Figure

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.

preceded by the word "cat" (Meyer & Schvaneveldt, 1971) can be easily modelled using such networks (see Chapter 12). Ayers and Reder (1998) have used semantic networks to understand misinformation effects in eyewitness testimony (see Chapter 8). At their best, semantic networks are both flexible and elegant modelling schemes.

Production systems

Another popular approach to modelling cognition involves production systems. These are made up of productions, where a production is an "IF... THEN" rule. These rules can take many forms, but an example that is very useful in everyday life is, "If the green man is lit up, then cross the road". In a typical production system model, there is a long-term memory that contains a large set of these IF.. .THEN rules. There is also a working memory (i.e., a system holding information that is currently being processed). If information from the environment that "the green man is lit up" reaches working memory, it will match the IF-part of the rule in long-term memory, and trigger the THEN-part of the rule (i.e., cross the road). Production systems have the following characteristics:

• They have a working memory containing information.

• The production system operates by matching the contents of working memory against the IF-parts of the rules and executing the THEN-parts.

• If some information in working memory matches the IF-part of many rules, there may be a conflict-resolution strategy selecting one of these rules as the best one to be executed.

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