TOWARD AN INSTANCE THEORY OF AUTOMATIZATION
TOWARD AN INSTANCE THEORY OF AUTOMATIZATION
复制标题
DOI：
10.1037/0033-295x.95.4.492
复制
发表时间：
1988-10-01
影响因子：
5.4
通讯作者：
LOGAN, GD
中科院分区：
文献类型：
--
作者：
LOGAN, GD
本文提出了一种理论，其中自动化被解释为获取特定领域的知识库，形成单独的表示，实例，每个暴露的任务。如果处理依赖于对存储实例的检索，则处理被认为是自动的，这将仅在一致环境中的实践之后发生。实践很重要，因为它增加了检索的数量和检索的速度;一致性很重要，因为它确保检索到的实例是有用的。该理论定量地解释了幂函数加速，并预测了标准差的幂函数减小，该标准差被约束为具有与加速的幂函数相同的指数。该理论也解释了定性性质，解释了一些性质如何随着实践而消失，另一些性质如何随着实践而出现。更一般地说，它提供了一种替代自动性的模态观点，认为新手的表现是有限的缺乏知识，而不是稀缺的资源。对学习的关注避免了模态观点的许多问题，这些问题源于它对资源限制的关注。
This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur only after practice in a consistent environment. Practice is important because it increases the amount retrieved and the speed of retrieval; consistency is important because it ensures that the retrieved instances will be useful. The theory accounts quantitatively for the power-function speed-up and predicts a power-function reduction in the standard deviation that is constrained to have the same exponent as the power function for the speed-up. The theory accounts for qualitative properties as well, explaining how some may disappear and others appear with practice. More generally, it provides an alternative to the modal view of automaticity, arguing that novice performance is limited by a lack of knowledge rather than a scarcity of resources. The focus on learning avoids many problems with the modal view that stem from its focus on resource limitations.