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)

Milner and Goodale have advanced a justly influential theory of the structure of the human visual system. In broad outline, Milner and Goodale hold that the ventral neural pathway is associated with recognition and experiential awareness, and with a kind of indirect control of action. And they hold that, by contrast, the dorsal neural stream is associated with the non-conscious, direct control of visually informed action. Most of the relevant empirical research has focused on the visual control of close-in, “personal (...) space,” reaching and/or grasping. While their view has not escaped controversy and debate, we think that Milner and Goodale have a compelling story to tell about the visual guidance of such close in, “personal space” action. However, the question of whether their scheme applies to behavior with visually specified targets that are outside of the space accessible by simply reaching and grasping is largely unstudied. And it should be studied, since this is the arena of much human behavior. We provide good reason to think that the Milner and Goodale scheme may well not apply to important classes of mid-range, ‘action space’ behavior. But the matter is in the end to be decided in the lab. We describe a research program to do this. (shrink)

Characterizing how activity in the central and autonomic nervous systems corresponds to distinct emotional states is one of the central goals of affective neuroscience. Despite the ease with which individuals label their own experiences, identifying specific autonomic and neural markers of emotions remains a challenge. Here we explore how multivariate pattern classification approaches offer an advantageous framework for identifying emotion-specific biomarkers and for testing predictions of theoretical models of emotion. Based on initial studies using multivariate pattern classification, we suggest that (...) central and autonomic nervous system activity can be reliably decoded into distinct emotional states. Finally, we consider future directions in applying pattern classification to understand the nature of emotion in the nervous system. (shrink)