How is knowledge stored?

Human knowledge comes in 2 varieties:


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.


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.

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?


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.


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?


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?


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.


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-onexperience, by making inferences.

Generalizing to create new knowledge.


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 .


Making inferences

Example: is spelt a food? Your knowledge store tells you 2 things:


You can answer the question even if you don’t have a card that says “spelt is a food”

Generalizing to create new knowledge

Suppose we learn that:

Tractors have large tires

Combines have large tires

We can now generalize: farm vehicles have large tires.


Do hay-balers have large tires? Yes. We can work that out even without explicitly learning it.

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.


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)



Quillian’s (1968) model

Quillian was a computer scientist. He wanted to build a program that would read and ‘understand’ English text.

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.

Basic elements of Quillian’s model



Nodes represent concepts.

They are ‘placeholders’.

They are empty.

Connections between nodes. Nodes send signals to each other down these links.



Air breathes isa

Animal isa


Bird isa has has

Feathers Wings

Mammal bears

Live young

Things to notice about Quillian’s model

All links are equivalent.

Structure was rigidly hierarchical. Time to retrieve information based on number of links

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.



Problems with Quillian’s model

1. How to explain typicality effect?

• Is a robin a bird?

• Is a chicken a bird?

• Easier to say ‘yes’ to robin. Why?

2. How to explain that it is easier to report that a bear is an animal than that a bear is a mammal?

3. Cognitive economy – do we learn by erasing links?

What’s new in Collins & Loftus (1975)

A. Structure

• responded to data accumulated since original

Collins & Quillian (1969) paper

• got rid of hierarchy

• got rid of cognitive economy

• allowed links to vary in length (not all equal)


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


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



An effect on response to one stimulus

( TARGET ) produced by processing another stimulus immediately before ( PRIME )

• If prime is related to target (e.g., bread-butter), reading prime improves response to target).

• Usually measured on RT; sometimes on accuracy

RT (related)



Related bread

Unrelated nurse

Task read only

BUTTER BUTTER read, respond

Difference in RT to two types of trials = priming effect. (Related shorter RT than unrelated.)


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 the kind of thing that could produce a priming effect.

• A network through which activation spreads is such a model. (Score one point for networks.)



• Knowledge has structure

• Our representation of that structure makes new knowledge available (things not experienced)

• The most popular models are network models, containing links and nodes .

• Nodes are empty. They are just placeholders.



• Knowledge is stored in the structure – the pattern of links, and the lengths of the links.

• The pattern of links and the lengths of links are consequences of experience (learning).

• Network models provide a handy explanation of priming effects.