Maria Serban

This paper examines the transformation of Craver’s (2009) mutual manipulability (MM) account into the matched interlevel experiments (MIE) framework (Craver et al., 2021) and argues that it amounts to a theoretical reduction of mechanistic constitutive relations to causal mediation. While the MIE account successfully resolves the incoherence challenge that plagued MM, it does so by eliminating the distinctive theoretical content that constitutive categories were supposed to provide. The processual reframing that enables this solution replaces hierarchical part-whole relationships with temporal causal sequences, changing what mechanistic explanations are understood to accomplish. Drawing on paradigmatic action potential experiments, I demonstrate that practices satisfying MIE’s formal requirements consistently establish causal mediation relationships without requiring constitutive interpretation. I address several theoretical defenses of constitutive categories—including interpretive objections about two types of constitution, arguments for distinctive explanatory value, and appeals to mechanistic levels—showing that none can rescue constitutive distinctiveness once constitution is explicitly identified with causal betweenness. Rather than undermining mechanistic approaches, this analysis suggests that their explanatory power derives from methodological sophistication in investigating complex, multi-scale causal structures rather than from categorically distinct constitutive relationships.

Philosophical accounts of scientific explanation are broadly divided into ontic and epistemic views. This paper explores the idea that the lexical ambiguity of the verb to explain and its nominalisation supports an ontic conception of explanation (Salmon 1989; Craver 2007). I analyse one argument which challenges this strategy by criticising the claim that explanatory talk is lexically ambiguous (Wright, European Journal of Philosophy of Science 2(3), 375–394, 2012). I propose that the linguistic mechanism of transfer of meaning (Nunberg, Journal of Semantics 12(2), 109–132, 1995) provides a better account of the lexical alternations that figure in the systematic polysemy of explanatory talk, and evaluate the implications of this proposal for the debate between ontic and epistemic conceptions of scientific explanation.

How do different experimental control strategies shape the production of biological knowledge? This paper examines Emil du Bois-Reymond’s electrophysiological investigations and Claude Bernard’s toxicological research in 19th-century physiology. Rather than viewing these as opposing methodologies, I analyze their approaches to nerve-muscle communication as complementary responses to shared epistemic challenges. Using Schickore’s framework for experimental control (2025), I show through a detailed historical analysis that experimental control functioned constitutively rather than merely technically in both research programs. Both scientists engaged in extensive second-order experimentation, systematically exploring their experimental setups to determine what factors needed controlling. Du Bois-Reymond’s non-polarizable electrodes made possible new ways of understanding biological electricity as quantifiable phenomena, while Bernard’s ligature preparation constituted a method for understanding physiological functions as systematically separable within integrated organisms. Control practices and theoretical commitments co-evolved such that what counted as legitimate operationalization of theoretical principles was shaped by experimental practice. This analysis shows how experimental methodology and biological knowledge co-develop, suggesting implications for understanding methodological tensions in contemporary biological research.

Recent work in philosophy of measurement has converged on a "theory-dependence consensus”, according to which measurement reliability requires sophisticated theoretical scaffolding. This consensus has been largely shaped by case studies from physics and high-precision metrology. This paper questions whether this consensus adequately captures measurement practices in biology, where researchers often operate under significant uncertainty about their target phenomena. Through detailed historical analysis of early electrophysiological research—from Carlo Matteucci through Emil du Bois-Reymond, Hermann von Helmholtz, and Ludimar Hermann—I examine how quantitative measurement practices emerged under theoretical uncertainty. The cases reveal recurring patterns including productive theoretical inadequacy and instrumental constraint-driven discovery, supporting an analytical framework that distinguishes multiple levels of theoretical involvement in measurement. Building on these cases, I argue that biological measurement practices function productively as strategies for causal discovery, and theoretically inadequate frameworks prove epistemically valuable by structuring empirical inquiry to reveal previously unrecognised causal factors.

The increasing application of network models to interpret biological systems raises a number of important methodological and epistemological questions. What novel insights can network analysis provide in biology? Are network approaches an extension of or in conflict with mechanistic research strategies? When and how can network and mechanistic approaches interact in productive ways? In this paper we address these questions by focusing on how biological networks are represented and analyzed in a diverse class of case studies. Our examples span from the investigation of organizational properties of biological networks using tools from graph theory to the application of dynamical systems theory to understand the behavior of complex biological systems. We show how network approaches support and extend traditional mechanistic strategies but also offer novel strategies for dealing with biological complexity.

Philosophical accounts of scientific explanation are broadly divided into ontic and epistemic views. This paper explores the idea that the lexical ambiguity of the verb to explain and its nominalisation supports an ontic conception of explanation (Salmon 1989; Craver 2007). I analyse one argument which challenges this strategy by criticising the claim that explanatory talk is lexically ambiguous (Wright, European Journal of Philosophy of Science 2(3), 375–394, 2012). I propose that the linguistic mechanism of transfer of meaning (Nunberg, Journal of Semantics 12(2), 109–132, 1995) provides a better account of the lexical alternations that figure in the systematic polysemy of explanatory talk, and evaluate the implications of this proposal for the debate between ontic and epistemic conceptions of scientific explanation.

Philosophical Perspectives on the Engineering Approach in Biology provides a philosophical examination of what has been called the most powerful metaphor in biology: The machine metaphor. The chapters collected in this volume discuss the idea that living systems can be understood through the lens of engineering methods and machine metaphors from both historical, theoretical, and practical perspectives. In their contributions the authors examine questions about scientific explanation and methodology, the interrelationship between science and engineering, and the impact that the use of engineering metaphors in science may have for bioethics and science communication, such as the worry that its wide application reinforces public misconceptions of the nature of new biotechnology and biological life. The book also contains an introduction that describes the rise of the machine analogy and the many ways in which it plays a central role in fundamental debates about e.g. design, adaptation and reductionism in the philosophy of biology. The book will be useful as a core reading to professionals as well as graduate and undergraduate students in courses of philosophy of science and for life scientists taking courses in philosophy of science and bioethics.

Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation.

One reason for the popularity of Craver’s mutual manipulability account of constitutive relevance is that it seems to make good sense of the experimental practices and constitutive reasoning in the life sciences. Two recent papers propose a theoretical alternative to in light of several important conceptual objections. Their alternative approach, the no de-coupling account, conceives of constitution as a dependence relation that 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 or abductive account recognize that their discussion leaves open a big question concerning the practical dimension of the notion of constitutive relevanssssce: Is it possible to faithfully reconstruct constitutional reasoning in science in terms of a failure to de-couple, via interlevel experiments, phenomena from their mechanistic constituents? Focusing on the field of memory and long-term potential research, this article argues that the abductive account provides a more adequate description of interlevel experiments in neuroscience. We also suggest that the account highlights some significant practical recommendations of how to interpret the findings of interlevel experiments. 1Introduction 2Mutual Manipulability and Constitutive Relevance 2.1Constitutive relevance through an interventionist lens 2.2Mutual manipulability: A methodological application 3Trouble for the Mutual Manipulability Account 4The Abductive Account of Constitution 5The Abductive Account: A Methodological Application 5.1Long-term potential and memory experiments 5.2A comparative summary 6Conclusions

An increasing number of philosophers have promoted the idea that mechanism provides a fruitful framework for thinking about the explanatory contributions of computational approaches in cognitive neuroscience. For instance, Piccinini and Bahar :453–488, 2013) have recently argued that neural computation constitutes a sui generis category of physical computation which can play a genuine explanatory role in the context of investigating neural and cognitive processes. The core of their proposal is to conceive of computational explanations in cognitive neuroscience as a subspecies of mechanistic explanations. This paper identifies several challenges facing their mechanistic account and sketches an alternative way of thinking about the epistemic roles of computational approaches used in the study of brain and cognition. Drawing on examples from both low-level and systems-level computational neuroscience, I argue that at least some computational explanations of neural and cognitive processes are partially independent from mechanistic constraints