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Cog5 lecppt chapter08

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  • Correct answer: b
    Feedback: Wittgenstein came up with the idea of family resemblance.
  • Correct answer: d
    Feedback: All three answers are correct.
  • Correct answer: a
    Feedback: Prototypes require exemplars and hence they emerge gradually.
  • Correct answer: b
    Feedback: Prototypes can be used in certain situations (under ideal circumstances), where exemplars may be available as well.
  • Correct answer: c
    Feedback: Heuristics are fast, efficient systems and hence categorization emphasizes superficial characteristics.
  • Correct answer: b
    Feedback: The first members are usually the prototypes and hence those most tightly linked to a category. The last members will be more loosely linked and hence last.
  • Correct answer: d
    Feedback: Exemplars are instances of an item. The idea is that we store these examples in our memory.
  • Transcript

    • 1. © 2010 by W. W. Norton & Co., Inc. Concepts and Generic Knowledge Chapter 8 Lecture Outline
    • 2. Chapter 8: Concepts and Generic Knowledge  Lecture Outline  Definitions  Prototypes and Typicality Effects  Exemplars  Difficulties with Categorizing via Resemblance  Concepts as Theories
    • 3. Definitions  Concepts like dogs or chairs  Building blocks  Simple but complex to explain
    • 4. Definitions  Dog  Definition  A mammal with four legs that barks and wags its tail  Exceptions  Dog that does not bark or that lost a leg  For any definition, we can always find such exceptions
    • 5. Definitions  Philosopher Ludwig Wittgenstein (1953)  Simple concepts have no definition  Consider a “game”  Played by children  Engaged in for fun  Has rules  Involves multiple people  Is competitive  Is played during leisure  For any set of definitive features, we can think of exceptions that are still considered games.
    • 6. Definitions Definition Exception Played by children Gambling? Engaged in for fun Professional sports Has rules Playing with Legos Involves multiple people Solitaire Is competitive Tea party Is played during leisure Flying simulators Games
    • 7. Definitions  Family resemblance  members of a category have a family resemblance to each other Ideal member Atypical member In the example, dark hair, glasses, a mustache, and a big nose are typical for this family but do not define the family.
    • 8. Definitions  A dog probably has four legs, probably barks, and probably wags its tail  A creature without these features is unlikely to be a dog
    • 9. Definitions  There may be no features that are shared by all dogs or all games, just as there are no features shared by every member of a family  The more characteristic features an object has, the more likely we are to believe it is part of the category
    • 10. Prototypes and Typicality Effects  Rosch’s prototype theory,  Prototypes  Rather than thinking about definitions that define the boundaries of a category  One that possesses all the characteristic features
    • 11. Prototypes and Typicality Effects  Prototype  An average of various category members that have been encountered  Differ across individuals (depending on their experiences)  May differ across countries  For example, the prototypical house in the United States compared to Japan
    • 12. Prototypes and Typicality Effects  Prototypes  Graded membership  Some members are closer to the prototype  Fuzzy boundaries  No clear dividing line for membership
    • 13. Prototypes and Typicality Effects  Which is the best red?
    • 14. Evidence Favoring the Network Approach  The sentence-verification task.  typicality effects  True or false?  Robins ( 知更鳥 ) are birds  Penguins are birds  This is because robins share more features with the prototypical “bird” than penguins do.
    • 15. Prototypes and Typicality Effects  using production tasks  Typicality effects  Name as many fruits as possible  Name as many birds as possible  If we ask people to name as many birds as they can, they typically start with category members that are closest to the prototype (e.g., robin). For fruit they are likely to start with bananas, apples, or oranges.
    • 16. Prototypes and Typicality Effects Does this picture show you a bird? [Insert typical bird] [Insert a penguin] Faster Slower picture-identification tasks
    • 17. Prototypes and Typicality Effects  The more prototypical category members are also “privileged” in rating tasks
    • 18. Prototypes and Typicality Effects Birds in a tree? Not thisImagine this
    • 19. Prototypes and Typicality Effects  Typicality also influences judgments about attractiveness. Which fish is the most attractive?
    • 20. Prototypes and Typicality Effects  Just as certain category members seem to be privileged, so are certain types of category  For example, what is this object?
    • 21. Prototypes and Typicality Effects Furniture Chair Upholstered armchair Too general Just right Too specific DetailExample Rosch argued that there is a basic level of categorization that is neither too general nor too specific, which we tend to use in speaking and reasoning about categories Here, “chair” is the basic-level category, as opposed to “furniture” (more general, or superordinate) or “wooden desk chair” (more specific, or subordinate)
    • 22. Prototypes and Typicality Effects  Basic-level categories  Single word.  The default for basic level  Easy-to-explain commonalities
    • 23. Prototypes and Typicality Effects  Basic categories are learned first  Used by children to describe most objects
    • 24. Exemplars  Exemplar  What is this?  An alternative to prototype theory is exemplar- based reasoning—drawing on knowledge of specific category members rather than on more general, prototypical information about the category.
    • 25. Exemplars Theory Prototype Exemplar Typicality Average of a category Encountered more often Graded membership Less similar to average How often it is encountered Illustration Ideal fruit (apple) vs. less ideal (fig 無花果 ) Apples (often) vs. figs (not as often) Both prototype theory and the exemplar view can explain the typicality and graded- membership effects that we have discussed.
    • 26. Exemplars  Prototypes  Economical but less flexible  Exemplars  More flexible but less economical  Chinese versus American Birds  A gift for a 4-year-old who recently broke her wrist  Our ability to “tune” our concepts to match circumstances may also fit better with the exemplar view than with prototype theory.
    • 27. Exemplars  Kermit the Frog  Prototypical features  Is green, eats flies  Exemplar (unique)  Sings, loves a pig Both prototype and exemplar provide information In sum, the evidence seems to suggest that we use a combination of prototypes and exemplars.
    • 28. Exemplars  Every concept is a mix of exemplar and prototype  Early learning involves exemplars  Experience involves averaging exemplars to get prototypes  With more experience, we can use both
    • 29. Difficulties with Categorizing via Resemblance  Category membership and typicality  Prototypes that are based on averaged exemplars  A process of triggering memories  This is because both judgments should be based in resemblance between the test case and the prototype or exemplar.
    • 30. Difficulties with Categorizing via Resemblance  Typicality and category membership sometimes dissociate  Moby Dick (白鯨) was a whale (鯨 魚) but not a typical one
    • 31. Difficulties with Categorizing via Resemblance The category is clear and yet typicality goes down
    • 32. Difficulties with Categorizing via Resemblance  Atypical features do not exclude category members  For example, a lemon that is painted with red and white stripes, injected with sugar to make it sweet, and then run over with a truck is still a lemon
    • 33. Difficulties with Categorizing via Resemblance  All the typical features but not category members  For example, a perfect counterfeit bill.
    • 34. Difficulties with Categorizing via Resemblance  Similar examples come from studies with children (Keil, 1986)  A skunk (臭鼬) cannot be turned into a raccoon (狸)  It has a raccoon mommy and daddy…  A toaster can be turned into a coffeepot  Just need to poke some holes in it…
    • 35. Difficulties with Categorizing via Resemblance  Essential properties  Those that define a category  Which are those?  some categories are reasoned about in terms of essential properties and not superficial attributes; for example, the abused lemon still has lemon DNA; it still has seeds that would grow into lemon trees
    • 36. Concepts as Theories  Resemblance  Prototypes and exemplars work  categorization is based in comparing the resemblance of the test case to prototypes and exemplars  Not enough  Perfect counterfeit bill resembles a bill but is not
    • 37. Concepts as Theories  Heuristic (捷思)  A reasonably efficient strategy that works most of the time  the resemblance of more superficial features is compared  Prototypes and exemplars  Heuristics allow some degree of error in exchange for efficiency
    • 38. Concepts as Theories  When heuristics fail, may need a more complete view  Concept-as-theory
    • 39. Concepts as Theories whipped cream airplanesReal airplanes resemble
    • 40. Concepts as Theories  Concepts are like schemas  They allow people to form generalizations  Related to typicality  Generalizations more likely from typical cases  Robins are more likely to be like all birds  Penguins are less likely  Research in this area shows that people are willing to make inferences from a typical case (e.g., robins) to an entire category (e.g., birds) but not from an atypical case (e.g., ducks).
    • 41. Concepts as Theories  Theories also explain cause and effect Lion Gazelle (羚羊) EnzymeEnzyme For instance, if told that gazelles have a particular enzyme, people conclude that lions have it as well. But they are not willing to make the reverse inference, given what they know about the food chain.
    • 42. Concepts as Theories  Natural kinds and artifacts are reasoned about differently  Natural kinds (e.g., the skunk and raccoon) have essential properties  These principles do not apply to artifacts (e.g., toaster and coffeepot)
    • 43. Concepts as Theories 43 Categories represented in different brain areas different sites are activated when people are thinking about living things than when they are thinking about nonliving things (e.g., Chao et al., 2002).
    • 44. Knowledge Network  Knowledge is represented via a vast network of connections and associations between all of the information you know
    • 45. Knowledge Network  Other evidence for the knowledge representation in a network comes from the sentence-verification task  Participants must quickly decide whether sentences like the following are true:  Robins are birds.  Robins are animals.  Cats have hearts.  Cats are birds.
    • 46. Knowledge Network  “Cats have hearts” requires two links  “Cats have claws” requires one link
    • 47. Knowledge Network Reaction time goes up for longer associative paths The time to answer these questions depends on the length of the associative path between the pieces of information (Collins & Quillian, 1969).
    • 48. Knowledge Network  Nodes can represent concepts  Links such as hasa or isa can associate each concept
    • 49. Knowledge Network Proposition = smallest unit that can be true or false Four propositions about dogs A more complex network (Anderson’s ACT) is designed around the notion of propositions—the smallest units of knowledge that can be true or false.
    • 50. Knowledge Network Abstract knowledge represented via time and location nodes
    • 51. Knowledge Network  Propositional networks  Localist representations—each node is equivalent to one concept  Connectionist networks (parallel distributed processing, PDP)  Distributed processing—information involves a pattern of activation  Parallel processing of information occurs at the same time
    • 52. Knowledge Network  How does learning take place in a connectionist or parallel distributed processing (PDP) network?  Changes in the connection weights or strength of connections
    • 53. Knowledge Network  Learning algorithms—how weights are changed  Both nodes firing together strengthen their connection  Error signals cause a node to decrease its connections to input nodes that led to the error (back propagation)
    • 54. Concepts  In sum, concepts are central to human reasoning, but are complex  We often reason about concepts using prototypes and exemplars, particularly in cases where fast judgments are required  However, for more sophisticated judgments, we also employ theories, represented by networks of interrelated conceptual knowledge  Finally, various computational networks have attempted to capture this complexity
    • 55. Chapter 8 Questions
    • 56. 1. According to Wittgenstein, a) we have no real general concept for each category we know but instead learn each category member individually. b) we assess category membership probabilistically, by family resemblance. c) we can find rigid features that define a category but only after intensive study. d) we first encounter the prototypical member of a category, and then we compare all other potential members to it.
    • 57. 2. Which of the following facts fits with the claims of prototype theory? a) Pictures of items similar to the prototype are identified as category members more quickly than pictures of items less similar to the prototype. b) Items close to the prototype are not the earliest (and most likely) to be mentioned in a production task. c) When making up sentences about a category, people tend to create sentences most appropriate for the prototype of that category, as opposed to a more peripheral member. d) all of the above
    • 58. 3. Which of the following claims is TRUE? a) Reliance on prototypes is likely to emerge gradually as a participant’s experience with a category grows. b) People are likely to rely strongly on prototypes early in their exposure to a particular category. c) People only rely on prototypes when they have time to make a decision. d) With exposure to many instances of a particular category, it becomes easier to remember each particular instance, and this contributes to the emergence of a prototype.
    • 59. 4. Which of the following is true? a) People only use prototypes when there are no clear definitions to fall back on. b) Just because people use prototypes does not mean that is the only information available to them. c) People use exemplars rather than prototypes whenever possible. d) Clearly defined category boundaries are necessary for deciding category membership.
    • 60. 5. Which of the following is true about heuristics? a) One way to ensure error-free decisions is to use the typicality heuristic. b) One example of a heuristic is determining cause and effect. c) The categorization heuristic emphasizes superficial characteristics. d) Using heuristics is an inefficient way to get things done.
    • 61. 6. In a production task, the ___ category members that a person mentions are the category members that produce the slowest reaction times in a sentence- verification task. a) first b) last c) loudest d) slowest
    • 62. 7. The idea that we categorize objects based on their similarity to previously stored instances is known as a) geometric theory. b) prototype theory. c) feature theory. d) exemplar theory.