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
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
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
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
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.
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.
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
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.
Editor's Notes
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.