PSY270H1 Lecture Notes - Lecture 12: German Shepherd, Counterargument, Temporal Lobe

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Published on 28 Mar 2019
PSY270 Lecture 8 Knowledge
How is knowledge organized?
Knowledge is interconnected, we are storing knowledge in an interconnected
network. One piece of information is connected to another piece of information.
Knowledge is divided into categories
A category is a group of objects that belong together and have something
in common
An exemplar is an item in the category
Ex: in the dog category, a German shepherd or poodle is an
exemplar in that category
Categories allow us to predict what is likely in new situations because
they treat all exemplars as similar
A concept is representation in our minds of that category
Allows us to predict behavior
3 types of categories
Natural category
Occur naturally (e.g., plant, trees, cats)
Artifact category
Created by humans as we need them (e.g., hammers, computers)
Ad hoc category
Ex: packing a suitcase. We do not have a set category for this, but
can easily come up with a different category if we’re going to the
beach vs. ski trip
Classical view of Categorization
Categories are defined by a list of necessary and sufficient features
Necessary: an item must have all defining features to be included
in the category
Sufficient: any other attributes are not required for category
Grandmothers are senior women, maybe with grey hair, etc. but
none of these are required, the only thing that is required is being
a mother of a mother
We can classify new items according to whether or not they satisfy the list
of necessary features
Not all categories have a list of defining features
Ex: is a 3-legged dog still a dog? What if it lost its fur, is it
still a dog? Yes
Counterargument: Maybe we’re just not very good at coming up
with lists of features, it’s a human cognitive problem rather than
the problem of categories
Some items are more typical examples of a category than others, leading
to typically effects
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Items that are more typical have privileged processing. We tend to
name typical items faster. For example, if we’re asked to name all
the birds we can think of, we’ll name the typical ones first.
We tend to see priming effects associated with typical exemplar,
but not atypical ones
We generate typical exemplars more frequently than typical ones
Classical view says exemplars either have defining features or
they don’t, there’s no “typical bird” or a “good example of a bird”,
there is a bird or isn’t a bird. However, when given a list of birds
and asked which ones are more “birdy”, we’ll rate bats lower than
a pigeon
Prototype Theory
Categories are fuzzy (they have a graded structure)
In the bird category, birds are likely to fly, they are likely to have
features, but these are not required for category membership
Rather than having a set of defining features, exemplars have
characteristic features
We determine category membership by matching item with prototype
stored in memory
Categories have a central tendency where exemplars with the most
characteristic features are found
The more features an exemplar shares with the prototype, the
more typical it is
All category members share a family resemblance even if they are not
typical members
This prototype doesn’t necessarily have to exist in reality, it’s an average
of everything you’ve experienced
3 levels of categories
Basic level: moderately specific; not too broad or too narrow; they
are informative and distinctive
“Dog” “piano” “tree”
Superordinate: one level above basic; a broad category
“Mammal” “musical instruments” “plants
Subordinate: one level below basic; includes specific instances of
a basic level category
“Poodle” “steinway” “maple”
Exemplar Theory
Instead of storing a prototype and matching specific exemplars to it, we
store specific exemplars and can create a prototype if necessary
We are storing individual category members. If someone asked us to
describe a prototypical dog, we could do it. Prototype theory says we are
storing one prototype but if we are coming up with specific exemplars
from that category we could do that
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