In the current advent of technological innovation allowing for precise neural manipulations and copious data collection, it is hardly questioned that the explanation of behavioral processes is to be chiefly found in neural circuits. Such belief, rooted in the exhausted dualism of cause and effect, is enacted by a methodology that promotes “necessity and sufficiency” claims as the goal-standard in neuroscience, thus instructing young students on what shall reckon as explanation. Here we wish to deconstruct and explicate the difference between (...) what is done, what is said, and what is meant by such causal circuit explanations of behavior. Well-known to most philosophers, yet ignored or at least hardly ever made explicit by neuroscientists, the original grand claim of “understanding the brain” is imperceptibly substituted by the methodologically sophisticated task of empirically establishing counterfactual dependencies. But for the 21st century neuroscientist, after so much pride, this is really an excess of humility. I argue that to upgrade intervention to explanation is prone to logical fallacies, interpretational leaps and carries a weak explanatory force, thus settling and maintaining low standards for intelligibility in neuroscience. To claim that behavior is explained by a “necessary and sufficient” neural circuit is, at best, misleading. In that, my critique is indeed mainly negative. Positively, I briefly suggest some available alternatives for conceptual progress, such as adopting circular causality, searching for principles of behavior, and embracing process philosophy. Overall, if the goal of neuroscience is to understand the relation between brain and behavior then, in addition to excruciating neural studies, we will need a strong theory of behavior and a solid foundation to establish their relation. (shrink)

Mechanism is undoubtedly a causal concept, in the sense that ordinary definitions and philosophical analyses explicate the concept in terms of other causal concepts such as production and interaction. Given this fact, many philosophers have supposed that analyses of the concept of mechanism, while they might appeal to philosophical theories about the nature of causation, could do little to inform such theories. On the other hand, methods of causal inference and explanation appeal to mechanisms. Discovering a mechanism is the gold (...) standard for establishing and explaining causal connections. This fact suggests that it might be possible to provide an analysis of causation that appeals to mechanisms. (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)

A number of areas of biology raise questions about what is of value in the natural environment and how we ought to behave towards it: conservation biology, environmental science, and ecology, to name a few. Based on my experience teaching students from these and similar majors, I argue that the field of environmental ethics has much to teach these students. They come to me with pent-up questions and a feeling that more is needed to fully engage in their subjects, and (...) I believe some exposure to environmental ethics can help focus their interests and goals. I identify three primary areas in which environmental ethics can con- tribute to their education. The first is an examination of who (or what) should be considered to be part of our moral community (i.e., the community to whom we owe direct duties). Is it humans only? Or does it include all sentient life? Or all life? Or ecosystems considered holistically? Often, readings implicitly assume one or more of these answers; the goal is to make the student more sensitive to these implicit claims and to get them to think about the different reasons that support them. The second area, related to the first, is the application of the different answers concerning the extent of the ethical community to real environmental issues and problems. Students need to be aware of how the different answers concerning the moral community can imply conflicting answers for how we should act in certain cases and to think about ways to move toward conflict resolution. The third area in which environmental ethics can contribute is a more conceptual one, focusing on central concepts such as biodiversity, sustainability, species, and ecosystems. Exploring and evaluating various meanings of these terms will make students more reflective and thoughtful citizens and biologists, sensitive to the implications that different conceptual choices make. (shrink)

In this paper, we critically analyze the mechanicist approach to scientific explanation and propose to complement this view with a conception of level as interval of quasi-decompositionality. This conception allows us to metaphysically base the mechanicist proposals on characteristic systemic features. By means of concrete examples, we claim that this proposal is able to overcome some of the alleged limitations of predominant models of mechanistic explanation.

In this thesis, I give a metascientific account of causality in medicine. I begin with two historical cases of causal discovery. These are the discovery of the causation of Burkitt’s lymphoma by the Epstein-Barr virus, and of the various viral causes suggested for cervical cancer. These historical cases then support a philosophical discussion of causality in medicine. This begins with an introduction to the Russo- Williamson thesis (RWT), and discussion of a range of counter-arguments against it. Despite these, I argue (...) that the RWT is historically workable, given a small number of modifications. I then expand Russo and Williamson’s account. I first develop their suggestion that causal relationships in medicine require some kind of evidence of mechanism. I begin with a number of accounts of mechanisms and produce a range of consensus features of them. I then develop this consensus position by reference to the two historical case studies with an eye to their operational competence. In particular, I suggest that it is mechanistic models and their representations which we are concerned with in medicine, rather than the mechanism as it exists in the world. -/- I then employ these mechanistic models to give an account of the sorts of evidence used in formulating and evaluating causal claims. Again, I use the two human viral oncogenesis cases to give this account. I characterise and distinguish evidence of mechanism from evidence of difference-making, and relate this to mechanistic models. I then suggest the relationship between types of evidence presents us with a means of tackling the reference-class problem. This sets the scene for the final chapter. Here, I suggest the manner in which these two different classes of evidence become integrated is also reflected in the way that developing research programmes change as their associated causal claims develop. (shrink)

How can we explain the intentional nature of an expert’s actions, performed without immediate and conscious control, relying instead on automatic cognitive processes? How can we account for the differences and similarities with a novice’s performance of the same actions? Can a naturalist explanation of intentional expert action be in line with a philosophical concept of intentional action? Answering these and related questions in a positive sense, this dissertation develops a three-step argument. Part I considers different methods of explanations in (...) cognitive neuroscience (Bennett & Hacker’s philosophical, conceptual analysis; Marr’s three levels of explanation; Neural Correlates of Consciousness research; mechanistic explanation), defending ‘mechanistic explanation’ as a method that provides the necessary tools for integrating interdisciplinary insights into human action. Furthermore, a dynamic, explanatory mechanism allows the assessment of the impact of learning and development on expert action in a valuable way that other methods don’t. Part II continues by scrutinizing several cognitive neuroscientific theories of learning and development (neuroconstructivism; dual-processing theories; simulation theory; extended mind/cognition hypothesis), arguing for the complex interactions between different types of processing and different action representations involved in expert action performances. Moreover, according to our discussion of a particular ‘simulation theory’ these interactions can be influenced in several ways with the use of language, allowing an agent to configure a specific action representation for performance at a later stage. The results of Parts I and II are then applied in Part III to a parallel discussion of philosophical analyses of intentional action (discussing i.a. Frankfurt, Bratman, Pacherie and Ricoeur) and cognitive neuroscientific insights in it. Both approaches are found to converge in emphasizing the importance for an expert to develop stable patterns of actions that comply maximally not only with his intentions, but also with his motor expertise and with situational conditions. Consequently, his actions – automatic, or not – rely on this ‘sculpted space of actions’. (shrink)

This thesis explores the issue of whether Aristotle's Rhetoric is consistent with the principles and tools of contemporary science. The approach is to review Aristotle's Rhetoric in light of explanatory mechanisms from psychology, biology, cognitive science and neuroscience. The thesis begins by reviewing Aristotle's Rhetoric and modern rhetorical contributions from Chaim Perelman and Christopher Tindale. A discussion of several psychological principles of reasoning and their relevance to philosophical rhetoric follows. Next, a computational cognitive science framework on emotions and cognition and (...) its applicability to rhetoric is provided, followed by a discussion from principles of evolutionary biology on language evolution and morality and their relevance to rhetoric. The thesis concludes with a brief discussion of rhetorical ideas relative to the neuroanatomy of deductive and inductive reasoning and relative to a view of morality founded on brain neurochemistry. (shrink)

What are the differences between philosophy and science, or between the methods of philosophy and the methods of science? Unlike some philosophers we do not find philosophy and the methods of philosophy to be sui generis. Science, and in particular neuroscience, has much to tell us about the nature of the world and the concepts that we must use to understand and explain it. Yet science cannot function well without reflective analysis of the concepts, methods, and practices that constitute it. (...) For example, experimental methods and their resulting empirical data are essential for understanding the world, yet such data is not a-conceptual. Understanding how and what theoretical assumptions, conceptual assumptions, and practical knowledge guide the use of experimental methods is relevant to understanding the results of that use. In this way, philosophy – whether done by philosophers or scientists – has a role to play in understanding the world. Neuroscience is typically individualistic in focus; nonetheless, the mechanisms of learning and linguistic ability that some neuroscientists study also have a role to play in understanding communication. Philosophy cannot offer adequate understanding, even conceptual clarity, in isolation from empirical investigation. Yet, this does not require that science or scientific concepts will replace or reduce philosophical concepts, let alone those of ordinary language. (shrink)

Following an analysis of the state of investigations and clinical outcomes in the Alzheimer's research field, I argue that the widely-accepted 'amyloid cascade' mechanistic explanation of Alzheimer's disease appears to be fundamentally incomplete. In this context, I propose that a framework termed 'principled mechanism' (PM) can help with remedying this problem. First, using a series of five 'tests', PM systematically compares different components of a given mechanistic explanation against a paradigmatic set of criteria, and hints at various ways of making (...) the mechanistic explanation more 'complete'. These steps will be demonstrated using the amyloid explanation, and its missing or problematic mechanistic elements will be highlighted. Second, PM makes an appeal for the discovery and application of 'biological principles' (BPs), which approximate ceteris paribus laws and are operative at the level of a biological cell. As such, although thermodynamic, evolutionary, ecological and other laws or principles from chemistry and the broader life sciences could inform them, BPs should be considered ontologically unique. BPs could augment different facets of the mechanistic explanation but also allow further independent nomological explanation of the phenomenon. Whilst this overall strategy can be complementary to certain 'New Mechanist' approaches, an important distinction of the PM framework is its equal attention to the explanatory utility of biological principles. Lastly, I detail two hypothetical BPs, and show how they could each inform and improve the potentially incomplete mechanistic aspects of the amyloid explanation and also how they could provide independent explanations of the cellular features associated with Alzheimer's disease. (shrink)

Recent discussion of mechanism has suggested new approaches to several issues in the philosophy of science, including theory structure, causal explanation, and reductionism. Here, I apply what I take to be the fruits of the 'new mechanical philosophy' to an analysis of a contemporary debate in evolutionary biology about the role of natural selection in speciation. Traditional accounts of that debate focus on the geographic context of genetic divergence--namely, whether divergence in the absence of geographic isolation is possible (or significant). (...) Those accounts are at best incomplete, I argue, because they ignore the mechanisms producing divergence and miss what is at stake in the biological debate. I argue that the biological debate instead concerns the scope of particular speciation mechanisms which assign different roles to natural selection at various stages of divergence. The upshot is a new interpretation of the crux of that debate--namely, whether divergence with gene flow is possible (or significant) and whether the isolating mechanisms producing it are adaptive. (shrink)

Richard Boyd has long campaigned for a view of natural kinds he calls the Homeostatic Property Cluster account. This account has been particularly exciting for philosophers of biology unhappy with traditional essentialism about natural kinds and the views that biological kinds are, in one way or another, “historical entities”. Though defenders of HPC kinds have done much to further articulate the view, many questions about the account remain. One pressing question concerns the way in which HPC kinds are supposed to (...) accommodate our inductive and explanatory practices via causal, homeostatic mechanisms. In addressing the question, I am led to an alternative conception of what a property cluster kind might be. I call my alternative, in deference to its Boydian origins, the Stable Property Cluster account of natural kinds. Rather than emphasizing homeostasis or causal mechanism, the SPC account emphasizes the stability of a property cluster over the various ways stability may be maintained. In virtue of this modest change of emphasis, the SPC account does not advertise itself as an account of a kind of natural kind — it is, I think, an attractive candidate for a general natural kind concept, able to accommodate the diversity of natural kinds we find in the world. (shrink)

One of the central aims of science is explanation: scientists seek to uncover why things happen the way they do. This chapter addresses what kinds of explanations are formulated in biology, how explanatory aims influence other features of the field of biology, and the implications of all of this for biology education. Philosophical treatments of scientific explanation have been both complicated and enriched by attention to explanatory strategies in biology. Most basically, whereas traditional philosophy of science based explanation on derivation (...) from scientific laws, there are many biological explanations in which laws play little or no role. Instead, the field of biology is a natural place to turn for support for the idea that causal information is explanatory. Biology has also been used to motivate mechanistic accounts of explanation, as well as criticisms of that approach. Ultimately, the most pressing issue about explanation in biology may be how to account for the wide range of explanatory styles encountered in the field. This issue is crucial, for the aims of biological explanation influence a variety of other features of the field of biology. Explanatory aims account for the continued neglect of some central causal factors, a neglect that would otherwise be mysterious. This is linked to the persistent use of models like evolutionary game theory and population genetic models, models that are simplified to the point of unreality. These explanatory aims also offer a way to interpret many biologists’ total commitment to one or another methodological approach, and the intense disagreements that result. In my view, such debates are better understood as arising not from different theoretical commitments, but commitments to different explanatory projects. Biology education would thus be enriched by attending to approaches to biological explanation, as well as the unexpected ways that these explanatory aims influence other features of biology. I suggest five lessons for teaching about explanation in biology that follow from the considerations of this chapter. (shrink)

The term emergence, or irreducibility, has been used in a great variety of senses over the years, and different senses are useful in different discursive contexts. In this paper the focus is on one specific context, that of methodologically oriented social theory, and the question to answer is, what might be the most useful sense of emergent irreducibility in that field? To answer that question, key intuitions of emergence are first abstracted from the concept’s history. Three main senses of the (...) term are distinguished based on those different intuitions. Then the likely linguistic functions of emergence in each of those main senses are gauged in social theory and methodology. It is argued that one of the senses – here called “contingently epistemological irreducibility” – is in fact more useful as regards social scientific methodology than the others. (shrink)

This paper presents an account of the senses and what differentiates them that is compatible with richly multisensory perception and consciousness. According to this proposal, senses are ways of perceiving. Each sense is a subfaculty that comprises a collection of perceptual capacities. What each sense shares and what differentiates one sense from another is the manner in which those capacities are exercised. Each way of perceiving involves a distinct type of information gathering, individuated by the information it functions to extract (...) and the medium from which it does so. This approach distinguishes the project of characterizing and differentiating senses from that of attributing experiences to sensory modalities. Perceptual experiences are episodes in which perceptual capacities are exer- cised. Conscious perceptual episodes may be ascribed to distinct sensory modalities, according to the manners in which perceptual capacities are deployed on an occasion. According to this account, senses are not exclusive. First, their capacities may overlap. Second, perceptual episodes, including conscious experiences, may belong to multiple senses. Indeed, some episodes require the joint use of several senses. In this account, subjects have only limited first-person knowledge of the senses they employ. (shrink)

In the contemporary life sciences more and more researchers emphasize the “limits of reductionism” (e.g. Ahn et al. 2006a, 709; Mazzocchi 2008, 10) or they call for a move “beyond reductionism” (Gallagher/Appenzeller 1999, 79). However, it is far from clear what exactly they argue for and what the envisioned limits of reductionism are. In this paper I claim that the current discussions about reductionism in the life sciences, which focus on methodological and explanatory issues, leave the concepts of a reductive (...) method and a reductive explanation too unspecified. In order to fill this gap and to clarify what the limits of reductionism are I identify three reductive methods that are crucial in the current practice of the life sciences: decomposition, focusing on internal factors, and studying parts in isolation. Furthermore, I argue that reductive explanations in the life sciences exhibit three characteristics: first, they refer only to factors at a lower level than the phenomenon at issue, second, they focus on internal factors and thus ignore or simplify the environment of a system, and, third, they cite only the parts of a system in isolation. (shrink)

Philosophical theories of explanation characterize the difference between correct and incorrect explanations. While remaining neutral as to which of these ‘first-order’ theories is right, this paper asks the ‘meta-explanatory’ question: is the difference between correct and incorrect explanation real, i.e., objective or mind-independent? After offering a framework for distinguishing realist from anti-realist views, I sketch three distinct paths to explanatory anti-realism.

We advance an account that grounds cognition, specifically decision-making, in an activity all organisms as autonomous systems must perform to keep themselves viable—controlling their production mechanisms. Production mechanisms, as we characterize them, perform activities such as procuring resources from their environment, putting these resources to use to construct and repair the organism's body and moving through the environment. Given the variable nature of the environment and the continual degradation of the organism, these production mechanisms must be regulated by control mechanisms (...) that select when a production is required and how it should be carried out. To operate on production mechanisms, control mechanisms need to procure information through measurement processes and evaluate possible actions. They are making decisions. In all organisms, these decisions are made by multiple different control mechanisms that are organized not hierarchically but heterarchically. In many cases, they employ internal models of features of the environment with which the organism must deal. Cognition, in the form of decision-making, is thus fundamental to living systems which must control their production mechanisms. (shrink)

Traditional accounts of explanation fail to illuminate the explanatory relevance of “models that are descriptively false” in the sense that the regularities they entail fail to obtain. In this paper, I propose an account of explanation, which I call ‘explanation by concretization’, that serves to explicate the explanatory relevance of such models. Starting from a highly abstract and idealized model, causal explanations of the absence of regularities are sought by adding complexity to the model or by concretizing it. Whether this (...) process is successful depends on whether the abstractions and idealizations in the basic model succeed in isolating a mechanism, i.e. in representing how it operates when interfering factors are absent. This account is developed in the context of economics and contrasted to those of Daniel Hausman and Nancy Cartwright. I go on to provide an account of how unrealistic models can be used for providing understanding of the way mechanisms work. (shrink)

There has been relatively little effort to provide a systematic overview of different forms of exploratory experimentation (EE). The present paper examines the growing subdiscipline of nanotoxicology and suggests that it illustrates at least four ways that researchers can engage in EE: searching for regularities; developing new techniques, simulation models, and instrumentation; collecting and analyzing large swaths of data using new experimental strategies (e.g., computer-based simulation and "high-throughput" instrumentation); and structuring an entire disciplinary field around exploratory research agendas. In order (...) to distinguish these and other activities more effectively, the paper proposes a taxonomy that includes three dimensions along which types of EE vary: (1) the aim of the experimental activity, (2) the role of theory in the activity, and (3) the methods or strategies employed for varying experimental parameters. (shrink)

As much as assumptions about mechanisms and mechanistic explanation have deeply affected psychology, they have received disproportionately little analysis in philosophy. After a historical survey of the influences of mechanistic approaches to explanation of psychological phenomena, we specify the nature of mechanisms and mechanistic explanation. Contrary to some treatments of mechanistic explanation, we maintain that explanation is an epistemic activity that involves representing and reasoning about mechanisms. We discuss the manner in which mechanistic approaches serve to bridge levels rather than (...) reduce them, as well as the different ways in which mechanisms are discovered. Finally, we offer a more detailed example of an important psychological phenomenon for which mechanistic explanation has provided the main source of scientific understanding. (shrink)

A large number of legal rules create an obligation to prevent, repair or otherwise mitigate damage to human health or the environment. Many of these rules require that a legally relevant causal relation between human behaviour and the damage at issue is established, and in the establishment of causal relations of this kind scientific information is often pressed into service. This thesis examines this specifically legal use of scientific information. It shows that many legally relevant causal relations cannot be established (...) in this way. It also shows that the legal strategy for dealing with epistemic difficulties by relaxing the standard of proof is of limited value in tackling this problem. Consequently, the legal rules focused upon are less efficient tools for combating damage to human health and the environment than their appearances suggest. Other legal means to achieve this aim should therefore be considered. The thesis begins by presenting a framework which can be used to solve some important problems with communication and the division of labour that arise when scientific information is used to answer legal questions. This framework recognizes both epistemological and ontological differences between law and science. The latter are differences between the kinds of entity which are relevant in distinct theoretical contexts; the former, by contrast, are differences between applicable standards of proof. In subsequent parts of the thesis this framework shapes the discussion of the question: To what extent can legally relevant causal relations, involving legally significant entities, be established through the application of scientific information about other, scientifically relevant entities and legal standards of proof. The thesis concludes with a discussion of some of the ways in which the legal regulation could, potentially, be adapted, so as to counteract damage to human health and the environment more efficiently. (shrink)

Philippe Huneman has recently questioned the widespread application of mechanistic models of scientific explanation based on the existence of structural explanations, i.e. explanations that account for the phenomenon to be explained in virtue of the mathematical properties of the system where the phenomenon obtains, rather than in terms of the mechanisms that causally produce the phenomenon. Structural explanations are very diverse, including cases like explanations in terms of bowtie structures, in terms of the topological properties of the system, or in (...) terms of equilibrium. The role of mathematics in bowtie structured systems and in topologically constrained systems has recently been examined in different papers. However, the specific role that mathematical properties play in equilibrium explanations requires further examination, as different authors defend different interpretations, some of them closer to the new-mechanistic approach than to the structural model advocated by Huneman. In this paper, we cover this gap by investigating the explanatory role that mathematics play in Blaser and Kirschner’s nested equilibrium model of the stability of persistent long-term human-microbe associations. We argue that their model is explanatory because: i) it provides a mathematical structure in the form of a set of differential equations that together satisfy an ESS; ii) that the nested nature of the ESSs makes the explanation of host-microbe persistent associations robust to any perturbation; iii) that this is so because the properties of the ESS directly mirror the properties of the biological system in a non-causal way. The combination of these three theses make equilibrium explanations look more similar to structural explanations than to causal-mechanistic explanation. (shrink)

The study of psychological mechanisms is an interdisciplinary endeavour, requiring insights from many different domains. In this article, I argue that philosophy plays an essential role in this interdisciplinary project, and that effective scientific study of psychological mechanisms requires that working scientists be responsible metaphysicians. This means adopting deliberate metaphysical positions when studying mechanisms that go beyond what is empirically justified regarding the nature of the phenomenon being studied, the conditions of its occurrence, and its boundaries. Such metaphysical commitments are (...) necessary in order to set up experimental protocols, determine which variables to manipulate under experimental conditions, and which conclusions to draw from different scientific models and theories. It is important for scientists to be aware of the metaphysical commitments they adopt, since they can easily be led astray if invoked carelessly. (shrink)

It is no secret that scientists argue. They argue about theories. But even more, they argue about the evidence for theories. Is the evidence itself trustworthy? This is a bit surprising from the perspective of traditional empiricist accounts of scientific methodology according to which the evidence for scientific theories stems from observation, especially observation with the naked eye. These accounts portray the testing of scientific theories as a matter of comparing the predictions of the theory with the data generated by (...) these observations, which are taken to provide an objective link to reality. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, contemporary biological research practice (...) shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex, the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates (...) models which are not intelligible, and hence not explanatory. (shrink)

In this paper, the role of the environment and physical embodiment of computational systems for explanatory purposes will be analyzed. In particular, the focus will be on cognitive computational systems, understood in terms of mechanisms that manipulate semantic information. It will be argued that the role of the environment has long been appreciated, in particular in the work of Herbert A. Simon, which has inspired the mechanistic view on explanation. From Simon’s perspective, the embodied view on cognition seems natural but (...) it is nowhere near as critical as its proponents suggest. The only point of difference between Simon and embodied cognition is the significance of body-based off-line cognition; however, it will be argued that it is notoriously over-appreciated in the current debate. The new mechanistic view on explanation suggests that even if it is critical to situate a mechanism in its environment and study its physical composition, or realization, it is also stressed that not all detail counts, and that some bodily features of cognitive systems should be left out from explanations. (shrink)

The Extended Mind Thesis (EMT) is traditionally formulated against the bedrock of functionalism, and ongoing debates are typically bogged down with questions concerning the exact relationship between EMT and different versions of functionalism. In this paper, I offer a novel ally for EMT: the new mechanistic approach to explanation. I argue that the mechanistic framework provides useful resources not just to disambiguate EMT, and to show which objections fail to pose a serious challenge, but also to answer some of the (...) deeper problems that stem from the functionalist roots of EMT. (shrink)

Whereas most branches of neuroscience are thought to provide mechanistic explanations, systems neuroscience is not. Two reasons are traditionally cited in support of this conclusion. First, systems neuroscientists rarely, if ever, rely on the dual strategies of decomposition and localization. Second, they typically emphasize organizational properties over the properties of individual components. In this paper, I argue that neither reason is conclusive: researchers might rely on alternative strategies for mechanism discovery, and focusing on organization is often appropriate and consistent with (...) the norms of mechanistic explanation. Thus, many explanations in systems neuroscience can also be viewed as mechanistic explanations. (shrink)

Polygenic effects have more than one cause. They testify to the fact that several causal contributors are sometimes simultaneously involved in causation. The importance of polygenic causation was noticed early on by Mill (1893). It has since been shown to be a problem for causal-law approaches to causation and accounts of causation cast in terms of capacities. However, polygenic causation needs to be examined more thoroughly in the emerging literature on causal mechanisms. In this paper I examine whether an influential (...) theory of mechanisms proposed by Peter Machamer, Lindley Darden and Carl Craver can accommodate polygenic effects and other forms of causal interaction. This theory is problematic, I will argue, because it ascribes a central role to activities. In it, activities are needed not only to constitute mechanisms but also to perform the causal role of mechanisms. Any such mechanism-as-activity will be incompatible with causal situations where either no or merely another kind of activity occurs. But, as I will try to illustrate in this paper, both kinds of situation may be frequent. If I am right, the view that Machamer and colleagues suggest leads to an impoverished conception of mechanism. (shrink)

The philosophical conception of mechanistic explanation is grounded on a limited number of canonical examples. These examples provide an overly narrow view of contemporary scientific practice, because they do not reflect the extent to which the heuristic strategies and descriptive practices that contribute to mechanistic explanation have evolved beyond the well-known methods of decomposition, localization, and pictorial representation. Recent examples from evolutionary robotics and network approaches to biology and neuroscience demonstrate the increasingly important role played by computer simulations and mathematical (...) representations in the epistemic practices of mechanism discovery and mechanism description. These examples also indicate that the scope of mechanistic explanation must be re-examined: With new and increasingly powerful methods of discovery and description comes the possibility of describing mechanisms far more complex than traditionally assumed. (shrink)

Many philosophers would concede that mathematics contributes to the abstractness of some of our most successful scientific representations. Still, it is hard to know what this abstractness really comes to or how to make a link between abstractness and success. I start by explaining how mathematics can increase the abstractness of our representations by distinguishing two kinds of abstractness. First, there is an abstract representation that eschews causal content. Second, there are families of representations with a common mathematical core that (...) is variously interpreted. The second part of the paper makes a connection between both kinds of abstractness and success by emphasizing confirmation. That is, I will argue that the mathematics contributes to the confirmation of these abstract scientific representations. This can happen in two ways which I label "direct" and "indirect". The contribution is direct when the mathematics facilitates the confirmation of an accurate representation, while the contribution is indirect when it helps the process of disconfirming an inaccurate representation. Establishing this conclusion helps to explain why mathematics is prevalent in some of our successful scientific theories, but I should emphasize that this is just one piece of a fairly daunting puzzle. (shrink)

Recently, Piccinini and Craver have stated three theses concerning the relations between functional analysis and mechanistic explanation in cognitive sciences: No Distinctness: functional analysis and mechanistic explanation are explanations of the same kind; Integration: functional analysis is a kind of mechanistic explanation; and Subordination: functional analyses are unsatisfactory sketches of mechanisms. In this paper, I argue, first, that functional analysis and mechanistic explanations are sub-kinds of explanation by scientific (idealized) models. From that point of view, we must take into account (...) the tradeoff between the representational/explanatory goals of generality and precision that govern the practice of model-building. In some modeling scenarios, it is rational to maximize explanatory generality at the expense of mechanistic precision. This tradeoff allows me to put forward a problem for the mechanist position. If mechanistic modeling endorses generality as a valuable goal, then Subordination should be rejected. If mechanists reject generality as a goal, then Integration is false. I suggest that mechanists should accept that functional analysis can offer acceptable explanations of cognitive phenomena. (shrink)

Concept empiricists are committed to the claim that the vehicles of thought are re-activated perceptual representations. Evidence for empiricism comes from a range of neuroscientific studies showing that perceptual regions of the brain are employed during cognitive tasks such as categorization and inference. I examine the extant neuroscientific evidence and argue that it falls short of establishing this core empiricist claim. During conceptual tasks, the causal structure of the brain produces widespread activity in both perceptual and non-perceptual systems. I lay (...) out several conditions on what is required for a neural state to be a realizer of the functional role played by concepts, and argue that no subset of this activity can be singled out as the unique neural vehicle of conceptual thought. Finally, I suggest that, while the strongest form of empiricism is probably false, the evidence is consistent with several weaker forms of empiricism. (shrink)

The naturalism versus interpretivism debate the in philosophy of social science is traditionally framed as the question of whether social science should attempt to emulate the methods of natural science. I show that this manner of formulating the issue is problematic insofar as it presupposes an implausibly strong unity of method among the natural sciences. I propose instead that what is at stake in this debate is the feasibility and desirability of what I call the Enlightenment ideal of social science. (...) I argue that this characterization of the issue is preferable, since it highlights the central disagreement between advocates of naturalism and interpretivism, makes connections with recent work on the topics of causal inference and social epistemology, while avoiding unfruitful comparisons between the social and natural sciences. (shrink)

In this paper, I would like to show that considering technological models as they arise in engineering disciplines can greatly enrich the philosophical perspective on models. In fluid mechanics, (at least) three types of models are distinguished: mathematical, computer and physical models. Very often, the choice of a particular mathematical, computer or physical model highly affects the type of solutions and the computational time needed for it. Technological models not only aim at a correct description of the physical phenomena, but (...) also for an efficient and accurate simulation. The problem arises how heterogeneous models of an engineering problem can be brought together and be compared to each other as regards their function and technological efficiency. There are two developments in the history of fluid mechanics that have greatly influenced the use of models in the field: The introduction of the concept of the boundary layer by Ludwig Prandtl in 1904 made it possible to apply ideal analytical solutions, which at the time were almost entirely based on Euler’s equations for inviscid fluids, to interesting real cases and to approximate the theoretical Navier-Stokes equations to practical engineering problems, i.e. to cases at high Reynolds numbers. This made it possible to link the empirical tradition of hydraulics with the theoretical tradition of analytical mechanics and therefore lead to a kind of equilibrium in the use of mathematical and physical models. In the 1970s the introduction of the computer has greatly pushed back the importance of both physical and mathematical (analytic) models alike without making them superfluous. There remain, however, three different ways to conceive of physical models in fluid mechanics, and thus of the experimental ingredient, depending on whether they are devised from an analytical, computational or measurement theoretical point of view. Yet even inside the tradition of computer simulation, different practices have formed according to the programming methods used. The choice of method either depends on considerations of efficiency in terms of costs and time, or on historically contingent factors, like availability of instruments and programming packages or the arbitrary choice of a forerunner. Seen from a technological point of view the factors that make models “autonomous agents” and thus (relatively) independent from theory depend on efficiency constraints. Models are means to solve problems in a certain practical perspective by the most efficient means available. To develop a model is a “fast and frugal way” to get to grips with a certain region of reality, whereas the theoretical approach stresses the importance of universal features. (shrink)