Semantic Memory

of 18

Please download to get full document.

View again

All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
18 pages
0 downs
Semantic Memory. Psychology 3717. Introduction. This is our memory for facts about the world How do we know that the capital of Viet Nam is Hanoi How is this type of information stored? Is there any difference between ‘natural’ and ‘logical’ concepts? How the heck could we figure this out?.
Semantic MemoryPsychology 3717Introduction
  • This is our memory for facts about the world
  • How do we know that the capital of Viet Nam is Hanoi
  • How is this type of information stored?
  • Is there any difference between ‘natural’ and ‘logical’ concepts?
  • How the heck could we figure this out?
  • TLC
  • No, not that lame TV channel ful of trading spaces and makeovers….
  • The Teachable Language Comprehender
  • Hierarchical associative network of concepts that began as a computer simulation
  • bacground
  • Quinlan and his colleagues tried to make a computer program that would simulate how a person learns language
  • Realized that concepts, not words, were the building blocks of knowledge
  • Example ‘the policeman held up his hand and the cars stopped (Collins & Quinlan, 1973)
  • You know exactly what that phrase entails
  • Cop is directing traffic
  • People push pedals etc
  • Tacit knowledge if you will
  • Three types of elements of semantic memory
  • The three elements
  • Units, properties and pointers
  • Unit is a thing (the cop, his hand, the car etc)
  • Properties are conceptual (raising the hand that sort of thing)
  • Pointers denote specific associations
  • So where does this get us?
  • Semantic memory then is a HUGE hierarchical network of relationships between elements
  • Is statements then are relationships between a superordinate element and a subordinate
  • A bird is a fish will not produce a yes
  • A fish is an animal and a bird is an animal will
  • Collins and Quinlan (1969)
  • A _____ is a ______ statements
  • Sentence verification
  • RT longer when number of associative links was greater
  • Less relevant property relationships, longer RT
  • (a canary is yellow is longer than a canary can breather)
  • However…
  • Ok, by this theory, the satement ‘a bear is a mammal’ should be quicker than ‘a bear is an animal’
  • Ummm, no….
  • So the ‘semantic distance effect’ does not show up here
  • More buts
  • Does not deal with the typicality effect at all
  • Does not explain why ‘a robin is a shark’ is more quickly rejected than ‘a robin is a salmon’
  • Conrad (1972) said that it is the typicality of the statement itself that is the issue
  • Feature set theory
  • Smith, Shoben and Rips (1974)
  • Concepts stored as sets of attributes
  • RT then depends on comparing features of exemplar with stored concept
  • Predicts the symbolic distance effect (very similar items take long RTs
  • Predicts the category size effect (concept to superordinate)
  • But….
  • You knew there would be a but
  • What are the defining features of a concept
  • What is a dog for example
  • Your idea is different than mine
  • But we gt the same effects with subjects
  • Spreading activation
  • Collins and Loftus (1975)
  • Network like TLC
  • NOT as rigidly hierarchical
  • Still have units and properties, but not so hierarchical
  • Distance is important
  • But..
  • This one sounds better but there is a but
  • Distantly related concepts should give longer RTs
  • But ‘a canary is a shark’ takes like no time
  • Propositional network theory
  • Anderson (1983)
  • ACT and ACT*
  • For example ‘Kurt’s mother sent him a package last week’
  • Three propositions
  • More propositions, sentences take longer, even with fewer words
  • Neural network models
  • Units or nodes that are like neurons, on and off
  • But, units have a threshold and once acticavted past this threshold you get ‘firing’
  • Units are connected either excitatorily or inhibtorily
  • There are activation rules
  • Output rules, how a unit sends info to next unit
  • Learning rules
  • Groups of units or modules, are devoted to specific cognitive functions
  • properties
  • Content addressable memory
  • Network makes guesses
  • Network makes spontaneous generalizations
  • conclusions
  • I don’t want to get too bogged down in this but, I think that we can make a few statements
  • All the theories are about connections
  • Probably NOT hierarchical, completely
  • Activation probably spreads
  • This stuff can get way hard….
  • Related Search
    We Need Your Support
    Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

    Thanks to everyone for your continued support.

    No, Thanks