2. PSY 369: Psycholinguistics Language Comprehension: Sentence comprehension

3. The Human Eye At its center is the fovea, a pit that is most sensitive to light and is responsible for our sharp central vision. The central retina is cone- dominated and the peripheral retina is rod- dominated.

4. Retinal Sampling

6. Eye Movements Within the visual field, eye movements serve two major functions Saccades to Fixations – Position target objects of interest on the fovea Tracking – Keep fixated objects on the fovea despite movements of the object or head

7. Fixations The eye is (almost) still – perceptions are gathered during fixations The most important of eye “movements” 90% of the time the eye is fixated duration: 150ms - 600ms

8. Saccades Saccades are used to move the fovea to the next object/region of interest. Connect fixations Duration 10ms - 120ms Very fast (up to 700 degrees/second) No visual perception during saccades Vision is suppressed Evidence that some cognitive processing may also be suppressed during eye-movements (Irwin, 1998)

9. Saccades Move to here

10. Saccade w/o suppression

11. Saccades Move to here

12. Saccades

13. Saccades are used to move the fovea to the next object/region of interest. Connect fixations Duration 10ms - 120ms Very fast (up to 700 degrees/second) No visual perception during saccades Vision is suppressed Ballistic movements (pre-programmed) About 150,000 saccades per day

14. Smooth Pursuit Smooth movement of the eyes for visually tracking a moving object Cannot be performed in static scenes (fixation/saccade behavior instead)

15. Smooth Pursuit versus Saccades Saccades Jerky No correction Up to 700 degrees/sec Background is not blurred (saccadic suppression) Smooth pursuit Smooth and continuous Constantly corrected by visual feedback Up to 100 degrees/sec Background is blurred

16. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

17. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

18. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

19. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

20. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

21. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

22. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

23. Eye-movements in reading Clothes make the man. Naked people have little or no influence on society. Eye-movements in reading are saccadic rather than smooth

24. Eye-movements in reading Limitations of the visual field 130 degrees vertically, 180 degrees horizontally (including peripheral vision Perceptual span for reading: 7-12 spaces Clothes make the man. Naked people have little or no influence on society.

25. Purkinje Eye Tracker Laser is aimed at the eye. Laser light is reflected by cornea and lens Pattern of reflected light is received by an array of light- sensitive elements. Very precise Also measures pupil accomodation No head movements Measuring Eye Movements

26. Video-Based Systems Infrared camera directed at eye Image processing hardware determines pupil position and size (and possibly corneal reflection) Good spatial precision (0.5 degrees) for head-mounted systems Good temporal resolution (up to 500 Hz) possible

27. S NP Ndet Themanhit dog withtheleash.the Theman

28. S NPVP V Ndet Themanhit dog withtheleash.the Themanhit

29. S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdogthe

30. S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdog PP withtheleash the Modifier

31. S NPVP VNP NdetN Themanhit dog withtheleash.the Themanhitdog PP withtheleash the Instrument

32. Themanhit dog withtheleash.the How do we know which structure to build?

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

34. 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.

35. Sentence Comprehension Modular

36. Sentence Comprehension Modular Interactive models

37. 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.

38. 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

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

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

41. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpast S NP The horse

42. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse

43. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse fell

44. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse raced is initially treated as a past tense verb

45. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse fell raced is initially treated as a past tense verb This analysis fails when the verb fell is encountered

46. Sentence Comprehension Garden path sentences The horse raced past the barn fell. VP VPP PNP racedpastthe barn S NP The horse 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. VP V raced PP PNP pastthe barn S NP The horsefell NPRR V

47. 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

48. 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 The girl hit the man with the umbrella.

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

50. 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

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

52. Minimal attachment Garden path sentences The spy saw the cop with a telescope. minimal attach non- minimal attach Modular prediction Build this structure first Interactive prediction Build this structure first (Rayner & Frazier, ‘83)

53. Minimal attachment Garden path sentences The spy saw the cop with a revolver. minimal attach non- minimal attach Modular prediction Build this structure first Interactive prediction Build this structure first Lexical information rules this one out (Rayner & Frazier, ‘83)

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

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

56. 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)

57. Semantic expectations 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. Other factors (e.g., semantic context, co-occurrence of usage & expectation) may provide cues about the likely interpretation of a sentence

58. The Non-MA structure may be favoured Semantic expectations Taraban & McCelland, 1988 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.

59. 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.

60. 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.

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

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

63. 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, …)