1. Categories
My dog sleeping. My dog. All golden retrievers. All dogs. All canines. All mammals…
Each of these is a category.
Categorization is the process of deciding which details matter, and which don’t, for some purpose.
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2. Bruner, Goodnow, & Austin (1956)
Categories
reduce complexity of environment
allow us to generalize lessons
guide choice of response
make hierarchical knowledge available
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3. Two questions about categories
1. What is the structure of natural categories like?
This is a question about the world.
2. How is information about natural categories represented in memory?
This is a question about your mind.
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4. 1. The structure of categories in the world
Is the question below a question about the world, or about us?
“Which two are most similar: sheep, goats, cows?”
To some extent, the structure of natural categories is given by the world.
To some extent, it is impressed upon the world by human cognition
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5. 1. The structure of categories in the world
Most important work done by Eleanor Rosch.
Rosch argued that our categorical knowledge is organized in a hierarchy:
Superordinate, basic level, subordinate.
This is a relation of containing.
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6. Collie Airedale Persian Siamese Arabian
Mammal
Dogs Cats Horses
Collie Airedale Persian Siamese Arabian
Superordinate
Basic Level
Subordinate
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7. The Basic Level
The basic level falls between superordinate and subordinate levels.
It’s the one we use when we name an object.
It’s the one children learn first.
Things in a basic level category look like other things in the same category but not like things in other categories. This quality is not true at other levels.
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8. Furniture Types of chair
Table
Chair
Lamp
Bookcase
This is a super-ordinate category
Dining room table
Patio table
Coffee table
Picnic table
This is a subordinate category
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9. Review: Hierarchy according to Rosch
Things in the world present themselves in a hierarchy of levels of categorization
Basic level, items in a category look like each other but not like members of other categories.
Basic level is first one learned and one used spontaneously in naming objects.
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10. Rosch’s second contribution - Typicality
Rosch argued that some members of a category are “better” than others – that is, more typical.
such members have ‘family resemblance.’
typical members can be verified fastest (implying fast access to their repns.)
typical members are similar to other members, unlike non-members of category
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11. Things in the world vs. in the head
So far, we’ve been talking about things in the world. We asked, how do these things assemble themselves into categories?
Now, we turn to the question of things in your head.
What is the nature of your knowledge about categories? How are they represented?
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12. 2. Mental representation of categories
Three kinds of categories:
Natural (e.g., Mammals)
Artificial (e.g., Animals that weigh > 100 pounds)
Functional (e.g., Things to bring out of the house in case of fire.)
We’ll consider only natural categories
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13. Mental representation of natural categories
There are four competing models. Each specifies how a decision on category membership is made (how do you decide if this is an X?). Note – they might all be wrong.
1. Prototype
2. Feature frequency
3. Nearest neighbour
4. Average distance
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14. Prototype models A prototype is a typical member of a category
Prototype theories say that, through experience, we create a central example of each category, and store that example.
A prototype captures what is typical of a category
A prototype may exist only in your mind (e.g., not as an actual object in the world).
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15. Feature frequency models
Categorization is based on how many features the to-be-classified object shares with each of the available categories.
E.g., a whale shares ‘breathes air’ and ‘gives birth to live young’ with mammals. It shares ‘lives in the ocean’ and ‘moves by tail and flipper action’ with fish.
So a whale could be a fish or a mammal…
Models predict confusion about whales
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16. Nearest neighbour models
New object is compared with each exemplar of each stored category.
Difference (on any dimensions) between object and each exemplar in each category is computed.
New object is classified in same category as object it is most similar to (smallest difference).
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17. Exemplar: New object: Duck Hat Category: Water fowl Category: Clothing
Has feathers
Quacks
Swims
Goose
Honks
Category: Water fowl
New object:
Hat
Textile
Has feathers
Worn on the body
Category: Clothing
Exemplar:
Tie
Textile
Worn on the body
Long and thin
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18. Average distance models
Comparison of new object to all stored exemplars of categories as in N. Neighbour.
Object goes in category with smallest average distance (exemplars to object).
Compare with Nearest Neighbour model – here, it is average distance for the category, not just which exemplar is closest, that counts.
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19. Review: models of representation
These models reflect 2 very different views of category representation:
Prototype model
‘what is generally true’ about something is stored and available when needed
this view emphasizes abstract representations (that is, not much detail)
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20. Review: models of representation
N.N. and A.D. models
‘what is generally true’ is not stored, but computed when needed.
these views emphasize storage of individual experiences with objects rather than storage of abstract ‘essences.’
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