How is knowledge stored?

1. How is knowledge stored? • Human knowledge comes in 2 varieties: • Concepts • Relations among concepts • So any theory of how knowledge is stored must explain both types. We’ll look at concepts a little later in the term. Today, it’s relations.

2. How are relations among concepts stored? • Rosch argued for hierarchical knowledge, that is, knowledge using the contains relation: • Animal contains mammal contains canine • She argued that this explains both the speed of knowledge retrieval and our ability to make inferences.

3. Retrieving knowledge • Is a mouse a mammal? • Yes. But how do I know? • How do I find this bit of information among all the many things that I know?

4. Making inferences • Does a mouse bear live young? • A mouse is a mammal. Mammals bear live young. Therefore, a mouse bears live young. • But in order for me to be able to reason like this, my knowledge store must connect mouse to mammal & mammal to live young. .

5. A mouse is a mammal Two ways we could store knowledge • Imagine that we have lots of facts that we need to store, and each fact is written on a 3X5 card. • We are going to store these cards on tables in a large room. • How do we do this?

6. Storing knowledge in a list • One way would be just to start piling cards on the nearest table as we get them. We would keep piling cards onto that table until they spilled onto the floor, then move on to the next table, and continue till all the tables were full. • If you wanted a piece of information that was on one of those cards, how would you get it?

7. A list of problems with lists • Retrieving any particular fact becomes more difficult the more facts you learn. • Lists do not capture relations between facts (e.g., dogs display dominance by snarling; wolves display dominance by snarling). • The list structure doesn’t have a mechanism for making inferences, so our knowledge would never be greater than the sum of the items on the list.

8. Advantages of structured knowledge • Faster access to concepts • E.g., if you want farm animal information, go to the farm animal table • Going beyond knowledge-based-on- experience, by making inferences. • Generalizing to create new knowledge.

9. Faster access to concepts • Continuing with the “tables” metaphor, we could assign each table to a topic (e.g., seven tables for politics, nine tables for animals, six for gardening… The animal tables could each be used for one class (e.g., reptiles, farm animals, sea animals…). • Now, if you wanted a particular piece of information about farm animals, what would you do? The principle, of course, is organization.

10. Spelt is a grain Making inferences • Example: is spelt a food? Your knowledge store tells you: And all of the grain cards are on a ‘food’ table, so spelt must be a food. That is, part of your knowledge is in the structure.

11. Generalizing to create new knowledge • Suppose we learn that: • Tractors have large tires • Combines have large tires • We generalize: farm vehicles have large tires. • Do hay-balers have large tires? Yes. • We can work that out, even if we never explicitly learn it.

12. What is the structure like? • We can all agree that having structure in our knowledge store offers advantages. • But what is that structure? A wall? A path? A tree? • The most widely-accepted answer is, a network. A semantic network.

13. Network models of semantic memory • Quillian (1968), Collins & Quillian (1969) • First network model of semantic memory • Collins & Loftus (1975) • Revised network model of semantic memory • Neural network models (later in the term)

14. Quillian’s (1968) model • Quillian was a computer scientist. He wanted to build a program that could read a newspaper and respond to questions about what it read. • To do this, he had to give the program the knowledge a reader has. • Constraint: computers were slow, and memory was very expensive, in those days.

15. Basic elements of Quillian’s model • Nodes • Nodes represent concepts. • They are ‘placeholders’. • They are empty. • Links • Connections between nodes. Nodes send signals to each other down these links. • Property links and isa links

16. breathes Air Animal isa isa Bird Mammal bears isa has has Feathers Wings Wren Live young

17. Things to notice about Quillian’s model • All links are equivalent. • Cognitive economy – properties stored only at highest possible level (e.g., birds have wings) • Made sense in late 1960s, when computer memory was very expensive, so efficiency was highly valued. • Structure was rigidly hierarchical. Time to retrieve information based on number of links

18. Problems with Quillian’s model • Cognitive economy – do we learn by erasing links? • How to explain typicality effect? • Is a robin a bird? • Is a chicken a bird? • Faster ‘yes’ to robin. Why? • How to explain that it is easier to report that a bear is an animal than that a bear is a mammal?

19. breathes Animal Air isa isa Bird Mammal isa isa isa has Bear Feathers Robin Chicken

20. What’s new in Collins & Loftus (1975) • A. Structure • responded to data accumulated after original Collins & Quillian (1969) paper • got rid of hierarchy • got rid of cognitive economy • allowed links to vary in length (not all equal) • this is ‘normal science’ – improving a model in response to criticism

21. skin cow animal mammal fly feathers bird robin wings fly ostrich bat

22. What’s new in Collins & Loftus (1975)? • B. Process – Spreading Activation • Activation – arousal level of a node • Spreading – down links • Mechanism used to extract information from network • Allowed neat explanation of a very important empirical effect: Priming

23. Priming • Task: read PRIME word then read and respond to TARGET word. • If prime is related to target (e.g., bread-butter), reading prime improves response to target). • Usually measured on RT; sometimes on accuracy • Effect: RT (unrelated) – RT (related) > 0

24. Priming – example of a Related trial • RelatedTask • bread read only • BUTTER read & respond • Response might be reading out loud or lexical decision (is target a word of English?) • Expect relatively fast responses in this condition

25. Priming – example of an Unrelated trial • UnrelatedTask • nurse read only • BUTTER read & respond • Expect relatively slow responses in this condition • Difference in average RT to two conditions is the priming effect

26. Why is the Priming effect important? • The priming effect is an important observation that models of semantic memory must account for. • Any model of semantic memory must be able to explain why the priming effect occurs. • A network through which activation spreads is such a model. (Score one point for networks.)

27. Review • Knowledge has structure • Our representation of that structure makes new knowledge available (knowledge of things not experienced) • The most popular models are network models, containing links and nodes. • Nodes are empty. They are just placeholders.

28. Review • Knowledge is stored in the structure – the pattern of which nodes are connected and how closely they are connected (link length). • The pattern of links and the lengths of links are consequences of experience (learning). • Network models provide a handy explanation of primingeffects.