PSY 369: Psycholinguistics Representing language Part 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 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 has fins can swim has gills has feathers can fly has wings Bird Fish Representation permits cognitive economy Reduce redundancy of semantic features Semantic Features 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms Testing the model SentenceVerification 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins eat worms Testing the model SentenceVerification 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers Testing the model SentenceVerification 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have feathers Testing the model SentenceVerification 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have skin Testing the model SentenceVerification 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 skin can move around breathes Bird has feathers can fly has wings Robin eats worms has a red breast Robins have skin Testing the model SentenceVerification 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 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 Verification 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)Rosch chaircouc h table desk 1) chair 1) sofa 2) couch 3) table : 12) desk 13) bed : 42) TV 54) 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)Rosch Prototypes: 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 car bus vehicle red Fire engine truck roses blue orange flowers fire house apple pear 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 car bus vehicle red Fire engine truck roses blue orange flowers fire house apple pear 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

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

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

Recognizing a word cat dog cap wolf tree yarn cat claw fur hat Search for a match Select word cat Retrieve lexical information Cat noun Animal, 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 Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Lexical Decision Task: Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin

Word frequency Gambastya Revery Voitle Chard Wefe Cratily Decoy Puldow Raflot Mulvow Governor Bless Tuglety Gare Relief Ruftily History Pindle Lexical Decision Task: Lexical Decision is dependent on word frequency Oriole Vuluble Chalt Awry Signet Trave Crock Cryptic Ewe Develop Gardot Busy Effort Garvola Match Sard Pleasant Coin Low frequencyHigh(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 Task PrimeTargetTime Nurse Butter940 msecs BreadButter855 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 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 task Context related:ant Context inappropriate:spy Context unrelatedsew 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 Logogen model (Morton 1969) Auditory stimuli Visual stimuli Auditory analysis Visual analysis Logogen system Output buffer 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’ [n ə :s] ‘doctor’ [dokt ə ] nurse doctor Spreading activation network doctornurse

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

Cohort 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 msec

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