Learning Flow of Control: Recursive and lterative Procedures

We present a formal model of the mental representation of task languages. The model is a metalanguage for defining task-action grammars: generative grammars which rewrite simple tasks into action specification. Important features of the model are: (1) Identification of the "simple tasks" that users can perform routinely and which require no control structure; (2) Representation of simple tasks by collections of semantic components reflecting a categorisation of the task world; (3) Marking of tokens in rewrite rules with the semantic features of the task world to supply selection restrictions on the rewriting of simple tasks into action specifications. This device allows the representation of family resemblances between individual task-action mappings. Simple complexity metrics over task-action grammars make predictions about the relative learnability of different task language designs. Some empirical support for these predictions is derived from the existing empirical literature on command language learning and from two unreported experiments. Task-action grammars also provide designers with an analytic tool for exposing the configural properties of task languages.

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