Knowing

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Knowing

• Semantic memory

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Semantic Memory

• Memory of the general knowledge of the world
• While episodic memory is personal events that
  happened to you semantic memory is more general
  information that everyone can learn about the
  world

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Two basic questions asked

• 1. What is the structure and content of semantic
  memory?
• Current perspective is that semantic memory is a
  network of nodes each representing a basic
  concept and nodes are linked together
• 2. How do we access the information in semantic
  memory?
• Accessing or retrieving information from the
  network involves spreading activation

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Semantic memory models

• Quillen and Collins network model
• Smiths feature comparison model

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Collin and Quillian Model

• A network model interrelated concepts or nodes
  are organized into an interconnected network
  these connections can be direct or indirect
• Memory is the activation of a node which can
  spread to other nodes activating other memories
• Two forms of connections or propositions
• Category membership is a
• Property statements has

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Collin and Quillian Model
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Collin and Quillian Model
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Collin and Quillian Model
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Smiths feature overlap model

• Showed significant problems of the Quillen and
  Collins model
• Used lists of characteristics instead of a
  network
• Concepts are defined by a list of features.
  These features are stored in a redundant manner
• The decision of whether one concept is an example
  of an another depends upon the level of overlap

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Smiths feature overlap model
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Smiths feature overlap model

• Feature comparison
• Where features of two concepts overlap a great
  deal or very little, the decision is made quickly
• If some features overlap and others do not, then
  a stage 2 comparison has to be made and the
  decision is slower

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Smiths feature overlap model
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Empirical Tests of Semantic Memory Models

• Sentence Verification Task Simple sentences are
  presented for the subjects yes/no decisions.
• Most early tests of semantic memory models
  adopted the sentence verification task.

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Challenges to Collin and Quillian Model

• Support for Collin and Quillian was cognitive
  economy only nonredundant facts stored in
  memory. Conrad (1972) found that high frequency
  properties were stored in a redundant fashion

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Challenges to Collin and Quillian Model

• Conrad (1972) found that high frequency
  properties were more highly associated with the
  concepts and are verified faster than low
  frequency properties not shown in network model

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Challenges to Collin and Quillian Model
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Challenges to Collin and Quillian Model

• Typicality The degree to which items are viewed
  as typical, central members of a category.
• Typicality Effect Typical members of a category
  can be judged more rapidly than atypical members.

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Challenges to Collin and Quillian Model
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Modified Collin and Quillian Model
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Semantic Relatedness

• Semantic Relatedness Effect Concepts that are
  more highly interrelated can be retrieved and
  judged true more rapidly than those with a lower
  degree of relatedness.
• Resulted in a third revision of the model which
  required a 3-dimensional model

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Knowing

• Categorization, classification, and prototypes

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Knowledge

• Knowledge is the acquisition of concepts and
  categories your mental representations that
  contain information about objects, events, etc.

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Categorization

• Concepts usually involve the creation of
  categories
• Categories grouping things into groups based
  upon similar characteristics
• Categories help organize information so that you
  do not have learn about every new thing you
  expereince

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Concepts and Categories

• Two basic questions
• What is the nature of concepts?
• How do we form concepts and categories?
• Three approaches to these questions, classical,
  prototype, and exemplar

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Classical Approach - Aristotle

• Categories have defining features semantic
  features that are necessary and sufficient to
  define the category
• Necessary features have to be present for
  inclusion
• Sufficient if these features are present no
  other features are necessary for inclusion
• Problem most members of a category do not have
  the same defining features

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Prototypes

• A prototype of the category is developed
• The prototype has the semantic features that are
  most typical of the members of the category
• New objects compared to different prototypes of
  different categories, and are included in
  category with the most similar prototype
• Members of a category that are less similar to
  the prototype require longer to verify their
  inclusion

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Prototypes (cont)

• Nonmembers of a category can be seen as members
  if they are similar to the prototype and the
  differences are not known
• When asked to name members of a category, those
  members most like the prototype are named first
• Priming most effected by prototypes

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Exemplars

• Identification of examples or exemplars of the
  category
• New objects are compared to to other objects you
  have seen in the past your exemplars
• Advantage of the use of exemplars it uses
  actual examples not just a constructed prototype
  atypical members can be exemplars of a category

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Prototypes and Exemplars

• Evidence supports both models of categorization
• One possibility is that we use prototypes in
  large categories and exemplars in defining
  smaller categories

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Feature comparison theory of determining category
membership

• This model focuses on the strategy used to decide
  whether an exemplar (i.e. a canary) is a member
  of a larger category (i.e. bird)
• This strategy consists of two rules
• If the feature associated with the exemplar
  (canary has feathers) is found to be associated
  with the larger category (birds have feathers),
  it provides positive proof the exemplar is a
  member of the larger category
• If the feature is not associated with the
  category (bats have fur), they are not members of
  the category (a bat is not a bird)

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Support for Feature comparison model

• Consistent with typicality effects typical
  exemplars have extensive overlap of features
  atypical exemplars have less overlap and require
  more time to determine their membership
• Consistent with the false relatedness effect-
  subjects respond faster when the exemplar is
  unrelated to the category than when it is
  somewhat related
• Also consistent with levels effects

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Level effects

• Categories are organized in a hierarchy one
  category is part of a larger category which is
  part of an even larger category
• Superordinate category largest and most
  abstract animal
• Subordinate category smallest and least level
  of abstraction a canary
• Base level category in the middle and at an
  intermediate level of abstraction - bird

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Base level categories

• Most useful and most likely to come to mind and
  tend to be the most important
• Children develop base categories before
  superordinate or subordinate categories
• When asked to identify pictures, people more
  likely to give base level category

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Category levels

• When asked for common attributes of superordinate
  category, people give very few (vehicle)
• When asked about attributes of base level
  categories, many more given (car)
• When asked about attributes of base level
  categories, not many more than those given at the
  base level are added (SUV)
• Movement from a superordinate category to a base
  level category results in a great increase in
  information, but movement to a subordinate
  category adds very little information

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Base level thinking

• Humans prefer to think a the base level of
  categorization because it provides the most
  useful information for predicting membership in a
  category
• Superordinate members of a category maybe very
  different with few similarities fruit
• Base level share many common features apples
• Subordinate categories are more informative , but
  are poor discriminators McIntosh apples share
  many features of other apples
• Subordinate level thinking most important in
  areas of expertise. Choosing wine for dinner

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Knowing

• Connectionism

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Importance of context

• Context can act as a prime to understanding
  correct meaning
• I saw a man eating fish.
• Visiting relatives can be boring
• Context can activate the meaning meant to be
  conveyed
• By understanding the context of a communication,
  you can understand and remember the material
  better

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Connectionist model of semantic memory

• Involves a network of interconnected nodes each
  node connected with specific information
• The connections between nodes vary in strength
  referred to as connection weights
• Nodes that are more strongly connected have
  greater connection weights
• Learning involves strengthening the connection by
  increasing connection weights

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A neural network
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A neural network example