Wright Jessey
Department of Psychology, Stanford University, Stanford, CA, United States.
Prog Brain Res. 2018;243:299-323. doi: 10.1016/bs.pbr.2018.10.025. Epub 2018 Nov 28.
Functional magnetic resonance imaging research is often associated with images of brains overlaid with patterns of color that indicate significant activity. These images are one of the most salient and recognizable pieces of evidence neuroscientists appeal to as justification for claims about the relationship between cognitive processes and human behavior. The strongest critics of neuroimaging research argue that the technology possesses little, if any, scientific value, in part because of the assumptions implicit in the complex analysis procedures used to transform the data into interpretable data patterns. In this chapter, I shift the focus of this debate away from assumptions implicit in the operation of techniques themselves, and toward the role data analysis techniques play as parts of the process of interpreting neuroimaging data. I propose that data analysis techniques can be conceived of as a lens that brings patterns within the data into focus through its selective transformation. This approach recognizes the double-edged nature of data analysis and interpretation: techniques render data interpretable, but their selection and application is often informed by the methodological and theoretical commitments of researchers using them.
功能磁共振成像研究通常与大脑图像相关联，这些图像上叠加着表示显著活动的颜色模式。这些图像是神经科学家用来支持有关认知过程与人类行为之间关系的主张的最突出、最容易识别的证据之一。对神经成像研究最强烈的批评者认为，这项技术即便有科学价值也微乎其微，部分原因在于将数据转化为可解释的数据模式所使用的复杂分析程序中隐含的假设。在本章中，我将这场辩论的焦点从技术本身操作中隐含的假设，转向数据分析技术作为解释神经成像数据过程的一部分所起的作用。我提出，数据分析技术可以被视为一个透镜，通过其选择性转换使数据中的模式清晰可见。这种方法认识到数据分析和解释的双刃剑性质：技术使数据变得可解释，但它们的选择和应用往往受到使用它们的研究人员的方法论和理论承诺的影响。
