1. Looking Ahead Remaining topics to be covered Remaining topics to be covered –Meanings and Concepts –Pragmatics and Language in Use November 20 th attendance (2 nd lecture) November 20 th attendance (2 nd lecture) –Language Acquisition –Bilingualism & Second Language Acquisition –Evolution of language One lecture One lecture One seminar – Discussion of Readings One seminar – Discussion of Readings –Relationship between Language and Thought

2. Looking Ahead Reading Homework for Seminar Reading Homework for Seminar December 13th December 13th –Hauser, Chomsky, & Fitch, W. T. (2002). –Pinker & Jackendoff (2005) –Fitch, Hauser, & Chomsky (2005) –Jackendoff & Pinker (2005)

3. Psy1302 Psychology of Language Lecture 15 Words and Meanings

4. What are we transmitting with language???

5. Language is a means for transmitting ideas and concepts… Language is a means for transmitting ideas and concepts… Words represent concepts… Words represent concepts…

6. How do we represent meanings of words? How do we represent meanings of words? How do word meanings (concepts) relate to each other? How do word meanings (concepts) relate to each other? How do we know what the word refers to? How do we know what the word refers to? How do we learn the word meanings? How do we learn the word meanings? Hodge-Podge of Difficult Questions…

7. Concepts A word denotes a concept Word meaning (lexical concepts) Combination of words could get you more complex concepts Categorization = psychological application of a concept

8. Theories of Concepts Meaning as Reference Meaning as Ideas Classical Theory of Concepts – –(Defining Features Representation) Prototype Theory – –(Probabilistic Feature Representation) Dual Theory – –(Both Classical and Prototype) Theory Theory of Concepts – –(Theory-based representations) These theories differ mostly in what they consider the structure of concepts to be

9. Hypothesis 1: Meaning as reference Child: What’s a fish? Child: What’s a fish? Adult: That’s a fish. Adult: That’s a fish.

10. Hypothesis 1: Meaning as reference Meaning = Reference Meaning = Reference The meaning of a word (or phrase) is whatever it refers to in the world The meaning of a word (or phrase) is whatever it refers to in the world –George Washington = a particular person a particular person –Fish = a kind of animal –Red = property of objects objects

11. Hypothesis 1: Meaning as reference Problems? Words can label non-existing real world referents Words can label non-existing real world referents –The Crown Prince of Massachusetts –unicorn Words can refer abstract referents Words can refer abstract referents –Infinity –Inevitability

12. Hypothesis 1: Meaning as reference Problems? Same referent, different meaning Same referent, different meaning –Morning star (the last visible star in the eastern sky as dawn breaks) –Evening star (the first star visible in the western sky as sun sets) –Creatures with a heart –Creatures with a kidney Learning: Many non-encountered instances Learning: Many non-encountered instances –Fish?

13. Hypothesis 2: Ideation Theory Words denote ideas rather than objects Words denote ideas rather than objects John Locke (1690, p. 225). John Locke (1690, p. 225). –Words in their primary and immediate signification stands for nothing but the ideas in the mind of him that uses them. Words allow us to talk about both imaginary and real objects Words allow us to talk about both imaginary and real objects –The Crown Prince of Massachusetts –Unicorn Potential problem: Potential problem: if words make no link to things in the world, how do we know our word meanings are the same as other peoples?

14. Hypothesis 3: Definitional theory Meanings are definitional. Meanings are definitional. Classical Theory of Concepts (Defining Features)

15. Hypothesis 3: Definitional theory The Classical Theory Word meanings are a set of properties that are necessary and sufficient for membership in the category. Word meanings are a set of properties that are necessary and sufficient for membership in the category. –Meanings are analyzable into bundles of semantic primitives (features). –Triangle: a closed, three sided figure, whose angles add up to 180 degrees. Classical Theory of Concepts (Defining Features)

16. The Classical Theory Fish Fish[aquatic][water-breathing][cold-blooded][animal] [chambered heart] Word meanings are a set of properties that are necessary and sufficient for membership in the category. Classical Theory of Concepts (Defining Features)

17. The Classical Theory Bachelor Bachelor –# My husband is a bachelor. Bachelor  UNMARRIED Bachelor  UNMARRIED –# I met a two-year-old bachelor. Bachelor  ADULT Bachelor  ADULT –# My sister is a bachelor. Bachelor  MALE Bachelor  MALE –# My dog Rex is a bachelor. Bachelor  HUMAN Bachelor  HUMAN Word meanings are a set of properties that are necessary and sufficient for membership in the category. [UNMARRIED] [ADULT] [MALE] [HUMAN] Classical Theory of Concepts (Defining Features)

18. What’s nice about this theory? What’s nice about this theory? Classical Theory of Concepts (Defining Features) Triangle:[closed][3-sided][figure] Is this a triangle?

19. Learning Word Meaning (Empiricist version) 1. Children enter the world with a limited set of primitive categories (features), given by neuropsychology Eg. RED; SALTY; OBJECT; CAUSE Eg. RED; SALTY; OBJECT; CAUSE 2. The sounds that label these primitive are learned through association: co-occurrence in space and time of word with sensory perceptual experience. 3. Words with complex meanings (not derivable from a single sensory perceptual system) are learned as lists of the innate features or through association. Classical Theory of Concepts (Defining Features)

20. Proposal for learning meaning of the word “red” Retina stimulated by red light – ~700nm triggers red receptor in brain Retina stimulated by red light – ~700nm triggers red receptor in brain Simultaneously ear stimulated by soundwave “red” Simultaneously ear stimulated by soundwave “red” Co-occurrence leads one to associate sensory category RED with the word “red”. Co-occurrence leads one to associate sensory category RED with the word “red”. “red” Classical Theory of Concepts (Defining Features)

21. Learning Word Meaning (Empiricist version) 1. Children enter the world with a limited set of primitive categories (features), given by neuropsychology Eg. RED; SALTY; OBJECT; CAUSE Eg. RED; SALTY; OBJECT; CAUSE 2. The sounds that label these primitive are learned through association: co-occurrence in space and time of word with sensory perceptual experience. 3. Words with complex meanings (not derivable from a single sensory perceptual system) are learned as lists of the innate features or through association. Classical Theory of Concepts (Defining Features)

22. Proposal for learning a more “complex” word Several innate categories combine to make meaning of lemon Several innate categories combine to make meaning of lemon –Yellow –Sour –Oval –Object If the sound /lemon/ occurs at the same times that these ideas are activated, we learn the word “lemon” If the sound /lemon/ occurs at the same times that these ideas are activated, we learn the word “lemon” Classical Theory of Concepts (Defining Features)

23. Creating new complex concepts via Compositional Semantics ModifierHead Noun Feature A Feature B Feature C Feature D Example of Semantic Composition Example of Semantic Composition NP Feature B Feature C Feature D Classical Theory of Concepts (Defining Features) Union of Features

24. Feature Union redtriangle red 3-sided closed figure red triangles [red] [3-sided] [closed] [figure] [red] [3-sided] [closed] [figure] NP Classical Theory of Concepts (Defining Features)

25. Explanatory Power of the Classical Theory 1. Explains category membership and its allowable variation in terms of necessary and sufficient features 2. Allows identification of new candidates 3. Explains how you learned the meanings of the words in a way that: –is grounded in perceptual experience –builds complex entities Classical Theory of Concepts (Defining Features)

26. Explanatory Power of the Classical Theory 4. Provides descriptions that can support semantic compositionality 5. Semantic feature can explain relationships between words Classical Theory of Concepts (Defining Features)

27. Problems? Difficulty in coming up with necessary and sufficient features Difficulty in coming up with necessary and sufficient features Is it always true that category membership is all or none? Is it always true that category membership is all or none? Classical Theory of Concepts (Defining Features)

28. What is a game? (Wittgenstein, 1953) Is it always amusing? Is it always competition? Is skill required? Must luck play a role? BOTTOM-LINE Difficult to come up with necessary features. Classical Theory of Concepts (Defining Features) – Problem #1: Coming up w/ features!

29. BACHELOR revisited Alfred is an unmarried adult male, but he has been living with his girl-friend for the last 23 yrs. Their relationship is happy and stale. Is Alfred a bachelor? Alfred is an unmarried adult male, but he has been living with his girl-friend for the last 23 yrs. Their relationship is happy and stale. Is Alfred a bachelor? BACHELOR: [UNMARRIED] [ADULT] [MALE] [HUMAN] Classical Theory of Concepts (Defining Features) – Problem #1: Coming up w/ features!

30. BACHELOR revisited Bernard is an unmarried adult male, and he does not have a partner. Bernard is a monk living in a monastery. Is Bernard a bachelor? Bernard is an unmarried adult male, and he does not have a partner. Bernard is a monk living in a monastery. Is Bernard a bachelor? BACHELOR: [UNMARRIED] [ADULT] [MALE] [HUMAN] Classical Theory of Concepts (Defining Features) – Problem #1: Coming up w/ features!

31. BACHELOR revisited Charles is a married adult male, but he has not seen his wife for many years. Charles is earnestly dating, hoping to find a new partner. Is Charles a bachelor? Charles is a married adult male, but he has not seen his wife for many years. Charles is earnestly dating, hoping to find a new partner. Is Charles a bachelor? BACHELOR: [UNMARRIED] [ADULT] [MALE] [HUMAN] Classical Theory of Concepts (Defining Features) – Problem #1: Coming up w/ features!

32. BACHELOR revisited Donald is a married adult male, but he lives in a culture that encourages men to take two wives. Donald is earnestly dating, hoping to find a new partner. Is Donald a bachelor? Donald is a married adult male, but he lives in a culture that encourages men to take two wives. Donald is earnestly dating, hoping to find a new partner. Is Donald a bachelor? BACHELOR: [UNMARRIED] [ADULT] [MALE] [HUMAN] Classical Theory of Concepts (Defining Features) – Problem #1: Coming up w/ features!

33. Labov (1973) Categorization Task Categorization Task –What is this? Classical Theory of Concepts (Defining Features) – Problem #2: Category membership?

34. What is it? CUP BOWL Classical Theory of Concepts (Defining Features) – Problem #2: Category membership?

35. What is it? CUP BOWL * DASHED LINE = FOOD CONTEXT (mashed potato) Classical Theory of Concepts (Defining Features) – Problem #2: Category membership?

36. Some Problems for Classical Theory Difficulty in coming up with a set of necessary and sufficient features Difficulty in coming up with a set of necessary and sufficient features –E.g. game Category membership may not be ALL-OR-NONE Category membership may not be ALL-OR-NONE –Category boundaries are fuzzy. Categories have graded membership Categories have graded membership Classical Theory of Concepts (Defining Features) – Problem #2: Category membership

37. Quillian’s Teachable Language Comprehender ANIMAL BIRD CANARY OSTRICH FISH SHARK SALMON Has skin Can move around Eats Breaths Has wings Can fly Has feathers Is Yellow Can Sing Digression: Semantic Networks

38. Verification Process in TLC “Does a cow produce Milk?” Mammal Has Fur Has Hooves Bovines Eats Plants Cow Produces milk for young 1. Start at node (COW) 2. Fan out through all links. 3. Unlimited energy search Digression: Semantic Networks

39. Collins & Quillian (1969) Test Search Hypothesis of TLC. Test Search Hypothesis of TLC. TRUE OR FALSE: TRUE OR FALSE: A canary can sing.A robin is a fish. A cow is an animal.A shark has skin. Digression: Semantic Networks

40. Collins & Quillian (1969) Test Search Hypothesis of TLC. Test Search Hypothesis of TLC. TRUE OR FALSE: TRUE OR FALSE: –A canary is a canary. (0 steps) –A canary is a bird.(1 step) –A canary is an animal.(2 steps) Digression: Semantic Networks

41. Collins & Quillian (1969) Digression: Semantic Networks

42. Typicality & Strength of Association (Conrad, 1972; Rips et al., 1973) 1. Collect “Property Norms” –What are typical properties of a cow? gives milk, eats grass, has hooves, etc.. gives milk, eats grass, has hooves, etc.. 2. Do Collins & Quillian Study: –“A cow gives milk.” (4 Steps) (but more typical) –“A cow has hooves.” (2 Steps!) (but less typical) Digression: Semantic Networks

43. Strength of Association (Conrad, 1972; Rips et al., 1973) RESULTS? RESULTS? –“A cow gives milk.” FAST (4 Steps) (but more typical) (4 Steps) (but more typical) –“A cow has hooves.” SLOW (2 Steps!) (but less typical) (2 Steps!) (but less typical) Digression: Semantic Networks

44. Hypothesis 4: Prototype Theory Alternative to definitional theory Alternative to definitional theory Prototype Theory (Probabilistic Features)

45. Prototype Theory and Family Resemblance Categories have graded membership: Some members of a category are reliably rated as “better” members than others Categories have graded membership: Some members of a category are reliably rated as “better” members than others –Rating task –Production task –Is-A verification task –Feature naming Prototype Theory (Probabilistic Features)

46. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Ostrich Good member Bad member Prototype Theory (Probabilistic Features)

47. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Robin Good member Bad member Prototype Theory (Probabilistic Features)

48. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Chicken Good member Bad member Prototype Theory (Probabilistic Features)

49. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Bat Good member Bad member Prototype Theory (Probabilistic Features)

50. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Wren Good member Bad member Prototype Theory (Probabilistic Features)

51. Rating of Prototypicality Please rate the following in the category BIRD 123 4 5 6 7 Eagle Good member Bad member Prototype Theory (Probabilistic Features)

52. Rating of Prototypicality Robin: 1.1 Robin: 1.1 Eagle: 1.2 Eagle: 1.2 Wren: 1.4 Wren: 1.4 Ostrich: 3.3 Ostrich: 3.3 Chicken: 3.8 Chicken: 3.8 Bat: 5.8 Bat: 5.8 Prototype Theory (Probabilistic Features)

53. Production Task In a minute’s time, list as many fruits as you can. In a minute’s time, list as many fruits as you can. People will generate prototypical fruit names earlier on the list (“apple, orange, peach, grapefruit, apricot, grapes, blueberries, honeydew”) People will generate prototypical fruit names earlier on the list (“apple, orange, peach, grapefruit, apricot, grapes, blueberries, honeydew”) Correlates with prototypical rating Correlates with prototypical rating Prototype Theory (Probabilistic Features)

54. Verification Task Determine whether each sentence is true or false Determine whether each sentence is true or falseE.g. –A robin is a bird. –A chicken is a bird. –An apple is a fruit. –A fig is a fruit. Prototype Theory (Probabilistic Features)

55. Verification Task High Freq Low Freq +Proto(orange-fruit)(peach-fruit) -Proto(fig-fruit)(coconut-fruit) Fast Slow Moderate > > Finding: Prototypical items categorized faster. Prototype Theory (Probabilistic Features)

56. Feature Naming Task: List all of the features for subordinate categories Task: List all of the features for subordinate categories E.g., FRUIT: apple; lemon; fig. E.g., FRUIT: apple; lemon; fig. Prototype Theory (Probabilistic Features)

57. Feature Naming Findings: 1. Necessary and sufficient features DO NOT emerge 2. Prototypical exemplars share more features with other exemplars Prototype Theory (Probabilistic Features)