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Saving Data Analysis: Epistemic Friction and Progress in Neuroimaging Research

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Neural Mechanisms

Part of the book series: Studies in Brain and Mind ((SIBM,volume 17))

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Abstract

Data must be manipulated for their evidential import to be assessed. However, data analysis is regarded as a source of inferential errors by scientists and critics of neuroscience alike. In this chapter I argue that of data analysis is epistemically challenged in part because data are causally separated from the events that they are intended to provide evidence for claims about. Experimental manipulations place researchers in epistemically advantageous positions by making contact with the objects and phenomena of interest. Data manipulations, on the other hand, are applied to material objects that are not in causal contact with the events they are used to learn about. I then propose that some of the inferential liabilities that go along with data manipulation are partly overcome through the occurrence of epistemic friction. I consider two forthcoming contributions to network neuroscience to illustrate the benefits, and risks, of the data analyst’s reliance on epistemic friction.

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Notes

  1. 1.

    At the time this chapter was written the cases examined were pre-prints. Pre-print material was chosen because I had the ability to observe as these contributions were conceived, developed, and written up. It was through observing and collaborating on these projects that the philosophical perspectives presented in this chapter were developed.

  2. 2.

    Those familiar with neuroimaging research may notice that experiments, and so experimental manipulations, are often designed with the data manipulations that will be carried out downstream in mind. In this way, experimental manipulations are methodologically beholden to data manipulations and so it may seem odd to classify one as epistemically inferior to the other. It is important here to note that the use of shared and otherwise open access data has begun to decouple experimental design from analysis design. As it becomes more common for researchers to analyze and interpret data that they did not produce it is important to consider the data and experimental manipulations as disentangled processes. I thank a reviewer for pressing this point.

  3. 3.

    The remainder of this section is partially autobiographical in content. The information reported here was obtained through conversations and collaborations with the scientist discussed.

  4. 4.

    I owe thanks to an anonymous reviewer for raising this challenge.

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Acknowledgements

I owe thanks to William Hedley Thompson for providing substantive comments on several drafts of this chapter, and the Poldrack lab at Stanford for the opportunity to be a member of their lab, as both an observer and collaborator. Adrian Currie, the editors of this book, and an anonymous reviewer provided incredibly helpful comments on an early draft. The National Science Foundation’s STS program, and Social Sciences and Humanities Research Council of Canada provided financial support for this research.

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  1. Department of Psychology, Jordan Hall, Stanford University, Stanford, CA, USA

    Jessey Wright

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  1. Department of Letter, Philosophy, Communication, University of Bergamo LLC, Turin, Italy

    Fabrizio Calzavarini

  2. Department of Philosophy and Education, University of Turin, Turin, Italy

    Marco Viola

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Wright, J. (2021). Saving Data Analysis: Epistemic Friction and Progress in Neuroimaging Research. In: Calzavarini, F., Viola, M. (eds) Neural Mechanisms. Studies in Brain and Mind, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-54092-0_8

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