Gilmore Rick O, Diaz Michele T, Wyble Brad A, Yarkoni Tal
Department of Psychology, the Pennsylvania State University, University Park, Pennsylvania.
Social, Life, & Engineering Sciences Imaging Center, the Pennsylvania State University, University Park, Pennsylvania.
Ann N Y Acad Sci. 2017 May;1396(1):5-18. doi: 10.1111/nyas.13325. Epub 2017 May 2.
Accumulating evidence suggests that many findings in psychological science and cognitive neuroscience may prove difficult to reproduce; statistical power in brain imaging studies is low and has not improved recently; software errors in analysis tools are common and can go undetected for many years; and, a few large-scale studies notwithstanding, open sharing of data, code, and materials remain the rare exception. At the same time, there is a renewed focus on reproducibility, transparency, and openness as essential core values in cognitive neuroscience. The emergence and rapid growth of data archives, meta-analytic tools, software pipelines, and research groups devoted to improved methodology reflect this new sensibility. We review evidence that the field has begun to embrace new open research practices and illustrate how these can begin to address problems of reproducibility, statistical power, and transparency in ways that will ultimately accelerate discovery.
越来越多的证据表明，心理科学和认知神经科学中的许多研究结果可能难以重现；脑成像研究的统计效力较低，且最近没有得到改善；分析工具中的软件错误很常见，并且可能多年都未被发现；此外，尽管有一些大规模研究，但数据、代码和材料的开放共享仍然是罕见的例外情况。与此同时，人们重新将可重复性、透明度和开放性视为认知神经科学的基本核心价值。数据档案库、元分析工具、软件管道以及致力于改进方法的研究小组的出现和迅速发展反映了这种新的认知。我们回顾了该领域已开始采用新的开放研究实践的证据，并说明了这些实践如何能够开始以最终加速发现的方式解决可重复性、统计效力和透明度问题。
