Advanced medical imaging, such as CT, fMRI and PET, has undergone enormous progress in recent years, both in accuracy and utilization. Such techniques often bring with them an illusion of immediacy, the idea that the body and its diseases can be directly inspected. In this paper we target this illusion and address the issue of the reliability of advanced imaging tests as knowledge procedures, taking positron emission tomography in oncology as paradigmatic case study. After individuating a suitable notion of reliability, (...) we argue that PET is a highly theory-laden and non-immediate knowledge procedure, in spite of the photographic-like quality of the images it delivers; the diagnostic conclusions based on the interpretation of PET images are population-dependent; PET images require interpretation, which is inherently observer-dependent and therefore variable. We conclude with a three-step methodological proposal for enhancing the reliability of advanced medical imaging. (shrink)

Neuroscience is a laboratory-based science that spans multiple levels of analysis from molecular genetics to behavior. At every level of analysis experiments are designed in order to answer empirical questions about phenomena of interest. Understanding the nature and structure of experimentation in neuroscience is fundamental for assessing the quality of the evidence produced by such experiments and the kinds of claims that are warranted by the data. This article provides a general conceptual framework for thinking about evidence and experimentation in (...) neuroscience with a particular focus on two research areas: cognitive neuroscience and cognitive neurobiology. (shrink)

Taking the visual appeal of the ‘bell curve’ as an example, this paper discusses in how far the availability of quantitative approaches (here: statistics) that comes along with representational standards immediately affects qualitative concepts of scientific reasoning (here: normality). Within the realm of this paper I shall focus on the relationship between normality, as defined by scientific enterprise, and normativity, that result out of the very processes of standardisation itself. Two hypotheses are guiding this analysis: (1) normality, as it is (...) defined by the natural and the life sciences, must be regarded as an ontological, but epistemological important fiction and (2) standardised, canonical visualisations (such as the ‘bell curve’) impact on scientific thinking and reasoning to a significant degree. I restrict my analysis to the epistemological function of scientific representations of data: This means identifying key strategies of producing graphs and images in scientific practice. As a starting point, it is crucial to evaluate to what degree graphs and images could be seen as guiding scientific reasoning itself, for instance in attributing to them a certain epistemological function within a given field of research. (shrink)