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

3. Announcements Exam 1, moved to Tuesday, Feb19 The next quiz date was also moved back Otherwise, things should be on schedule

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

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

6. Lexical organization Another possibility is that there are multiple levels of representation, with different organizations at each level Sound based representationsMeaning based representationsGrammatical based representations

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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.

18. 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

19. Semantic Networks Alternative account: store feature information with most “prototypical” instance 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

20. 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

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

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

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

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

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

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

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

28. 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

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

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

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

32. 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

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

34. 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

35. 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

36. Word frequency The kite fell on the dog Eyemovement studies:

37. Word frequency The kite fell on the dog Eyemovement studies:

38. Word frequency The kite fell on the dog Eyemovement studies:

39. 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

40. Semantic priming Meyer & Schvaneveldt (1971) Lexical Decision Task PrimeTargetTime Nurse Butter940 msecs BreadButter855 msecs Evidence that associative relations influence lexical access

41. 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.

42. Role of prior context ant

43. 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

44. Lexical ambiguity Hogaboam and Pefetti (1975) Words can have multiple interpretations The role of frequency of meaning Task, is the last word ambiguous? The jealous husband read the letter (dominant meaning) The antique typewriter was missing a letter (subordinate meaning) Participants are faster on the second sentence.

45. 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.

46. 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)

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

48. 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

49. Think of a logogen as being like a ‘strength-o-meter’ at a fairground When the bell rings, the logogen has ‘fired’

50. ‘cat’ [kæt] What makes the logogen fire? – seeing/hearing the word What happens once the logogen has fired? – access to lexical entry!

51. – 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

52. 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

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

54. 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 200-250 msec

55. 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

56. 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