Computational modelling techniques

The general characteristics of computational models of cognition have been discussed at some length. It is now time to deal with some of the main types of computational model that have been used in recent years. Three main types are outlined briefly here: semantic networks; production systems; and connectionist networks.

Semantic networks

Consider the problem of modelling what we know about the world (see Chapter 9). There is a long tradition from Aristotle and the British empiricist school of philosophers (Locke, Hume, Mill, Hartley, Bain) which proposes that all knowledge is in the form of associations. Three main principles of association have been proposed:

• Contiguity: two things become associated because they occurred together in time.

• Similarity: two things become associated because they are alike.

• Contrast: two things become associated because they are opposites.

There is a whole class of cognitive models owing their origins to these ideas; they are called associative or semantic or declarative networks. Semantic networks have the following general characteristics:

• Concepts are represented by linked nodes that form a network.

• These links can be of various kinds; they can represent very general relations (e.g., is-associated-with or is-similar-to) or specific, simple relations like is-a (e.g., John is-a policeman), or more complete relations like play, hit, kick.

• The nodes themselves and the links among nodes can have various activation strengths representing the similarity of one concept to another. Thus, for example, a dog and a cat node may be connected by a link with an activation of 0.5, whereas a dog and a pencil may be connected by a link with a strength of 0.1.

• Learning takes the form of adding new links and nodes to the network or changing the activation values on the links between nodes. For example, in learning that two concepts are similar, the activation of a link between them may be increased.

• Various effects (e.g., memory effects) can be modelled by allowing activation to spread throughout the network from a given node or set of nodes.

• The way in which activation spreads through a network can be determined by a variety of factors For example, it can be affected by the number of links between a given node and the point of activation, or by the amount of time that has passed since the onset of activation.

Part of a very simple network model is shown in Figure 1.3. It corresponds closely to the semantic network model proposed by Collins and Loftus (1975). Such models have been successful in accounting for a various findings. Semantic priming effects in which the word "dog" is recognised more readily if it is

A schematic diagram of a simple semantic network with nodes for various concepts (i.e., dog, cat), and links between these nodes indicating the differential similarity of these concepts to each other.

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