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.
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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.
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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?
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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. .
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5. 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?
A mouse is a mammal
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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?
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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.
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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.
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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.
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10. Example: is spelt a food? Your knowledge store tells you:
Making inferences
Example: is spelt a food? Your knowledge store tells you:
Spelt is a grain
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.
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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.
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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.
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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)
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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.
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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
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16. Wren Animal Mammal Bird Feathers Wings Air Live young breathes isa
has
bears
isa
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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
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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?
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19. Animal Air Bird Mammal Bear Feathers Robin Chicken breathes isa has
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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
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21. animal mammal bird robin ostrich feathers wings fly bat skin cow
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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
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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
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24. Priming – example of a Related trial
Related Task
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
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25. Priming – example of an Unrelated trial
Unrelated Task
nurse read only
BUTTER read & respond
Expect relatively slow responses in this condition
Difference in average RT to two conditions is the priming effect
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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.)
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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.
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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 priming effects.
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