University of Illinois-Chicago, Chicago, IL, USA.
Northwestern University, Evanston, IL, USA.
Cogn Res Princ Implic. 2020 Apr 18;5(1):18. doi: 10.1186/s41235-020-00217-6.
Working memory capacity is known to predict the performance of novices and experts on a variety of tasks found in STEM (Science, Technology, Engineering, and Mathematics). A common feature of STEM tasks is that they require the problem solver to encode and transform complex spatial information depicted in disciplinary representations that seemingly exceed the known capacity limits of visuospatial working memory. Understanding these limits and how visuospatial information is encoded and transformed differently by STEM learners presents new avenues for addressing the challenges students face while navigating STEM classes and degree programs. Here, we describe two studies that explore student accuracy at detecting color changes in visual stimuli from the discipline of chemistry. We demonstrate that both naive and novice chemistry students' encoding of visuospatial information is affected by how information is visually structured in "chunks" prevalent across chemistry representations. In both studies we show that students are more accurate at detecting color changes within chemistry-relevant chunks compared to changes that occur outside of them, but performance was not affected by the dimensionality of the structure (2D vs 3D) or the presence of redundancies in the visual representation. These studies support the hypothesis that strategies for chunking the spatial structure of information may be critical tools for transcending otherwise severely limited visuospatial capacity in the absence of expertise.
工作记忆容量被认为可以预测新手和专家在 STEM(科学、技术、工程和数学)领域的各种任务中的表现。STEM 任务的一个共同特点是,它们要求解决问题者对学科表示法中描述的复杂空间信息进行编码和转换,这些信息似乎超出了已知的视空间工作记忆容量限制。了解这些限制以及 STEM 学习者如何以不同的方式对空间信息进行编码和转换,为解决学生在 STEM 课程和学位项目中面临的挑战提供了新的途径。在这里,我们描述了两项研究,这些研究探讨了学生在检测来自化学学科的视觉刺激的颜色变化方面的准确性。我们证明,即使是没有经验的和新手化学学生,他们对空间信息的编码也会受到“块”中信息的视觉结构的影响,而这些“块”在化学表示法中很常见。在这两项研究中,我们都表明,与发生在它们之外的变化相比,学生在检测与化学相关的块内的颜色变化时更准确,但性能不受结构的维度(2D 与 3D)或视觉表示中冗余的影响。这些研究支持了这样一种假设,即用于对信息的空间结构进行分组的策略可能是在没有专业知识的情况下克服视空间容量严重受限的关键工具。