The dangers of character reification for cladistic inference are explored. The identification and analysis of characters always involves theory-laden abstraction—there is no theory-free “view from nowhere.” Given theory-ladenness, and given a real world with actual objects and processes, how can we separate robustly real biological characters from uncritically reified characters? One way to avoid reification is through the employment of objectivity criteria that give us good methods for identifying robust primary homology statements. I identify six such criteria and explore each (...) with examples. Ultimately, it is important to minimize character reification, because poor character analysis leads to dismal cladograms, even when proper phylogenetic analysis is employed. Given the deep and systemic problems associated with character reification, it is ironic that philosophers have focused almost entirely on phylogenetic analysis and neglected character analysis. (shrink)

Here I consider the relative merits of two recent models of explanation, James Woodward's interventionist-counterfactual model and the model model. According to the former, explanations are largely constituted by information about the consequences of counterfactual interventions. Problems arise for this approach because countless relevant interventions are possible in most cases and because it overlooks other kinds of equally relevant information. According the model model, explanations are largely constituted by cognitive models of actual mechanisms. On this approach, explanations tend not to (...) represent any of the aforementioned information explicitly but can instead be used to produce it on demand. The model model thus offers the more plausible account of the information of which we are aware when we have an explanation and of the ratiocinative process through which we derive many kinds of information that are relevant to the evaluation of explanations. (shrink)

Scientists of many disciplines use theoretical models to explain and predict the dynamics of the world. They often have to rely on digital computer simulations to draw predictions fromthe model. But to deliver phenomenologically adequate results, simulations deviate from the assumptions of the theoretical model. Therefore the role of simulations in scientific explanation demands itself an explanation. This paper analyzes the relation between real-world system, theoretical model, and simulation. It is argued that simulations do not explain processes in the real (...) world directly. The way in which simulations help explaining real-world processes is conceived as indirect, mediated by the theoretical model. Simulacra are characterized further, and turn out to be a priori measurable. This gives a clue to a better understanding of the epistemic role of computer simulations in scientific research. (shrink)

Philosophers of the social sciences are increasingly convinced that macro-and micro-explanations are complementary. Whereas macro-explanations are broad, micro-explanations are deep. I distinguish between weak and strong complementarity: Strongly complementary explanations improve one another when integrated, weakly complementary explanations do not. To demonstrate the explanatory autonomy of different levels of explanation, explanatory pluralists mostly presuppose the weak form of complementarity. By scrutinizing the notions of explanatory depth and breadth, I argue that macro- and micro-accounts of the same phenomenon are more often (...) strongly complementary. This invites a revision of the pluralist position in which integration promotes explanatory progress. Key Words: explanatory pluralism • social science • explanatory depth • explanatory breadth • mechanism. (shrink)

I analyze the importance of parts in the style of biological theorizing that I call compositional biology. I do this by investigating various aspects, including partitioning frames and explanatory accounts, of the theoretical perspectives that fall under and are guided by compositional biology. I ground this general examination in a comparative analysis of three different disciplines with their associated compositional theoretical perspectives: comparative morphology, functional morphology, and developmental biology. I glean data for this analysis from canonical textbooks and defend the (...) use of such texts for the philosophy of science. I end with a discussion of the importance of recognizing formal and compositional biology as two genuinely different ways of doing biology – the differences arising more from their distinct methodologies than from scientific discipline included or natural domain studied. Ultimately, developing a translation manual between the two styles would be desirable as they currently are, at times, in conflict. (shrink)

From its genesis in the 1960s, the focus of inquiry in neuroscience has been on the cellular and molecular processes underlying neural activity. In this pursuit neuroscience has been enormously successful. Like any successful scientific inquiry, initial successes have raised new questions that inspire ongoing research. While there is still much that is not known about the molecular processes in brains, a great deal of very important knowledge has been secured, especially in the last 50 years. It has also attracted (...) the attention of a number of philosophers, some of whom have viewed it as evidence for a ruthlessly reductionistic program that will eventually explain how mental processes are performed in the brain in purely molecular terms. As neuroscience developed, however, there emerged a smaller group of researchers who focused on systems, behavioral, and cognitive neuroscience. These investigators have also made impressive advances in the last 50 years and they have been the focus of an even larger group of philosophers, who have appealed to systems level understanding of the brain as providing the appropriate point of connection to the information processing accounts advanced in psychology. (shrink)

Arguments for the autonomy of psychology or other higher-level sciences have often taken the form of denying the possibility of reduction. The form of reduction most proponents and critics of the autonomy of psychology have in mind is theory reduction. Mechanistic explanations provide a different perspective. Mechanistic explanations are reductionist insofar as they appeal to lower-level entities—the component parts of a mechanism and their operations— to explain a phenomenon. However, unlike theory reductions, mechanistic explanations also recognize the fundamental role of (...) organization in enabling mechanisms to engage their environments as units (as well as the role of yet higher-level structures in constraining such engagement). Especially when organization is non-linear, it can enable mechanisms to generate phenomena that are quite surprising given the operations of the components taken in isolation. Such organization must be discovered—it cannot simply be derived from knowledge of lower-level parts and their operations. Moreover, the organized environments in which mechanisms operate must also be discovered. It is typically the higher-level disciplines that have the tools for discovering the organization within and between mechanisms. Although these inquiries are constrained by the knowledge of the parts and operations constituting the mechanism, they make their own autonomous contribution to understanding how a mechanism actually behaves. Thus, mechanistic explanations provide a strong sense of autonomy for higher levels of organization and the inquiries addressing them even while recognizing the distinctive contributions of reductionistic research investigating the operations of the lower level components. (shrink)

trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in.

Neuroscience and cognitive science seek to explain behavioral regularities in terms of underlying mechanisms. An important element of a mechanistic explanation is a characterization of the operations of the parts of the mechanism. The challenge in characterizing such operations is illustrated by an example from the history of physiological chemistry in which some investigators tried to characterize the internal operations in the same terms as the overall physiological system while others appealed to elemental chemistry. In order for biochemistry to become (...) successful, researchers had to identify a new level of operations involving operations over molecular groups. Existing attempts at mechanistic explanation of behavior are in a situation comparable to earlier approaches to physiological chemistry, drawing their inspiration either from overall psychology activities or from low-level neural processes. Successful mechanistic explanations of behavior require the discovery of the appropriate component operations. Such discovery is a daunting challenge but one on which success will be beneficial to both behavioral scientists and cognitive and neuroscientists. (shrink)

Not all models are explanatory. Some models are data summaries. Some models sketch explanations but leave crucial details unspecified or hidden behind filler terms. Some models are used to conjecture a how-possibly explanation without regard to whether it is a how-actually explanation. I use the Hodgkin and Huxley model of the action potential to illustrate these ways that models can be useful without explaining. I then use the subsequent development of the explanation of the action potential to show what is (...) required of an adequate mechanistic model. Mechanistic models are explanatory. (shrink)

1. A Historical Look at Unity 2. Field Guide to Modern Concepts of Reduction and Unity 3. Kitcher's Revisionist Account of Unification 4. Critics of Unity 5. Integration Instead of Unity 6. Reduction via Mechanisms 7. Case Studies in Reduction and Unification across the Disciplines.

This paper argues that the form of explanation at issue in the hard problem of consciousness is scientifically irrelevant, despite appearances to the contrary. In particular, it is argued that the 'sense of understanding' that plays a critical role in the form of explanation implicated in the hard problem provides neither a necessary nor a sufficient condition on satisfactory scientific explanation. Considerations of the actual tools and methods available to scientists are used to make the case against it being a (...) necessary condition, and work by J.D. Trout that exploits psychological research on the hindsight and overconfidence biases is used to show that it is not a sufficient condition. It is argued, however, that certain intellectual and moral concerns give us good reason to still try to meet the hard problem's explanatory challenge, despite its extrascientific nature. (shrink)

In this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the TM (...) does not provide a mechanistic explanation for interactive computing. The reason is that the fundamental phenomena relevant to interactive computing are out of the scope of classical computability theory. Part of the explanatory power of the TM relies on what we propose to call an execution model. An execution model belongs to a level of abstraction where it is possible to describe both the functional architecture and the execution in mechanistic terms. An updated execution model is warranted to provide the minimal mechanistic description for interactive computation as a counterpart of what the TM could explain regarding Church-Turing computation. It would support an explanation of the ubiquitous computing devices we know - those interacting with humans, e.g., through digital interfaces. We show that such a model is not available within interactive models of computation and that relevant abstractions and concerns are available in computer engineering but need to be identified and gathered. To fill this void, we propose to reflect on the level of abstraction required to support the mechanistic description of an interactive execution and propose some preliminary requirements. (shrink)

Causal mediation mechanisms are well supported by available experimental evidence and provide a practicable way to reductive physicalism. According to the causal mediation account of mechanistic explanation, descriptions as diverse as ‘black-box’ phenomena, mechanistic sketches and schemas mixing physically interpreted and operationalized biological, psychological and social variables, and detailed descriptions of mechanisms refer to the same causal structure circumscribed within the spatiotemporal boundaries of a replicable experimental setup. The coreference of coarser- and finer-grained descriptions of causal structures opens new possibilities (...) for testing the reductive physicalism conjecture. I discuss experimental designs supporting the causal mediation account and show how recent studies demonstrating the biological mediation of mind-mind causal processes can provide evidence for reductive physicalism. (shrink)

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

In recent years, an increasing number of theoretical biologists and philosophers of biology have been opposing reductionist research agendas by appealing to the concept of biological autonomy which draws on the older concept of autopoiesis. In my paper, I investigate some of the ontological implications of this approach. The emphasis on autonomy and autopoiesis, together with the associated idea of organisational closure, might evoke the impression that organisms are to be categorised ontologically as substances: ontologically independent, well-individuated, discrete particulars. However, (...) I argue that this is mistaken. Autopoiesis and biological autonomy, properly understood, require a rigorous commitment to a process ontological view of life. (shrink)

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

According to an influential epistemological tradition, science explains phenomena on the basis of laws, but the last two decades have witnessed a neo-mechanistic movement that emphasizes the fundamental role of mechanism-based explanations in science, which have the virtue of opening the “black box” of correlations and of providing a genuine understanding of the phenomena. Mechanisms enrich the empirical content of a theory by introducing a new set of variables, helping us to make causal inferences that are not possible on the (...) basis of macro-level correlations. However, the appeal to mechanisms has also a methodological price. They are vulnerable to interference effects; they also face underdetermination problems, because the available evidence often allows different interpretations of the underlying structure of a correlation; they are strongly context-dependent and their individuation as causal patterns can be controversial; they present specific testability problems; finally, mechanism-based extrapolations can be misleading due to the local character of mechanisms. At any rate, the study of mechanisms is an indispensable part of the human sciences, and the problems that they raise can be controlled by quantitative and qualitative methods, and an epistemologically informed exercise of critical thinking. (shrink)

Despite the miraculous progress of science—it’s practitioners continue to run into mistakes, either discrediting research unduly or making leaps of causal inference where none are warranted. In this we isolate two of the reasons for such behavior involving the misplaced understanding of the role of mechanisms and mechanistic knowledge in the establishment of cause-effect relationships. We differentiate causal knowledge into causes, effects, mechanisms, cause-effect relationships, and causal stories. Failing to understand the role of mechanisms in this picture, including their absence (...) of knowledge or incorrect specification, leads to errors of wrongly excluding cause-effect relationships or wrongly inferring their existence. We highlight the primary of causality over mechanistic knowledge and causal stories in scientific reasoning. (shrink)

I argue that difference-making should be a crucial element for evaluating the quality of evidence for mechanisms, especially with respect to the robustness of mechanisms, and that it should take central stage when it comes to the general role played by mechanisms in establishing causal claims in medicine. The difference- making of mechanisms should provide additional compelling reasons to accept the gist of Russo-Williamson thesis and include mechanisms in the protocols for Evidence- Based Medicine, as the EBM+ research group has (...) been advocating. (shrink)

Biological processes are often explained by identifying the underlying mechanisms that generate a phenomenon of interest. I characterize a basic account of mechanistic explanation and then present three challenges to this account, illustrated with examples from molecular biology. The basic mechanistic account is insufficient for explaining nonsequential and nonlinear dynamic processes, is insufficient for explaining the inherently stochastic nature of many biological mechanisms, and fails to give a proper framework for analyzing organization. I suggest that biological processes are best approached (...) as a multidimensional gradient—with some processes being paradigmatic cases of mechanisms and some being marginal cases. (shrink)

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

The first part of this paper argues that if Craver’s ([2007a], [2007b]) popular mutual manipulability account (MM) of mechanistic constitution is embedded within Woodward’s ([2003]) interventionist theory of causation--for which it is explicitly designed--it either undermines the mechanistic research paradigm by entailing that there do not exist relationships of constitutive relevance or it gives rise to the unwanted consequence that constitution is a form of causation. The second part shows how Woodward’s theory can be adapted in such a way that (...) (MM) neither undermines the mechanistic paradigm nor reduces constitution to causation. However, it turns out that this modified theoretical embedding of (MM) makes it impossible to produce empirical evidence for constitutive relations. The paper ends by suggesting an additional criterion, the fat-handedness criterion, which, when combined with (MM), generates indirect empirical evidence for constitutive relevance. (shrink)

In the recent philosophical literature, two questions have arisen concerning the status of natural selection: (1) Is it a population-level phenomenon, or is it an organism-level phenomenon? (2) Is it a causal process, or is it a purely statistical summary of lower-level processes? In an earlier work (Millstein, Br J Philos Sci, 57(4):627–653, 2006), I argue that natural selection should be understood as a population-level causal process, rather than a purely statistical population-level summation of lower-level processes or as an organism-level (...) causal process. In a 2009 essay entitled “Productivity, relevance, and natural selection,” Stuart Glennan argues in reply that natural selection is produced by causal pro- cesses operating at the level of individual organisms, but he maintains that there is no causal productivity at the population level. However, there are, he claims, many population-level properties that are causally relevant to the dynamics of evolution- ary processes. Glennan’s claims rely on a causal pluralism that holds that there are two types of causes: causal production and causal relevance. Without calling into question Glennan’s causal pluralism or his claims concerning the causal relevance of natural selection, I argue that natural selection does in fact exhibit causal production at the population level. It is true that natural selection does not fit with accounts of mechanisms that involve decomposition of wholes into parts, such as Glennan’s own. However, it does fit with causal production accounts that do not require decomposition, such as Salmon’s Mark Transmission account, given the extent to which populations act as interacting “objects” in the process of natural selection. (shrink)

I argue that examining the explanatory power of the hypothesis of extended cognition (HEC) offers a fruitful approach to the problem of cognitive system demarcation. Although in the discussions on HEC it has become common to refer to considerations of explanatory power as a means for assessing the plausibility of the extended cognition approach, to date no satisfying account of explanatory power has been presented in the literature. I suggest that the currently most prominent theory of explanation in the special (...) sciences, James Woodward’s contrastive-counterfactual theory, and an account of explanatory virtues building on that theory can be used to develop a systematic picture of cognitive system demarcation in the psychological sciences. A major difference between my differential influence (DI) account and most other theories of cognitive extension is the cognitive systems pluralism implied by my approach. By examining the explanatory power of competing traditions in psychological memory research, I conclude that internalist and externalist classificatory strategies are characterized by different profiles of explanatory virtues and should often be considered as complementary rather than competing approaches. This suggests a deflationary interpretation of HEC. (shrink)

In this study the notion of mechanistic entities is analyzed as it has been conceptualized by Hermann Lotze in his article Life. Vital Force (1842), the metaphysical foundation of which has recourse to his Metaphysik (1841) and Logik (1843). According to Lotze, explanations in the sciences are arguments which have a syntactic and a semantic structure—similar to that which became later known as the DN-model of explanation. The syntactic structure is delineated by ontological forms, the semantic by cosmological ones; the (...) latter comprise the preconditions for the construction of appearances in accord with the ontological forms. Mechanisms are embedded into this logical framework by representing the more complex spatio-temporal arrangements of cosmological entities. The coordinated model of a mechanism is a reductive type of explanation. This study also demonstrates how Lotze made use of his concept of mechanisms in order to explain law-like and probabilistic events in organic and inorganic nature, thereby establishing an original ‘oligomeric’ (i. e., a fraction of the parts of a system determines its development) variant of a preformative theory of ontogenesis which anticipates modern concepts of genetic determination. In this context, Lotze alludes to paradigms of dissipative structures. The relevance of these reflections for subsequent theories is shown by contrasting them with Schrödinger’s theory of organisms. Finally, a comparison of some aspects of Lotze’s concept of mechanisms with equivalent aspects of current normative approaches confirms that essential elements of the latter versions can be retrieved in the former one. Above that, Lotze employs the teleological aspect of ontological forms in order to determine the extent of the mechanistic system under consideration. He further differentiates three modal states of mechanisms and includes a concept to explain exceptions or irregularities. The concept of ‘activity’ is strictly excluded from his account and shown to be a metaphysical illusion. (shrink)

Abstract Although noting the importance of organization in mechanisms, the new mechanistic philosophers of science have followed most biologists in focusing primarily on only the simplest mode of organization in which operations are envisaged as occurring sequentially. Increasingly, though, biologists are recognizing that the mechanisms they confront are non-sequential and the operations nonlinear. To understand how such mechanisms function through time, they are turning to computational models and tools of dynamical systems theory. Recent research on circadian rhythms addressing both intracellular (...) mechanisms and the intercellular networks in which these mechanisms are synchronized illuminates this point. This and other recent research in biology shows that the new mechanistic philosophers of science must expand their account of mechanistic explanation to incorporate computational modeling, yielding dynamical mechanistic explanations. Developing such explanations, however, is a challenge for both the scientists and the philosophers as there are serious tensions between mechanistic and dynamical approaches to science, and there are important opportunities for philosophers of science to contribute to surmounting these tensions. Content Type Journal Article Category Original paper in Philosophy of Science Pages 1-16 DOI 10.1007/s13194-012-0046-x Authors William Bechtel, Department of Philosophy, Center for Chronobiology, and Science Studies Program, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0119, USA Journal European Journal for Philosophy of Science Online ISSN 1879-4920 Print ISSN 1879-4912. (shrink)

In recent debates mechanisms are often discussed in the context of ‘complex systems’ which are understood as having a complicated compositional structure. I want to draw the attention to another, radically different kind of complex system, in fact one that many scientists regard as the only genuine kind of complex system. Instead of being compositionally complex these systems rather exhibit highly non-trivial dynamical patterns on the basis of structurally simple arrangements of large numbers of non-linearly interacting constituents. The characteristic dynamical (...) patterns in what I call “dynamically complex systems” arise from the interaction of the system’s parts largely irrespective of many properties of these parts. Dynamically complex systems can exhibit surprising statistical characteristics, the robustness of which calls for an explanation in terms of underlying generating mechanisms. However, I want to argue, dynamically complex systems are not sufficiently covered by the available conceptions of mechanisms. I will explore how the notion of a mechanism has to be modified to accommodate this case. Moreover, I will show under which conditions the widespread, if not inflationary talk about mechanisms in complex systems stretches the notion of mechanisms beyond its reasonable limits and is no longer legitimate. (shrink)

This paper proposes an extended version of the interventionist account for causal inference in the practical context of biological mechanism research. This paper studies the details of biological mechanism researchers’ practices of assessing the evidential legitimacy of experimental data, arguing why quantity and variety are two important criteria for this assessment. Because of the nature of biological mechanism research, the epistemic values of these two criteria result from the independence both between the causation of data generation and the causation in (...) question and between different interventions, not techniques. The former independence ensures that the interventions in the causation in question are not affected by the causation that is responsible for data generation. The latter independence ensures the reliability of the final mechanisms not only in the empirical but also the formal aspects. This paper first explores how the researchers use quantity to check the effectiveness of interventions, where they at the same time determine the validity of the difference-making revealed by the results of interventions. Then, this paper draws a distinction between experimental interventions and experimental techniques, so that the reliability of mechanisms, as supported by the variety of evidence, can be safely ensured in the probabilistic sense. The latter process is where the researchers establish evidence of the mechanisms connecting the events of interest. By using case studies, this paper proposes to use ‘intervention’ as the fruitful connecting point of literature between evidence and mechanisms. (shrink)

In this paper, we adopt a physiological perspective in order to produce an intelligible overview of biological transmission in all its diversity. This allows us to put forward the analysis of transmission mechanisms, with the aim of complementing the usual focus on transmitted factors. We underline the importance of the structural, dynamical, and functional features of transmission mechanisms throughout organisms’ life cycles in order to answer to the question of what is passed on across generations, how and why. On this (...) basis, we propose a vision of biological transmission as networks of heterogeneous physiological mechanisms, not restricted to transmission mechanisms stricto sensu. They prove to be themselves suited candidates for evolutionary explanations. They are processes both necessary for evolution to happen and resulting themselves from evolution. This leads us to call for a strategy of endogenization to account for transmission, and more specifically inheritance, as evolved and evolving physiological mechanisms. (shrink)

Volume 32, Issue 1, March 2019, Page 69-72.

Stemming from the realization of the importance of the role of explanation in the science classroom, the Next Generation Science Standards call for appropriately supporting students to learn science, argue from evidence, and provide explanations. Despite the ongoing emphasis on explanations in the science classroom, there seems to be no well-articulated framework that supports students in constructing adequate scientific explanations, or that helps teachers assess student explanations. Our motivation for this article is twofold: First, we think that the ways in (...) which researchers in science education have studied scientific explanation are, at best, leaves much to be desired and, at worst, simply incomplete. Second, we believe that research about the teaching and learning of explanation in science classrooms must be guided by explicit models or frameworks that specify elements involved in constructing explanations particularly applicable to science. More importantly, we think that the development of such models or guidelines should be based on theoretical and philosophical foundations. In order to develop these frameworks or guidelines, we first outline and clarify models of scientific explanation developed by philosophers of science over the last few decades. In the second section of this article, we present a more recent philosophical work on scientific explanation, the pragmatic approach to studying scientific explanations. This approach suggests a toolbox for analyzing scientists’ scientific explanations, which provides a useful instrument to science education. In Section 3, we discuss the ways by which the previous two sections are useful in developing a K-12 scientific explanation schema. Implications for future research on students’ explanations are discussed. (shrink)

In recent years, the ‘mechanistic view’ has developed as a popular alternative to the ‘semantic view’ concerning the identity of physical computation. However, semanticists have provided powerful arguments that suggest the mechanistic view fails to deliver essential distinctions between paradigmatic computational operations. This article reviews responses on behalf of the mechanist and uses this opportunity to propose a type of pluralism about computational identity. This pluralism contends that there are multiple ‘levels’ of properties and relations pertaining to computation that can (...) inform different kinds of individuation. As such, for the pluralist, there are multiple legitimate ways of classifying computation, depending on the nature of the system in question, and one’s own epistemic priorities. (shrink)

In contemporary philosophy of science, the consensus view seems to be that scientific explanations describe mechanisms responsible for the phenomena to be explained. Two kinds of explanatory relevance figure in mechanistic accounts of explanation: causal and constitutive. Following prominent accounts, it seems natural to analyze both these relations in terms of systematic interventions into some factor X with respect to another factor Y. However, such interventions are tailored to uncover causal relations only. Construing the constitutive relationship between parts and wholes (...) in terms of interventions thus raises metaphysical, conceptual, and epistemological questions. We here review the barriers that intervention-based inquiry into mechanisms encounters and consider some solutions. (shrink)

The terms “perspectivism” and “perspectivalism” have been the focus of an intense philosophical discussion with important repercussions for the debate about the role of mechanisms in scientific explanations. However, leading exponents of the new mechanistic philosophy have conceded more than was necessary to the radically subjectivistic perspectivalism, and fell into the opposite error, by retaining not negligible residues of objectivistic views about mechanisms. In order to remove this vacillation between the subjective-cultural and the objective-natural sides of mechanisms, we shall raise (...) the question about theory-ladenness over again and interpret it in its connection with the technical–experimental nature of scientific knowledge, as affirming the perspectival character of scientific knowledge: It is because of the character at once theory-laden and practice-laden, i.e. technique-laden, of our putting questions to nature that empirical reality must be investigated from particular perspectives: nature can be known scientifically only from a potentially infinite number of perspectives or theoretical points of view, concretely exemplified by mechanisms or experimental ‘machines’ that allow specific access to specific aspects of sensible reality. (shrink)

Computational explanations focus on information processing required in specific cognitive capacities, such as perception, reasoning or decision-making. These explanations specify the nature of the information processing task, what information needs to be represented, and why it should be operated on in a particular manner. In this article, the focus is on three questions concerning the nature of computational explanations: What type of explanations they are, in what sense computational explanations are explanatory and to what extent they involve a special, “independent” (...) or “autonomous” level of explanation. In this paper, we defend the view computational explanations are genuine explanations, which track non-causal/formal dependencies. Specifically, we argue that they do not provide mere sketches for explanation, in contrast to what for example Piccinini and Craver :283–311, 2011) suggest. This view of computational explanations implies some degree of “autonomy” for the computational level. However, as we will demonstrate that does not make this view “computationally chauvinistic” in a way that Piccinini or Kaplan :339–373, 2011) have charged it to be. (shrink)

Much of the philosophical discussion of explanations has centered around two broad conceptions of what sorts of ‘things’ explanations are – namely, the descriptive and ontic conceptions. Defenders of each argue that scientific psychology has at best little to contribute to the study of explanations. These anti-psychologistic arguments come in two main varieties, the metaphysical and the epistemic. Both varieties trace back to Hempel and recur in the more recent writings of prominent mechanists. The metaphysical arguments attempt to combat psychologism (...) about explanation by doubly dissociating explanations themselves from the commonly associated phenomenology of explanation. The epistemic arguments attempt to combat psychologism by doubly dissociating the good-making features of explanations from the CAPE. I focus on the metaphysical arguments here, for only they directly concern the nature of, as opposed to the evaluation conditions for, explanations. The metaphysical arguments are all shown to equivocate over ‘explanation’, and thus they are no threat to the proposal that sometimes explanations just are psychological events. Such arguments do, nonetheless, gesture towards something important – namely, that the CAPE is neither necessary nor sufficient for explanation in any sense. Similar considerations lead another latter-day mechanist to conclude that no phenomenology of any sort is essential for explanation. What all of these thinkers overlook, however, is that there is another important phenomenological correlate of explanation – namely, finding a happening intelligible or understanding how or why-possibly – and it may well be of the very essence of explanation, at least in the psychological sense. I propose a psychomechanistic theory of intelligibility that accounts for how we can, through finite means, make boundless, endlessly qualified inferences about the implications of our explanations. In closing I note that, although finding a happening intelligible does sometimes engender an unjustified confidence in correctness, we should not overlook how attaining this state does at least put us in a kind of epistemic base camp. I also hint at how intelligibility could turn out to be the ultimate source of the descriptive and ontic conceptions of explanation. (shrink)

The mechanistic and causal accounts of explanation are often conflated to yield a ‘causal-mechanical’ account. This paper prizes them apart and asks: if the mechanistic account is correct, how can causal explanations be explanatory? The answer to this question varies according to how causality itself is understood. It is argued that difference-making, mechanistic, dualist and inferentialist accounts of causality all struggle to yield explanatory causal explanations, but that an epistemic account of causality is more promising in this regard.

Ever since Wesley Salmon’s theory, the mechanical approach to causality has found an increasing number of supporters who have developed it in different directions. Mechanical views such as those advanced by Stuart Glennan, Jim Bogen and Peter Machamer, Lindley Darden and Carl Craver have met with broad consensus in recent years. This paper analyses the main features of these mechanical positions and some of the major problems they still face, referring to the latest debate on mechanisms, causal explanation and the (...) relationship between mechanisms and counterfactuals. I shall claim that the mechanical approach can be recognised as having a fundamental explanatory power, whereas the counterfactual approach, recently developed mainly by Jim Woodward and essentially linked to the notion of intervention, has an important heuristic role. Claiming that mechanisms are by no means to be seen as parasitic on counterfactuals or less fundamental than them – as it has been recently suggested –, and that yet counterfactuals can play a part in a conceptual analysis of causation, I shall look for hints in support of the peaceful coexistence of the two. (shrink)

This article addresses the question of whether we should conceive of mechanisms as productive of change in a regular way. I argue that, if mechanisms are characterized as fully regular, on the one hand, then not enough processes will count as mechanisms for them to be interesting or useful. If no appeal to regularity is made at all in their characterization, on the other hand, then mechanisms can no longer be useful for grounding prediction and supporting intervention strategies. I conclude (...) that, if the New Mechanistic Philosophy is to be successful, a stochastic characterization of mechanisms must be adopted. (shrink)

Most philosophical accounts of causation take causal relations to obtain between individuals and events in virtue of nomological relations between properties of these individuals and events. Such views fail to take into account the consequences of the fact that in general the properties of individuals and events will depend upon mechanisms that realize those properties. In this paper I attempt to rectify this failure, and in so doing to provide an account of the causal relevance of higher-level properties. I do (...) this by critiquing one prominent model of higher-level properties—Kim’s functional model of reduction—and contrasting it with a mechanistic approach to higher-level properties and causation. (shrink)