1. Thesis Proposal Raluca Budiu February 9, 2000 The Role of Background Knowledge in Sentence and Discourse Processing

2. 02/ 09/ 2000Thesis proposal --- Raluca Budiu2 Metaphors Time is money.  People from all cultures use metaphors on an every-day basis, irrespective of their level of education.  Language is full of frozen metaphors (Adam’s apple, leg of a table, etc.)  People understand (most) metaphors easily.

3. 02/ 09/ 2000Thesis proposal --- Raluca Budiu3 “Mistakes”  People make mistakes when they speak.  Often people do not notice mistakes and can understand the message communicated: How many animals of each kind did Moses take on the ark?  It’s hard for people not to ignore mistakes.

4. 02/ 09/ 2000Thesis proposal --- Raluca Budiu4 Memory for Text  People interpret new stories in terms of past experiences.  Doing that helps them remember the new stories better.  Doing than makes them deform the actual facts.

5. 02/ 09/ 2000Thesis proposal --- Raluca Budiu5 Motivation Metaphors “Mistakes” Memory for text Claim: all are facets of the same cognitive mechanism, which: accounts for both fallibility and robustness uses background knowledge as a heuristic in service of the current goal.

6. 02/ 09/ 2000Thesis proposal --- Raluca Budiu6 At the semantic level, comprehension works bottom-up: all the information available is used to find an interpretation; top-down: the interpretation is further used to help comprehension or recall. Proof: a unique computational model in ACT-R (Anderson & Lebiere, 1998) explaining and unifying phenomena from various domains; satisfying a number of computational and empirical (i.e. fitting actual behavioral data) constraints. Thesis Topic: Comprehension

7. 02/ 09/ 2000Thesis proposal --- Raluca Budiu7 Behavioral Data  Metaphor understanding;  Semantic illusions;  Text memory:  Lexical Ambiguities.

8. 02/ 09/ 2000Thesis proposal --- Raluca Budiu8 Overview Thesis topic;  A model for sentence comprehension;  Empirical constraints;  Computational constraints;  Summary and work plan.

9. 02/ 09/ 2000Thesis proposal --- Raluca Budiu9 Semantic Interpretation Model Semantic Background Words + thematic roles knowledge interpretation Understanding a sentence = finding a matching interpretation/context in the background knowledge. take ark animals Noah Ark prop agent verb place-oblique patient

10. 02/ 09/ 2000Thesis proposal --- Raluca Budiu10 How Does the Model Work? Ark context ark How manydid Farm context Ark context animalsNoahtake Farm context raise farm animals father Farm prop agent verb place-oblique patient on the take ark animals Noah Ark prop agent verb place-oblique patient Incremental From left to right omitting Incremental From left to right omitting

11. 02/ 09/ 2000Thesis proposal --- Raluca Budiu11 Model in the Absence of Context Priming Context found? Read word Extract Word Meaning Context ? Word matches context? Find context Old words match? yes no yesno yes = here the model may omit to check all the previous words

12. 02/ 09/ 2000Thesis proposal --- Raluca Budiu12 Context Priming How many animals did Noah take on the ark? 1. Boat or ship held to resemble that in which Noah and his family were preserved from the Deluge 2. A repository traditionally in or against the wall of a synagogue for the scrolls of the Torah Ark story Noah animals took ark(1) agent verb place-oblique patient Different processing at the beginning and at the end of the sentence.

13. 02/ 09/ 2000Thesis proposal --- Raluca Budiu13 Model With Context Priming Read word Extract Context Role Context role matches word? Find context Old words match? yes no yes Context found? Sentence not comprehended = here the model may omit to check all the previous words no

14. 02/ 09/ 2000Thesis proposal --- Raluca Budiu14 Distributed Meaning Assumption Bible charNavigator MarriedPatriarch Noah “Noah” meaning word meaning Meaning retrieval = extracting word features; Replace word meaning with feature as unit of processing; Model remains the same. Speak very briefly

15. 02/ 09/ 2000Thesis proposal --- Raluca Budiu15 Context Finding With Distributed Meanings Bible charMarriedPatriarch Noah word meaning Jesus context Moses context Jesus context Noah context Moses context took the animals on the ark. Show It only if you get questions

16. 02/ 09/ 2000Thesis proposal --- Raluca Budiu16 Summary of the Model  Incremental;  Trial-and-error strategy;  Mixture of bottom-up and top-down strategies;  Incomplete processing (aka symbolic partial matching) at the word meaning level (not all features extracted); at the sentence level;  No syntactic processing: thematic roles are inputs.

17. 02/ 09/ 2000Thesis proposal --- Raluca Budiu17 Overview Thesis Topic; Model;  Empirical constraints;  Computational constraints;  Summary and work plan.

18. 02/ 09/ 2000Thesis proposal --- Raluca Budiu18 Metaphor-related Phenomena  Effects of position on metaphor understanding (Gerrig & Healy, 1983) ; Effects of metaphoric truth on the judgement and recall of sentences of the type Some As are Bs (Glucksberg, Glidea & Bookin, 1982) ; Interferences of literal and metaphoric truth on sentence judgements (Keysar, 1989) ; Effects of context length on metaphor understanding (Ortony, Schallert, Reynolds & Antos, 1978) ; Comprehension differences between different types of metaphors (Gibbs, 1990; Ortony et al. 1978; our data).

19. 02/ 09/ 2000Thesis proposal --- Raluca Budiu19 Metaphor Position Effects Metaphor-first sentences take longer to comprehend than metaphor-second sentences( Gerrig & Healy, 1983 ). Container context Stars context Drops of molten silver filledthe sky The sky was filled withdrops of molten silver 4.21s(4.23s) 3.53s(2.84s) * * Predictions *

20. 02/ 09/ 2000Thesis proposal --- Raluca Budiu20 Effects of Metaphoric Truth  Some roads are snakes > Some flutes are jails (Glucksberg et al., 1982) : snakes needs to be processed more deeply in order for Some roads are snakes to be judged as false.  Congruent sentences < incongruent sentences (Keysar, 1989) : All features are equally informative in the congruent conditions. RT hide

21. 02/ 09/ 2000Thesis proposal --- Raluca Budiu21 Types of Metaphors  Literal sentences are as fast to understand as metaphorical sentences (Ortony et al., 1978) : The hens clucked noisily.  Metaphoric anaphoras are sometimes harder to understand than equivalent literals (Gibbs, 1990) : The creampuff did not show up for the box match.  Does the literality of a metaphoric sentence make a difference? The hens/women clucked/talked noisily. hide

22. 02/ 09/ 2000Thesis proposal --- Raluca Budiu22 What Are Semantic Illusions?  How many animals of each kind did Moses take on the ark?  Semantic illusions are very robust (Reder & Kusbit, 1991) ; however, not anything can make an illusion.  Good vs. bad illusions: How many animals did Adam take on the ark?

23. 02/ 09/ 2000Thesis proposal --- Raluca Budiu23 Semantic Illusion Datasets  Illusion rates for good and bad distortions (Ayers, Reder & Anderson, 1996) ;  Percent correct for good and bad distortions in the gist task (Ayers et al., 1996) ;  Latencies in the literal and gist task (Reder & Kusbit, 1991) ;  Processing of semantic anomalies and contradictions (Barton & Sanford, 1993); When an aircraft crashes, where should the survivors be buried? vs. When a bicycle accident occurs where should the survivors be buried?

24. 02/ 09/ 2000Thesis proposal --- Raluca Budiu24 Good vs. Bad Illusions All levels of distortion are significantly different from one another.

25. 02/ 09/ 2000Thesis proposal --- Raluca Budiu25 Gist Task  People are faster and very good at performing the gist task (Reder & Kusbit, 1991) ; Undistorted > Bad Hide this;

26. 02/ 09/ 2000Thesis proposal --- Raluca Budiu26 Meaning Overlap Moses Egyptian Patriarch First manEve Adam Navigator Noah MarriedBible char Eden born “Noah”“Moses” “Adam” hide

27. 02/ 09/ 2000Thesis proposal --- Raluca Budiu27 Modeling Semantic Illusions take ark animals Noah Ark prop agent verb place-oblique patient Moses Adam  Model says “Distorted” if it finds no interpretation;  Key idea: meaning overlap ( supported by van Oostendorp & Mul, 1990; van Oostendorp & Kok, 1990);  Model predicts an effect of position of distortion in the sentence: late distortions are harder to detect.

28. 02/ 09/ 2000Thesis proposal --- Raluca Budiu28 Memory for Text  Prior schemas can influence text memory (Bartlett, 1932; Bransford & Johnson, 1972; etc.);  If a text is consistent with a pre-existent script (paradigmatic situation/previous experience) subjects recall more propositions from the text, but also make more script-consistent intrusions (Owens, Bower & Black, 1979).

29. 02/ 09/ 2000Thesis proposal --- Raluca Budiu29 Text Memory Datasets  Recall and recognition of sentences from multiple episodes related or not by a common setting (Owens et al., 1979);  Interferences from related stories on recall and recognition of text (Bower, Black & Turner, 1979); Text recall in the presence or absence of a topic (Bransford & Johnson, 1972); Recall of single, related and unrelated facts (Bradshaw and Anderson, 1982).

30. 02/ 09/ 2000Thesis proposal --- Raluca Budiu30 Interferences Among Related Stories The number of intrusions can increase if subjects study more variants of the same script (Bower, Black & Turner, 1979) : At the Dentist’s --- about Bill At the Doctor’s --- about Tom

31. 02/ 09/ 2000Thesis proposal --- Raluca Budiu31 Modeling Script Effects Story 2 (doctor’s) Story 1 (dentist’s) Visiting-healthcare-professional script Studied Propositions Script Propositions

32. 02/ 09/ 2000Thesis proposal --- Raluca Budiu32 Elaborations  recall improved when subjects were shown the topic of a passage before studying the passage (Bransford & Johnson, 1972);  recall improved when subjects studied a number of related sentences about one historical figure, compared with the conditions in which they studied unrelated sentences about that figure or a single fact (Bradshaw & Anderson, 1979). mihaib: hide mihaib: hide

33. 02/ 09/ 2000Thesis proposal --- Raluca Budiu33 Difficulties With Modeling Script Effects  Parsing the discourse into a unitary and coherent representation (solve the problem of binding);  Text representation that allows recursive schemas;  Modeling different types of intrusions, especially abstract intrusions: Studied Intruded Bill paid the bill. Tom paid the bill. The nurse x-rayed Bill’s The nurse checked Tom’s teeth. blood pressure.

34. 02/ 09/ 2000Thesis proposal --- Raluca Budiu34 Lexical Ambiguity Resolution  Although not designed for data from this domain, our model makes strong predictions about ambiguity resolution.  Does context influence meaning access for an ambiguous word?  Possible answer: both meanings are activated, but activation depends additively on both context and individual meaning frequency (Tabossi, 1988; Duffy, Morris & Rayner, 1988; Rayner & Duffy, 1986; Rayner & Frazier, 1989; Lucas, 1999).

35. 02/ 09/ 2000Thesis proposal --- Raluca Budiu35 Lexical Ambiguity Datasets  Gaze duration on balanced and unbalanced homophones (Duffy et al., 1988);  Mean reading time per character in the disambiguation region (Duffy et al., 1988);

36. 02/ 09/ 2000Thesis proposal --- Raluca Budiu36 Lexical Ambiguity: An Eye Movement Study (Duffy et al., 1988) Because it was kept on the back of a high shelf, the pitcher (whiskey) was often forgotten. Of course the pitcher (whiskey) was often forgotten because it was kept on the back of a high shelf. When she finally served it to her guests, the port (soup) was a great success. Last night the port (soup) was a great success, when she finally served it to her guests. Disambiguation-beforeDisambiguation-after Balanced Unbalanced Context always supports subordinate meaning for unbalanced words. Mention controls hide Mention controls hide

37. 02/ 09/ 2000Thesis proposal --- Raluca Budiu37 Gaze Durations on Homophones Duffy et al. (1988) manipulated position of disambiguating region and relative frequency of the homophone’s meanings: –Disambiguating region before/after the homophone; –Homophone could be balanced (pitcher) or unbalanced (port);

38. 02/ 09/ 2000Thesis proposal --- Raluca Budiu38 Gaze Duration on Homophones Times longer than controls reflect multiple access. Times equal with controls reflect selective access.

39. 02/ 09/ 2000Thesis proposal --- Raluca Budiu39 Time Spent on Disambiguating Region mihaib: hide mihaib: hide

40. 02/ 09/ 2000Thesis proposal --- Raluca Budiu40 Fitting the Data  Disambiguation-after: no context priming; individual meaning activation is proportional with meaning frequency (ACT-R assumption); ACT-R is serial (no multiple access), but close competitors can slow down retrieval (tentative ACT-R assumption).  Disambiguation-before: context priming: context is an extra source of activation; If the wrong meaning is more frequent, context priming may not be enough.

41. 02/ 09/ 2000Thesis proposal --- Raluca Budiu41 Overview Thesis Topic; Model; Empirical constraints:  Computational constraints;  Summary and work plan.

42. 02/ 09/ 2000Thesis proposal --- Raluca Budiu42 Computational Constraints  Realistic reaction times;  Integration with background knowledge;  Allowing for errors of the syntactic processor (i.e. wrong thematic roles). Model Semantic Background Words + Thematic roles knowledge interpretation

43. 02/ 09/ 2000Thesis proposal --- Raluca Budiu43 Syntactic Ambiguity As a Computational Constraint Garden path effects have been largely documented in the literature: The horse raced past the barn fell ; The cop arrested by the detective was guilty of taking bribes. Solution: thematic roles as meaning features later omitted. Model Semantic Background Words + Candidate thematic roles knowledge interpretation

44. 02/ 09/ 2000Thesis proposal --- Raluca Budiu44 Summary  Language comprehension theory to be embodied in a unique ACT-R model;  Semantic rather than syntactic level of processing (no parser);  The theory should satisfy: Computational constraints: –Realistic reaction times; –Integration with background knowledge; –Syntactic ambiguity. Empirical constraints –Metaphor understanding; –Semantic illusions; –Lexical ambiguity; –Memory for text: script effects and elaborations.

45. 02/ 09/ 2000Thesis proposal --- Raluca Budiu45 Empirical Constraints Metaphor understanding: Effects of position on metaphor understanding (Gerrig & Healy, 1983); Effects of metaphoric truth on the judgement and recall of sentences of the type Some As are Bs (Glucksberg et al., 1982); Interferences of literal and metaphoric truth on sentence judgements (Keysar, 1989); Effects of context length on metaphor understanding (Ortony et al., 1978); Comprehension differences between different types of metaphors (Gibbs, 1990; Ortony et al. 1979; our data).

46. 02/ 09/ 2000Thesis proposal --- Raluca Budiu46 Empirical Constraints (contd.)  Semantic illusions: Illusion rates for good and bad distortions in the literal and gist tasks (Ayers et al., 1996); Latencies in the literal and gist task (Reder & Kusbit, 1991); Processing of semantic anomalies and contradictions (Barton & Sanford, 1993).  Lexical ambiguity: Gaze duration on balanced and unbalanced homophones (Duffy et al., 1988); Mean reading time per character in the disambiguation region (Duffy et al., 1988);

47. 02/ 09/ 2000Thesis proposal --- Raluca Budiu47 Empirical Constraints (contd.)  Memory for text (script effects and elaborations): Recall and recognition of sentences from multiple episodes related or not by a common setting (Owens et al., 1979); Interferences from related stories on recall and recognition of text (Bower et al., 1979); Text recall in the presence or absence of a topic (Bransford & Johnson, 1972); Recall of single, related and unrelated facts (Bradshaw and Anderson, 1982).

48. 02/ 09/ 2000Thesis proposal --- Raluca Budiu48 Model Validation  Collect new empirical data to validate “side effects” or other predictions of the model, not covered by the previous list of empirical phenomena: E.g.: position effects for Moses’ illusion.  Test it on other sets of data (for the same phenomena) than the ones it has been built for in order to avoid “overfitting”.

49. 02/ 09/ 2000Thesis proposal --- Raluca Budiu49 Work Plan Garden path Lexical ambiguity Text memory Semantic illusions Metaphor 20%10%15%30% Modeling and parameter fitting; Data collection : metaphors and semantic illusions; The model still has to solve the more difficult problems of discourse representation. 25%