Kessler Franziska, Proske Antje, Urbas Leon, Goldwater Micah, Krieger Florian, Greiff Samuel, Narciss Susanne
Faculty of Psychology, Technische Universität Dresden, 01069 Dresden, Germany.
Department of Electrical Engineering, Technische Universität Dresden, 01069 Dresden, Germany.
Behav Sci (Basel). 2023 Aug 23;13(9):701. doi: 10.3390/bs13090701.
The ability to recognize key causal models across situations is associated with expertise. The acquisition of schema-governed category knowledge of key causal models may underlie this ability. In an experimental study ( = 183), we investigated the effects of promoting the construction of schema-governed categories and how an enhanced ability to recognize the key causal models relates to performance in complex problem-solving tasks that are based on the key causal models. In a 2 × 2 design, we tested the effects of an adapted version of an intervention designed to build abstract mental representations of the key causal models and a tutorial designed to convey conceptual understanding of the key causal models and procedural knowledge. Participants who were enabled to recognize the underlying key causal models across situations as a result of the intervention and the tutorial (i.e., causal sorters) outperformed non-causal sorters in the subsequent complex problem-solving task. Causal sorters outperformed the control group, except for the subtask in the experimental group that did not receive the tutorial and, hence, did not have the opportunity to elaborate their conceptual understanding of the key causal models. The findings highlight that being able to categorize novel situations according to their underlying key causal model alone is insufficient for enhancing the transfer of the according concept. Instead, for successful application, conceptual and procedural knowledge also seem to be necessary. By using a complex problem-solving task as the dependent variable for transfer, we extended the scope of the results to dynamic tasks that reflect some of the typical challenges of the 21st century.
跨情境识别关键因果模型的能力与专业知识相关。对关键因果模型的图式主导类别知识的掌握可能是这种能力的基础。在一项实验研究（n = 183）中，我们调查了促进图式主导类别的构建的效果，以及增强的识别关键因果模型的能力如何与基于关键因果模型的复杂问题解决任务中的表现相关。在一个2×2设计中，我们测试了一种改编版干预措施的效果，该干预旨在构建关键因果模型的抽象心理表征，以及一个旨在传达对关键因果模型的概念理解和程序性知识的教程。由于干预和教程而能够跨情境识别潜在关键因果模型的参与者（即因果分类者）在随后的复杂问题解决任务中表现优于非因果分类者。因果分类者的表现优于对照组，但实验组中未接受教程、因此没有机会详细阐述其对关键因果模型的概念理解的子任务除外。研究结果突出表明，仅根据潜在的关键因果模型对新情境进行分类不足以增强相应概念的迁移。相反，为了成功应用，概念性和程序性知识似乎也是必要的。通过使用复杂问题解决任务作为迁移的因变量，我们将结果范围扩展到反映21世纪一些典型挑战的动态任务。
