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Lecture

Lecture on Knowledge & Categorization

2 Pages
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Department
Psychology
Course Code
PSY270H1
Professor
Gillian Rowe

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Lecture 3
Definitional approach
By checking potential members against a list of defining criteria
Problems: most members fit in the category, but not all / some are better examples of a concept than
others / difficult to specify that are necessary features
Prototype approach
Family resemblance: many overlapping features = high resemblance = high prototypicality
Typicality effect: faster verification for high-prototypicality than low ones
Good point: not all members are the same
Problem: not necessarily one single average / wide variations of items within a categor y
Useful when initially average into prototype (and for large size)
Exemplar approach
Using real examples from the past, consider atypical cases as well
Problem: need to know the example
Useful when including exceptions (and for small size)
What? strengthweakness
Semantic
network model
Knowledge organized
in hierarchical manner
Cognitive economy:
shared properties
stored at higher-
level node
Cant explain typicality
effect
Predominant feature
effect: pig is an animal
faster than pig is a
mammal
Things can be classified
in different ways as well
Spreading
activation
model
Nodes interconnected
by links with
differential strength
(distance) = related
concepts are more
easily accessible =
faster recall
Explain semantic
priming effect
Explain typicality
effect
Too flexible
Length of link can be
determined by many
factors like personal
experience
Double dissociation between living & non-living things
Some patients impaired wit h living things, others wit h non-living things
Argument against this category-specific impairment (differences in defining characteristics)
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Description
Lecture 3 Definitional approach By checking potential members against a list of defining criteria Problems: most members fit in the category, but not all some are better examples of a concept than others difficult to specify that are necessary features Prototype approach Family resemblance: many overlapping features = high resemblance = high prototypicality Typicality effect: faster verification for high-prototypicality than low ones Good point: not all members are the same Problem: not necessarily one single average wide variations of items within a category Useful when initially average into prototype (and for large size) Exemplar approach Using real examples from the past, consider atypical cases as well Problem: need to know the example Useful when including exceptions (and for small size) What? strength weakness Semantic Knowledge organized Cognitive economy: Cant explain typicality network model in hierarchical manner shared properties effect stored at higher- Predominant feature leve
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