7 - Conceptual Learning and Semantic Knowledge Flashcards

1
Q

define typicality

A

object showing most usual characteristics of a particular type or thing therefore being a good example of that type

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2
Q

why can items be a less typical example even when they show similarities to the prototype

A

share fewer similarities than other category members

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3
Q

define economy of storage

A

propositions of many items stored once and apply to anything underneath it

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4
Q

define generalisation

A

inference of unseen properties about a concept to new members based on what we already know

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5
Q

The defining attributes model is set up how

A

categories stored as concepts from super to subordinate and categories get narrower in lower levels

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6
Q

why is defining attributes bottom-up

A

look in the environment and informed by experiences as look at if novel things have certain attributes to determine its category

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7
Q

properties and concepts stored where are more easily accessible

A

at the bottom so accessed faster

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8
Q

define prototype approach

A

categories have a central description or prototype standing for the whole category which the concept is represented by

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9
Q

(prototype) category members share what

A

family resemblance as have common features but look different

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10
Q

(prototype) what is the prototype informed by

A

experiences and change more w more experience

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11
Q

(prototype) the prototype has what attributes

A

average and characteristic ones of all things in that category, not defining

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12
Q

(prototype) how are items categorised

A

if they’re similar enough to the prototype but don’t have to have exact same attributes unlike defining attributes

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13
Q

define graded membership in the prototype approach

A

members closer to similarity to the prototype are better members of the category than those further away

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14
Q

(prototype) we compare novel things to what

A

things we have knowledge and representations of already

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15
Q

define the exemplar approach

A

compare novel thing to set of all known instances of examples of a category that come to mind which we have encountered

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16
Q

what is exemplar based reasoning

A

categorisation relies on knowledge about specific members rather than the prototype

17
Q

(exemplar) how do we categorise

A

based on similarity to exemplars and if similar enough

18
Q

(exemplar) what information is preserved

A

how there is variability between things’ attributes

19
Q

what does the prototype approach do to all instances of something

A

averages them and excludes info about their variability

20
Q

why can objects be categorised in the same category after superficial changes

A

deep properties count more than superficial ones

21
Q

define connectionist approach

A

brain is like a computer and deals with input, processing, and outputs and makes mdoels

22
Q

what is parallel distributed processing

A

experience-driven network, as initially have no knowledge or connections

23
Q

(PDP) what is each idea represented by

A

pattern of activation across the network

24
Q

(PDP) how can we train the network

A

present it with experiences based on info in the hierarchy which the network leads to map onto different attributes

25
Q

(PDP) what do left units represent

A

basic levels of categories of things we physically encounter

26
Q

(PDP) what do left units project through

A

weighted connections to hidden units

27
Q

(PDP) what do hidden untis do

A

abstract units connecting to attributes and concepts on the right and are activated by left units

28
Q

(PDP) what do hidden units capture

A

complex relationships between input and output units

29
Q

(PDP) what are relational units

A

when looking at things, make inferences ab the attributes to store info about its functions and what it is (can, has, is)

30
Q

(PDP) what are the right units for

A

all possible completions of three term postpropositions true of all the concepts

31
Q

(PDP) what happens when the output is generated using error-driven learning

A

model told correct or not then in/decreases weights to increase link (assoc) between the input, hidden, and correct output units

32
Q

define progressive differentiation

A

differentiating between superordinate categories in EL which being reinforced by parents when successfully differentiating

33
Q

define coherent covariation

A

items within a specific category share many attributes and more than with items from different categories

34
Q

what is there consistent correlation between

A

sets of attributes between diff objects which are true for all things in one category but not for things in another category of the same level