Multivariate pattern analysis, or MVPA, has become one of the most popular analytic methods in cognitive neuroscience. Since its inception, MVPA has been heralded as offering much more than regular univariate analyses, for—we are told—it not only can tell us which brain regions are engaged while processing particular stimuli, but also which patterns of neural activity represent the categories the stimuli are selected from. We disagree, and in the current paper we offer four conceptual challenges to the use of MVPA (...) to make claims about neural representation. Our view is that the use of MVPA to make claims about neural representation is problematic. (shrink)

The neural reuse framework developed primarily by Michael Anderson proposes that brain regions are involved in multiple and diverse cognitive tasks and that brain regions flexibly and dynamically interact in different combinations to carry out cognitive functioning. We argue that the evidence cited by Anderson and others falls short of supporting the fundamental principles of neural reuse. We map out this problem and provide solutions by drawing on recent advances in network neuroscience, and we argue that methods employed in network (...) neuroscience provide the means to fully engage in a research program operating under the principles of neural reuse. (shrink)

ABSTRACTÉmilie du Châtelet’s recently-discovered Essai sur l’optique offers new insights into her early natural philosophy. Here I analyse the Essai in detail, focusing on Du Châtelet’s use of attr...

In the last two decades, philosophy of neuroscience has predominantly focused on explanation. Indeed, it has been argued that mechanistic models are the standards of explanatory success in neuroscience over, among other things, topological models. However, explanatory power is only one virtue of a scientific model. Another is its predictive power. Unfortunately, the notion of prediction has received comparatively little attention in the philosophy of neuroscience, in part because predictions seem disconnected from interventions. In contrast, we argue that topological predictions (...) can and do guide interventions in science, both inside and outside of neuroscience. Topological models allow researchers to predict many phenomena, including diseases, treatment outcomes, aging, and cognition, among others. Moreover, we argue that these predictions also offer strategies for useful interventions. Topology-based predictions play this role regardless of whether they do or can receive a mechanistic interpretation. We conclude by making a case for philosophers to focus on prediction in neuroscience in addition to explanation alone. (shrink)

Does the brain behave indeterministically? I argue that accounting for ion channels, key functional units in the brain, requires indeterministic models. These models are probabilistic, so the brain does behave indeterministically in a weak sense. I explore the implications of this point for a stronger sense of indeterminism. Ultimately I argue that it is not possible, either empirically or through philosophical argument, to show that the brain is indeterministic in that stronger sense.