Prediction and Topological Models in Neuroscience

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

Multivariate pattern analysis and the search for neural representations

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

The discontinuity of levels in cognitive science

Comenzamos caracterizando la concepción “funcionalista homuncular” de la mente de Dennett, tal como es descrita en su obra temprana. A continuación, la comparamos con la bosquejada en From Bacteria to Bach and Back. Argumentamos que los cambios recientes en la posición de Dennett han producido cierta tensión en su visión de las descomposiciones funcionales. Supuestamente, las descomposiciones funcionales basadas en la postura intencional terminan en un nivel inferior, no inteligente, que puede explicarse mecánicamente. Sin embargo, puesto que Dennett cree ahora que las neuronas podrían tener que ser descritas en términos intencionales, no queda claro si nuestras explicaciones de las funciones cognitivas van a casar con nuestras explicaciones de la conducta de neuronas y redes. Exploramos las consecuencias de esta tensión para la teoría de Dennett y para la neurociencia cognitiva en general.We begin by characterizing Dennett’s “homuncular functionalist” view of the mind, as described in his early work. We then contrast that view with the one outlined in From Bacteria to Bach and Back. We argue that recent changes in Dennett’s view have produced tension in the way he conceives of functional decompositions. Functional decompositions based on the intentional stance are supposed to reach a bottom, “dumb” level which can be explained mechanically; however, since Dennett now believes that neurons may need to be described intentionally, it is not clear whether our explanations of cognitive functions can ever align with our explanations of neuronal and network behaviors. We explore the consequences of this tension for Dennett’s view, and for cognitive neuroscience in general