Psy1302 Psychology of Language

1. Psy1302 Psychology of Language Lecture 16 Words and Meanings II

2. Review: Classical Theory of Concepts Review • Classical Theory of Concepts • Other Names: • Defining Features Theory • Definitional Theory • Pros and Cons

3. Review: Classical Theory of Concepts Pros: Explanatory Power • Explains category membership in terms of necessary and sufficient features • Allows identification of new candidates • Explains how you learned the meanings of the words. • Provides descriptions that can support semantic compositionality. • Semantic feature can explain relationships between words.

4. Review: Classical Theory of Concepts Cons: Problems for Classical Theory • Difficulty in coming up with a set of necessary and sufficient features • E.g. game, bachelor • Feature Naming: list features of “apple”, “lemon”, “fig”. • Necessary and sufficient features DO NOT emerge • Category membership may not be ALL-OR-NONE • Category boundaries are fuzzy. • Cup vs. Bowl • Categories have graded membership • Typicality effects: some members are better than others

5. Review: Prototype Theory of Concepts Pro Prototype Theory(Experiments showing Graded Membership) • Prototypicality Ratings correlates with Production Task (Rate 1-7 Good/Bad) (List Members) • Prototypical members are listed early. • Prototypicality Ratings correlates with Verification Task (Rating 1-7 Good/Bad) (RT of X is a ___.) • Prototypical members are responded to faster. • Prototypicality Rating correlates with Feature Naming (Rate 1-7 Good/Bad) (List features of…) • Prototypical members share more features with other members.

6. Prototype Theory (Probabilistic Features) Prototype Theory • Concepts are made of: • features • perceptually grounded (like the classical theory) • How are features combined: • family resemblance: no single feature necessary • more shared features = better category member (different than the classical theory)

7. Smith Family Prototype Theory (Probabilistic Features) – Family Resemblance Structure One formalization ofFamily Resemblance Structure

8. Prototype Theory (Probabilistic Features) – Family Resemblance Structure Family Resemblance “Smith” family • Degree of Category Membership (“Smithness”) depends on • the number of features and • how central they are to “Smithness”

9. Prototype Theory (Probabilistic Features) – Family Resemblance Structure Family Resemblance “Smith” family • Smith Features • Beard 8/8 • Brown hair 6/8 • Big nose 6/8 • Big ears 6/8 • Mustache 5/8 • Smith Features • Beard • Brown hair • Big nose • Big ears • Mustache • Smith Features • Beard 8/8 = 1 • Brown hair6/8 = .75 • Big nose6/8 = .75 • Big ears6/8 = .75 • Mustache5/8 = .625

10. Prototype Theory (Probabilistic Features) – Family Resemblance Structure • Middle Smith has all features • beard 1 * 1.0 • brown hair 1 *.75 • big nose 1 * .75 • big ears 1 * .75 • mustache 1* .625 --------------------------- • Total 3.8

11. Prototype Theory (Probabilistic Features) – Family Resemblance Structure • Smith #3 a few features • beard 1* 1.0 • brown hair 1* .75 • big nose 0 * .75 • big ears 1 * .75 • mustache 0 * .625 -------------------------- • Total 2.5 • poorer instance than middle Smith

12. Prototype Theory (Probabilistic Features) – Family Resemblance Structure • Item with too few features is not a member of the category • beard 0* 1 • brown hair 0 * .75 • big nose 1 * .75 • big ears 0 * .75 • mustache 0 * .625 ----------------------- • Total .75 • not a Smith

13. Prototype Theory (Probabilistic Features) – Family Resemblance Structure One formalization ofFamily Resemblance Structure • Features have associated probability • These probabilities may be thought of as weights on the features for membership/identification purposes • Category membership is based on a weighted sum of the features.

14. fast Typicality effect slow Prototype Theory (Probabilistic Features) – Applied to Experimental Data Verification Task • Press a button to answer TRUE or FALSE to the following statements • QuestionResponse A canary is a bird A ostrich is a bird TRUE TRUE

15. = ? = ? Typicalityeffect! Prototype Theory (Probabilistic Features) – Applied to Experimental Data Prototype Theory(Rosch & Mervis, 1975) • Conceptual category (e.g. birds) is represented by a prototype • an average of all the exemplars in the category • not a real instance, just an abstraction • the ‘average bird’ will be more like a canary than an ostrich • Verification task: compare the exemplar to the prototype • A canary is a bird • big overlap = FAST • An ostrich is a bird • small overlap = SLOW

16. Prototype Theory (Probabilistic Features) Prototype Theory Summary • Certain members of a category are prototypical – or instantiate the prototype • Categories form around prototypes; new members added on basis of resemblance to prototype • No requirement that a property or set of properties be shared by all members • Features/attributes generally gradable • Category membership a matter of degree • Categories do not have clear boundaries

17. Prototype Theory (Probabilistic Features) Pros: Explanatory power of Prototype Theory • Explains category membership ratings – membership is graded not absolute • Explains sentence verification results: • more typical instances quickly identified because they have more of the category features. • Consistent with feature listing results – more typical instances have more features in common with other members

18. Prototype Theory (Probabilistic Features) – PROBLEM B A Armstrong, Gleitman & GleitmanStructure of the Argument Prototype Claim: If a category shows typicality effects, then it must have a prototype structure Contrapositive: Whenever “if A then B” is true. “If not B, then not A” must also be true.

19. Prototype Theory (Probabilistic Features) – PROBLEM B A Armstrong, Gleitman & GleitmanStructure of the Argument Prototype Claim: • If a category shows typicality effects, then it must have a prototype structure Contrapositive of Prototype Claim: • If a category does not have a prototype structure then it will not show typicality effects.

20. Prototype Theory (Probabilistic Features) – PROBLEM B A Armstrong, Gleitman & GleitmanStructure of the Argument • If you can disprove the contrapositive then the original claim must be false. Contrapositive of Prototype Claim: • If a category does not have a prototype structure then it will not show typicality effects.

21. Prototype Theory (Probabilistic Features) – PROBLEM Armstrong, Gleitman & GleitmanStructure of the Argument • AGG set out to disprove the contrapositive by conducting typicality tests on well-defined categories: • mathematical categories: even number, triangle, plane • gender categories: female, male • kinship terms: uncle, grandmother • Contrapositive: Well-defined categories (those that do not have prototypical structure) should not show typicality effect.

22. FRUIT apple 1.3 strawberry 2.1 pineapple 2.7 fig 5.2 olive 6.4 SPORT football 1.4 hockey 1.8 gymnastics 2.8 archery 4.8 weight-lifting 5.1 Prototype Theory (Probabilistic Features) – PROBLEM Exp. 1: Exemplar Ratings forordinary categories

23. EVEN NUMBER 4 1.1 8 1.5 18 2.6 34 3.4 106 3.9 FEMALE mother 1.7 housewife 2.4 princess 3.0 policewoman 3.9 comedienne 4.5 Prototype Theory (Probabilistic Features) – PROBLEM Exp. 1: Exemplar Ratings forwell-defined categories EVEN NUMBER • 4 • 8 • 18 • 34 • 106 FEMALE • mother • housewife • princess • policewoman • comedienne

24. Prototype Theory (Probabilistic Features) – PROBLEM Fast Fast > > Slow Slow Verification Task

25. Prototype Theory (Probabilistic Features) – PROBLEM Armstrong, Gleitman & GleitmanConclusion • Since well-defined categories also show typicality effects • then the presence of these effects, does not prove that ordinary categories have a prototype structure

26. Dual Theory • Hypothesis 5: Dual Theory • Dual Route: Classical and Prototype

27. Dual Theory Grandmothers

28. Definitional Features for reasoning with words and determining category membership Who is a grandmother? The mother of a parent Prototypes for quick identification How do you find a grandmother is a crowd? look for the prototypical features (kindly, gray-haired) Dual Theory Dual Theory

29. Dual Theory SNL Women? J. Reno Pat Mango Tina Fey

30. General Challenges Challenges for any feature theory • What are the features which determine category membership? • What are the rules which describe how meanings combine.

31. General Challenges Classical Theory(definitional) PET • [animal] • [kept for amusement]

32. General Challenges Classical Theory(definitional) FISH • [aquatic] • [water-breathing] • [cold blooded] • [chambered heart] • [animal]

33. General Challenges Classical Theory(definitional) PET+FISH • [aquatic] • [water-breathing] • [cold blooded] • [chambered heart] • [animal] • [kept for amusement]

34. General Challenges Prototype Theory PET • [animal] • [kept for amusement] • [cute] • [friendly] • [mammal] • [furry] • [smallish]

35. General Challenges Prototype Theory FISH • [aquatic] • [water-breathing] • [cold blooded] • [chambered heart] • [animal] • [elongated] • [spindle shaped] • [broad caudal fin]

36. Predicted Prototype for PetFish: a catlike trout? a fuzzy salmon? General Challenges Prototype Theory

37. General Challenges Prototype Theory • Rating Task • Is guppy a good PET? • Is guppy a good FISH? • Is guppy a good PET FISH? • Rating Task • Is guppy a good PET? Bad Member • Is guppy a good FISH? Bad Member • Is guppy a good PET FISH? Good Member

38. General Challenges • A theory of concepts should explain how words combine to yield the meanings of phrases. • Prototype Theory fails to do this

39. General Challenges More challenges…(for any feature theory) • Differing inferences: • SKILLFUL + SURGEON • SKILLFUL + CARPENTER (is skillful adding the same features in each case?) • Feature eating modifiers • STONE LION: • COUNTERFEIT DOLLAR: • FORMER SENATOR: [animate] [legal tender] [member of congress]

40. General Challenges Challenges for any feature theory • What are the features which determine category membership? • What are the rules which describe how meanings combine.

41. TIGER [JUNGLE-DWELLING] [4-LEGGED] [FUR-COVERED] [GROWLY] [FIERCE] [STRIPPED] [ANIMAL] General Challenges Are these any of these features necessary? What if it lives in a zoo? What if it lost a leg in an accident? What if I shaved it? What if it lost its voice? What if it is a scaredy cat? What if I dye its hair?

42. General Challenges What is necessary? • Has tiger DNA? • Has tiger essence? • What the experts call a tiger? • But how can these be reduced to sensory primitives?

43. General Challenges Are these any of these features sufficient? What if I transplanted a mean striped tomcat to the jungle? • [JUNGLE-DWELLING] • [4-LEGGED] • [FUR-COVERED] • [GROWLY] • [FIERCE] • [STRIPPED] • [ANIMAL]

44. Doctors took a raccoon and shaved away some of its fur. They dyed what was left all black. Then they bleached a single stripe all white down the center of tits back. Then, with surgery, they put in its body a sac of super smelly yucky stuff, just like a skunk has. When they were all done, the animal looked like this. After the operation, was this a skunk or a raccoon?

45. Doctors took a coffeepot that looked like this. They sawed off the handle, sealed the top, took off the top knob, closed the spout, and sawed it off. They also sawed off the base and attached a flat piece of metal. They attached a little stick, cut a window in it, and filled the metal container with bird food. When they were done, it looked like this After the operation, was this a coffeepot or a birdfeeder?

46. Results

47. Results • Children know that appearances aren't everything. • Animal generalizations should be based on membership in a category, not on appearance. • This ability increases with age. • Children also shift from using characteristic properties to categorize to using defining ones.

48. Theory-Based Theory (Theory Theory) Hypothesis 6: Theory-Based Theory of Concepts (Theory theory) • Concepts are representations whose structure consists in their relations to other concepts as specified by a mental theory

49. Theory-Based Theory (Theory Theory) Hypothesis 6: Theory-Based Theory (Theory theory) Causal theory of category membership.

50. Causal knowledge is critical to concept learning in at least three ways: Causal knowledge helps us decide which features are relevant to category membership Causal knowledge helps us decide which features are central and which peripheral. Causal knowledge affects our intuitions about when category members will retain their identity and when they will be transformed. Theory-Based Theory (Theory Theory) Theory Theory