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Representation and organization of knowledge in memory

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Representation and organization of knowledge in memory

  1. 1. Representation and Organization of Knowledge in Memory: Concepts, Categories, Networks, and Schemas CHAPTER 8 COGNITIVE PSYCHOLOGY PSYC 60
  2. 2. Declarative versus Procedural Knowledge
  3. 3. Declarative Knowledge  “Knowing that”  Knowledge of facts about cognitive psychology, about world history, about your personal history, and about mathematics.  Describing
  4. 4. Procedural Knowledge  “Knowing how”  Knowledge about how to follow procedural steps for performing actions.  Example: how to drive a car, how to write your signature, how to ride a bicycle to the nearest grocery store, and how to catch a ball.  Doing
  5. 5. ACTIVITY #1
  6. 6.  As quickly and as legibly as possible, write your normal signature, from the first letter of your first name to the last letter of your last name. Don’t stop to think about which letters come next. Just write as quickly as possible.  Turn the paper over. As quickly and as legibly as possible, write your signature backward. Start with the last letter of your last name and work toward the first letter of your first name.  Now, compare the two signatures. Which signature was more easily and accurately created?  For both signatures, you had available extensive declarative knowledge of which letters preceded or followed one another. But for the first task, you also could call on procedural knowledge, based on years of knowing how to sign your name.
  7. 7. CMLIX 959 xLVIII x58
  8. 8. Organization of Declarative Knowledge
  9. 9. Concept  The fundamental unit of symbolic knowledge (knowledge of correspondence between symbols and their meaning, for example, that the symbol “3” means three) is the concept—an idea about something that provides a means of understanding the world.  Ex. Apple (concept), which can relates to redness, roundness, or fruit.
  10. 10. Category  A group of items into which different objects or concepts can be placed that belong together because they share some common features, or because they are all similar to a certain prototype.  Ex. Apple (category), as in a collection of different kinds of apples. Apple (concept), within the category of fruit
  11. 11. Networks  How concepts can be organized by means of hierarchically organized semantic networks.
  12. 12. Schemas  Mental frameworks of knowledge that encompass a number of interrelated concepts.
  13. 13. Concepts and Categories  Natural Categories are groupings that occur naturally in the world, like birds or trees.  Artifact categories are groupings that are designed or invented by humans to serve particular purposes or functions.  Examples: automobiles, kitchen appliances
  14. 14. Concepts and Categories  Natural and artifact categories are relatively stable and people tend to agree on criteria for membership in them.  Ex. Tiger is always a mammal. Knife is always an implement used for cutting.
  15. 15. Concepts and Categories  Concepts, on the contrary, are not always stable but can change.  Some categories are created just for the moment or for specific purpose, for example, “things you can write on.”  These categories are called ad hoc categories. They are described in words but rather in phrases.  Ex. People in Uganda will probably name different things that you can write on than will urban Americans or Inuit Eskimos.
  16. 16. Concepts and Categories  Concepts appear to have a basic level (sometimes termed a natural level) of specificity, a level within a hierarchy that is preferred to other levels.  Ex. Apple – might characterize as a fruit, apple, red delicious apple, so on. The basic, preferred level is apple.  In general, the basic level is neither the most abstract nor the most specific.
  17. 17. Concepts and Categories  The basic level is the one that most people find to be maximally distinctive.  When people are shown pictures of objects, they identify the objects at a basic level more quickly than they identify objects at higher or lower levels.  Thus, the picture of the roundish red, edible object from a tree probably first would be identified as an apple. Only then, if necessary would it be identified as a fruit or a Red Delicious apple.
  18. 18. Feature-Based Categories: A Defining View  All those features are then necessary (and sufficient) to define the category. This means that each feature is an essential element of the category.  Together, the features uniquely define the category; they are defining features (or necessary attributes):  For a thing to be an X, it must have that feature. Otherwise, it is not an X.
  19. 19. Feature-Based Categories: A Defining View  Ex. Bachelor (male, unmarried, adult)  The features are each single necessary.  If one feature is absent, the object cannot belong to the category.  The three features are jointly sufficient.  If a person has all three features, then he is automatically a bachelor.
  20. 20. Prototype Theory: A Characteristic View  Prototype Theory – grouping of things together not by their defining features but rather by their similarity to an averaged model of the category.  Prototype is an abstract average of all objects in the category we have encountered before.  Crucial are characteristic features, which describe (characterize or typify) the prototype but are not necessary for it. They are commonly present in typical examples of concepts, but they are not always present.
  21. 21. Prototype Theory: A Characteristic View  Ex. Prototype of a game Prototype of a bird (robin or ostrich)  Whereas a defining feature is shared by every single object in a category, a characteristic feature need not be.
  22. 22. Prototype Theory: A Characteristic View  Classical concepts are categories that can be readily defined through defining features, such as bachelor.  Fuzzy concepts are categories that cannot be so easily defined, such as game or death.
  23. 23. Prototype Theory: A Characteristic View  Exemplars are typical representatives of a category.  Ex. Birds, we might think not only of the prototypical songbird, which is small, flies, builds nest, sings, and so on. We also might think of exemplars for birds of prey, for large flightless birds, for medium-sized waterfowl, and so on.
  24. 24. A Synthesis: Combining Feature-Based and Prototype Theories  Core refers to the defining features something must have to be considered an example of a category.  Ex. Robber (the core requires that someone labeled as a robber be a person who takes things from others without permission) white-collar criminals vs. unkempt denizens
  25. 25. A Synthesis: Combining Feature-Based and Prototype Theories  First person: a smelly, mean old man with a gun in his pocket who came to your house and took your TV set because your parents didn’t want it anymore and told him he could have it.  Second person: a very friendly and cheerful who gave you a hug, but then disconnected your toilet bowl and took it away without permission and with no intention to return it.
  26. 26. Theory-Based View of Categorization  Also called an explanation-based view.  A theory-based view of meaning holds that people understand and categorize concepts in terms of implicit theories, or general ideas they have regarding those concepts.
  27. 27. Theory-Based View of Categorization  Ex. What makes someone a “good sport” ?  Componential view, isolate features of a good sport.  Prototype view, find characteristic features of a good sport.  Exemplar view, find some good examples you have known in your life  Theory-based view, use your experience to construct an explanation for what makes someone a good sport.
  28. 28. Theory-Based View of Categorization  A good sport is someone who, when he or she wins, is gracious in victory and does not mock losers or otherwise make them feel bad about losing. It is also someone who, when he or she loses, loses graciously and does not blame the winner, the referee, or find excuses.  Note: it is difficult to capture the essence of the theory in a word or two.  The theory-based view suggests that people can distinguish between essential and incidental, or accidental, features of concepts because they have complex mental representations of these concepts.
  29. 29. Semantic-Network Models  Suggest that knowledge is represented in our minds in the form of concepts that are connected with each other in a web-like form.
  30. 30. Collins and Quillan’s Network Model  An older model still in use today is that knowledge is represented in terms of hierarchical semantic (related to meaning as expressed in language—i.e., in linguistic symbols) network.  A semantic network is a web of elements of meaning (nodes) that are connected with each other through links.  The elements are called nodes; they are typically concepts.
  31. 31. Collins and Quillan’s Network Model  The connections between the nodes are labeled relationships.  They might indicate category membership (an “is a” relationship connecting “pig” to “mammal”), attributes ( connecting “furry” to “mammal”), or some other semantic relationship.  A network provides a means for organizing concepts.  The labeled relationships form links that enable the individual to connect the various nodes in a meaningful way.
  32. 32. a b Structure of a Semantic Network In a simple semantic network, nodes serve as junctures representing concepts linked by labeled relationships: a basic network structure showing that relationship R links the nodes a and b. R Labeled relationship (link)
  33. 33. Hierarchical Structure of a Semantic Network A semantic network has a hierarchichal structure. The concepts (represented through the nodes) are connected by means of relationships (arrows) like “is” or “has.”
  34. 34. Schematic Representations  Schemas – mental framework for organizing knowledge. It creates a meaningful structure of related concepts.  A cognitive structure that organizes related concepts and integrates past events.  Ex. Kitchen (tells us the kind of things we might find in a kitchen and where we might find them)
  35. 35. Schematic Representations  Schemas have several characteristics that ensure wide flexibility in their use.  Schemas can include other schemas. Ex. A schema for animals includes a schema for cows, a schema for apes, and so on.  Schemas encompass typical, general facts that can vary slightly from one specific instance to another.  Schemas can vary in their degree of abstraction.
  36. 36. Schematic Representations  Script contains information about the particular order in which things occur.  Ex. Restaurant script (coffee shop)  Props: tables, a menu, food, a check, and money  Roles to be played: a customer, a waiter, a cook, a cashier, and an owner.  Opening conditions for the script: the customer is hungry, and he or she has money  Scenes: entering, ordering, eating, and exiting  A set of results: the customer has less money; the owner has more money; the customer is no longer hungry; and sometimes the customer and the owner are pleased.
  37. 37. Schematic Representations  Jargon – specialized vocabulary commonly used within a group, such as a profession or a trade.  Imaging studies reveal that the frontal and parietal lobes are involved in the generation of scripts. The generation of scripts requires a great deal of working memory. Further script generation involves the use of both temporal and spatial information.
  38. 38. Schematic Representations  Scripts enable us to use a mental framework for acting in certain situations when we must fill in apparent gaps within a given context.
  39. 39. Representations of How We Do Things: Procedural Knowledge
  40. 40. The “Production” of Procedural Knowledge  Serial Processing of information, in which information is handled through a linear sequence of operations, one operation at a time.  Production, which includes the generation and output of a procedure.(“if-then” rules)  If you want to complete a particular task or use a skill, you use a production system that comprises the entire set of rules (productions) for executing the task or using the skill
  41. 41. The “Production” of Procedural Knowledge  Ex. A pedestrian to cross the street at an intersection with a traffic light.  Traffic-light red stop  Traffic-light green move  Move and left foot on pavement step with right foot  Move and right foot on pavement step with left foot
  42. 42. Nondeclarative Knowledge  Specifically, in addition to declarative knowledge, we mentally represent the following forms of nondeclarative knowledge:  Perceptual, motor, and cognitive skills (procedural knowledge)  Simple associative knowledge (classical and operant knowledge)  Simple non-associative knowledge (habituation and sensitization); and  Priming
  43. 43. Activity #2 PROCEDURAL KNOWLEDGE
  44. 44. Procedural Knowledge  Ask a friend if he or she would like to win $20. The $20 can be won if your friend can recite the months of the year within 30 seconds—in alphabetical order. Go!  In the years that we have offered this cash to the students in our courses, not a single student has ever won, so your $20 is probably safe. This demonstration shows how something as common and frequently used as the months of the year is bundled together in a certain order. It is very difficult to rearrange their names in an order that is different from their commonly used or more familiar order.
  45. 45. Activity #3 PRIMING
  46. 46. Priming  Recruit at least two (and preferably more) volunteers. Separate them into two groups. For one group, ask them to unscramble the following anagrams (puzzles in which you must figure out the correct order of letters to make a sensible words): ZAZIP, GASPETHIT, POCH YUSE, OWCH MINE, ILCHI, ACOT.  Ask the members of the other group to unscramble the following anagrams: TECKAJ, STEV, ASTEREW, OLACK, ZELBAR, ACOT.
  47. 47. Priming  For the first group, the correct answers are pizza, spaghetti, chop suey, chow mien, chili, and a sixth item.  The correct answers for the second group are jacket, vest, sweater, cloak, blazer, and a sixth item.  The sixth item in each group may be either taco or coat.  Did your volunteers show a tendency to choose one or the other answer, depending on the preceding list with which they were primed?
  48. 48. Two types of Priming  Semantic priming – we are primed by a meaningful context or by meaningful information. Such information typically is a word or cue that is meaningfully related to the target that is used.  Ex. Fruits or green things, which may prime lime.  Repetition priming – a prior exposure to a word or other stimulus primes a subsequent retrieval of that information.  Ex. Hearing the word lime primes subsequent stimulation for the word lime.
  49. 49. Integrative Models for Representating Declarative and NonDeclarative Knowledge
  50. 50. Combining Representations: ACT-R  Adaptive Control of Thought  John Anderson  In ACT, procedural knowledge is represented in the form of productive systems. Declarative knowledge is represented in the form of propositional networks.  Anderson (1985) defined a proposition as being the smallest unit of knowledge that can be judged to be either true or false.  ACT-R (R stands for rational) most recent version, is a model of information that integrates a network representation for declarative knowledge and a production-system representation for procedural knowledge.
  51. 51. Components of the ACT-R model and Propositional Network
  52. 52. Declarative Knowledge within ACT-R  Given each node’s receptivity to stimulation from neighboring nodes, there is spreading activation within the network from one node to another.  Therefore, the nodes closely related to the original node have a great deal of activation.  Ex. When the node for mouse is activated, the node for cat also is strongly activated. At the same time, the node for deer is activated (because a deer is an animal as well), but to a much lesser degree.
  53. 53. Declarative Knowledge within ACT-R  Thus, within semantic networks, declarative knowledge may be learned and maintained through the strengthening of connections as a result of frequent use.
  54. 54. Procedural Knowledge within ACT-R  Knowledge representation of procedural skills occurs in three stages: cognitive, associative, and autonomous.
  55. 55. 1. Cognitive Stage  We think about explicit rules for implementing the procedure.  Ex. We must explicitly think about each rule for stepping on the clutch pedal, the gas pedal, or the break pedal. Simultaneously, we also try to think about when and how to shift gears.
  56. 56. 2. Associative Stage  We consciously practice using the explicit rules extensively, usually in a highly consistent manner.  Ex. We carefully and repeatedly practice following the rules in a consistent manner. We gradually become more familiar with the rules. We learn when to follow which rules and when to implement which procedures.
  57. 57. 3. Autonomous Stage  We use these rules automatically and implicitly without thinking about them. We show a high degree of integration and coordination, as well as speed and accuracy.  Ex. At this time we have integrated all the various rules into a single, coordinated series of actions. We no longer need to think about what steps to take to shift gears. We can concentrate instead on listening to our favorite radio station. We simultaneously can think about going to our destination, avoiding accidents, stopping for pedestrians, and so on.
  58. 58.  Our progress through these stages is called proceduralization.  Proceduralization is the overall process by which we trans form slow, explicit information about procedures (“knowing that”) into speedy, implicit, implementations of procedures (“knowing how”).
  59. 59. Parallel Processing: The Connectionist Model  Multiple operations go on all at once.  According to parallel distributed processing (PDP) models or connectionist models, we handle very large numbers of cognitive operations at once through a network distributed across incalculable numbers of locations in the brain.
  60. 60. Knowledge Represented by Patterns of Connections Each individual unit (dot) is relatively uninformative, but when the units are connected into various patterns, each pattern may be highly informative, as illustrated in the patterns at the top of this figure. Similarly, individual letters are relatively uninformative, but patterns of letters may be highly informative. Using just three-letter combinations, we can generate many different patterns, such as DAB, FED, and other patterns shown in the bottom of this figure.
  61. 61. Parallel Processing: The Connectionist Model  In the brain, at any one time, a given neuron may be inactive, excitatory, or inhibitory.  Inactive neurons are not stimulated beyond their threshold of excitation. They do not release any neurotransmitters into the synapse.  Excitatory neurons release neurotransmitters that stimulate receptive neurons at the synapse. They increase the likelihood that the receiving neurons will reach their threshold of excitation.  Inhibitory neurons release neurotransmitters that inhibit receptive neurons. They reduce the likelihood that the receiving neurons will reach their threshold of excitation.
  62. 62. QUIZ 1. Define declarative knowledge and procedural knowledge, and give examples of each. 2. What is a script that you use in your daily life? How might you make it work better for you?

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