Work in progress

Constitutive relevance in interlevel experiments
One reason for the popularity of Craver's mutual manipulability account of constitutive relevance (MM) is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers (Baumgartner and Gebharter2016 & Baumgartner and Casini2017) propose a theoretical revision of (MM) in light of several important conceptual objections. Their alternative approach, the No De-Coupling account (NDC) conceives of constitution as a dependence relation which, once postulated, provides the best explanation of the impossibility of breaking the common cause coupling of a macro-level mechanism and its micro-level components. This entails an abductive view of constitutive inference. Proponents of the NDC account recognise that their discussion leaves open a big question concerning the practical dimension of the notion of constitutive relevance: Is it possible to faithfully reconstruct constitutional reasoning in science in terms of NDC? Focusing on the field of memory and LTP research, this paper argues that NDC provides a more adequate description of inter-level experiments in neuroscience. We also suggest that NDC highlights some significant practical recommendations of how to interpret the findings of interlevel experiments.
Emergence, robustness and mechanisms

How common are emergent phenomena in the biological realm? The rise of the mechanistic outlook challenges the idea that biological phenomena are emergent in any substantive sense. Mechanism has a complicated relationship to emergence. Philosophical mechanism stresses the non-reductionist and hierarchical character of mechanistic models and explanations of biological phenomena. This maps onto the characterization of emergence, but the causal-constitutive character of mechanistic explanations seems to be at odds with the positive features of emergence: the novelty and independence of macrolevel phenomena. Current and past discussions of the relation between mechanism and emergence have overlooked the problem of biological robustness. Some kinds of robustness are not amenable to a mechanistic treatment. I claim that they hold the key to strengthening the positive characterization of emergent biological phenomena. Focusing on a case of multiscale kinetic modeling, I show how the investigation of biological robustness can alleviate the tension between mechanism and emergence.

Computational explanations through a mechanistic lens
Piccinini's mechanistic account of physical computation is currently one of the most well-developed non-reductionist views of the modeling and explanatory strategies used in computational neuroscience. The key idea of the account is that aligning the notion of physical computation and mechanism helps elucidate the distinctive character of computational models developed in the field and provides insight into the norms for evaluating their explanatory value. This paper explores two problems for the mechanistic account of physical computation: the non-decompositional features of some computational explanatory models used in cognitive neuroscience and the non-mechanistic character of the distinctive features of computational systems.