Given that many imaging technologies in biology and medicine are non-optical and generate data that is essentially numerical, it is a striking feature of these technologies that the data generated using them are most frequently displayed in the form of semi-naturalistic, photograph-like images. In this paper, I claim that three factors underlie this: (1) historical preferences, (2) the rhetorical power of images, and (3) the cognitive accessibility of data presented in the form of images. The third of these can be (...) argued to provide an epistemic advantage to images, but I will further argue that this is often misleading and that images can in many cases be less informative than the corresponding mathematical data. (shrink)

Philosophers of science turned to historical case studies in part in response to Thomas Kuhn's insistence that such studies can transform the philosophy of science. In this issue Joseph Pitt argues that the power of case studies to instruct us about scientific methodology and epistemology depends on prior philosophical commitments, without which case studies are not philosophically useful. Here I reply to Pitt, demonstrating that case studies, properly deployed, illustrate styles of scientific work and modes of argumentation that are not (...) well handled by currently standard philosophical analyses. I illustrate these claims with exemplary findings from case studies dealing with exploratory experimentation and with interdisciplinary cooperation across sciences to yield multiple independent means of access to theoretical entities. The latter cases provide examples of ways that scientists support claims about theoretical entities that are not available in work performed within a single discipline. They also illustrate means of correcting systematic biases that stem from the commitments of each discipline taken separately. These findings illustrate the transformative power of case study methods, allow us to escape from the horns of Pitt's "dilemma of case studies," and vindicate some of the post-Kuhn uses to which case studies have been put. (shrink)

T. H. Morgan, A. H. Sturtevant, H. J. Muller and C. B. Bridges published their comprehensive treatise "The Mechanism of Mendelian Heredity" in 1915. By 1920 Morgan 's "Chromosome Theory of Heredity" was generally accepted by geneticists in the United States, and by British geneticists by 1925. By 1930 it had been incorporated into most general biology, botany, and zoology textbooks as established knowledge. In this paper, I examine the reasons why it was accepted as part of a series of (...) comparative studies of theory-acceptance in the sciences. In this context it is of interest to look at the persuasiveness of confirmed novel predictions, a factor often regarded by philosophers of science as the most important way to justify a theory. Here it turns out to play a role in the decision of some geneticists to accept the theory, but is generally less important than the CTH's ability to explain Mendelian inheritance, sex-linked inheritance, non-disjunction, and the connection between linkage groups and the number of chromosome pairs; in other words, to establish a firm connection between genetics and cytology. It is remarkable that geneticists were willing to accept the CTH as applicable to all organisms at a time when it had been confirmed only for Drosophila. The construction of maps showing the location on the chromosomes of genes for specific characters was especially convincing for non-geneticists. (shrink)

The use of multiple means of determination to “triangulate” on the existence and character of a common phenomenon, object, or result has had a long tradition in science but has seldom been a matter of primary focus. As with many traditions, it is traceable to Aristotle, who valued having multiple explanations of a phenomenon, and it may also be involved in his distinction between special objects of sense and common sensibles. It is implicit though not emphasized in the distinction between (...) primary and secondary qualities from Galileo onward. It is arguably one of several conceptions involved in Whewell’s method of the “consilience of inductions” (Laudan 1971) and is to be found in several places in Peirce. (From M. Brewer and B. Collins, eds., (1981); Scientific Inquiry in the Social Sciences (a festschrift for Donald T. Campbell), San Francisco: Jossey-Bass, pp. 123–162.). (shrink)

Let me first state that I like Antti Revonsuo’s discussion of the various methodological and interpretational problems in neuroscience. It shows how careful and methodologically reflected scientists have to proceed in this fascinating field of research. I have nothing to add here. Furthermore, I am very sympathetic towards Revonsuo’s general proposal to call for a Philosophy of Neuroscience that stresses foundational issues, but also focuses on methodological and explanatory strategies.2 In a footnote of his paper, Revonsuo complains – as many (...) others do today – about what is sometimes called “physics imperialism”. This is the view that physics dominates the philosophy of science. I am not sure if this is still the case nowadays, but it is certainly historically correct that almost all work in the field of methodology centered around cases from physics. Although this has been changing, there are still plenty of special sciences philosophers did not worry about much. Admittedly, I am myself a trained physicist and not a neuroscientist and will therefore probably be biased negatively. As it is, I will discuss some examples from physics in order to illustrate my points. (shrink)

A square tabular array was introduced by R. C. Punnett in (1907) to visualize systematically and economically the combination of gametes to make genotypes according to Mendel’s theory. This mode of representation evolved and rapidly became standardized as the canonical way of representing like problems in genetics. Its advantages over other contemporary methods are discussed, as are ways in which it evolved to increase its power and efficiency, and responded to changing theoretical perspectives. It provided a natural visual decomposition of (...) a complex problem into a number of inter-related stages. This explains its computational and conceptual power, for one could simply “read off” answers to a wide variety of questions simply from the “right” visual representation of the problem, and represent multiple problems, and multiple layers of problems in the same diagram. I relate it to prior work on the evolution of Weismann diagrams by Griesemer and Wimsatt (What Philosophy of Biology Is, Martinus-Nijhoff, the Hague, 1989), and discuss a crucial change in how it was interpreted that midwifed its success. (shrink)

An important function of scientific diagrams is to identify causal relationships. This commonly relies on contrasts that highlight the effects of specific difference-makers. However, causal contrast diagrams are not an obvious and easy to recognize category because they appear in many guises. In this paper, four case studies are presented to examine how causal contrast diagrams appear in a wide range of scientific reports, from experimental to observational and even purely theoretical studies. It is shown that causal contrasts can be (...) expressed in starkly different formats, including photographs of complexly visualized macromolecules as well as line graphs, bar graphs, or plots of state spaces. Despite surface differences, however, there is a measure of conceptual unity among such diagrams. In empirical studies they generally serve not only to infer and communicate specific causal claims, but also as evidence for them. The key data of some studies is given nowhere except in the diagrams. Many diagrams show multiple causal contrasts in order to demonstrate both that an effect exists and that the effect is specific -- that is, to narrowly circumscribe the phenomenon to be explained. In a large range of scientific reports, causal contrast diagrams reflect the core epistemic claims of the researchers. (shrink)

There is currently a gap in our understanding of how figures produced by mechanical imaging techniques play evidential roles: several studies based on close examination of scientific practice show that imaging techniques do not yield data whose significance can simply be read off the image. If image-making technology is not a simple matter of nature re-presenting itself to us in a legible way, just how do the images produced provide support for scientific claims? In this article I will first show (...) that there is a distinct question about the semiotics of mechanically produced images that has not yet been answered. I show that my account of visual representations can do so, and I argue that the role of convention involved in my account is compatible with the view that visual representations produced through mechanized imaging techniques can play genuine evidential roles in scientific reasoning. (shrink)

Scientific images represent types or particulars. According to a standard history and epistemology of scientific images, drawings are fit to represent types and machine-made images are fit to represent particulars. The fact that archaeologists use drawings of particulars challenges this standard history and epistemology. It also suggests an account of the epistemic quality of archaeological drawings. This account stresses how images integrate non-conceptual and interepretive content.

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)