Improving practices and inferences in developmental cognitive neuroscience
Improving practices and inferences in developmental cognitive neuroscience
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DOI：
10.1016/j.dcn.2020.100807
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发表时间：
2020-10-01
影响因子：
4.7
通讯作者：
Pfeifer, Jennifer H.
中科院分区：
文献类型：
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作者：
Flournoy, John C.;Vijayakumar, Nandita;Pfeifer, Jennifer H.
在过去的十年里，人们越来越关注认知神经科学的研究实践，以及更广泛的心理学，这些研究实践动摇了我们对这些领域许多推论的信心。我们考虑这些问题如何影响发育认知神经科学，目标是推进我们的领域，以支持从我们的神经生物学数据中得出的强有力的、可辩护的推论。这篇论文的重点是在发展认知神经科学中区分验证性与探索性数据分析方法的重要性。关于验证性研究，我们讨论了分析灵活性，适当实例化假设以及控制错误率的问题，因为我们如何阈值数据并纠正多个比较。为了用验证性分析来抵消这些担忧，我们提出了两个互补的策略。首先，我们讨论在探索性分析框架内工作的优点，包括估计和报告效果大小，使用分组，以及进行规格曲线分析。其次，我们总结了验证性分析中零假设显著性检验的可辩护方法，重点是我们领域的透明和可重复实践。给出了具体的建议，并且在可能的情况下提供了模板、脚本或其他资源的超链接。
The past decade has seen growing concern about research practices in cognitive neuroscience, and psychology more broadly, that shake our confidence in many inferences in these fields. We consider how these issues affect developmental cognitive neuroscience, with the goal of progressing our field to support strong and defensible inferences from our neurobiological data. This manuscript focuses on the importance of distinguishing between confirmatory versus exploratory data analysis approaches in developmental cognitive neuroscience. Regarding confirmatory research, we discuss problems with analytic flexibility, appropriately instantiating hypotheses, and controlling the error rate given how we threshold data and correct for multiple comparisons. To counterbalance these concerns with confirmatory analyses, we present two complementary strategies. First, we discuss the advantages of working within an exploratory analysis framework, including estimating and reporting effect sizes, using parcellations, and conducting specification curve analyses. Second, we summarize defensible approaches for null hypothesis significance testing in confirmatory analyses, focusing on transparent and reproducible practices in our field. Specific recommendations are given, and templates, scripts, or other resources are hyperlinked, whenever possible.