Knowledge

Question 1

What does obcject recognition rely on?
(conceptual knowledge)

Answer 1

Bottom-Up
-> Visual Analysis of a stimulus

Top-Down
-> Knowledge structures to match the input to

Question 2

What is a concept / category?
(conceptual knowledge)

Answer 2

Concept
-> Mental representation of a class or individual
-> used for different cognitive functions

Category
-> Collection of all possible examples of a perticular
concept

Categorization
-> The process by which things are placed into categories

Question 3

Why are categories useful?
(conceptual knowledge)

Answer 3

=> knowledge is organized in categories

= Categories as “pointers to nowledge”
-> allows acces to the knowledge associated with the
concept of the category

-> store information common to all category members
- helps generelize new cases
- crucial for everyday life and survival
- focus on what makes individual members different
from others (efficient)

Question 4

How is category membership determined?
(approach to categorization)

Answer 4

1. Definitional approach
2. Protype approach
3. Exemplar approach

Question 5

What is the definitional approach to categorization?
(approach to categorization)

Answer 5

= Determine category membership based on whether the object meets the category definition

-> does not work well

-> not all members of everyday categories have the same defining features

Definitions often do not include all members of a category
-> instead members of a category resemble one another in several ways
-> not all members will be similar in all ways, but in some

Question 6

What is the prototype approach to categorization?
(approach to categorization)

Answer 6

= Determine category membership by comparing an
object to a prototype from the category

-> Abstract representation of the typical member of a category
-> has characteristic features describing what members of that concepts are like

Prototype as the average of commonly experienced
members of a category
-> created by adding together many pictures
-> if objects have some commonalities, they appear as a pattern in the average

Question 7

What is the attribute of prototypicality?
(approach to categorization)

Answer 7

High prototypicality
-> Category member closely resembles the prototype

Low procotypicality
-> Category member doesn’t closely resemble the
prototype

Prototypicality is correlated with family resemblance
-> Typical objects share more properties than untypical ones

Question 8

Whay are prototypical objects “special”
(approach to categorization)

Answer 8

Typicality effect
(Medin & Smith, 1984)
-> sentence verification technique
(compare un/typical object on category membership)
=> highly prototypical objects are judged quickly

Prototypical objects are named first in category
(Mervis, 1976)

Priming of prototypes
(Rosch, 1975)
Task: judge whether two color patches are same color
Prime: Hear the word green
Result:
-> fastest when a prototypical green was presented
-> slower for a light green

Interpretation
-> Prime activated prototypical image of green in
participants minds

Question 9

What is the exemplar approach to categorization?
(approach to categorization)

Answer 9

= Concept is representated by multiple exemplars
-> exemplars are actual category members a person
has encountered
-> to categorize, new object is compared to stored
exemplars

Question 10

What is the favored approach between Prototype and exemplar ?
(approach to categorization)

Answer 10

= the more similar an object is to the category prototype or a known exemplar, the faster it will be categorized

Exemplar
-> takes atypical cases into account and does not discard
information through averaging
-> represents highly variable categories better

Prototype
-> represents large categories better

=> We initially use prototype approch and later add
exemplars

Question 11

How are categories hierarchically organized?
(hierarchical categorization system)

Answer 11

Some categories are more general than others
-> categories form a hierarchical system with at least
three levels
- global
- basic
- specific

Question 12

What makes the basic level of a category special?
(hierarchical categorization system)

Answer 12

= optimal balance between being informative and economic when listing objects of one category

Free naming Task
-> Basic level names are preferred when naming objects

Category verification task
-> Category name first than image of object
-> Decide whether object belongs to category or not
=> fastest for basic-level category

Children
-> learn basic level concepts sooner

=> Basic level used in everyday language
=> Different cultures use same basic-level categories

Question 13

Are objects categorized uniform for every one?
(hierarchical categorization system)

Answer 13

Expertise
-> changes preferred level of response

Factors that influence our experience
-> Age
-> Gender
-> Culture
-> Profession
-> Object availabiity

Question 14

What is a semantic network
(semantic networks)

Answer 14

Sematic Networks
(Collins & Quillian, 1969)
Model of how the mind represents and organizes categories and concepts

Consists of
-> Nodes as Categories
-> Links between Categories
-> Properties of the categories

Network is hierarchical, related concepts are conected

Principle of cognitive economy
-> shared properties are stored at higher nodes and
inherited
-> Exceptions are stored at lower nodes

Question 15

What is the principle of cognitive economy?
(semantic networks)

Answer 15

= Shared properties are stored at higher nodes and inherited

Example: Canary
Canary: Can sing, is yellow
Bird: Can fly, has wings, has feathers
Animal: Can move, has skin
Living thing: Can grow, is living

-> by not storing “can fly” for each bird, system “saves storage space”

Question 16

What is the evidence in support of Semanic networks?
(semantic networks)

Answer 16

Collins & Quillian, 1969
Prediction:
-> Time to retrieve information about a concept should
be determined by the distance one would have to
travel in the network
Task
-> Sentence verification task using category membership
and feature descriptions at different levels
- canary is canary (0-level jump)
- canary is a bird (1-level jump)
- canary is an animal (2-level jump)

Results
-> Reaction Time well predicted by distance

Meyer & Schvaneledt, 1971
Prediction
-> if a node is activated, activation will spread along the connected links (spread activition)

=> Conceps that receive activation are primed and more easily accessed from memory

Task
-> Lexical decision task
(decide if both words are real english or not)

Result
-> Reaction time of words associated in same category faster than not associated words

Question 17

What criticism do Collins and Quillian’s model face?
(semantic networks)

Answer 17

Cannot explain typicality effect
-> apple / pomegranate is a fruit not equally fast
responded although same distance from fruit

Cognitive economy and inheritance of properties called into question
-> same distance properties have different RTs
-> properties are not equally frequent

Question 18

What is the parallel distributed processing model?
(connectionist network aproach)

Answer 18

Model inspired by neural architecture
-> circle = neuron-like units
-> lines = axon-like connections between units

Signal flow: Input to hidden to output units
-> Input units: Activated by stimulation from environment (receptor)

Connections have different connection weights
-> weights determine how strongly incoming signal will activate the next unit

Model represents concepts by distributed activity across network
-> some units are activated more strongly compared to others

Question 19

How is a PDP model structured?
(connectionist network approach)

Answer 19

McClelland & Rogers, 2003
Representing concepts and properties

Knowledge is represented in seperate nodes
-> Concepts
-> Relations
-> Properties

Knowledge is reconstructed
-> activate the concept and the relation
-> activity spread along the network (determined by
weights)
-> activation from concept and relation units converge in
hidden layers
-> property units are activated accordingly

Question 20

How does the network learn?
(connectionist network approach)

Answer 20

Two critical features for learning in these models
-> weights determine the activation of following units
-> sending an error signal backwards (back propagation)

Untrained model (all weights equal)
= All representation units respond equally to input
Canary -> Hidden1 -> fly, sing, swim
Can -> Hidden1 -> fly, sing, swim
(Back propagation of error signal)

Trained model (weights adjusted, many repetitions)
= Representation units respond similarly to similar input
Canary -> Hidden1 -> fly, sing
Can -> Hidden1 -> fly, sing
(When input from these two units received, dont send signal to that unit)

Question 21

What are the advantages of connectionist networks?
(connectionist network approach)

Answer 21

Biological plausibility
-> Proposed structure and function is based on
information representation in the brain

Can explain several controversial findings
-> typicality
-> graded category membership
-> generelization of learning

Gradual disruption of performance
-> Damage to a network does not destroy it completely

=> Strong influence on cognitive science and machine learning / AI development