In this paper, we compare the mechanisms of protein synthesis and natural selection. We identify three core elements of mechanistic explanation: functional individuation, hierarchical nestedness or decomposition, and organization. These are now well understood elements of mechanistic explanation in fields such as protein synthesis, and widely accepted in the mechanisms literature. But Skipper and Millstein have argued that natural selection is neither decomposable nor organized. This would mean that much of the current mechanisms literature does not apply to the mechanism (...) of natural selection.We take each element of mechanistic explanation in turn. Having appreciated the importance of functional individuation, we show how decomposition and organization should be better understood in these terms. We thereby show that mechanistic explanation by protein synthesis and natural selection are more closely analogous than they appear—both possess all three of these core elements of a mechanism widely recognized in the mechanisms literature. (shrink)

Radical enactive and embodied approaches to cognitive science oppose the received view in the sciences of the mind in denying that cognition fundamentally involves contentful mental representation. This paper argues that the fate of representationalism in cognitive science matters significantly to how best to understand the extent of cognition. It seeks to establish that any move away from representationalism toward pure, empirical functionalism fails to provide a substantive “mark of the cognitive” and is bereft of other adequate means for individuating (...) cognitive activity. It also argues that giving proper attention to the way the folk use their psychological concepts requires questioning the legitimacy of commonsense functionalism. In place of extended functionalism—empirical or commonsensical—we promote the fortunes of extensive enactivism, clarifying in which ways it is distinct from notions of extended mind and distributed cognition. (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)

Besides mechanistic explanations of phenomena, which have been seriously investigated in the last decade, biology and ecology also include explanations that pinpoint specific mathematical properties as explanatory of the explanandum under focus. Among these structural explanations, one finds topological explanations, and recent science pervasively relies on them. This reliance is especially due to the necessity to model large sets of data with no practical possibility to track the proper activities of all the numerous entities. The paper first defines topological explanations (...) and then explains why topological explanations and mechanisms are different in principle. Then it shows that they are pervasive both in the study of networks—whose importance has been increasingly acknowledged at each level of the biological hierarchy—and in contexts where the notion of selective neutrality is crucial; this allows me to capture the difference between mechanisms and topological explanations in terms of practical modelling practices. The rest of the paper investigates how in practice mechanisms and topologies are combined. They may be articulated in theoretical structures and explanatory strategies, first through a relation of constraint, second in interlevel theories, or they may condition each other. Finally, I explore how a particular model can integrate mechanistic informations, by focusing on the recent practice of merging networks in ecology and its consequences upon multiscale modelling. (shrink)

The debate over the explanatory nature of cognitive models has been waged mostly between two factions: the mechanists and the dynamical systems theorists. The former hold that cognitive models are explanatory only if they satisfy a set of mapping criteria, particularly the 3M/3m* requirement. The latter have argued, pace the mechanists, that some cognitive models are both dynamical and constitute covering-law explanations. In this paper, I provide a minimal model interpretation of dynamical cognitive models, arguing that this both provides needed (...) clarity to the mechanist versus dynamicist divide in cognitive science and also paves the way towards further insights about scientific explanation generally. (shrink)

There have been recent disagreements in the philosophy of neuroscience regarding which sorts of scientific models provide mechanistic explanations, and which do not. These disagreements often hinge on two commonly adopted, but conflicting, ways of understanding mechanistic explanations: what I call the “representation-as” account, and the “representation-of” account. In this paper, I argue that neither account does justice to neuroscientific practice. In their place, I offer a new alternative that can defuse some of these disagreements. I argue that individual models (...) do not provide mechanistic explanations by themselves. Instead, individual models are always used to complement a huge body of background information and pre-existing models about the target system. With this in mind, I argue that mechanistic explanations are distributed across sets of different, and sometimes contradictory, scientific models. Each of these models contributes limited, but essential, information to the same mechanistic explanation, but none can be considered a mechanistic explanation in isolation of the others. (shrink)

The article examines the role of natural kinds in semantic theorizing, which has largely been conducted in isolation from relevant work in science, metaphysics, and philosophy of science. We argue that the Kripke–Putnam account of natural kind terms, despite recent claims to the contrary, depends on a certain metaphysics of natural kinds; that the metaphysics usually assumed—micro-essentialism—is untenable even in a ‘placeholder’ version; and that the currently popular homeostatic property cluster theory of natural kinds is correct only to an extent (...) that fails to vindicate the Kripke–Putnam account. This undermines the metasemantics required for anti-descriptivist semantics. _1_ Introduction _2_ From Semantics to Metaphysics _3_ Metaphysics, Part I: The Demise of Micro-essentialism _3.1_ Original micro-essentialism _3.2_ Placeholder essentialism _4_ Metaphysics, Part II: Homeostatic Property Cluster Theory _5_ Prospects for Natural Kind Term Semantics. (shrink)

We give a general background describing how the notion of levels of reality comes about in contemporary nonreductive approaches to the special sciences, what the notion of levels means in these approaches, its role in our scientific outlook of the world, and why it is important.

Several philosophers (e.g., Ehring (Nous (Detroit, Mich.) 30:461–480, 1996 ); Funkhouser (Nous (Detroit, Mich.) 40:548–569, 2006 ); Walter (Canadian Journal of Philosophy 37:217–244, 2007 ) have argued that there are metaphysical differences between the determinable-determinate relation and the realization relation between mental and physical properties. Others have challenged this claim (e.g., Wilson (Philosophical Studies, 2009 ). In this paper, I argue that there are indeed such differences and propose a “mechanistic” account of realization that elucidates why these differences hold. This (...) account of realization incorporates two distinct roles that mechanisms play in the realization of mental (and other special science) properties which are implicit, but undeveloped, in the literature—what I call “constitutive” and “integrative” mechanisms. I then use these two notions of mechanism to clarify some debates about the relations between realization, multiple realizability, and irreducibility. (shrink)

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale (...) techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system. (shrink)

In this paper, I argue that looking at the concept of neural function through the lens of cognition alone risks cognitive myopia: it leads neuroscientists to focus only on mechanisms with cognitive functions that process behaviorally relevant information when conceptualizing “neural function”. Cognitive myopia tempts researchers to neglect neural mechanisms with noncognitive functions which do not process behaviorally relevant information but maintain and repair neural and other systems of the body. Cognitive myopia similarly affects philosophy of neuroscience because scholars overlook (...) noncognitive functions when analyzing issues surrounding e.g., functional decomposition or the multifunctionality of neural structures. I argue that we can overcome cognitive myopia by adopting a patchwork approach that articulates cognitive and noncognitive “patches” of the concept of neural function. Cognitive patches describe mechanisms with causally specific effects on cognition and behavior which are likely operative in transforming sensory or other inputs into motor outputs. Noncognitive patches describe mechanisms that lack such specific effects; these mechanisms are enabling conditions for cognitive functions to occur. I use these distinctions to characterize two noncognitive functions at the mesoscale of neural circuits: subsistence functions like breathing are implemented by central pattern generators and are necessary to maintain the life of the organism. Infrastructural functions like gain control are implemented by canonical microcircuits and prevent neural system damage while cognitive processing occurs. By adding conceptual patches that describe these functions, a patchwork approach can overcome cognitive myopia and help us explain how the brain’s capacities as an information processing device are constrained by its ability to maintain and repair itself as a physiological apparatus. (shrink)

This paper is concerned with the notions of supervenience and mechanistic constitution as they have been discussed in the philosophy of neuroscience. Since both notions essentially involve specific dependence and determination relations among properties and sets of properties, the question arises whether the notions are systematically connected and how they connect to science. In a first step, some definitions of supervenience and mechanistic constitution are presented and tested for logical independence. Afterwards, certain assumptions fundamental to neuroscientific inquiry are made explicit (...) in order to show that the presented definitions of supervenience are virtually uninteresting for theory construction in this field. In a third step, a new formulation of supervenience is developed that makes explicit reference to the notion of constitution and that bridges the gap between the philosophical concepts and explanatory practice in neuroscience. (shrink)

This paper discusses the relevance of models for cognitive science that integrate mechanistic and computational aspects. Its main hypothesis is that a model of a cognitive system is satisfactory and explanatory to the extent that it bridges phenomena at multiple mechanistic levels, such that at least several of these mechanistic levels are shown to implement computational processes. The relevant parts of the computation must be mapped onto distinguishable entities and activities of the mechanism. The ideal is contrasted with two other (...) accounts of modeling in cognitive science. The first has been presented by David Marr in combination with a distinction of “levels of computation”. The second builds on a hierarchy of “mechanistic levels” in the sense of Carl Craver. It is argued that neither of the two accounts secures satisfactory explanations of cognitive systems. The mechanistic-computational ideal can be thought of as resulting from a fusion of Marr’s and Craver’s ideals. It is defended as adequate and plausible in light of scientific practice, and certain metaphysical background assumptions are discussed. (shrink)

This paper discusses two challenges for a Boolean method for establishing constitutive regularity statements which, according to the regularity theory of mechanistic constitution, form the core of any mechanistic explanation in neuroscience. After presenting the regularity definition for the constitution relation and a methodology for constitutive inference, the paper discusses the problem of full variation of tested mechanistic factors and the problem of informational redundancy. A solution is offered for each problem. The first requires some adjustments to the original theory (...) by introducing the technical notion of a set of types satisfying independent instantiability. The second one is resolved by demonstrating that the problem of informational redundancy is based on a confusion that fails to challenge the theory. It is concluded that the methodology of constitutive inference is consistent and plausible with respect to actual practice in neuroscience. (shrink)

This paper discusses the role of levels and level-bound theoretical terms in neurobiological explanations under the presupposition of a regularity theory of constitution. After presenting the definitions for the constitution relation and the notion of a mechanistic level in the sense of the regularity theory, the paper develops a set of inference rules that allow to determine whether two mechanisms referred to by one or more accepted explanations belong to the same level, or to different levels. The rules are characterized (...) as underlying level distinctions in neuroscience research. (shrink)

Carl Craver’s mutual manipulability criterion aims to pick out all and only those components of a mechanism that are constitutively relevant with respect to a given phenomenon. In devising his criterion, Craver has made heavy use of the notion of an ideal intervention, which is a tool for illuminating causal concepts in causal models. The problem is that typical mechanistic models contain non-causal relations in addition to causal ones, which is why the standard concept of an ideal intervention is not (...) appropriate in that context. In this paper, I first show how top-down interventions in mechanistic models violate the conditions for ideal interventions. Drawing from recent developments in the causal exclusion literature, I then argue for extended interventionism better suited for the purposes of the new mechanist. Finally, I show why adopting such an extended account leads to the surprising consequence that an important subset of mechanistic interlevel relations comes out as causal. (shrink)

This paper discusses the constitution relation within the framework of the mechanistic approach to neurobiological explanation. It develops a regularity theory of constitution as an alternative to the manipulationist theory of constitution advocated by some of the proponents of the mechanistic approach. After the main problems of the manipulationist account of constitution have been reviewed, the regularity account is developed based on the notion of a minimal type relevance theory. A minimal type relevance theory expresses a minimally necessary condition of (...) a given type that consists of a disjunction of minimally sufficient conditions of that type. Afterwards, the attained definition is successfully applied in an analysis of the logical structure of the explanation of spatial learning and memory in rats as a paradigm neurobiological explanation. The overall result is a more robust and more precise version of the mechanistic approach to neurobiological explanations. (shrink)

Counterfactual conditionals have been appealed to in various ways to show how the mind can be causally efficacious. However, it has often been overestimated what the truth of certain counterfactuals actually indicates about causation. The paper first identifies four approaches that seem to commit precisely this mistake. The arguments discussed involve erroneous assumptions about the connection of counterfactual dependence and genuine causation, as well as a disregard of the requisite evaluation conditions of counterfactuals. In a second step, the paper uses (...) the insights of the foregoing analyses to formulate a set of counterfactuals-based conditions that are characterized as sufficient to establish singular causal claims. The paper concludes that there are ample reasons to believe that some mental events satisfy all these conditions with respect to certain further events and, hence, that mental events sometimes are causes. (shrink)

Representations seem to play a major role in many neuroscientific explanations. Philosophers have long attempted to properly define what it means for a neural state to be a representation of a specific content. Teleosemantic theories of content which characterize representations, in part, by appealing to a historical notion of function, are often regarded as our best path towards an account of neural representations. This paper points to the anti-representationalist consequences of these accounts. I argue that assuming such teleosemantic views will (...) deprive representations of their explanatory role in computational explanations. My argument rests on the claim that many explanations in cognitive neuroscience are entirely independent of any historical considerations. In making this claim, I will also offer an adapted version of the famous Swampperson thought experiment, which is better suited to discussions of subpersonal neural representations. (shrink)

We present two case studies from contemporary biology in which we observe conflicts between established and emerging approaches. The first case study discusses the relation between molecular biology and systems biology regarding the explanation of cellular processes, while the second deals with phylogenetic systematics and the challenge posed by recent network approaches to established ideas of evolutionary processes. We show that the emergence of new fields is in both cases driven by the development of high-throughput data generation technologies and the (...) transfer of modeling techniques from other fields. New and emerging views are characterized by different philosophies of nature, i.e. by different ontological and methodological assumptions and epistemic values and virtues. This results in a kind of conflict we call “epistemic competition” that manifests in two ways: On the one hand, opponents engage in mutual critique and defense of their fundamental assumptions. On the other hand, they compete for the acceptance and integration of the knowledge they provide by a broader scientific community. Despite an initial rhetoric of replacement, the views as well as the respective audiences come to be seen as more clearly distinct during the course of the debate. Hence, we observe—contrary to many other accounts of scientific change—that conflict results in the formation of new niches of research, leading to co-existence and perceived complementarity of approaches. Our model thus contributes to the understanding of the pluralization of the scientific landscape. (shrink)

Even though complexity is a concept that is ubiquitously used by biologists and philosophers of biology, it is rarely made precise. I argue that a clarification of the concept is neither trivial nor unachievable, and I propose a unifying framework based on the technical notion of “effective complexity” that allows me to do justice to conflicting intuitions about biological complexity, while taking into account several distinctions in the usage of the concept that are often overlooked. In particular, I propose a (...) distinction between two kinds of complexity, “mechanical” and “emergent”, which can be understood as different ways of relating the effective complexity of mechanisms and of behaviors in biological explanations. I illustrate the adequacy of this framework by discussing different attempts to understand intracellular organization in terms of pathways and networks. My framework provides a different way of thinking about recent philosophical debates, for example, on the difference between mechanistic and topological explanations and about the concept of emergence. Moreover, it can contribute to a proper assessment of metascientific arguments that invoke biological complexity. (shrink)

The distinction between explanation and understanding was foundational to Jaspers’ ‘phenomenological’ approach to psychiatry. It makes sense that those now calling for a phenomenological approach to psychiatry would look to Jaspers for inspiration, and that in doing so, they would take up this distinction. However, I argue that it is and was a mistake to use the distinction in work on psychiatry: adhering to the distinction now would undermine, rather than support, the goals of those advocating a phenomenological approach to (...) psychiatry; furthermore, if the distinction had been adhered to, then contemporary psychiatry and its patients would be worse off. (shrink)

In this paper we identify six theses that constitute core results of philosophical investigation into the nature of mechanisms, and of the role that the search for and identification of mechanisms play in the sciences. These theses represent the fruits of the body of research that is now often called New Mechanism. We concisely present the main arguments for these theses. In the literature, these arguments are scattered and often implicit. Our analysis can guide future research in many ways: it (...) provides critics of New Mechanism with clear targets, it can reduce misunderstandings, it can clarify differences of opinion among New Mechanists and it helps to define a research agenda for New Mechanists. (shrink)

While much of the recent literature on mechanisms has emphasized the superiority of mechanisms and mechanistic explanation over laws and nomological explanation, paradigmatic mechanisms—e.g., clocks or synapses—actually exhibit a great deal of stability in their behavior. And while mechanisms of this kind are certainly of great importance, there are many events that do not occur as a consequence of the operation of stable mechanisms. Events of natural and human history are often the consequence of causal processes that are ephemeral and (...) capricious. In this paper I shall argue that, notwithstanding their ephemeral nature, these processes deserve to be called mechanisms. Ephemeral mechanisms share important characteristics with their more stable cousins, and these shared characteristics will help us to understand connections between scientific and historical explanation. (shrink)

What is it for an individual thing in the natural world—a rock, a mouse, a family or a planet—to be made of other things—crystals, organs, animals, soil, water, or dirt? Rocks, mice, families and planets are composites, but how are we to understand the relation that holds between these composites and their component parts? My aim is to offer a new account of this relation, which I shall call corporeal composition. A central claim of my account is that corporeal composition (...) is grounded in causal relations between components. I will show how this claim, combined with a minimalist approach to causation—that causation is not one thing, but many—entails a position I call compositional minimalism—that composition is not one thing, but many. (shrink)

Carl Craver and Lindley Darden are two of the foremost proponents of a recent approach to the philosophy of biology that is often called the New Mechanism. In this book they seek to make available to a broader readership insights gained from more than two decades of work on the nature of mechanisms and how they are described and discovered. The book is not primarily aimed at specialists working on the New Mechanism, but rather targets scientists, students and teachers who (...) are looking for a broad, philosophically and historically informed image of discovery in the life sciences. (shrink)

Understanding the 'making-up' relations, to put things neutrally, posited in mechanistic explanations the sciences is finally an explicit topic of debate amongst philosophers of science. In particular, there is now lively debate over the nature of the so-called 'realization' relations between properties posited in such explanations. Despite criticism (Gillett, Analysis 62: 316-323, 2002a), the most common approach continues to be that of applying machinery developed in the philosophy of mind to scientific concepts in what is known as the 'Flat' or (...) 'Subset' model of 'realization' (Kim, Mind in a physical world, 1998; Shapiro, J Philos XCVII: 635-654, 2000; Clapp, J Philos XCVIII: 111-136,2001 ; Wilson, Philos Stud 145: 149-169,2009). My primary goal in this paper is to show in still more detail that the Subset model of realization is inadequate for the descriptive task of describing the 'making-up' relations posited between properties or their instances in mechanistic explanations in the higher sciences. And my secondary goal is to highlight why this critique of the Subset view as a first-order descriptive account also shows there are deep difficulties in using the Subset account to address second-order issues in the philosophy of science as well. (shrink)

Inter-level mechanistic explanations in the sciences have long been a focus of philosophical interest, but attention has recently turned to the compositional character of these explanations which work by explaining higher level entities, whether processes, individuals or properties, using the lower level entities they take to compose them. However, we still have no theoretical account of the constitution or parthood relations between individuals deployed in such explanations, nor any accounts of multiple constitution. My primary focus in this paper is to (...) outline a positive account of the constitution/part-whole relations between individuals posited in inter-level mechanistic explanations that takes constituents in the sciences to be ‘working parts’. Using this account, I then go on to illuminate the nature, and varieties, of multiple constitution that we find in the sciences and provide a starting theoretical framework for multiple constitution as well. (shrink)

In this paper, I argue that explaining cognitive behavior can be achieved through what I call hybrid explanatory inferences: inferences that posit mechanisms, but also draw on observed regularities. Moreover, these inferences can be used to achieve unification, in the sense developed by Allen Newel in his work on cognitive architectures. Thus, it seems that explanatory pluralism and unification do not rule out each other in cognitive science, but rather that the former represents a way to achieve the latter.

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather (...) than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not. (shrink)

In this paper, I explicate and discuss performance-similarity reasoning as a strategy for mechanism schema evaluation, understood in Lindley Darden’s sense. This strategy involves inferring hypotheses about the mechanism responsible for cognitive capacities from premises describing the performance of those capacities; performance-similarity reasoning is a type of Inference to the Best Explanation, or IBE. Two types of such inferences are distinguished: one in which the performance of two systems is compared, and another when the performance of two systems under intervention (...) is compared. Both types of inferences are illustrated with examples taken from cognitive science. I conclude that performance-similarity reasoning is an important strategy for evaluating mechanistic hypotheses. (shrink)

Explanations are supposed to provide us with understanding. It is common to make a distinction between genuine, scientific understanding, and the phenomenological, or ‘aha’ notion of understanding, where the former is considered epistemically relevant, the latter irrelevant. I argue that there is a variety of phenomenological understanding that does play a positive epistemic role. This phenomenological understanding involves a similarity between bodily sensations that is used as evidence for mechanistic hypotheses. As a case study, I will consider 17th and 18th (...) century research into the mechanism behind the electric eel’s power to shock. Se supone que las explicaciones nos dotan de comprensión. Es habitual hacer una distinción entre compresión cientifica, genuina, y la noción fenomenológica, o ‘ahá’, de comprensión, donde la primera se considera epistemológicamente relevante y esta última irrelevante. Argumento que hay un tipo de compresión fenomenológica que sí juega un papel positivo epistémico. Esta comprensión epistemológica conlleva cierta semejanza con las sensaciones corporales que se usan como evidencia de algunas hipótesis mecanicistas. Como caso de estudio, consideraré la investigación en los siglos xvii y xviii del mecanismo tras la capacidad de la anguila eléctrica para provocar un shock. (shrink)

In the literature on dynamical models in cognitive science, two issues have recently caused controversy. First, what is the relation between dynamical and mechanistic models? I will argue that dynamical models can be upgraded to be mechanistic as well, and that there are mechanistic and non-mechanistic dynamical models. Second, there is the issue of explanatory power. Since it is uncontested the mechanistic models can explain, I will focus on the non-mechanistic variety of dynamical models. It is often claimed by proponents (...) of mechanistic explanations that such models do not really explain cognitive phenomena . I will argue against this view. Although I agree that the three arguments usually offered to vindicate the explanatory power of non-mechanistic dynamical models are not enough, I consider a fourth argument, namely that such models provide understanding. The Voss strong anticipation model is used to illustrate this. (shrink)

In this paper I argue that constitutive relevance relations in mechanisms behave like a special kind of causal relation in at least one important respect: Under suitable circumstances constitutive relevance relations produce the Markov factorization. Based on this observation one may wonder whether standard methods for causal discovery could be fruitfully applied to uncover constitutive relevance relations. This paper is intended as a first step into this new area of philosophical research. I investigate to what extent the PC algorithm, originally (...) developed for causal search, can be used for constitutive relevance discovery. I also discuss possible objections and certain limitations of a constitutive relevance discovery procedure based on PC. (shrink)

Causal Bayes nets (CBNs) can be used to model causal relationships up to whole mechanisms. Though modelling mechanisms with CBNs comes with many advantages, CBNs might fail to adequately represent some biological mechanisms because—as Kaiser (2016) pointed out—they have problems with capturing relevant spatial and structural information. In this paper we propose a hybrid approach for modelling mechanisms that combines CBNs and cellular automata. Our approach can incorporate spatial and structural information while, at the same time, it comes with all (...) the merits of a CBN representation of mechanisms. (shrink)

Mechanisms, in the prominent biological sense of the term, are historical entities. That is, whether or not something is a mechanism for something depends on its history. Put differently, while your spontaneously-generated molecule-for-molecule double has a heart, and its heart pumps blood around its body, its heart does not have a mechanism for pumping, since it does not have the right history. My argument for this claim is that mechanisms have proper functions; proper functions are historical entities; so, mechanisms are (...) historical entities, too. This thesis runs against the mainstream new mechanist way of thinking about mechanisms, where mechanisms are generally thought of in an ahistorical way. After arguing for this thesis, I draw out some consequences for philosophy of science and metaphysics. (shrink)

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using (...) system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two. (shrink)

Although there is a consensus among philosophers of mathematics and mathematicians that mathematical explanations exist, only a few authors have proposed accounts of explanation in mathematics. These accounts fit into the unificationist or top-down approach to explanation. We argue that these models can be complemented by a bottom-up approach to explanation in mathematics. We introduce the mechanistic model of explanation in science and discuss the possibility of using this model in mathematics, arguing that using it does not presuppose a Platonist (...) view of mathematics and allows one to gain insight into why a theorem is true by answering what-if-things-had-been-different questions. (shrink)

The interventionist account of causal explanation, in the version presented by Jim Woodward, has been recently claimed capable of buttressing the widely felt—though poorly understood—hunch that high-level, relatively abstract explanations, of the sort provided by sciences like biology, psychology and economics, are in some cases explanatorily optimal. It is the aim of this paper to show that this is mistaken. Due to a lack of effective constraints on the causal variables at the heart of the interventionist causal-explanatory scheme, as presently (...) formulated it is either unable to prefer high-level explanations to low, or systematically overshoots, recommending explanations at so high of a level as to be virtually vacuous. (shrink)

The central task of cognitive neuroscience to map cognitive capacities to neural mechanisms faces three interlocking conceptual problems that together frame the problem of cognitive ontology. First, they must establish which tasks elicit which cognitive capacities, and specifically when different tasks elicit the same capacity. To address this operationalization problem, scientists often assess whether the tasks engage the same neural mechanisms. But to determine whether mechanisms are of the same or different kinds, we need to solve the abstraction problem by (...) determining which mechanistic differences are and are not relevant, and also the boundary problem by distinguishing the mechanism from its background conditions. Solving these problems, in turn, requires understanding how cognitive capacities are elicited in tasks. These three problems, which have been noted and discussed elsewhere in the literature, together form a ‘cycle of kinds’ that frames the central problem-space of cognitive ontology. We describe this cycle to clarify the intellectual challenges facing the cognitive ontologist and to reveal the kind of iterative process by which ontological revision in cognitive neuroscience is likely to unfold. (shrink)

The use of “levels of abstraction” in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section “Some Definitions and Preliminary Examples” the nature and applicability of the epistemological method of levels of abstraction is clarified. In section “A Classic Application of the Method ofion”, the philosophical fruitfulness of the new (...) method is shown by using Kant’s classic discussion of the “antinomies of pure reason” as an example. In section “The Philosophy of the Method of Abstraction”, the method is further specified and supported by distinguishing it from three other forms of “levelism”: (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems. (shrink)

It is widely believed that neural elements interact by communicating messages. Neurons, or groups of neurons, are supposed to send packages of data with informational content to other neurons or to the body. Thus, behavior is traditionally taken to consist in the execution of commands or instructions sent by the nervous system. As a consequence, neural elements and their organization are conceived as literally embodying and transmitting representations that other elements must in some way read and conform to. In opposition (...) to this conception, growing approaches such as enactivism and ecological psychology hold that neurons are not in the business of representing. However, by insisting that neural causation is not of a representational kind, these anti-representationalist approaches seem to be left with only one rather implausible alternative, viz. that behavior is the result of nothing but basic physical causation such as push-pull forces. In this paper it is argued that a third form of causation—termed “modulation”—exists and is at work in the coordination of animal behavior. Modulation is the quasi-direct guidance of dynamical systems through specific yet emerging trajectories. By setting the constraints that coordinate the free interaction of multi-element systems, modulation influences without forcing nor representing goal states. The basic properties of modulatory causation are analyzed and shown to be present in some fundamental aspects of neural and bodily interaction. (shrink)

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about (...) how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding. (shrink)

This paper investigates the relationship between structural explanation and the New Mechanistic account of explanation. The aim of this paper is twofold: firstly, to argue that some phenomena in the domain of fundamental physics, although mechanically brute, are structurally explained; and secondly, by elaborating on the contrast between SE and mechanistic explanation to better clarify some features of SE. Finally, this paper will argue that, notwithstanding their apparently antithetical character, SE and ME can be reconciled within a unified account of (...) general scientific explanation. (shrink)

The aim of this paper is to explore the ways in which Axiomatic Reconstructions of Quantum Theory in terms of Information-Theoretic principles can contribute to explaining and understanding quantum phenomena, as well as to study their explanatory limitations. This is achieved in part by offering an account of the kind of explanation that axiomatic reconstructions of Quantum Theory provide, and re-evaluating the epistemic status of the program in light of this explanation. As illustrative case studies, I take Clifton's, Bub's and (...) Halvorson's characterization theorem and Popescu's and Rohrlich's toy models, and their explanatory contribution with respect to quantum nonlocality. On the one hand, I argue that ARQITs can aspire to provide genuine explanations of quantum nonlocality. On the other hand, I argue that such explanations cannot rule out a mechanical quantum theory. (shrink)

We argue that recent empirical findings and theoretical models shed new light on the nature of attention. According to the resulting amplification view, attentional phenomena can be unified at the neural level as the consequence of the amplification of certain input signals of attention-independent perceptual computations. This way of identifying the core realizer of attention evades standard criticisms often raised against sub-personal accounts of attention. Moreover, this approach also reframes our thinking about the function of attention by shifting the focus (...) from the function of selection to the function of amplification. (shrink)

Within the field of neuroscience, it is assumed that the central nervous system is divided into two functionally distinct components: the brain, which does the cognizing, and the spinal cord, which is a conduit of information enabling the brain to do its job. We dub this the “Cinderella view” of the spinal cord. Here, we suggest it should be abandoned. Marshalling recent empirical findings, we claim that the spinal cord is best conceived as an intrabodily cognitive extension: a piece of (...) biological circuitry that, together with the brain, constitutes our cognitive engine. To do so, after a brief introduction to the anatomy of the spinal cord, we briefly present a number of empirical studies highlighting the role played by the spinal cord in cognitive processing. Having so done, we claim that the spinal cord satisfies two popular and often endorsed criteria used to adjudicate cases of cognitive extension; namely the parity principle and the so-called “trust and glue” criteria. This, we argue, is sufficient to vindicate the role of the spinal cord as an intrabodily mental extension. We then steel our case considering a sizable number of prominent anti-extension arguments, showing that none of them poses a serious threat to our main claim. We then conclude the essay, spelling out a number of far-from trivial implications of our view. (shrink)

There is a growing consensus among philosophers of science that scientific endeavors of understanding the human mind or the brain exhibit explanatory pluralism. Relatedly, several philosophers have in recent years defended an interventionist approach to causation that leads to a kind of causal pluralism. In this paper, I explore the consequences of these recent developments in philosophy of science for some of the central debates in philosophy of mind. First, I argue that if we adopt explanatory pluralism and the interventionist (...) approach to causation, our understanding of physicalism has to change, and this leads to what I call pluralistic physicalism. Secondly, I show that this pluralistic physicalism is not endangered by the causal exclusion argument. (shrink)