Task-Action Grammars: A Model of the Mental Representation of Task Languages
Human-Computer InteractionPublished 1 June 1986
Stephen A. Payne, T. R. G. Green
Citations352
SJR quartileQ1
SJR score1.03
SNIP1.76
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The findings reported in this paper indicate that graphical presentations are more useful when evaluating information in order to determine promising directions in the search for an optimal solution bit when the task requires the determination of exact data values for computational purposes, graphical reports are less useful than tabular ones.
Abstract
Abstract A formal model of the mental representation of task languages is presented. The model is a metalanguage for defining task-action grammars (TAG): generative grammars that rewrite simple tasks into action specifications. Important features of the model are (a) Identification of the "simple-tasks" that users can perform routinely and that require no control structure; (b) Representation of simple-tasks by collections of semantic components reflecting a categorization of the task world; (c) 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.
Keywords
PsychologyComputer ScienceSocial Sciences
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