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Common Process Demands of Two Complex Dynamic Control Tasks: Transfer Is Mediated by Comprehensive Strategies

机译:两项复杂的动态控制任务的通用流程需求:转移由综合策略来调节

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摘要

Although individual differences in complex problem solving (CPS) are well–established, relatively little is known about the process demands that are common to different dynamic control (CDC) tasks. A prominent example is the VOTAT strategy that describes the separate variation of input variables (“Vary One Thing At a Time”) for analyzing the causal structure of a system. To investigate such comprehensive knowledge elements and strategies, we devised the real-time driven CDC environment Dynamis2 and compared it with the widely used CPS test MicroDYN in a transfer experiment. One hundred sixty five subjects participated in the experiment, which completely combined the role of MicroDYN and Dynamis2 as source or target problem. Figural reasoning was assessed using a variant of the Raven Test. We found the expected substantial correlations among figural reasoning and performance in both CDC tasks. Moreover, MicroDYN and Dynamis2 share 15.4% unique variance controlling for figural reasoning. We found positive transfer from MicroDYN to Dynamis2, but no transfer in the opposite direction. Contrary to our expectation, transfer was not mediated by VOTAT but by an approach that is characterized by setting all input variables to zero after an intervention and waiting a certain time. This strategy (called PULSE strategy) enables the problem solver to observe the eigendynamics of the system. We conclude that for the study of complex problem solving it is important to employ a range of different CDC tasks in order to identify components of CPS. We propose that besides VOTAT and PULSE other comprehensive knowledge elements and strategies, which contribute to successful CPS, should be investigated. The positive transfer from MicroDYN to the more complex and dynamic Dynamis2 suggests an application of MicroDYN as training device.
机译:尽管在复杂问题解决(CPS)中存在个体差异,但对于不同动态控制(CDC)任务共有的过程需求了解得很少。一个著名的例子是VOTAT策略,它描述了输入变量的单独变化(“一次变化”),用于分析系统的因果结构。为了研究这样的综合知识要素和策略,我们设计了实时驱动的CDC环境Dynamis2,并将其与广泛使用的CPS测试MicroDYN进行了转移实验。 165名受试者参加了该实验,该实验完全结合了MicroDYN和Dynamis2作为源问题或目标问题的作用。图形推理是使用Raven测试的一种变体进行评估的。我们发现在两项CDC任务中,图形推理与绩效之间存在预期的实质相关性。此外,MicroDYN和Dynamis2在图形推理中共享15.4%的独特方差控制。我们发现从MicroDYN到Dynamis2发生了正向转移,但没有相反方向的转移。与我们的预期相反,转移不是由VOTAT介导的,而是一种方法,其特征在于在干预后等待所有时间将所有输入变量设置为零。这种策略(称为PULSE策略)使问题解决者能够观察系统的本征动力学。我们得出结论,对于研究复杂的问题解决方案,重要的是采用一系列不同的CDC任务来识别CPS的组成部分。我们建议,除了VOTAT和PULSE外,还应研究有助于成功进行CPS的其他综合知识要素和策略。从MicroDYN到更复杂和动态的Dynamis2的积极转移表明,将MicroDYN用作训练设备。

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