This document summarizes different theories of how knowledge is organized in memory. It discusses declarative versus procedural knowledge, with declarative being "knowing that" facts and procedural being "knowing how" to perform skills. Concepts, categories, networks and schemas are reviewed as ways to organize declarative knowledge. Prototype and exemplar theories are described as alternatives to defining categories solely based on necessary features. The ACT-R model integrates propositional networks to represent declarative knowledge and production systems for procedural knowledge.

1. Representation and Organization of
Knowledge in Memory: Concepts,
Categories, Networks, and Schemas
CHAPTER 8
COGNITIVE PSYCHOLOGY
PSYC 60

2. Declarative versus Procedural
Knowledge

3. Declarative Knowledge
 “Knowing that”
 Knowledge of facts about cognitive psychology, about
world history, about your personal history, and
about mathematics.
 Describing

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. ACTIVITY #1

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. CMLIX 959
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8. Organization of Declarative
Knowledge

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. 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. Networks
 How concepts can be organized by means of
hierarchically organized semantic networks.

12. Schemas
 Mental frameworks of knowledge that encompass a
number of interrelated concepts.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Representations of How We Do
Things: Procedural Knowledge

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. 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. 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. Activity #2
PROCEDURAL KNOWLEDGE

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. Activity #3
PRIMING

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. 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. 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. Integrative Models for
Representating Declarative and
NonDeclarative Knowledge

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. Components of the ACT-R model and
Propositional Network

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. 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. Procedural Knowledge within ACT-R
 Knowledge representation of procedural skills occurs
in three stages: cognitive, associative, and
autonomous.

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. 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. 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.  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. 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. 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. 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.

63. 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?