Learning Flow of Control: Recursive and lterative Procedures
Learning Flow of Control: Recursive and lterative Procedures
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学习控制流程：递归和迭代过程
DOI：
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发表时间：
1986
期刊：
影响因子：
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通讯作者：
John R. Anderson
中科院分区：
文献类型：
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作者：
C. M. Kessler;John R. Anderson
我们提出了一个正式的任务语言的心理表征模型。该模型是一种元语言，用于定义任务-动作语法：生成语法，将简单的任务重写为动作规范。该模型的重要特征是：（1）识别用户可以例行执行的、不需要控制结构的“简单任务”;（2）通过反映任务世界分类的语义成分集合来表示简单任务;（三）利用任务世界的语义特征对重写规则中的标记进行标记，以提供将简单任务重写为动作的选择限制规范.该设备允许表示各个任务-动作映射之间的家族相似性。简单的复杂性指标的任务动作语法预测不同的任务语言设计的相对易学性。这些预测的一些实证支持来自现有的实证文献的命令语言学习和两个未报告的实验。任务动作语法也为设计者提供了一个分析工具，用于揭示任务语言的语法属性。
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.