Schoppek Wolfgang, Fischer Andreas
University of Bayreuth, Bayreuth, Germany.
Forschungsinstitut Betriebliche Bildung, Nürnberg, Germany.
Front Psychol. 2017 Dec 19;8:2145. doi: 10.3389/fpsyg.2017.02145. eCollection 2017.
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.
尽管复杂问题解决（CPS）中的个体差异已得到充分证实，但对于不同动态控制（CDC）任务共有的过程要求却知之甚少。一个突出的例子是VOTAT策略，该策略描述了输入变量的单独变化（“一次改变一件事”），用于分析系统的因果结构。为了研究此类综合知识元素和策略，我们设计了实时驱动的CDC环境Dynamis2，并在一项迁移实验中将其与广泛使用的CPS测试MicroDYN进行了比较。165名受试者参与了该实验，实验完全结合了MicroDYN和Dynamis2作为源问题或目标问题的角色。使用瑞文测试的一个变体评估图形推理。我们发现图形推理与两个CDC任务的表现之间存在预期的显著相关性。此外，在控制图形推理的情况下，MicroDYN和Dynamis2共享15.4%的独特方差。我们发现从MicroDYN到Dynamis2存在正向迁移，但反向没有迁移。与我们的预期相反，迁移不是由VOTAT介导的，而是由一种在干预后将所有输入变量设置为零并等待一定时间的方法介导的。这种策略（称为脉冲策略）使问题解决者能够观察系统的本征动力学。我们得出结论，对于复杂问题解决的研究，采用一系列不同的CDC任务以识别CPS的组成部分很重要。我们建议，除了VOTAT和脉冲策略外，还应研究其他有助于成功解决复杂问题的综合知识元素和策略。从MicroDYN到更复杂、更动态的Dynamis2的正向迁移表明MicroDYN可作为一种训练工具。
