PSY 369: Psycholinguistics

1. PSY 369: Psycholinguistics Language Comprehension: Sentence comprehension

2. Center embedded structures • The house burned down.

3. Center embedded structures • The house burned down. • The house the handyman painted burned down.

4. This one may be legal, but that doesn’t mean that it is (easily) comprehensible • Center embedded structures • The house burned down. • The house the handyman painted burned down. • The house the handyman the teacher hired painted burned down. • (the handyman that the teacher hired painted the house that burned down)

5. dog The man hit the with the leash. S NP det N The man

6. dog The man hit the with the leash. S NP VP V det N The man hit

7. dog The man hit the with the leash. S NP VP V NP NP det N det N The man hit the dog

8. PP with the leash dog The man hit the with the leash. S NP VP V NP NP Modifier det N det N The man hit the dog

9. PP with the leash dog The man hit the with the leash. S NP VP V NP Instrument NP det N det N The man hit the dog

10. dog The man hit the with the leash. • How do we know which structure to build?

11. Parsing • The syntactic analyser or “parser” • Main task: To construct a syntactic structure from the words of the sentence as they arrive

12. Different approaches • 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 • Parallel Analysis: Build both alternative structures at the same time • Minimal Commitment: Stop building - and wait until later material clarifies which analysis is the correct one.

13. Sentence Comprehension • Modular

14. Interactive models Sentence Comprehension • Modular

15. Sentence Comprehension • Garden path sentences • A garden path sentence invites the listener to consider one possible parse, and then at the end forces him to abandon this parse in favor of another.

16. S NP VP The horse Sentence Comprehension • Garden path sentences • The horse raced past the barn fell.

17. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. S NP VP V The horse raced

18. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. S NP VP V PP P NP The horse raced past

19. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. S NP VP V PP P NP The horse raced past the barn

20. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. S NP VP V PP P NP The horse raced past the barn fell

21. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. • raced is initially treated as a past tense verb S NP VP V PP P NP The horse raced past the barn

22. Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. • raced is initially treated as a past tense verb • This analysis fails when the verb fell is encountered S NP VP V PP P NP The horse raced past the barn fell

23. S VP NP V NP RR PP V P NP The horse raced past the barn fell Sentence Comprehension • Garden path sentences • The horse raced past the barn fell. • raced is initially treated as a past tense verb • This analysis fails when the verb fell is encountered • raced can be re-analyzed as a past participle. S NP VP V PP P NP The horse raced past the barn fell

24. Real Headlines • Juvenile Court to Try Shooting Defendant • Red tape holds up new bridge • Miners Refuse to Work after Death • Retired priest may marry Springsteen • Local High School Dropouts Cut in Half • Panda Mating Fails; Veterinarian Takes Over • Kids Make Nutritious Snacks • Squad Helps Dog Bite Victim • Hospitals are Sued by 7 Foot Doctors

25. A serial model • Formulated by Lyn Frazier (1978, 1987) • Build trees using syntactic cues: • phrase structure rules • plus two parsing principles • Minimal Attachment • Late Closure

26. A serial model • Minimal Attachment • Prefer the interpretation that is accompanied by the simplest structure. • simplest = fewest branchings (tree metaphor!) • Count the number of nodes = branching points Marcie kissed Ernie and his brother… The girl hit the man with the umbrella.

27. Minimal attachment S 8 Nodes NP VP the girl V NP Preferred S hit NP PP NP VP the man P NP the girl V NP PP with the umbrella hit the man P NP with the umbrella 9 nodes The girl hit the man with the umbrella.

28. Modular prediction Interactive prediction Minimal attachment • Garden path sentences The spy saw the cop with a telescope. minimal attach Build this structure first non-minimal attach Build this structure first

29. Modular prediction Lexical information rules this one out Interactive prediction Sentence Comprehension • Garden path sentences The spy saw the cop with a revolver. minimal attach Build this structure first non-minimal attach Build this structure first

30. S S NP VP NP the spy V NP VP S’ S’ the spy saw NP PP V PP NP the cop P NP saw P NP with the revolver but the cop didn’t see him the cop but the cop didn’t see him with the revolver MA Non-MA The spy saw the cop with the binoculars.. The spy saw the cop with the revolver … (Rayner & Frazier, ‘83) <- takes longer to read

31. A serial model • Late Closure • Incorporate incoming material into the phrase or clause currently being processed. OR • Associate incoming material with the most recent material possible. She said he tickled her yesterday Tom said that Bill had written his paper yesterday. They were cooking apples.

32. Parsing Preferences .. late closure S Preferred S np vp np vp she v S' adv she v S' said np vp yesterday said np vp he v np he v np adv tickled her tickled her yesterday (Both have 10 nodes, so use LC not MA) She said he tickled her yesterday

33. evidence typically gets questioned, but can’t do the questioning Interactive Models • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence • The evidence questioned in the trial … • The person questioned in the trial …

34. Interactive Models • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence • The evidence questioned in the trial … • The person questioned in the trial … A lawyer often asks questions (more often than answering them)

35. Semantic expectations • Taraban & McCelland (1988) • Expectation • Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence • The couple admired the house with a friendbut knew that it was over-priced. • The couple admired the house with a gardenbut knew that it was over-priced.

36. Semantic expectations • Taraban & McCelland, 1988 • The couple admired the house with a friendbut knew that it was over-priced. • The couple admired the house with a gardenbut knew that it was over-priced. The Non-MA structure may be favoured

37. Intonation as a cue A: I’d like to fly to Davenport, Iowa on TWA. B: TWA doesn’t fly there ... B1: They fly to Des Moines. B2: They fly to Des Moines. A1: I met Mary and Elena’s mother at the mall yesterday. A2: I met Mary and Elena’s mother at the mall yesterday.

38. Chunking, or “phrasing” A1: I met Mary and Elena’s mother at the mall yesterday. A2: I met Mary and Elena’s mother at the mall yesterday.

39. Phrasing can disambiguate Mary & Elena’s mother mall I met Mary and Elena’s mother at the mall yesterday One intonation phrase with relatively flat overall pitch range.

40. Phrasing can disambiguate Elena’s mother mall Mary I met Mary and Elena’s mother at the mall yesterday Separate phrases, with expanded pitch movements.

41. 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, …)