Semantic Memory

1. Semantic Memory Psychology 3717

2. 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?

3. 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

4. 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)

5. 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

6. 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

7. 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

8. 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)

9. 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

10. 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

11. 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)

12. 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

13. 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

14. 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

15. 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

16. 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

17. properties • Content addressable memory • Network makes guesses • Network makes spontaneous generalizations

18. 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….