pets nor birds do)

Extending salient characteristics? When nouns are “alignable” (zebra horse) But non-alignable nouns are combined using a different

mechanism (zebra house)