Knowledge Flashcards

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1
Q

What does obcject recognition rely on?
(conceptual knowledge)

A

Bottom-Up
-> Visual Analysis of a stimulus

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

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2
Q

What is a concept / category?
(conceptual knowledge)

A

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

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3
Q

Why are categories useful?
(conceptual knowledge)

A

=> 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)

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4
Q

How is category membership determined?
(approach to categorization)

A
  1. Definitional approach
  2. Protype approach
  3. Exemplar approach
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5
Q

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

A

= 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

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6
Q

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

A

= 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

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7
Q

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

A

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

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8
Q

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

A

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

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9
Q

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

A

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

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10
Q

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

A

= 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

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11
Q

How are categories hierarchically organized?
(hierarchical categorization system)

A

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

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12
Q

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

A

= 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

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13
Q

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

A

Expertise
-> changes preferred level of response

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

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14
Q

What is a semantic network
(semantic networks)

A

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

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15
Q

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

A

= 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”

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16
Q

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

A

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

17
Q

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

A

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

18
Q

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

A

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

19
Q

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

A

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

20
Q

How does the network learn?
(connectionist network approach)

A

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)

21
Q

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

A

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