Cog5 lecppt chapter08

© 2010 by W. W. Norton & Co., Inc.

Concepts and Generic Knowledge

Chapter 8Lecture Outline

Chapter 8: Concepts and Generic Knowledge

Lecture OutlineDefinitionsPrototypes and Typicality EffectsExemplarsDifficulties with Categorizing via ResemblanceConcepts as Theories

Definitions

Concepts like dogs or chairs Building blocks Simple but complex to

explain

Definitions

DogDefinition

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

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.

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

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.

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

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

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


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


PrototypesGraded membership

Some members are closer to the prototype

Fuzzy boundaries No clear dividing line for membership


Which is the best red?

Evidence Favoring the Network Approach

The sentence-verification task. typicality effects

True or false?Robins (知更鳥 ) are birdsPenguins are birdsThis is because robins share more features

with the prototypical “bird” than penguins do.


using production tasks Typicality effects

Name as many fruits as possibleName 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.


Does this picture show you a bird?

[Insert typical bird]

[Insert a penguin]

Faster

Slower

picture-identification tasks


The more prototypical category members are also “privileged” in rating tasks


Birds in a tree?

Not thisImagine this


Typicality also influences judgments about attractiveness. Which fish is the most attractive?


Just as certain category members seem to be privileged, so are certain types of category

For example, what is this object?


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 categoriesHere, “chair” is the basic-level category, as opposed to “furniture” (more general, or superordinate) or “wooden desk chair” (more specific, or subordinate)


Basic-level categories Single word. The default for basic level Easy-to-explain commonalities


Basic categories are learned firstUsed by children to describe most objects

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.

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.

Exemplars

Prototypes Economical but less flexible

ExemplarsMore 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.

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.

Exemplars

Every concept is a mix of exemplar and prototypeEarly learning involves exemplarsExperience involves averaging exemplars to

get prototypesWith more experience, we can use both

Difficulties with Categorizing via Resemblance

Category membership and typicality Prototypes that are based on averaged

exemplarsA process of triggering memories

This is because both judgments should be based in resemblance between the test case and the prototype or exemplar.


Typicality and category membership sometimes dissociate

Moby Dick （白鯨） was a whale （鯨魚） but not a typical one


The category is clear and yet typicality goes down


Atypical features do not exclude category membersFor 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


All the typical features but not category members For example, a perfect

counterfeit bill.


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…


Essential propertiesThose that define a categoryWhich 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

Concepts as Theories

ResemblancePrototypes and exemplars work

categorization is based in comparing the resemblance of the test case to prototypes and exemplars

Not enoughPerfect counterfeit bill resembles a bill but is

not


Heuristic（捷思）A reasonably efficient strategy that works

most of the time the resemblance of more superficial features is

compared

Prototypes and exemplarsHeuristics allow some degree of error in

exchange for efficiency


When heuristics fail, may need a more complete viewConcept-as-theory


whipped cream airplanesReal airplanes resemble


Concepts are like schemasThey allow people to form generalizationsRelated 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).


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.


Natural kinds and artifacts are reasoned about differentlyNatural kinds (e.g., the skunk and raccoon)

have essential propertiesThese principles do not apply to artifacts (e.g.,

toaster and coffeepot)


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).

Knowledge Network

Knowledge is represented via a vast network of connections and associations between all of the information you know

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.

Knowledge Network

“Cats have hearts” requires two links “Cats have claws” requires one link

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).

Knowledge Network

Nodes can represent concepts Links such as hasa or isa can associate

each concept

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.

Knowledge Network

Abstract knowledge represented via time and location nodes

Knowledge Network

Propositional networksLocalist representations—each node is

equivalent to one concept Connectionist networks (parallel

distributed processing, PDP)Distributed processing—information involves

a pattern of activationParallel processing of information occurs at

the same time

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

Knowledge Network

Learning algorithms—how weights are changedBoth nodes firing together strengthen their

connectionError signals cause a node to decrease its

connections to input nodes that led to the error (back propagation)

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

Chapter 8 Questions

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.

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

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.

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.

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.

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

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.

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Cog5 lecppt chapter08

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