Duhem’s problem arises especially in scientific contexts where the tools and procedures of measurement and analysis are numerous and complex. Several philosophers of cognitive science have cited its manifestations in fMRI as grounds for skepticism regarding the epistemic value of neuroimaging. To address these Duhemian arguments for skepticism, I offer an alternative approach based on Deborah Mayo’s error-statistical account in which Duhem's problem is more fruitfully approached in terms of error probabilities. This is illustrated in examples such as the use (...) of probabilistic brain atlases, comparison of different preprocessing protocols with respect to their error characteristics, and statistical modeling of fMRI data. These examples demonstrate the ways in which we can better understand and formulate the general methodological problem and direct the way toward alternative approaches to neuroimaging in philosophy of cognitive science, in which we can be more balanced and productive in our scrutiny of the epistemic value of neuroimaging studies. (shrink)

Brain images are used both as scientific evidence and to illustrate the results of neuroimaging experiments. These images are apt to be viewed as photographs of brain activity, and in so viewing them people are prone to assume that they share the evidential characteristics of photographs. Photographs are epistemically compelling, and have a number of characteristics that underlie what I call their inferential proximity. Here I explore the aptness of the photography analogy, and argue that although neuroimaging does bear important (...) similarities to photography, the details of the generation and analysis of neuroimages significantly complicate the relation of the image to the data. Neuroimages are not inferentially proximate, but their seeming so increases the potential for misinterpretation. This suggests caution in appealing to such images in the public domain. (shrink)

fMRI promises to uncover the functional structure of the brain. I argue, however, that pictures of ‘brain activity' associated with fMRI experiments are poor evidence for functional claims. These neuroimages present the results of null hypothesis significance tests performed on fMRI data. Significance tests alone cannot provide evidence about the functional structure of causally dense systems, including the brain. Instead, neuroimages should be seen as indicating regions where further data analysis is warranted. This additional analysis rarely involves simple significance testing, (...) and so justified skepticism about neuroimages does not provide reason for skepticism about fMRI more generally. (shrink)

There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every level. This (...) sentiment, however, is much less congenial to the tried‐and‐true experimental protocols available today and to theorizing about the brain in general. This essay examines the tension between current experimental methods and large-scale views of the brain. We argue that this disconnection between experiment and what really are guiding theoretical metaphors seriously impedes progress in neuroscience. (shrink)