Developing a neurally informed ontology of creativity measurement
Developing a neurally informed ontology of creativity measurement
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DOI：
10.1016/j.neuroimage.2020.117166
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发表时间：
2020-07
期刊：
影响因子：
5.7
通讯作者：
Yoed N. Kenett;David J. M. Kraemer;Katherine L. Alfred;Griffin A. Colaizzi;Robert A. Cortes;Adam E. Green
中科院分区：
文献类型：
--
作者：
Yoed N. Kenett;David J. M. Kraemer;Katherine L. Alfred;Griffin A. Colaizzi;Robert A. Cortes;Adam E. Green
创造力研究的一个核心挑战--就像实验心理学和认知神经科学的所有领域一样--是在结构和测量之间建立一个映射（即，识别最好地捕获一组创造性能力的一组任务）。一个相关的挑战是实现更大的一致性，不同的研究人员使用的措施;不一致的测量阻碍了对创造力的认知和神经成分的共同理解的进展。聚集神经影像学数据的新资源，以及识别多变量数据结构的方法的出现，为解决这些挑战提出了新方法的潜力。识别元分析结构（即，相似性）可能有助于识别这些任务的子集，这些任务最好地反映了创造性相关结构的相似性结构。在这里，我们展示了这种方法的初步概念验证。为了建立一个模型的相似性之间的创造力相关的结构，我们首先调查了创造力的研究人员。接下来，我们使用NeuroSynth元分析软件生成与旨在测量同一组创造力相关结构的任务密切相关的神经活动图。一个代表性的相似性分析为基础的方法确定特定的结构和特定的任务，旨在衡量这些结构，积极或消极影响模型拟合。这种方法指出了识别最佳任务集以捕捉创造力元素的方法（即，创造力结构之间的相似性空间的维度），并具有长期的潜力，有意义地推进创造力研究的本体论发展与创造力神经科学的快速增长。因为它依赖于神经影像学荟萃分析，这种方法有更直接的潜力，为更广泛的神经影像学数据集已经可用的长期建立的领域提供信息。
A central challenge for creativity research—as for all areas of experimental psychology and cognitive neuroscience—is to establish a mapping between constructs and measures (i.e., identifying a set of tasks that best captures a set of creative abilities). A related challenge is to achieve greater consistency in the measures used by different researchers; inconsistent measurement hinders progress toward shared understanding of cognitive and neural components of creativity. New resources for aggregating neuroimaging data, and the emergence of methods for identifying structure in multivariate data, present the potential for new approaches to address these challenges. Identifying meta-analytic structure (i.e., similarity) in neural activity associated with creativity tasks might help identify subsets of these tasks that best reflect the similarity structure of creativity-relevant constructs. Here, we demonstrated initial proof-of-concept for such an approach. To build a model of similarity between creativity-relevant constructs, we first surveyed creativity researchers. Next, we used NeuroSynth meta-analytic software to generate maps of neural activity robustly associated with tasks intended to measure the same set of creativity-relevant constructs. A representational similarity analysis-based approach identified particular constructs—and particular tasks intended to measure those constructs—that positively or negatively impacted the model fit. This approach points the way to identifying optimal sets of tasks to capture elements of creativity (i.e., dimensions of similarity space among creativity constructs), and has long-term potential to meaningfully advance the ontological development of creativity research with the rapid growth of creativity neuroscience. Because it relies on neuroimaging meta-analysis, this approach has more immediate potential to inform longer-established fields for which more extensive sets of neuroimaging data are already available.