lecture 2- categorization

Question 1

cognitive economy

Answer 1

-by dividing the world into classes of things we decrease the amount of information we need to learn, perceive, remember and recognize

Question 2

superordinate category

Answer 2

• high coverage, low predictive value
• often applicable
• yields little information
• example: animal

Question 3

basic level category

Answer 3

• trade-off usefulness/information
• example: dog
• basic level shifts downward as expertise grows -> in subordinate categories

Question 4

subordinate category

Answer 4

• low coverage

- high predictive value

Question 5

concept formation

Answer 5

• abstraction of feature set

- e.g. child acquires representation of concept ‘apple’

Question 6

concept learning

Answer 6

• by applying concept and getting feedback

- e.g. child learns that/why tomato is not an apple

Question 7

similarity based categorization (3)

Answer 7

1) classical theories
2) prototype- or probabilistic theories
3) exemplar- or instance-based theories

Question 8

classical theory

Answer 8

-category is represented by a series of defining features that specify category boundary

Question 9

flaws classical theory

Answer 9

• categorization would require perfect match between object and definition
• some objects are more typical members than others
• > these are learned and recognized faster

Question 10

Prototype-or probabilistic theories

Answer 10

• category is represented by a series of characteristic features
• prototype specifies category center

Question 11

prototype flaws

Answer 11

• list of attribute values ignores feature correlations?
• no information saved on the range of attribute values ?
• similarity to earlier example influences categorization

Question 12

exemplar-based/instance-based model

Answer 12

-category consists of set of examples

Question 13

exemplar based

flaws

Answer 13

• defining features ?

- memory capacity

Question 14

prototope or specific examples?

Answer 14

research on language acquisition and expertise development suggest:

• exemplars are more important during early learning
• abstract information (prototype) becomes more important as experience grows

Question 15

experts keep using examples

Answer 15

-physicians are more likely to diagnose correctly when they have recently seen a specific case that presents like the current one

Question 16

when having time pressure, do we use rules or similarities?

Answer 16

-similarities

Question 17

when having high task load, do we use rules or similarities?

Answer 17

-similarities

Question 18

when we have the instruction to use rule and be careful we use…

Answer 18

-rules

Question 19

when we have instruction to give first impression, do we use rules or similarities?

Answer 19

-similarities

Question 20

when we have random trading, do we use rules or similarities?

Answer 20

-similarities

Question 21

blocked training - rules/similarities?

Answer 21

-rules

Question 22

neuro-imaging , determining similarity with exemplars

Answer 22

-

Question 23

neuro-imaging, applying rule

Answer 23

-

Question 24

tabula rasa and the theories

Answer 24

• prototype and exemplar models start from a kind of tabula rasa
• > concept representations are built up solely by experience with exemplars
• ignore knowledge effects

Question 25

category types (4)

Answer 25

1) natural categories -> plants, animals, reflect correlational structure of environment 2) formal categories -> like adverbs, prime numbers 3) functional categories -> items for traveling, study materials 4) ad hoc categories ->things to save from a burning house - > no stable metal representations of categories