An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result (...) in divergent explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

Between 1940 and 1970 pioneers in the new field of cell biology discovered the operative parts of cells and their contributions to cell life. They offered mechanistic accounts that explained cellular phenomena by identifying the relevant parts of cells, the biochemical operations they performed, and the way in which these parts and operations were organised to accomplish important functions. Cell biology was a revolutionary science but in this book it also provides fuel for yet another revolution, one that focuses on (...) the very conception of science itself. Laws have traditionally been regarded as the primary vehicle of explanation, but in the emerging philosophy of science it is mechanisms that do the explanatory work. Bechtel emphasises how mechanisms were discovered, focusing especially on the way in which new instruments made these inquiries possible. He also describes how new journals and societies provided institutional structure to this new enterprise. (shrink)

Between 1940 and 1970 pioneers in the new field of cell biology discovered the operative parts of cells and their contributions to cell life. They offered mechanistic accounts that explained cellular phenomena by identifying the relevant parts of cells, the biochemical operations they performed, and the way in which these parts and operations were organised to accomplish important functions. Cell biology was a revolutionary science but in this book it also provides fuel for yet another revolution, one that focuses on (...) the very conception of science itself. Laws have traditionally been regarded as the primary vehicle of explanation, but in the emerging philosophy of science it is mechanisms that do the explanatory work. Bechtel emphasises how mechanisms were discovered, focusing especially on the way in which new instruments made these inquiries possible. He also describes how new journals and societies provided institutional structure to this new enterprise. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic or epistemic norms. Still, mechanistic philosophers on both sides agree that there is no sharp distinction between the processes of discovery and explanation. Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic (...) and epistemic accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Autoinhibition is a design principle realized in many molecular mechanisms in biology. After explicating the notion of a design principle and showing that autoinhibition is such a principle, we focus on how researchers discovered instances of autoinhibition, using research establishing the autoinhibition of the molecular motors kinesin and dynein as our case study. Research on kinesin and dynein began in the fashion described in accounts of mechanistic explanation but, once the mechanisms had been discovered, researchers discovered that they exhibited a (...) second phenomenon, autoinhibition. The discovery of autoinhibition not only reverses the pattern in terms of which philosophers have understood mechanism discovery but runs counter to the one phenomenon-one mechanism principle assumed to relate mechanisms and the phenomena they explain. The ubiquity of autoinhibition as a design principle, therefore, necessitates a philosophical understanding of mechanisms that recognizes how they can participate in more than one phenomenon. Since mechanisms with this design are released from autoinhibition only when they are acted on by control mechanisms, we advance a revised account of mechanisms that accommodates attribution of multiple phenomena to the same mechanism and distinguishes them from other processes that control them. (shrink)

When cognitive scientists are looking for the neural basis of consciousness or the computational processes underlying vision, what are they looking to find? I argue for a new account of this explanatory project in cognitive science (and the special sciences more generally) on which it is best understood on close analogy with causal explanation in the special sciences. Causal explanations cite causal difference-makers: they explain how certain events causally depend on other events. Generative explanations cite generative difference-makers: they (...) explain how certain events obtain in virtue of (and so are generated by) other events. I show how that this generative difference-making account (GDM) extends well regarded developments in philosophical work on causal explanation, helps to usefully clarify and expands the hypothesis space of a key project in cognitive science – that of understanding how underlying nonmental processes give rise to the mental, including consciousness. In section 2, I develop GDM by close analogy with causal explanation, and locate both of them as species of a more general project in the special sciences: that of discovering what makes a difference to what. In section 3, I motivate GDM by contrasting it with other rival accounts, including correlationist, identity, and grounding accounts, as well as Carl Craver’s constitutive relevance account. In section 4, I tie all of the strands of the preceding discussion together to show how GDM provides a new and compelling response to the so-called hard problem of consciousness. (shrink)

Pseudo‐mechanistic explanations in psychology and cognitive neuroscienceThis paper focuses on the level of systems/cognitive neuroscience. It argues that the great majority of explanations in psychology and cognitive neuroscience is “pseudo‐mechanistic.” On the basis of various case studies, Hommel argues that cognitive neuroscience should move beyond what he calls an “Aristotelian phase” to become a mature “Galilean” science seeking to discover actual mechanisms of cognitive phenomena.

If the decades of the forties through the sixties were dominated by discussion of Hempel's “covering law“ explication of explanation, that of the seventies was preoccupied with Salmon's “statistical relevance” conception, which emerged as the principal alternative to Hempel's enormously influential account. Readers of Wesley C. Salmon's Scientific Explanation and the Causal Structure of the World, therefore, ought to find it refreshing to discover that its author has not remained content with a facile defense of his previous investigations; on the (...) contrary, Salmon offers an original account of different kinds of explications, advances additional criticisms of various alternative theories, and elaborates a novel “two-tiered“ analysis of explanation that tacitly depends upon a “two-tiered” account of homogeneity. Indeed, if the considerations that follow are correct, Salmon has not merely refined his statistical relevance account but has actually abandoned it in favor of a “causal/mechanistic“ construction. This striking development suggests that the theory of explanation is likely to remain as lively an arena of debate in the eighties as it has been in the past. (shrink)

What kinds of norms constrain mechanistic discovery and explanation? In the mechanistic literature, the norms for good explanations are directly derived from answers to the metaphysical question of what explanations are. Prominent mechanistic accounts thus emphasize either ontic (Craver, in: Kaiser, Scholz, Plenge, Hüttemann (eds) Explanation in the special sciences: the case of biology and history, Springer, Dordrecht, pp 27–52, 2014) or epistemic norms (Bechtel in Mental mechanisms: philosophical perspectives on cognitive neuroscience, Routledge, London, 2008). Still, mechanistic philosophers (...) on both sides agree that there is no sharp distinction between the processes of discovery and explanation (Bechtel and Richardson in Discovering complexity. Decomposition and localization as strategies in scientific research, MIT Press, Cambridge, 2010; Craver and Darden in In search of mechanisms: discoveries across the life sciences, University of Chicago Press, Chicago, 2013). Thus, it seems reasonable to expect that ontic and epistemic accounts of explanation will be accompanied by ontic and epistemic accounts of discovery, respectively. As we will show here, however, recent discovery accounts implicitly rely on both ontic and epistemic norms to characterize the discovery process. In this paper, we develop an account that makes explicit that, and how, ontic and epistemic norms work together throughout the discovery process. By describing mechanism discovery as a process of pattern recognition (Haugeland, in: Having thought. Essays in the metaphysics of mind, Harvard University Press, Cambridge, pp 267–290, 1998) we demonstrate that scientists have to develop epistemic activities to distinguish a pattern from its background. Furthermore, they have to determine which epistemic activities successfully describe how the pattern is implemented by identifying the pattern’s components. Our approach reveals that ontic and epistemic norms are equally important in mechanism discovery. (shrink)

Costa and Shrapnel have recently proposed an interventionist theory of quantum causation. The formalism generalizes the classical methods of Pearl and allows for the discovery of quantum causal structure via localized interventions. Classical causal structure is presented as a special case of this more general framework. I introduce the account and consider whether this formalism provides a causal explanation for the Bell correlations.

If one had all the true particular statements that there are would such a collection be deficient for the purpose of science? In particular, would we still require a type of explanation that requires irreducible appeal to universals, and modalities. An argument to this conclusion is examined. In the situation envisaged, the realists needed distinctions such as between accidental and essential properties, or generalizations that are accidently true and those that are lawful and true, cannot be made. The argument then (...) is rejected: appeal to universals, and the need for explanations td tell us what might have happened, cannot be secured when science no longer need discover the truth about the world. Particulars maintain their claim to epistemological and ontological priority over universals. (shrink)

I examine the status of Aristotle’s science of soul and argue that it is trans-generic in the way that Aristotle's universal mathematics is. For just as the branches of the latter differ considerably, so too do the sciences of life: botany, zoology, psychology, and (in Aristotle’s view) astronomy and theology. Discovering the correct definition of soul, which is their starting point or first principle, as with other scientific starting points, involves both induction and dialectic. Induction uses scientific observation of (...) living things to move toward this starting point. Dialectic enables the scientist to assemble endoxa, or reputable beliefs, that allow us to solve each puzzle (aporia) that clouds our understanding (nous) of the starting point that induction enables us to reach. (shrink)

This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of constraints (...) are used in their design, and how their results are evaluated. It argues that the point of implementing networks roughly analogous to neural structures is to discover and explores the generic mechanisms at work in the brain, not to deduce the precise activities of specific structures. The chapter explores a formal analysis of the explanations offered, which interprets connectionist models and the cognitive theories they represent as sharing membership in a type of mechanism. (shrink)

In this essay we argue that the notion of machine necessarily includes its being designed for a purpose. Therefore, being a mechanical system is not enough for being a machine. Since the experimental scientific method excludes any consideration of finality on methodological grounds, it is then also insufficient to fully understand what machines are. Instead in order to understand a machine it is first required to understand its purpose, along with its structure, in clear parallel with Aristotle’s final and formal (...) causes. Obviously, purpose and structure are not machine components that can physically interact with other components; nonetheless they are essential to understanding their operation. This casts an interesting light on the relationship between mind and body: for just as an artifact’s finality and structure explain its operation, so also consciousness is the explanation—not the efficient cause—of specifically human behavior. What machines and human beings have in common is that, in order to understand them, it is necessary to appeal to the principle of finality. Yet while finality is given and extrinsic in the case of machines, we human beings are characterized by the ability to self-propose our own ends. Since the principle of finality is essential to understanding the production of machines, the traditional view in modern Western philosophy that finality lies beyond the scope of objective/scientific knowledge should be rectified to allow for a genuine science of the artificial. We think a correct understanding of final causality will overcome current resistance to this principle. (shrink)

ABSTRACT: I argue that there is no evidence that Epicurus dealt with the kind of free-will problem he is traditionally associated with; i.e. that he discussed free choice or moral responsibility grounded on free choice, or that the "swerve" was involved in decision processes. Rather, for Epicurus, actions are fully determined by the agent's mental disposition at the outset of the action. Moral responsibility presupposes not free choice but that the person is unforced and causally responsible for the action. This (...) requires the agent's ability to influence causally, on the basis of their beliefs, the development of their behavioral dispositions. The "swerve" was intended to explain the non-necessity of agency without undermining Epicurus' atomistic explanation of the order in the universe, viz. by making the mental dispositions of adults non-necessary. (shrink)

If we assume that consciousness is a natural biological phenomenon in the brain, should we expect the current brain sensing and imaging methods to somehow ‘discover’ consciousness? The answer depends on the following points: What kind of level of biological organization do we assume consciousness to be? What would count as the discovery of this level? What are the levels of organization from which the currently available research instruments pick signals and acquire data? Single-cell recordings, PET, fMRI, EEG and MEG (...) pick different types of signals from different levels of organization in the brain. However, it seems they do not manage to pick signals that would allow the direct visualization and reconstruction of the higher levels of electrophysiological organization that are crucial for the empirical discovery and theoretical explanation of consciousness. The message of the present paper is twofold: On the one hand, we should be aware of the practical limitations of the currently available methods of cognitive neuroscience and not read too much into the images produced by them. On the other hand, the present limitations could be overcome by more sophisticated methods in the future. Therefore, contrary to what several philosophers have argued, the empirical discovery of consciousness in the brain is not impossible in principle. (shrink)

A large part of the explainable AI literature focuses on what explanations are in general, what algorithmic explainability is more specifically, and how to code these principles of explainability into AI systems. Much less attention has been devoted to the question of why algorithmic decisions and systems should be explainable and whether there ought to be a right to explanation and why. We therefore explore the normative landscape of the need for AI to be explainable and individuals having a (...) right to such explanation. This exploration is particularly relevant to the medical domain where the (im)possibility of explainable AI is high on both the research and practitioners’ agenda. The dominant intuition overall is that explainability has and should play a key role in the health context. Notwithstanding the strong normative intuition for having a right to explanation, intuitions can be wrong. So, we need more than an appeal to intuitions when it comes to explaining the normative significance of having a right to explanation when being subject to AI-based decision-making. The aim of the paper is therefore to provide an account of what might underlie the normative intuition. We defend the ‘symmetry thesis’ according to which there is no special normative reason to have a right to explanation when ‘machines’ in the broad sense, make decisions, recommend treatment, discover tumors, and so on. Instead, we argue that we have a right to explanation in cases that involve automated processing that significantly affect our core deliberative agency and which we do not understand, because we have a general moral right to explanation when choices are made which significantly affect us but which we do not understand. (shrink)

Creatures that have different physical realizations than human beings may or may not be conscious. Ned Block’s ‘harder problem of consciousness’ is that naturalistic phenomenal realists have no conception of a rational ground for belief that they have or have not discovered consciousness in such a creature. Drawing on the notion of inference to the best explanation, it appears the arguments to these conclusions beg the question and ignore that explanation may be a guide to discovery. Thus, best explanation can (...) both validate an interpretation of the evidence and lead to the discovery of consciousness. (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)

In scientific explanations, the explanans theory is sometimes incommensurable with the explanandum empirical data. How is this possible, especially when the explanation is deductive in nature? This paper attempts to solve the puzzle without relying on any particular theory of reference. For us, it is rather obvious that the geometric idea of projection plays a key role in Keplers explanation of Tycho Brahes empirical data. We discover that a similar mechanism operates in theoretic explanations in general. In short, (...) all theoretic explanations are projective explanations. If so, there should be no logical reason why explanans theories cannot be incommensurable with explanandum data. For illustration, we analyse Einsteins explanation of the results of the Michelson–Morley experiment in some detail. (shrink)

A great deal of research has demonstrated that learning is influenced by the learner’s prior background knowledge (e.g. Murphy, 2002; Keil, 1990), but little is known about the processes by which prior knowledge is deployed. We explore the role of explanation in deploying prior knowledge by examining the joint effects of eliciting explanations and providing prior knowledge in a task where each should aid learning. Three hypotheses are considered: that explanation and prior knowledge have independent and additive effects on (...) learning, that their joint effects on learning are subadditive, and that their effects are superadditive. A category learning experiment finds evidence for a superadditive effect: explaining drives the discovery of regularities, while prior knowledge constrains which regularities learners discover. This is consistent with an account of explanation’s effects on learning proposed in Williams & Lombrozo (in press). (shrink)

This paper presents a new approach to resolving an apparent tension in Descartes’ discussion of scientific theories and explanations in the Principles of Philosophy. On the one hand, Descartes repeatedly claims that any theories presented in science must be certain and indubitable. On the other hand, Descartes himself presents an astonishing number of speculative explanations of various scientific phenomena. In response to this tension, commentators have suggested that Descartes changed his mind about scientific theories having to be certain (...) and indubitable, that he lacked the conceptual resources to describe the appropriate epistemic attitude towards speculative theories, or that the presence of geometrical principles in these explanations guarantee their certainty. I argue that none of these responses is satisfactory and suggest a different resolution to the tension by examining Descartes’ notion of explanation. On Descartes’ view, providing an adequate explanation does not require being certain of the theories that constitute the explanans. Relatedly, the purpose of Cartesian explanations is not to discover the truth about the various underlying mechanisms that such explanations appeal to, but to support his general philosophical thesis that all natural phenomena can be explained by appealing to the extension of matter. (shrink)

In recent work, Andrew T. Forcehimes and Robert B. Talisse correctly note that G.A. Cohen’s fact-insensitivity thesis, properly understood, is explanatory. This observation raises an important concern. If fact-insensitive principles are explanatory, then what role can they play in normative deliberations? The purpose of my paper is, in part, to address this question. Following David Miller, I indicate that on a charitable understanding of Cohen’s thesis, an explanatory principle explains a justificatory fact by completing an otherwise logically incomplete inference. As (...) a result, the explanatory role such a principle plays is inseparable from its status as a (not necessarily successful) justificatory reason. With this interpretation in hand, I then proceed to argue that Lea Ypi’s and Robert Jubb’s recent criticisms fail to undermine Cohen’s thesis, and that fact-insensitive principles, once discovered, are especially helpful for purposes of deliberation in circumstances characterized by changing and changeable feasibility constraints. (shrink)

It is argued that de Vries did not see Mendel's paper until 1900, and that, while his own theory of inheritance may have incorporated the notion of independent units, this pre-Mendelian formulation was not the same as Mendel's since it did not apply to paired hereditary units. Moreover, the way in which the term ‘segregation’ has been applied in the secondary literature has blurred the distinction between what is explained and the law which facilitates explanation.

The assumption that commonsense psychological explanations of behavior are causal underlies current debate between simulation theory and theory theory regarding the nature of cognitive mechanism responsible for our folk psychological practices. Theory theorists claim that these explanations are subsumed by the covering law model of causal explanation. Simulationists are not explicit about the nature of the explanations produced by simulation. In what follows, I propose a set of plausible conditions that a correct causal simulation-produced folk psychological explanation (...) will satisfy and point out two prima facie problems. In discussing a possible solution, I discover that the latter incurs the need for some sort of causal law. An examination of two likely candidates for these laws reveals that neither is capable of playing the role required. I then suggest alternative routes that simulationists might explore in order to provide simulation theory with a sorely-needed account of the nature of the explanations produced by simulation. (shrink)

This work is mainly concerned with the notion of hierarchical modularity and its use in explaining structure and dynamical behavior of complex systems by means of hierarchical modular models, as well as with a concept of my proposal, antimodularity, tied to the possibility of the algorithmic detection of hierarchical modularity. Specifically, I highlight the pragmatic bearing of hierarchical modularity on the possibility of scientific explanation of complex systems, that is, systems which, according to a chosen basic description, can be considered (...) as composed of elementary, discrete, interrelated parts. I stress that hierarchical modularity is also required by the experimentation aimed to discover the structure of such systems. Algorithmic detection of hierarchical modularity turns out to be a task plagued by the demonstrated computational intractability of the search for the best hierarchical modular description, and by the high computational expensiveness of even approximated detection methods. Antimodularity consists in the lack of a modular description fitting the needs of the observer, a lack due either to absence of modularity in the system’s chosen basic description, or to the impossibility, due to the excessive size of the system under assessment in relation to the computational cost of algorithmic methods, to algorithmically produce a valid hierarchical description. I stress that modularity and antimodularity depend on the pragmatic choice of a given basic description of the system, a choice made by the observer based on explanatory goals. I show how antimodularity hinders the possibility of applying at least three well-known types of explanation: mechanistic, deductive-nomological and computational. A fourth type, topological explanation, remains unaffected. I then assess the presence of modularity in biological systems, and evaluate the possible consequences, and the likelihood, of incurring in antimodularity in biology and other sciences, concluding that this eventuality is quite likely, at least in systems biology. I finally indulge in some metaphysical and historical speculations: metaphysically, antimodularity seems to suggest a possible position according to which natural kinds are detected modules, and as such, due to the computational hardness of the detection of the best hierarchical modular description, they are unlikely to be the best possible way to describe the world, because the modularity of natural kinds quite probably does not reflect the best possible modularity of the world. From an historical point of view, the growing use of computational methods for modularity detection or simulation of complex systems, especially in certain areas of scientific research, hints at the envisioning of a multiplicity of emerging scientific disciplines guided by a self- sustained, growing production of possibly human-unintelligible explanations. This, I suggest, would constitute an historical change in science, which, if has not already occurred, could well be on the verge of happening. (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)

According to conceptual reductive accounts, if properties of one domain can be conceptually reduced to properties of another domain, then the former properties are ontologically reduced to the latter properties. I will argue that conceptual reductive accounts face problems: either they do not recognise that many higher-level properties are correlated with multiple physical properties, or they do not clarify how we can discover new truthmakers of sentences about a higher-level property. Still, there is another way to motivate ontological reduction, the (...) truthmaker reductive explanations (TRE). TRE can be given by using resources from John Heil’s truthmaker theory and the a priori entailment view or the a posteriori entailment view. I will argue that we can give these truthmaker reductive explanations if there are various less-than-perfectly similar physical properties that can be the truthmakers of sentences about higher-level properties and the physical similarity between them can explain why an irreducible higher-level property is not needed. (shrink)

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

It seems that the above account of explanation-strategy in the area of temperature adaptation underscores many of the points made earlier. First, it discloses the fruitful interaction of classical, evolutionary, and molecular approaches. Secondly, it indicates that biological characterizations are not rival accounts to chemical ones. Thirdly, it stresses the importance of the DNA sequence order in chemical explanations of biological organisms.One feature which this area does not seem to reveal, which genetics does, is the development of a biological (...) (that is, non-chemical) theory which would explain temperature adaptation in nonchemical terms. What this shows is that one should not always expect systematization at the biological level, in the sense of being able to refer many phenomena to a small number of basic principles. The future development of laws and theories in biological terms in this area is, of course, not precluded by the fact that none have yet been discovered. The case does indicate that physical and chemical language and theories are extremely useful in biological investigations, and the physicochemical approach may well be the best approach in this area. In any event, ultimate reduction of the biological to the chemical seems to be a foregone non-controversial conclusion here as elsewhere. (shrink)

Explanatory problems in the philosophy of neuroscience are not well captured by the division between the radical and the trivial neuron doctrines. The actual problem is, instead, whether mechanistic biological explanations across different levels of description can be extended to account for psychological phenomena. According to cognitive neuroscience, some neural levels of description at least are essential for the explanation of psychological phenomena, whereas, in traditional cognitive science, psychological explanations are completely independent of the neural levels of description. (...) The challenge for cognitive neuroscience is to discover the levels of description appropriate for the neural explanation of psychological phenomena. (shrink)

There are two reasons for asking such an apparently unanswerable question. First, Max Born's recollections of what Minkowski had told him about his research on the physical meaning of the Lorentz transformations and the fact that Minkowski had created the full-blown four-dimensional mathematical formalism of spacetime physics before the end of 1907, both indicate that Minkowski might have arrived at the notion of spacetime independently of Poincare and at a deeper understanding of the basic ideas of special relativity independently of (...) Einstein. So, had he lived longer, Minkowski might have employed successfully his program of regarding four-dimensional physics as spacetime geometry to gravitation as well. Moreover, Hilbert had derived the equations of general relativity simultaneously with Einstein. Second, even if Einstein had arrived at what is today called Einstein's general relativity before Minkowski, Minkowski would have certainly reformulated it in terms of his program of geometrizing physics and might have represented gravitation fully as the manifestation of the non-Euclidean geometry of spacetime exactly like he reformulated Einstein's special relativity in terms of spacetime. [NOTE: The text of two explanations was taken from my previous paper "Is Gravitation Interaction or just Curved-Spacetime Geometry?" because those explanations had to be repeated in the present paper anyway.]. (shrink)

Will we ever discover a single scientific theory that explains everything that has ever happened and everything that will happen - a key that unlocks the ...

Corroborative evidence may be understood as having two epistemic effects: a primary effect by which it offers direct evidence for some claim, and a secondary effect by which it bolsters the appraised probative, or evidential, value of some other piece of evidence for that claim. This paper argues that the bolstering effect of corroborative evidence is epistemically legitimate because corroboration provides a reason to count the belief based on the initial evidence as sensitive to, and safe from, defeat in a (...) way that it was not previously recognized to be. Discovering that our initial evidence tracks the truth in a way we previously did not recognize provides a reason to positively reappraise the probative value of that evidence. The final section of the paper relates the proposed sensitivity- and safety-based account of corroboration to an explanation-based account. (shrink)

This chapter focuses on the experimental practices and reasoning strategies employed in nineteenth century investigations on the causal origin of the phenomenon of Brownian movement. It argues that there was an extensive and sophisticated experimental work done on the phenomenon throughout the nineteenth century. Investigators followed as rigorously as possible the methodological standards of their time to make causal claims and advance causal explanations of Brownian movement. Two major methodological strategies were employed. The first was the experimental strategy of (...) varying the circumstances. Suspected causal factors were varied and the resulting effect on Brownian movement was studied. The main goal of this strategy was the identification of difference-making factors, i.e., factors having a causal influence on the phenomenon. The second was the so-called method of hypothesis. Rather than relying exclusively on the ability of experiments to identify difference-making factors, its proponents tried to show how the independently developed tenets of the new kinetic-molecular conception of matter provided a plausible causal explanation of Brownian movement. Each one of these methodological strategies had its distinctive practices and notions of control. None of these strategies could, on its own, establish molecular motion as the cause of Brownian movement. It was only the fruitful combination of the two strategies and of their accompanying practices and notions of control that led, at the end of the nineteenth century, to the recognition of molecular motion as the most probable cause of Brownian movement. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Ideas 62.3 (2001) 445-462 [Access article in PDF] Hobbes on Explanation and Understanding Ioli Patellis One aspect of Hobbes's philosophy of science which has not been explored in any depth is his view of the explanatory import of science. This is possibly because of the important passages in Hobbes awarding primary, if not sole, significance to the practical dimension of scientific knowledge, i.e., to (...) science as a means of controlling and manipulating nature, an emphasis which is in tune with the general prudential tenor of Hobbes's philosophy. In addition the Hobbesian view of explanation seems straightforward enough: one explains a phenomenon when one assigns a cause to it. On the face of it there does not seem much more to be said about the matter. Hobbesian science explains because it provides knowledge of causes and causes explain because that is what causes do by definition. A problem with the explanatory force of causes arises when one surveys the great variety of conceptions of causality which have been proposed. Do they all have explanatory value and for the same reasons? Are Aristotelian causes and Humean ones of equal explanatory import and in virtue of the same traits? The case of Hobbes is of particular interest in this respect because in his work we find discussions not only of his own notion of causality as a species of efficient cause but also of Scholastic causes, deriving from Aristotle, and of prudence which is based on Humean-type regularities. One can then ask why it is that, according to Hobbes, the type of causes he espouses explain, while the other varieties do not. In attempting to answer this question I shall suggest that one of the reasons they do so is because they afford a particular sort of understanding. This in turn will provide a link with contemporary discussions of the relations between explanation and understanding, and in particular it will provide some insight into the kind of understanding afforded by systematicity by construing understanding as the integration of what is to be explained within one's body of knowledge.In this paper I attempt to piece together from hints in Hobbes's work a Hobbesian theory of explanation as understanding of a certain sort. In the first [End Page 445] section I argue that, notwithstanding his official pronouncements to the contrary, Hobbes does believe that science has theoretical as well as practical value. The second section discusses two counts on which Hobbes finds School science non-explanatory: the causes it posits are not productive processes in Hobbes's sense and the language it employs is often meaningless. The third section discusses the latter accusation in relation to Hobbes's views on understanding. The fourth section reverts to the former accusation by way of a discussion of Hobbes's views on prudence and the curiosity which leads to the positing of God as the first cause. The last brings together the results of the former two sections and suggests that Scholastic causes have no explanatory value because they do not provide understanding of the required kind. It further briefly attempts to link the latter with recent unification theories of explanation.I. "The goal or scope of philosophy is that we might be able to use previously observed effects for our benefit or that, after the effects have been discovered by the mind, similar effects might be produced through the application of bodies to bodies by the industry of men, insofar as human power and the matter of things will allow for the use of human life." 1 In this passage from De Corpore the goal of science is restricted to the prediction and manipulation of nature. This goal is eminently compatible with the Hobbesian view of science as providing infallible rational knowledge ofcausal relations. First, science provides knowledge of effects given their causes and of possible causes given their effects. 2 For according to Hobbes, a cause is the (manner of) generation or production of effects. 3 "For you enquire not so much, when you see a change... (shrink)

The ninth and tenth chapters of the Origin mark a profound, if perhaps difficult to detect, shift in the book’s argumentative structure. In the previous few chapters and in the ninth, Darwin has been exploring a variety of objections to natural selection, some more obvious (where are all the fossils of transitional forms?) and some showing careful attention to challenging consequences of evolution (could selection really produce instincts?). Starting in the tenth, however, Darwin turns to showing us what kinds of (...) new and unexpected results evolutionary theory might be able to offer us, again in domains both predictable (extinction) and unexpected (biogeography, embryology). It is notable that it is the fossil record that serves as this pivot point, being both a source of potentially powerful objections to evolutionary theory and home to some of its most compelling new explanations. In this chapter, I present both sets of arguments and consider what role Darwin gave to fossil evidence, in the process attempting to discover why it might have played this unique role in two different parts of Darwin’s “long argument” for evolution by natural selection. Geology’s special place, I argue, derives at least in part from its particular importance in Darwin’s social and intellectual context. (shrink)

Alvin Goldman's early work in action theory and theory of knowledge was a major influence on my own thinking and writing about emotions. For that reason and others, it was a very happy moment in my professional life when I learned, in 1988, that in his presidential address to the Society for Philosophy and Psychology Goldman endorsed and defended the “simulation” theory I had put forward in a 1986 article. I discovered afterward that we share a strong conviction that empirical (...) evidence is relevant to a full assessment of the theory. We both find the burgeoning evidence from cognitive neuroscience to be of particular interest, I believe, in part because it makes possible a major departure for the philosophy of mind: turning its attention from " the neural basis of mental states, " to. (shrink)

In the works which constitute his distinguished contribution to philosophy, Emile Meyerson has advanced and defended the opinion that scientific explanation consists in transforming empirically discovered natural laws into statements of identity in time. This contention, which it is the purpose of the present paper to examine, is of great interest both on its own account and by reason of its intimate connection with Meyerson's central thesis that all thought consists essentially in similar processes of identification. Indeed so intimate is (...) this connection that our present inquiry although it deals explicitly only with the nature of scientific explanation nevertheless constitutes by implication an examination of the more general thesis also, for if scientific explanation is not a process of identification then obviously not all thought is identificatory. (shrink)

Turing patterns are a class of minimal mathematical models that have been used to discover and conceptualize certain abstract features of early biological development. This paper examines a range of these minimal models in order to articulate and elaborate a philosophical analysis of their epistemic uses. It is argued that minimal mathematical models aid in structuring the epistemic practices of biology by providing precise descriptions of the quantitative relations between various features of the complex systems, generating novel predictions that can (...) be compared with experimental data, promoting theory exploration, and acting as constitutive parts of empirically adequate explanations of naturally occurring phenomena, such as biological pattern formation. Focusing on the roles that minimal model explanations play in science motivates the adoption of a broader diachronic view of scientific explanation. (shrink)

In a contribution to a symposium on “Causality in the Social Sciences,” Lewis Feuer remarks in passing that “Functionalism, in the form which Malinowski gave it, affirms that culture is an ‘organic unity’; it is the principle that in every culture, each custom, belief, and behavioral form ‘represents an indespensible part within a working whole.’” That culture is an integrated and organic unity is a view found quite often in the writings of Malinowski, though he does not maintain it with (...) full consistency. Sometimes he seems to say that a culture is made up of a number of grand institutions, each of which is an integrated unity, though the totality need not be. But whether affirmed or denied this thesis is not the doctrine, characteristic of almost every attempt made by Malinowski to explain cultural phenomena during the last quarter century of his life, which may be properly called “Malinowskian functionalism.” And that the doctrine cited by Feurer is sometimes formulated in such a way that the term “functional” or some variant is used, as in “culture is a functionally integrated organic unity,” makes no difference to my claim. I am not at all certain that the addition of the word “functionally” does anything for the view in question; it surely makes no difference to its scientific sterility. But that apart, the point I wish to make here is that a good many scholars who make references to Malinowski's functionalism have never bothered to discover just what in the world this is. They have been all too ready to cite the vague programmatic pronouncements in which Malinowski's writings abound, while seemingly oblivious to the fact that with rare exception every major attempt by Malinowski to account systematically for some kind of sociocultural phenomena reveals one and the same pattern of explanation. And it is this pattern alone which may be spoken of as Malinowski's method of functional analysis. (shrink)

Jeffrey has devised a probability revision method that increases the probability of hypothesis H when it is discovered that H implies previously known evidence E. A natural extension of Jeffrey's method likewise increases the probability of H when E has been established with sufficiently high probability and it is then discovered, quite apart from this, that H confers sufficiently higher probability on E than does its logical negation H̄.

Since its origins as a distinct philosophical discipline during the first quarter of the present century, philosophy of science has been largely a matter of logical analysis. Only in relatively recent times have the historically minded philosophers attacked the logical empiricist account of the scientific enterprise. Among the pioneers of this revolution, however, Kuhn, along with Popper and Feyerabend, have also challenged the idea that a linear growth in scientific knowledge is either possible or desirable. Though partial to the historical (...) approach, Wallace will not accept this negativistic thesis. On the other hand, his conviction that most scientists are realists at heart makes him reject the positivistic account of science proposed by Duhem, Mach and the Vienna Circle. Science, he claims, aims at discovering the truth about the world. It is not content simply to describe how things are, but wants to know why. Now it is in their search for causes that scientists reveal their realistic convictions. And it is because science throughout its long history has made causal explanations the paradigm of scientific knowledge that its growth is not the haphazard or irrational sort of thing that Kuhn envisions. In this first of a proposed two-volume work, Wallace traces the origins of this paradigm to Aristotle’s Posterior Analytics. Part I, ‘Medieval Science', sketches the influence of this work on medieval scientists at the universities of Oxford, Paris and Padua from the 13th to the 16th centuries. Part II, entitled ‘Early Classical Science', shows how the earlier paradigm continues to influence such men as Gilbert, Kepler, Galileo, Harvey and Newton. Both historians and philosophers can profit from this original and controversial study. Even those who would not accept the author’s thesis that "the reconstruction of contemporary philosophy of science necessitates a return to some of the values of medieval and early classical science" will be grateful for the great amount of factual information and wealth of primary source material the book provides. We look forward to the publication of the companion volume on "Classical and Contemporary Science."—A. B. W. (shrink)

It is one of Karl Popper's great distinctions that he has an intense—some would say too intense—awareness of the history of philosophy within which he works. He knows not only its patterns, but also its comedies, and sometimes he plays rhetorically against their grain. He knows, for example, that the drive to consistency tends to turn philosophy into compositions of related doctrines, each seeming to involve the others. Religious belief, for example, tends to go with idealism and free will, religious (...) scepticism with materialism and determinism. Popper does not believe in a religion, was for long some kind of a socialist, and takes his bearings from the philosophy of science. Aha! it seems we have located him. Here is a positivist, a materialist, probably a determinist. But of course he denies he is any of these things. Again, like many modern thinkers, he wants to extend scientific method not only to the social sciences but also to history. So far so familiar, until we discover that he regards nature as no less ‘cloudy’ than human societies. (shrink)

EDITOR’S ABSTRACTThis paper argues that we cannot determine with certainty the sequence of the two parts of the Laozi text: “Way” and “Virtue”. These two parts were originally written independently by Lao Zi and in an uncertain chronological order. They originally circulated separately, and were later combined differently by various editors. Thus emerged the two Laozi versions: The one with “Way” preceding “Virtue” has dominated the transmission; the alternative order can be retrieved from recently discovered sources such as the Mawangdui (...) Silk Manuscripts and Han Bamboo Slips. (shrink)

Many philosophers insist that moral facts or properties play no role in explaining (non-normative) natural phenomena. The problem of moral explanation has raised metaphysical, semantic and epistemic challenges to contemporary moral realism. In my dissertation, I attempt to vindicate the explanatory efficacy of moral properties, while at the same time respecting the autonomy and normativity of morality. In doing so, I will advocate a sort of non-reductive ethical naturalism, according to which moral properties are natural properties (in the sense that (...) they are open to empirical investigation), and yet remain irreducible to non-normative natural properties, such as psychological, biological, and sociological properties. More specifically, I develop a form of moral functionalism to vindicate moral explanation. Moral functionalism understands moral properties as second-order, functional properties, the natures of which are characterized in terms of functional roles. My version of moral functionalism has two particular features. First, it is a form of a posteriori moral functionalism. The moral theory used to characterize the functional roles of moral properties can only be discovered by appeal to empirical investigation (for this reason, my a posteriori version of moral functionalism is a sort of ethical naturalism). Second, it is a holistic version of moral functionalism. The functional role of a moral property cannot be identified solely in terms of non-normative properties; rather, it essentially involves a network of connections to both non-normative and normative properties (for this reason, my holistic version of moral functionalism is a sort of ethical anti-reductionism). It is important to note that the question of whether moral properties have explanatory power bears on the question of what explanation or explanatoriness is. In my dissertation, I attempt to show that my vindication of moral explanation presupposes a plausible account of explanation. Obviously I cannot discuss all theories of explanation; my dissertation will rather focus on two influential accounts of explanation: the causalist model and the unificationist model. Then I argue that moral properties understood as functional properties of the kind I have described can do causal-explanatory work and play a distinctive unifying role. (shrink)

Hume’s Science of Human Nature is an investigation of the philosophical commitments underlying Hume's methodology in pursuing what he calls ‘the science of human nature’. It argues that Hume understands scientific explanation as aiming at explaining the inductively-established universal regularities discovered in experience via an appeal to the nature of the substance underlying manifest phenomena. For years, scholars have taken Hume to employ a deliberately shallow and demonstrably untenable notion of scientific explanation. By contrast, Hume’s Science of Human Nature sets (...) out to update our understanding of Hume’s methodology by using a more sophisticated picture of science as a model. (shrink)

A mechanistic explanation is a desired outcome in many studies of perception. Such explanations require discovering the processes that contribute to the realization of a perceptual phenomenon at different levels of information processing. The present analysis aims at investigating the obstacles to develop such mechanistic explanations and their potential solutions in the context of psychopathology. Geometric-Optical Illusions (GOIs) are among perceptual phenomena that have been studied to understand psychopathology in various clinical populations. In the present analysis, the (...) GOIs will be discussed as a methodological tool in the investigation of the mechanistic approach to understanding perception in such populations. I will argue that the fact that most such studies are mainly focused on the investigation of susceptibility to GOIs can only lead to classifying such phenomena or to reporting some correlations between different aspects of such phenomena with certain brain activities, without formulating a mechanistic explanation. The investigation of the variables that mediate perceptual phenomena at earlier stages of information processing is required for the formulation of such explanations. Thus, the objective of the present analysis is twofold: 1) showing how ignoring such variables have prevented the formulation of mechanistic explanations of responses to GOIs in clinical populations, such as schizophrenia and autism, 2) analyzing the methodological aspects of an effective approach in which a top-down guidance based on phenomenological insights (the first-person data) along with the insights provided by the existing theories help overcome obstacles in developing mechanistic explanations of GOIs. (shrink)