This paper unpacks the claim that network neuroscience provides a fruitful theoretical framework for the discovery and evaluation of mechanistic models of the human brain. I argue that as a theoretical framework, network neuroscience makes it possible for us to infer and articulate specific functional hypotheses about different parts of the human cortex. I describe these hypotheses as search tools to emphasise the multiple roles they play in neuroscientific research, which, among others are likely to include: (i) refining or discovering mechanistic models of (parts of) the brain, (ii) providing correlational evidence for specific mechanistic models, and (iii) inferring general principles of brain function by linking such mechanistic models across scales. The underlying assumption of network neuroscience is that the brain can be separated into units (network nodes) with well-defined interactions (network edges), and that the pattern of interunit interactions (network topology) makes possible the rich complex dynamics observed in the brain to support cognitive function. Armed with this general assumption, network neuroscience has approached the study of the human brain (and its associated cognitive functions) across a wide range of spatial scales. It is fair to say that so far the field has demonstrated its potential to offer both correlational and causal evidence for mechanistic explanations of the brain. My analysis will focus on two aims: (1) explicating the appeal of network neuroscience in a way that does not hide away its limitations, and (2) illustrating the general claims about the theoretical usefulness/fruitfulness of the network approach with one example drawn from the study of the human visual system.

Mechanistic experiments have been consistently linked to the project of turning the black boxes of input-output relations of biological phenomena into grey boxes and then into glass boxes: discovering which part does what and thereby brings about an observable effect. Emphasizing their involvement in these discovery practices, philosophers have argued that mechanistic experiments fuel explanations of biological processes. One of the most influential philosophical analyses characterises mechanistic experiments in terms of surgical interventions. An important divide in this analysis is drawn between intra-level causal experiments and inter-level constitutive explanations, both being required to achieve mechanistic explanations of biological processes. This paper takes a broader methodological angle on the epistemic contributions of inter-level mechanistic experiments in particular, shifting the focus from inter-level explanations to the inter-level investigative practices they make possible. Joining the debate about how to capture the distinctive character of inter-level experiments [citations], I will argue for the substantive role of conditional abductive reasoning in mechanistic discovery practices. In doing so, I show how a conditional account of abductive inferences can offer a good stand-in [approximation] for a logic of mechanistic discovery. This will allow us to compare the epistemic contributions of different kinds of interventions, including interventions that are closer to the surgical interventions.

The concept of biological measurement covers a wide spectrum of research strategies. On one end of this spectrum, certain methodologies aim to generalise biological entities, occasionally intervening and modifying their inherent uniqueness. At the opposite extreme, there are biological measurement practices that prioritise the examination of highly diverse and genetically varied entities, taking into account their evolutionary histories and the resulting variability. In the middle, various methods are employed to selectively reduce diversity in the specific aspect under investigation while concurrently preserving overall diversity within the broader context. This compatibility between biological measurement and an extensive array of experimental and theoretical practices requires elucidation. Biological measurement may be viewed as an extension of classical measurement, intended to accommodate the historicity and variability of biological entities. But also, it may be treated as a distinct concept altogether, primarily due to the absence of theoretical descriptions grounded in underlying equations for these objects. I argue that looking into the nuances inherent to biological measurement practices can yield a more systematic understanding of the role that mathematics plays in shaping the theoretical constructs of biological entities through measurement practices and in facilitating experimental reproducibility.

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 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 or abductive account recognize 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 a failure to de-couple, via interlevel experiments, phenomena from their mechanistic constituents? Focusing on the field of memory and LTP research, this paper 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.

Network theoretical approaches have shaped our understanding of many different kinds of biological modularity. This essay makes the case that to capture these contributions, it is useful to think about the role of network models in exploratory research. The overall point is that it is possible to provide a systematic analysis of the exploratory functions of network models in bioscientific research. Using two examples from molecular and developmental biology, I argue that often the same modelling approach can perform one or more exploratory functions, such as: introducing new directions of research, offering a complementary set of concepts, methods and algorithms for individuating important features of natural phenomena, generating proofs of principle demonstrations and potential explanations for phenomena of interest, and enlarging the scope of certain research agendas.

Recent engineering-based modelling efforts in systems biology suggest that some forms of biological robustness depend on structural features of the internal organization of living systems. To explain such features, abstract representations of patterns of organization or design principles within the framework of control theory have been proposed. We characterize such model-based explanations as structural-causal explanations and situate our account in the broader philosophical debate about explanation in biology.

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. I analyse one argument which challenges this strategy by criticising the claim that explanatory talk is lexically ambiguous, 375–394, 2012). I propose that the linguistic mechanism of transfer of meaning, 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.

Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed in neuroscience. I distinguish two types of applications of the simulation methodology in neuroscientific research. Model-oriented applications aim to use the simulation outputs to derive new hypotheses about brain organization and functioning and thus to extend current theoretical knowledge and understanding in the field. Data-oriented applications of the simulation methodology target the collection and analysis of data relevant for neuroscientific research that is inaccessible via more traditional experimental methods. I argue for a two-stage evaluation schema which helps clarify the differences and similarities between three current large-scale simulation projects pursued in neuroscience.

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.

Cognitive approaches to translation studies are driven by three interrelated aims: to understand the structure and organization of the capacities of cognitive agents involved in processes of translation, to build better theories and models of translation, and to develop more efficient methods and programs for translator training. Meeting the goals of such a broad agenda requires the fusion of different theoretical and experimental tools, from fields such as cognitive psychology, linguistics, and artificial intelligence. From exploratory studies that aimed to carve out the problem space for cognitive approaches to translation through methodologically refined studies based on triangulation and statistical analysis, to large scale projects that promise helpful technological innovations for translation studies, the current landscape of research programs that investigate the cognitive underpinnings of translation is both varied and constantly developing. This essay showcases some current research programs that reflect the fruitfulness of the interdisciplinary structure of translation studies. Instead of thinking about cognitive research on translation as being driven by a master cognitive theory, it is more descriptively adequate and more fruitful to understand it as a family of projects based on multiple theories that are relevant for studying different aspects of the translation process. This perspective allows us to extract the erotetic structure of these programs which are organized around specific problems or questions that have been shaped by previous research, by well-established cognitive hypotheses and by the current interests of the discipline of translation studies. Comparing different studies and models of translation will serve to illustrate how different theoretical and experimental approaches contribute to organizing and addressing specific problems on the agenda of a multidisciplinary field such as that of translation studies.

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.

My aim in this paper is to investigate what epistemic role, if any, do appeals to representations play in cognitive neuroscience. I suggest that while at present they seem to play something in between a minimal and a substantive explanatory role, there is reason to believe that representations have a substantial contribution to the construction of neuroscientic explanations of cognitive phenomena.