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对具有“或”门的非循环任务网络中选择性影响各过程的因素的响应时间的影响。

Effects on response time of factors selectively influencing processes in acyclic task networks with OR gates.

作者信息

Schweickert R, Wang Z

机构信息

Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907-1364.

出版信息

Br J Math Stat Psychol. 1993 May;46 ( Pt 1):1-30. doi: 10.1111/j.2044-8317.1993.tb00999.x.

DOI:10.1111/j.2044-8317.1993.tb00999.x
PMID:8393336
Abstract

The mental processes involved in performing some tasks can be represented as directed arcs in an acyclic network. A path directed from the head of one arc to the tail of another indicates that the process represented by the first arc must be executed prior to the process represented by the second arc. If there is no directed path from one arc to another, the corresponding processes can be executed concurrently. Information about the arrangement of processes in an acyclic network can be found from the effects on response times of factors selectively influencing the processes. The methodology was developed earlier for critical path networks, in which a process begins execution when all its immediate predecessors have finished. This paper considers shortest path networks, in which a process begins execution as soon as any immediate predecessor is finished. Results analogous to those for critical path networks are reported. New results are presented enabling investigators to distinguish sequential and concurrent processes in both critical path and shortest path networks. This information is sufficient to construct an acyclic network representing the processes. Further, by examining the effects of selectively influencing processes, one can determine whether a task network is a critical path network or a shortest path network.

摘要

执行某些任务所涉及的心理过程可以表示为无环网络中的有向弧。从一条弧的头部指向另一条弧的尾部的路径表明,第一条弧所代表的过程必须在第二条弧所代表的过程之前执行。如果从一条弧到另一条弧没有有向路径,则相应的过程可以并发执行。关于无环网络中过程排列的信息可以从选择性影响这些过程的因素对响应时间的影响中找到。该方法早些时候是为关键路径网络开发的,在关键路径网络中,一个过程在其所有直接前驱都完成后开始执行。本文考虑最短路径网络,在最短路径网络中,一个过程在任何一个直接前驱完成后立即开始执行。报告了与关键路径网络类似的结果。提出了新的结果,使研究人员能够区分关键路径网络和最短路径网络中的顺序和并发过程。这些信息足以构建一个表示这些过程的无环网络。此外,通过检查选择性影响过程的效果,可以确定任务网络是关键路径网络还是最短路径网络。

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