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)

The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. (...) Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (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)

Functional neuroimaging (NI) technologies like Positron Emission Tomography and functional Magnetic Resonance Imaging (fMRI) have revolutionized neuroscience, and provide crucial tools to link cognitive psychology and traditional neuroscientific models. A growing discipline of 'neurophilosophy' brings fMRI evidence to bear on traditional philosophical issues such as weakness of will, moral psychology, rational choice, social interaction, free will, and consciousness. NI has also attracted critical attention from psychologists and from philosophers of science. I review debates over the evidential status of fMRI, including (...) the differences between brain scans and ordinary images, the legitimacy of forward inference and reverse inference, and deductive versus probabilistic accounts of NI evidence. I conclude with a discussion of fMRI as exploratory rather than confirmatory evidence, linking this debate to the growing literature on cognitive ontology. (shrink)