PSY 369 Psycholinguistics Representing language Part II Semantic

PSY 369: Psycholinguistics Representing language Part II: Semantic Networks & Lexical Access

Announcements n n n Exam 1, moved to Tuesday, Feb 19 The next quiz date was also moved back Otherwise, things should be on schedule

Storing linguistic information n How are words stored? What are they made up of? How are word related to each other? How do we use them? n n Mental lexicon The representation of words in long term memory Lexical Access: How do we activate (retrieve) the meanings (and other properties) of words?

Lexical organization n Factors that affect organization n n Phonology Frequency Imageability, concreteness, abstractness Grammatical class Semantics

Lexical organization n Another possibility is that there are multiple levels of representation, with different organizations at each level Meaning based representations Grammatical based representations Sound based representations

Semantic Networks n Words can be represented as an interconnected network of sense relations n Each word is a particular node n Connections among nodes represent semantic relationships

Collins and Quillian (1969) Animal Semantic Features has skin can move around breathes Lexical entry n Collins and Quillian Hierarchical Network model n Lexical entries stored in a hierarchy Semantic features attached to the lexical entries

Collins and Quillian (1969) Animal Bird n has feathers can fly has wings has skin can move around breathes Fish has fins can swim has gills Representation permits cognitive economy n Reduce redundancy of semantic features

Collins and Quillian (1969) n Testing the model n Semantic verification task n An A is a B True/False An apple has teeth Use time on verification tasks to map out the structure of the lexicon.

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins eat worms n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins eat worms n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins have feathers n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins have feathers n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins have skin n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) has skin can move around breathes Animal Bird Robin has feathers can fly has wings Robins have skin n Testing the model Sentence Robins eat worms Robins have feathers Robins have skin Verification time 1310 msecs 1380 msecs 1470 msecs eats worms n has a red breast Participants do an intersection search

Collins and Quillian (1969) n Problems with the model n Effect may be due to frequency of association n n “A robin breathes” is less frequent than “A robin eats worms” Assumption that all lexical entries at the same level are equal n The Typicality Effect n n 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 Bird Robin has feathers can fly has wings eats worms Ostrich has a red breast has skin can move around breathes Fish has fins can swim has gills has long legs is fast can’t fly

Semantic Networks n Alternative account: store feature information with most “prototypical” instance n Prototypes: n Some members of a category are better instances of the category than others n n Fruit: apple vs. pomegranate What makes a prototype? n n More central semantic features n What type of dog is a prototypical dog n What are the features of it? We are faster at retrieving prototypes of a category than other members of the category

Spreading Activation Models n Collins & Loftus (1975) n n Words represented in lexicon as a network of relationships Organization is a web of interconnected nodes in which connections can represent: n n n categorical relations degree of association typicality

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks n Retrieval of information n Spreading activation Limited amount of activation to spread Verification times depend on closeness of two concepts in a network

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks street vehicle car bus truck house orange blue Fire engine fire red apple tulips flowers roses fruit pear

Semantic Networks n Advantages of Collins and Loftus model n n n Recognizes diversity of information in a semantic network Captures complexity of our semantic representation Consistent with results from priming studies

Lexical access n How do we retrieve the linguistic information from Long-term memory? n n What factors are involved in retrieving information from the lexicon? Models of lexical retrieval

Recognizing a word Input Search for a match cat dog cap wolf tree yarn cat claw fur hat

Recognizing a word Input Search for a match cat dog cap wolf tree yarn cat claw fur hat

Recognizing a word Input Search for a match cat dog cap wolf tree yarn cat claw fur hat Select word Retrieve lexical information Cat cat noun Animal, pet, Meows, furry, Purrs, etc.

Lexical access n Factors affecting lexical access n n n Frequency Semantic priming Role of prior context Phonological structure Morphological structure Lexical ambiguity

Word frequency n Lexical Decision Task: Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin

Word frequency n Lexical Decision Task: Low frequency Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot n Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe High(er) frequency Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin Lexical Decision is dependent on word frequency

Word frequency n Eyemovement studies: The kite fell on the dog

Word frequency n Eyemovement studies: The kite fell on the dog

Word frequency n Eyemovement studies: The kite fell on the dog

Word frequency n Eyemovement studies: n Subjects spend about 80 msecs longer fixating on lowfrequency words than highfrequency words The kite fell on the dog

Semantic priming n Meyer & Schvaneveldt (1971) n n Lexical Decision Task Prime Target Time Nurse Butter 940 msecs Bread Butter 855 msecs Evidence that associative relations influence lexical access

Role of prior context Listen to short paragraph. At some point during the Paragraph 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 n Swinney (1979) n n 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 task Context related: Context inappropriate: Context unrelated sew n ant spy Results and conclusions n n Within 400 msecs of hearing "bugs", both ant and spy are primed After 700 msecs, only ant is primed

Lexical ambiguity n Hogaboam and Pefetti (1975) n n Words can have multiple interpretations The role of frequency of meaning n Task, is the last word ambiguous? n n n 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 n Snodgrass and Jarvell (1972) n n Do we strip off the prefixes and suffixes of a word for lexical access? Lexical Decision Task: n n 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 n Serial comparison models n n Search model (Forster, 1976, 1979, 1987, 1989) Parallel comparison models n n Logogen model (Morton, 1969) Cohort model (Marslen-Wilson, 1987, 1990)

Logogen model (Morton 1969) Context system Auditory stimuli Visual stimuli Auditory analysis Visual analysis Semantic Attributes Logogen system Available Responses Output buffer Responses

Logogen model n n n 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? n 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!

‘cat’ [kæt] • So how does this help us to explain the frequency effect? – High frequency words have a lower threshold for firing –e. g. , cat vs. cot ‘cot’ [kot] Low freq takes longer

‘doctor’ [doktə] • Spreading activation from doctor lowers the threshold for nurse to fire – So nurse take less time to fire doctor ‘nurse’ [nə: s] Spreading activation network doctor nurse

Search model Visual input Pointers Decreasing frequency Entries in order of Access codes Auditory input /kat/ cat Mental lexicon mat cat mouse

Cohort model n Three stages of word recognition 1) Activate a set of possible candidates 2) Narrow the search to one candidate n 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 n n Specifically for auditory word recognition Speakers can recognize a word very rapidly n Usually within 200 -250 msec

Cohort model n Prior context: “I took the car for a …” /s/ /sp/ … soap spinach psychologist spin spit sun spank … spinach spin spit spank … time /spi/ spinach spin spit … /spin/ spin

Comparing the models n Each model can account for major findings (e. g. , frequency, semantic priming, context), but they do so in different ways. n Search model is serial and bottom-up n Logogen is parallel and interactive (information flows up and down) n Cohort is bottom-up but parallel initially, but then interactive at a later stage