The philosophical literature on scientific explanation in neuroscience has been dominated by the idea of mechanisms. The mechanist philosophers often claim that neuroscience is in the business of finding mechanisms. This view has been challenged in numerous ways by showing that there are other successful and widespread explanatory strategies in neuroscience. However, the empirical evidence for all these claims was hitherto lacking. Empirical evidence about the pervasiveness and uses of various explanatory strategies in neuroscience is particularly needed because examples and (...) case studies that are used to illustrate philosophical claims so far tend to be hand-picked. The risk of confirmation bias is therefore considerable: when looking for white swans, all one finds is that swans are white. The more systematic quantitative and qualitative bibliometric study of a large body of relevant literature that we present in this paper can put such claims into perspective. Using bibliometric tools, we identify the typical linguistic patterns used in the alleged mechanistic, dynamical, and topological explanations in the literature, their preponderance and how they change over time. Our findings show abundant use of mechanistic language, but also the presence of a significant neuroscience literature using topological and dynamical explanatory language, which grows over time and increasingly differentiates from each other and from mechanistic explanations. (shrink)

According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers consider it important to scientific explanation. What is surprising is that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this concept is importantly different from the (...) notion of mechanism and that this difference matters for our understanding of causation and explanation in science. (shrink)

We provide three innovations to recent debates about whether topological or “network” explanations are a species of mechanistic explanation. First, we more precisely characterize the requirement that all topological explanations are mechanistic explanations and show scientific practice to belie such a requirement. Second, we provide an account that unifies mechanistic and non-mechanistic topological explanations, thereby enriching both the mechanist and autonomist programs by highlighting when and where topological explanations are mechanistic. Third, we defend this view against some powerful mechanist objections. (...) We conclude from this that topological explanations are autonomous from their mechanistic counterparts. (shrink)

Transdisciplinary research challenges the divide between Indigenous and academic knowledge by bringing together epistemic resources of heterogeneous stakeholders. The aim of this article is to explore causal explanations in a traditional fishing community in Brazil that provide resources for transdisciplinary collaboration, without neglecting differences between Indigenous and academic experts. Semi-structured interviews were carried out in a fishing village in the North shore of Bahia and our findings show that community members often rely on causal explanations for local ecological phenomena with (...) different degrees of complexity. While these results demonstrate the ecological expertise of local community members, we also argue that recognition of local expertise needs to reflect on differences between epistemic communities by developing a culturally sensitive model of transdisciplinary knowledge negotiation. (shrink)

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

The emphasis on the organization of entities and their activities and interactions has been labeled one of the most distinct contributions of mechanistic philosophy. In this paper I discuss the manner in which the organization of entities and their activities and interactions participates in bringing about phenomena. I present a well-known example from molecular biology—the functioning of the genetic switch in phage lambda—and discuss Marco J. Nathan’s notion of causation by concentration. Nathan introduces causation by concentration to account for the (...) irreducible causal role that the concentration ratio between two kinds of proteins possesses in the genetic switch mechanism in phage lambda. I discuss what the irreducibility of this causal role amounts to and provide a mechanistic interpretation of Nathan’s causation by concentration; that is, I explain this irreducible causal role as one organizational feature of this mechanism. The paper concludes that biological mechanisms need a causal pluralist framework [similar to Glennan’s account in, and but slightly modified] where organizational features such as the concentration ratio have a causally relevant role, yet all the causally productive relations occur at the level of entities or individuals. (shrink)

In the last two decades few topics in philosophy of science have received as much attention as mechanistic explanation. A significant motivation for these accounts is that scientists frequently use the term “mechanism” in their explanations of biological phenomena. While scientists appeal to a variety of causal concepts in their explanations, many philosophers argue or assume that all of these concepts are well understood with the single notion of mechanism. This reveals a significant problem with mainstream mechanistic accounts– although philosophers (...) use the term “mechanism” interchangeably with other causal concepts, this is not something that scientists always do. This paper analyses two causal concepts in biology–the notions of “mechanism” and “pathway”–and how they figure in biological explanation. I argue that these concepts have unique features, that they are associated with distinct strategies of causal investigation, and that they figure in importantly different types of explanation. (shrink)

ABSTRACTIn this paper I offer an interventionist perspective on the explanatory structure and explanatory power of dynamical models in cognitive science: I argue that some “pure” dynamical models – ones that do not refer to mechanisms at all – in cognitive science are “contextualized causal models” and that this explanatory structure gives such models genuine explanatory power. I contrast this view with several other perspectives on the explanatory power of “pure” dynamical models. One of the main results is that dynamical (...) models need not refer to underlying mechanisms in order to be explanatory. I defend and illustrate this position in terms of dynamical models of the A-not-B error in developmental psychology as elaborated by Thelen and colleagues, and dynamical models of unintentional interpersonal coordination developed by Richardson and colleagues. (shrink)

I examine to what extent accounts of mechanisms based on formal interventionist theories of causality can adequately represent biological mechanisms with complex dynamics. Using a differential equation model for a circadian clock mechanism as an example, I first show that there exists an iterative solution that can be interpreted as a structural causal model. Thus, in principle, it is possible to integrate causal difference-making information with dynamical information. However, the differential equation model itself lacks the right modularity properties for a (...) full integration. A formal mechanistic model will therefore have to leave out either noncausal or causal explanatory relations. (shrink)

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...) articulate norms of completeness for mechanistic explanations that have no such unwanted implications. _1_ Introduction _2_ A Balancing Act: When Do Details Matter? _3_ The Norms of Causal Explanation _4_ The Norms of Constitutive Explanation _5_ Salmon-Completeness _6_ From More Details to More Relevant Details _7_ Non-explanatory Virtues of Abstraction _8_ From Explanatory Models to Explanatory Knowledge _9_ Mechanistic Completeness Reconsidered _10_ Conclusion. (shrink)

In this paper, we offer reasons to justify the explanatory credentials of dynamical modeling in the context of the metaplasticity thesis, located within a larger grouping of views known as 4E Cognition. Our focus is on showing that dynamicism is consistent with interventionism, and therefore with a difference-making account at the scale of system topologies that makes sui generis explanatory differences to the overall behavior of a cognitive system. In so doing, we provide a general overview of the interventionist approach. (...) We then argue that recent mechanistic attempts at reducing dynamical modeling to a merely descriptive enterprise fail given that the explanatory standard in dynamical modeling can be shown to rest on interventionism. We conclude that dynamical modeling captures features of nested and developmentally plastic cognitive systems that cannot be explained by appeal to underlying mechanisms alone. (shrink)

Rational analysis is an influential but contested account of how probabilistic modeling can be used to construct non-mechanistic but self-standing explanatory models of the mind. In this paper, I disentangle and assess several possible explanatory contributions which could be attributed to rational analysis. Although existing models suffer from evidential problems that question their explanatory power, I argue that rational analysis modeling can complement mechanistic theorizing by providing models of environmental affordances.

This article examines three candidate cases of non-causal explanation in computational neuroscience. I argue that there are instances of efficient coding explanation that are strongly analogous to examples of non-causal explanation in physics and biology, as presented by Batterman, Woodward, and Lange. By integrating Lange’s and Woodward’s accounts, I offer a new way to elucidate the distinction between causal and non-causal explanation, and to address concerns about the explanatory sufficiency of non-mechanistic models in neuroscience. I also use this framework to (...) shed light on the dispute over the interpretation of dynamical models of the brain. _1_ Introduction _1.1_ Efficient coding explanation in computational neuroscience _1.2_ Defining non-causal explanation _2_ Case I: Hybrid Computation _3_ Case II: The Gabor Model Revisited _4_ Case III: A Dynamical Model of Prefrontal Cortex _4.1_ A new explanation of context-dependent computation _4.2_ Causal or non-causal? _5_ Causal and Non-causal: Does the Difference Matter? (shrink)

Batterman and Rice ([2014]) argue that minimal models possess explanatory power that cannot be captured by what they call ‘common features’ approaches to explanation. Minimal models are explanatory, according to Batterman and Rice, not in virtue of accurately representing relevant features, but in virtue of answering three questions that provide a ‘story about why large classes of features are irrelevant to the explanandum phenomenon’ ([2014], p. 356). In this article, I argue, first, that a method (the renormalization group) they propose (...) to answer the three questions cannot answer them, at least not by itself. Second, I argue that answers to the three questions are unnecessary to account for the explanatoriness of their minimal models. Finally, I argue that a common features account, what I call the ‘generalized ontic conception of explanation’, can capture the explanatoriness of minimal models. (shrink)

This paper argues that in some explanations mathematics are playing an explanatory rather than a representational role, and that this feature unifies many types of non-causal or non-mechanistic explanations that some philosophers of science have been recently exploring under various names. After showing how mathematics can play either a representational or an explanatory role by considering two alternative explanations of a same biological pattern—“Bergmann’s rule”—I offer an example of an explanation where the bulk of the explanatory job is done by (...) a mathematical theorem, and where mechanisms involved in the target systems are not explanatorily relevant. Then I account for the way mathematical properties may function in an explanatory way within an explanation by arguing that some mathematical propositions involving variables non directly referring to the target system features constitute constraints to which a whole class of systems should comply, provided they are describable by a mathematical object concerned by those propositions. According to such “constraint account”, those mathematical facts are directly entailing the explanandum, as a consequence of such constraints. I call those explanations “structural”, because here properties of mathematical structures are accounting for the explanandum; various kinds of mathematical structures thereby define various types of structural explanations. (shrink)

Although it has been argued that mechanistic explanation is compatible with abstraction, there are still doubts about whether mechanism can account for the explanatory power of significant abstract models in computational neuroscience. Chirimuuta has recently claimed that models describing canonical neural computations must be evaluated using a non-mechanistic framework. I defend two claims regarding these models. First, I argue that their prevailing neurocognitive interpretation is mechanistic. Additionally, a criterion recently proposed by Levy and Bechtel to legitimize mechanistic abstract models, and (...) also a criterion proposed by Chirimuuta herself aimed to distinguish between causal and non-causal explanation, can be employed to show why these models are explanatory only under this interpretation. Second, I argue that mechanism is able to account for the special epistemic achievement implied by CNC models. Canonical neural components contribute to an integrated understanding of different cognitive functions. They make it possible for us to explain these functions by describing different mechanisms constituted by common basic components arranged in different ways. (shrink)

Though the realm of biology has long been under the philosophical rule of the mechanistic magisterium, recent years have seen a surprisingly steady rise in the usurping prowess of process ontology. According to its proponents, theoretical advances in the contemporary science of evo-devo have afforded that ontology a particularly powerful claim to the throne: in that increasingly empirically confirmed discipline, emergently autonomous, higher-order entities are the reigning explanantia. If we are to accept the election of evo-devo as our best conceptualisation (...) of the biological realm with metaphysical rigour, must we depose our mechanistic ontology for failing to properly “carve at the joints” of organisms? In this paper, I challenge the legitimacy of that claim: not only can the theoretical benefits offered by a process ontology be had without it, they cannot be sufficiently grounded without the metaphysical underpinning of the very mechanisms which processes purport to replace. The biological realm, I argue, remains one best understood as under the governance of mechanistic principles. (shrink)

Most philosophical debate over mental causation has been concerned with reconciling commonsense intuitions that there are causal interactions between the mental and the physical with philosophical theories of the nature of the mental that seem to suggest otherwise. My concern is with a different and more practical problem. We often confront some cognitive, affective, or bodily phenomenon, and wonder about its source – its etiology or its underlying causal basis. For instance, you might wonder whether your queasiness due to something (...) you ate, or whether it is just nervousness, or whether your aunt’s memory loss is a neurological problem or a psychological response to trauma. Such questions attempt to localize the causes of a phenomenon at some level in the complex multi-level systems that we human animals are. In this paper I will attempt to tease out the sense of level implicit in such questions, and to show how it is related to current mechanistic accounts of levels. I will argue that the explanation of our practices of level attribution is deeply pragmatic. Such attributions are often attempts to locate the causes of problems, and to identify interventions that could solve those problems. (shrink)

An important strategy in the discovery of biological mechanisms involves the piecing together of experimental results from interventions. However, if mechanisms are investigated by means of ideal interventions, as defined by James Woodward and others, then the kind of information revealed is insufficient to discriminate between modular and non-modular causal contributions. Ideal interventions suffice for constructing webs of causal dependencies that can be used to make some predictions about experimental outcomes, but tell us little about how causally relevant factors are (...) organized together and how they interact with each other in order to produce a phenomenon. I argue that lab research relies on more elaborated types of interventions targeting in a controlled fashion multiple variables at the same time in order to probe the temporal organization of causally-relevant factors along distinct causal pathways and to test for non-modular interaction effects, thus providing crucial spatial-temporal constraints guiding the formulation of more detailed mechanistic explanations. (shrink)

In robustness analysis, hypotheses are supported to the extent that a result proves robust, and a result is robust to the extent that we detect it in diverse ways. But what precise sense of diversity is at work here? In this paper, I show that the formal explications of evidential diversity most often appealed to in work on robustness – which all draw in one way or another on probabilistic independence – fail to shed light on the notion of diversity (...) relevant to robustness analysis. I close by briefly outlining a promising alternative approach inspired by Horwich’s (1982) eliminative account of evidential diversity. (shrink)

This paper examines the adequacy of causal graph theory as a tool for modeling biological phenomena and formalizing biological explanations. I point out that the causal graph approach reaches it limits when it comes to modeling biological phenomena that involve complex spatial and structural relations. Using a case study from molecular biology, DNA-binding and -recognition of proteins, I argue that causal graph models fail to adequately represent and explain causal phenomena in this field. The inadequacy of these models is due (...) to their failure to include relevant spatial and structural information in a way that does not render the model non-explanatory, unmanageable, or inconsistent with basic assumptions of causal graph theory. (shrink)

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. (shrink)

A tension exists between those who do—e.g. Meyer (The British Journal for the Philosophy of Science 71:959–985, 2020 ) and Chemero ( 2011 )—and those who do not—e.g. Kaplan and Craver (Philosophy of Science 78:601–627, 2011 ) Piccinini and Craver (Synthese 183:283–311, 2011 )—afford nonmechanistic explanations a role in cognitive science. Here, I argue that one’s perspective on this matter will cohere with one’s interpretation of the tasks of cognitive science; that is, of the actions for which cognitive scientists are (...) specially fitted. To make this point concrete, I consider two tasks of cognitive science—establishing causal claims and unifying—and show how these tasks are interpreted differently by mechanists and nonmechanists respectively. I then argue that, as a result of these different interpretations, we cannot expect to resolve the tension between mechanists and nonmechanists by appeal to cognitive scientific practice. I conclude that we should give up on the debate between mechanists and nonmechanists, and search for progress elsewhere. (shrink)

In this essay I propose that what design principles in systems biology and systems neuroscience do is to present abstract characterizations of mechanisms, and thereby facilitate mechanistic explanation. To show this, one design principle in systems neuroscience, i.e., the multilayer perceptron, is examined. However, Braillard contends that design principles provide a sort of non-mechanistic explanation due to two related reasons: they are very general and describe non-causal dependence relationships. In response to this, I argue that, on the one hand, all (...) mechanisms are more or less general, and on the other, many design principles are causal systems. (shrink)

This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really that (...) way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

Mechanistic explanations are often said to explain because they reveal the causal structure of the world. Conversely, dynamical models supposedly lack explanatory power because they do not describe causal structure. The only way for dynamical models to produce causal explanations is via the 3M criterion: the model must be mapped onto a mechanism. This framing of the situation has become the received view around the viability of dynamical explanation. In this paper, I argue against this position and show that dynamical (...) models can themselves reveal causal structure and consequently produce nonmechanistic, dynamical explanations. Taking the example of cell fates from systems biology, I show how dynamical models, and specifically the attractor landscapes they describe, identify the causes of cell differentiation and explain why cells select particular fates. These dynamical features of the system better fit Woodward’s (2010, 2018) criteria of specificity and proportionality and make them the best candidate causes of cell fates than mechanisms. I also show how these causes are irreducible and inaccessible to mechanistic models, making 3M unworkable and counterproductive in this case. Dynamical models can reveal dynamical causes and thereby provide causal explanations. (shrink)

This paper defends three claims about concrete or physical models: these models remain important in science and engineering, they are often essentially idealized, in a sense to be made precise, and despite these essential idealizations, some of these models may be reliably used for the purpose of causal explanation. This discussion of concrete models is pursued using a detailed case study of some recent models of landslide generated impulse waves. Practitioners show a clear awareness of the idealized character of these (...) models, and yet address these concerns through a number of methods. This paper focuses on experimental arguments that show how certain failures to accurately represent feature X are consistent with accurately representing some causes of feature Y, even when X is causally relevant to Y. To analyse these arguments, the claims generated by a model must be carefully examined and grouped into types. Only some of these types can be endorsed by practitioners, but I argue that these endorsed claims are sufficient for limited forms of causal explanation. (shrink)

This paper considers the way mathematical and computational models are used in network neuroscience to deliver mechanistic explanations. Two case studies are considered: Recent work on klinotaxis by Caenorhabditis elegans, and a longstanding research effort on the network basis of schizophrenia in humans. These case studies illustrate the various ways in which network, simulation and dynamical models contribute to the aim of representing and understanding network mechanisms in the brain, and thus, of delivering mechanistic explanations. After outlining this mechanistic construal (...) of network neuroscience, two concerns are addressed. In response to the concern that functional network models are non-explanatory, it is argued that functional network models are in fact explanatory mechanism sketches. In response to the concern that models which emphasize a network’s organization over its composition do not explain mechanistically, it is argued that this emphasis is both appropriate and consistent with the principles of mechanistic explanation. What emerges is an improved understanding of the ways in which mathematical and computational models are deployed in network neuroscience, as well as an improved conception of mechanistic explanation in general. (shrink)

The article analyses in detail the Meselson–Stahl experiment, identifying two novel difficulties for the crucial experiment account, namely, the fragility of the experimental results and the fact that the hypotheses under scrutiny were not mutually exclusive. The crucial experiment account is rejected in favour of an experimental-mechanistic account of the historical significance of the experiment, emphasizing that the experiment generated data about the biochemistry of DNA replication that is independent of the testing of the semi-conservative, conservative, and dispersive hypotheses. _1_ (...) Introduction _2_ The Meselson–Stahl Experiment _3_ Some Difficulties for the Crucial Experiment Account _3.1_ Additional experiments were required _3.2_ Simplicity considerations are unconvincing _3.3_ The fragility of the experimental results _3.4_ The problematic interpretation of falsifyng results _3.5_ Not all possible hypotheses considered or tested _3.6_ The tested hypotheses were not mutually exclusive _4_ The Historical and Scientific Significance of the Meselson–Stahl Experiment _4.1_ The challenge for the crucial experiment account _4.2_ A mechanistic perspective on the experiment _4.3_ The experimental design and its independence from theoretical speculations _4.4_ The advantages of a ‘bottom-up’ mechanistic account _5_ Conclusion. (shrink)

In the last 20 years or so, since the publication of a seminal paper by Watts and Strogatz :440–442, 1998), an interest in topological explanations has spread like a wild fire over many areas of science, e.g. ecology, evolutionary biology, medicine, and cognitive neuroscience. The topological approach is still very young by all standards, and even within special sciences it still doesn’t have a single methodological programme that is applicable across all areas of science. That is why this special issue (...) is important as a first systematic philosophical study of topological explanations and their relation to a well understood and widespread explanatory strategy, such as mechanistic one. (shrink)

Recently, it has been provocatively claimed that dynamical modeling approaches signal the emergence of a new explanatory framework distinct from that of mechanistic explanation. This paper rejects this proposal and argues that dynamical explanations are fully compatible with, even naturally construed as, instances of mechanistic explanations. Specifically, it is argued that the mathematical framework of dynamics provides a powerful descriptive scheme for revealing temporal features of activities in mechanisms and plays an explanatory role to the extent it is deployed for (...) this purpose. It is also suggested that more attention should be paid to the distinctive methodological contributions of the dynamical framework including its usefulness as a heuristic for mechanism discovery and hypothesis generation in contemporary neuroscience and biology. (shrink)

This paper is aimed at identifying how a model’s explanatory power is constructed and identified, particularly in the practice of template-based modeling (Humphreys, Philos Sci 69:1–11, 2002; Extending ourselves: computational science, empiricism, and scientific method, 2004), and what kinds of explanations models constructed in this way can provide. In particular, this paper offers an account of non-causal structural explanation that forms an alternative to causal–mechanical accounts of model explanation that are currently popular in philosophy of biology and cognitive science. Clearly, (...) defences of non-causal explanation are far from new (e.g. Batterman, Br J Philos Sci 53:21–38, 2002a; The devil in the details: asymptotic reasoning in explanation, reduction, and emergence, 2002b; Pincock, Noûs 41:253–275, 2007; Mathematics and scientific representation 2012; Rice, Noûs. doi:10.1111/nous.12042, 2013; Biol Philos 27:685–703, 2012), so the targets here are focused on a particular type of robust phenomenon and how strong invariance to interventions can block a range of causal explanations. By focusing on a common form of model construction, the paper also ties functional or computational style explanations found in cognitive science and biology more firmly with explanatory practices across model-based science in general. (shrink)

Mechanistic explanations, according to one prominent account, are derived from objective explanations (Craver 2007, 2014 ). Mechanistic standards of explanation are in turn pulled from nature, and are thereby insulated from the values of investigators, since explanation is an objectively defined achievement grounded in the causal structure of the world (Craver 2014 ). This results in the closure of mechanism’s explanatory standards—it is insulated from the values, norms and goals of investigators. I raise two problems with this position. First, it (...) relies on several ontological claims which, while plausible, fail to guarantee the objectivity of mechanistic explanatory standards to the degree of certainty required. Second, Craver’s position itself introduces a value–laden explanatory standard—the 3M requirement (Kaplan & Craver 2011 )—which undermines the closure of explanatory standards. I show how in practice mechanistic explanation is in part guided by explanatory taste, shorthand for background contextual values that influence our standards of explanation. Mechanism often has a particular pragmatically-oriented taste for control, and gerrymanders explanatory standards in order to obtain it. I conclude by arguing that objectivity, rather than being obtained through the right set of explanatory standards, is better thought of as the result of processes of intersubjective criticism, which renders visible the contextual values of communities of investigators and allows them to be controlled for (Longino 1990 ). (shrink)

Causal approaches to explanation often assume that a model explains by describing features that make a difference regarding the phenomenon. Chirimuuta claims that this idea can be also used to understand non-causal explanation in computational neuroscience. She argues that mathematical principles that figure in efficient coding explanations are non-causal difference-makers. Although these principles cannot be causally altered, efficient coding models can be used to show how would the phenomenon change if the principles were modified in counterpossible situations. The problem is (...) that efficient coding models also involve difference-makers that, prima facie, cannot be characterized as non-causal in this sense. Mathematical principles always involve variables which have counterfactual relations between them. However, we cannot simply assume that these difference-makers are causal. They can also be found in paradigmatic non-causal explanations and therefore they must be characterized as non-causal in some sense. I argue that, despite appearances, Chirimuuta's view can be applied to these cases. The mentioned counterfactual relations presuppose the counterpossible conditionals that describe the modification of a relevant mathematical principle. If these conditionals are the hallmark of non-causal relations, then Chirimuuta’s criterion has the desired implication that variables in mathematical principles are non-causal difference-makers. (shrink)

It is widely assumed that mechanistic explanations are causal explanations. Many prominent new mechanists endorse interventionism as the correct analysis of explanatory causal models in biology and other fields. This article argues that interventionism is not entirely satisfactory in this regard. A case study of Jacob and Monod’s operon model shows that at least some important mechanistic explanations in biology present significant contrasts with the interventionist account. This result motivates a more inclusive approach to mechanistic explanation, allowing for noncausal aspects.

Philosophers have traditionally treated physicalism as an empirically informed metaphysical thesis. This approach faces a well-known problem often referred to as Hempel’s dilemma: formulations of physicalism tend to be either false or indeterminate. The generally preferred strategy to address this problem involves an appeal to a hypothetical complete and ideal physical theory. After demonstrating that this strategy is not viable, I argue that we should redefine physicalism as an interdisciplinary research program seeking to explain the mental in terms of the (...) physical that encompasses the physical sciences, the psychological and brain sciences, and philosophy. Redefining physicalism in this way improves upon previous reconstructive accounts while avoiding the indeterminacy associated with orthodox forms of futurist physicalism. (shrink)

Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction. In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics. Network medicine is a recent discipline that relies on the analysis of various (...) disease networks in order to find organizing principles in disease explanation. My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases. However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons. First, in network medicine, topological explanations depend on and use mechanistic information. Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations. Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases. This last point may have major consequences for biomedical research. (shrink)

We address the question of whether and to what extent explanatory and modelling strategies in systems biology are mechanistic. After showing how dynamic mathematical models are actually required for mechanistic explanations of complex systems, we caution readers against expecting all systems biology to be about mechanistic explanations. Instead, the aim may be to generate topological explanations that are not standardly mechanistic, or to arrive at design principles that explain system organization and behaviour in general, but not specific mechanisms. These abstraction (...) strategies serve various aims, including prediction and control, that are central to understanding the epistemic diversity of systems biology. (shrink)

Causal relations among components and activities are intentionally misrepresented in mechanistic explanations found routinely across the life sciences. Since several mechanists explicitly advocate accurately representing factors that make a difference to the outcome, these idealizations conflict with the stated rationale for mechanistic explanation. We argue that these idealizations signal an overlooked feature of reasoning in molecular and cell biology—mechanistic explanations do not occur in isolation—and suggest that explanatory practices within the mechanistic tradition share commonalities with model-based approaches prevalent in population (...) biology. (shrink)

Current debates about the integration of traditional and academic ecological knowledge struggle with a dilemma of division and assimilation. On the one hand, the emphasis on differences between traditional and academic perspectives has been criticized as creating an artificial divide that brands TEK as “non-scientific” and contributes to its marginalization. On the other hand, there has been increased concern about inadequate assimilation of Indigenous and other traditional perspectives into scientific practices that disregards the holistic nature and values of TEK. The (...) aim of this article is to develop a practice-based account of the epistemic relations between TEK and AEK that avoids both horns of the dilemma. While relations between TEK and AEK are often described in terms of the “holistic” nature of the former and the “mechanistic” character of the latter, we argue that a simple holism–mechanism divide misrepresents the epistemic resources of both TEK and AEK. Based on the literature on mechanistic explanations in philosophy of science, we argue that holders of TEK are perfectly capable of identifying mechanisms that underlie ecological phenomena while AEK often relies on non-mechanistic strategies of dealing with ecological complexity. Instead of generic characterizations of knowledge systems as either mechanistic or holistic, we propose to approach epistemic relations between knowledge systems by analyzing their heuristics in practice. (shrink)

Journal of Social Philosophy, EarlyView.

It is a widespread assumption in philosophy of science that data is what is explained by theory—that data itself is not explanatory. I draw on instances of representational and explanatory practice from mammalian chronobiology to suggest that this assumption is unsustainable. In many instances, biologists employ representations of data in explanatory ways that are not reducible to constraints on or evidence for representations of mechanisms. Data graphs are used to exemplify relationships between quantities in the mechanism, and often these representations (...) are necessary for explaining particular aspects of the phenomena under study. I argue that this kind of representation is distinct from representing laws or generalizations, and its primary purpose is to convey particular types or patterns of quantitative relationships. The benefit of the analysis is two-fold. First, it provides a more accurate account of explanatory practice in broadly mechanistic analysis in biology. Second, it suggests that there is not an explanatory “fundamental” type of representation in biology. Rather, the practice of explanation consists in the construction of different types of representations and their employment for distinct explanatory purposes. (shrink)

Colombo’s (Phenomenology and the Cognitive Sciences, 2013) plea for neural representationalism is the focus of a recent contribution to Phenomenology and Cognitive Science by Daniel D. Hutto and Erik Myin. In that paper, Hutto and Myin have tried to show that my arguments fail badly. Here, I want to respond to their critique clarifying the type of neural representationalism put forward in my (Phenomenology and the Cognitive Sciences, 2013) piece, and to take the opportunity to make a few remarks of (...) general interest concerning what Hutto and Myin have dubbed “the Hard Problem of Content.”. (shrink)

The received view of computation is methodologically bifurcated: it offers different accounts of computation in the mathematical and physical cases. But little in the way of argument has been given for this approach. This article rectifies the situation by arguing that the alternative, a unified account, is untenable. Furthermore, once these issues are brought into sharper relief we can see that work remains to be done to illuminate the relationship between physical and mathematical computation.

I examine the adequacy of the causal graph-structural equations approach to causation for modeling biological mechanisms. I focus in particular on mechanisms with complex dynamics such as the PER biological clock mechanism in Drosophila. I show that a quantitative model of this mechanism that uses coupled differential equations – the well-known Goldbeter model – cannot be adequately represented in the standard causal graph framework, even though this framework does permit causal cycles. The reason is that the model contains dynamical information (...) about the mechanism that concerns causal properties but that does not correspond to variables that could be subject to independent interventions. Thus, a representation of the mechanisms as a causal structural model necessarily suppresses causally relevant information. (shrink)

In this paper, I argue that the newly developed network approach in neuroscience and biology provides a basis for formulating a unique type of realization, which I call topological realization. Some of its features and its relation to one of the dominant paradigms of realization and explanation in sciences, i.e. the mechanistic one, are already being discussed in the literature. But the detailed features of topological realization, its explanatory power and its relation to another prominent view of realization, namely the (...) semantic one, have not yet been discussed. I argue that topological realization is distinct from mechanistic and semantic ones because the realization base in this framework is not based on local realisers, regardless of the scale but on global realizers. In mechanistic approach, the realization base is always at the local level, in both ontic and epistemic accounts. The explanatory power of realization relation in mechanistic approach comes directly from the realization relation-either by showing how a model is mapped onto a mechanism, or by describing some ontic relations that are explanatory in themselves. Similarly, the semantic approach requires that concepts at different scales logically satisfy microphysical descriptions, which are at the local level. In topological framework the realization base can be found at different scales, but whatever the scale the realization base is global, within that scale, and not local. Furthermore, topological realization enables us to answer the “why” questions, which according to Polger 2010 make it explanatory. The explanatoriness of topological realization stems from understanding mathematical consequences of different topologies, not from the mere fact that a system realizes them. (shrink)

The history of developmental biology is interwoven with debates as to whether mechanistic explanations of development are possible or whether alternative explanatory principles or even vital forces need to be assumed. In particular, the demonstrated ability of embryonic cells to tune their developmental fate precisely to their relative position and the overall size of the embryo was once thought to be inexplicable in mechanistic terms. Taking a causal perspective, this Element examines to what extent and how developmental biology, having turned (...) molecular about four decades ago, has been able to meet the vitalist challenge. It focuses not only on the nature of explanations but also on the usefulness of causal knowledge – including the knowledge of classical experimental embryology – for further scientific discovery. It also shows how this causal perspective allows us to understand the nature and significance of some key concepts, including organizer, signal and morphogen. This title is also available as Open Access on Cambridge Core. (shrink)