Using Jim Woodward's Counterfactual Dependency account as an example, I argue that causal claims about indeterministic systems cannot be satisfactorily analysed as including counterfactual conditionals among their truth conditions because the counterfactuals such accounts must appeal to need not have truth values. Where this happens, counterfactual analyses transform true causal claims into expressions which are not true.

How do people understand questions about cause and prevent? Some theories propose that people affirm that A causes B if A's occurrence makes a difference to B's occurrence in one way or another. Other theories propose that A causes B if some quantity or symbol gets passed in some way from A to B. The aim of our studies is to compare these theories' ability to explain judgements of causation and prevention. We describe six experiments that compare judgements for causal (...) paths that involve a mechanism, i.e. a continuous process of transmission or exchange from cause to effect, against paths that involve no mechanism yet a change in the cause nevertheless brings about a change in the effect. Our results show that people prefer to attribute cause when a mechanism links cause to effect. In contrast, prevention is sensitive both to the presence of an interruption to a causal mechanism and to a change in the outcome in the absence of a mechanism. In this sense, ‘prevent’ means something different than ‘cause not'. We discuss the implications of our results for existing theories of causation. (shrink)

Among the current philosophical attempts to understand causation two seem to be the most prominent. The first is James Woodward’s counterfactual approach; the second is the mechanistic approach advocated by Peter Machamer, Lindley Darden, Carl Craver, Jim Bogen and Stuart Glennan. The counterfactual approach takes it that causes make a difference to their effects, where this difference-making is cashed out in terms of actual and counterfactual interventions. The mechanistic approach takes it that two events are causally related if and only (...) if there is a mechanism that connects them. On the face of it, the two approaches need not be in conflict. The mechanisms might satisfy (or depend on) certain interventionist counterfactuals and, conversely, the interventionist counterfactuals might be made true by the presence of certain mechanisms. But, overall, both approaches tend to be imperialistic. Advocates of each argue that their own approach fairs much better than their opponents’. The question then is this: are we forced to choose between the mechanistic approach and the counterfactual one? In this paper, I argue that, as they stand, both approaches face some important problems that need to be fixed. I shall also argue that there is a sense in which the counterfactual approach is more basic than the mechanistic, though the former will benefit from a better understanding of the mechanisms that are at work in causal connections. So both approaches can work together to offer a better understanding of causation. If they work in tandem, they can offer us a glimpse of what Hume famously called “the secret connexion”. But in so far as the ‘secret connexion’ is an intrinsic relation between the causal relata, neither of the above approaches tells us what this relation is. (shrink)

This paper presents a counterfactual account of what a mechanism is. Mechanisms consist of parts, the behavior of which conforms to generalizations that are invariant under interventions, and which are modular in the sense that it is possible in principle to change the behavior of one part independently of the others. Each of these features can be captured by the truth of certain counterfactuals.

Woodward's long awaited book is an attempt to construct a comprehensive account of causation explanation that applies to a wide variety of causal and explanatory claims in different areas of science and everyday life. The book engages some of the relevant literature from other disciplines, as Woodward weaves together examples, counterexamples, criticisms, defenses, objections, and replies into a convincing defense of the core of his theory, which is that we can analyze causation by appeal to the notion of manipulation.

This article defends the use of interventionist counterfactuals to elucidate causal and explanatory claims against criticisms advanced by James Bogen and Peter Machamer. Against Bogen, I argue that counterfactual claims concerning what would happen under interventions are meaningful and have determinate truth values, even in a deterministic world. I also argue, against both Machamer and Bogen, that we need to appeal to counterfactuals to capture the notions like causal relevance and causal mechanism. Contrary to what both authors suppose, counterfactuals are (...) not "unscientific" - a substantial tradition within statistics and the causal modelling literature makes heavy use of them. (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 and laypeople alike use the sense of understanding that an explanation conveys as a cue to good or correct explanation. Although the occurrence of this sense or feeling of understanding is neither necessary nor sufficient for good explanation, it does drive judgments of the plausibility and, ultimately, the acceptability, of an explanation. This paper presents evidence that the sense of understanding is in part the routine consequence of two well-documented biases in cognitive psychology: overconfidence and hindsight. In light of (...) the prevalence of counterfeit understanding in the history of science, I argue that many forms of cognitive achievement do not involve a sense of understanding, and that only the truth or accuracy of an explanation make the sense of understanding a valid cue to genuine understanding. (shrink)

Stuart Glennan, and the team of Peter Machamer, Lindley Darden, and Carl Craver have recently provided two accounts of the concept of a mechanism. The main difference between these two versions rests on how the behavior of the parts of the mechanism is conceptualized. Glennan considers mechanisms to be an interaction of parts, where the interaction between parts can be characterized by direct, invariant, change-relating generalizations. Machamer, Darden, and Craver criticize traditional conceptualizations of mechanisms which are based solely on parts (...) interacting and introduce a new conceptactivity. This essay is an attempt at carving out a relationship between these two philosophical interpretations of a mechanism. I will claim that, rather than being in conflict, Glennan's concept of interaction and Machamer, Darden, and Craver's notion of activity actually complement one another, each emphasizing a missing element of the other. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

Wesley Salmon is renowned for his seminal contributions to the philosophy of science. He has powerfully and permanently shaped discussion of such issues as lawlike and probabilistic explanation and the interrelation of explanatory notions to causal notions. This unique volume brings together twenty-six of his essays on subjects related to causality and explanation, written over the period 1971-1995. Six of the essays have never been published before and many others have only appeared in obscure venues. The volume includes a section (...) of accessible introductory pieces, as well as more advanced and technical pieces, and will make essential work in the philosophy of science readily available to both scholars and students. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

This article deals with mechanisms conceived as composed of entities and activities. In response to many perplexities about the nature of activities, a number of arguments are developed concerning their epistemic and ontological status. Some questions concerning the relations between cause and causal explanation and mechanisms are also addressed.

Towards the end of Beyond Reduction Horst hypothesizes that ‘it is a general design principle of the cognitive architecture of humans that the mind possesses multiple models for understanding and interacting practically with different aspects of the world’. The suggestion is made following a discussion of recent research in cognitive science. According to Horst, the hypothesis is also consistent with what recent non-reductionist tendencies in the philosophy of science teach us. Taken together, Horst claims these two sets of evidence motivate (...) a new post-reductionist approach to the philosophy of mind. After outlining the route Horst takes to reach this claim, I shall raise a worry I have about the claim and the route taken to it.Beyond Reduction is in three parts. Part one frames the debate. In Chapter 1 Horst notes how naturalism is a view to which nearly all philosophers of mind, some of whom hold quite disparate views, would subscribe. He formulates a schema for naturalism that he thinks most would accept: ‘naturalism about domain D is the view that all features of D are to be accommodated within the framework of nature as it is understood by the natural sciences’. Horst acknowledges that the schema under-describes matters. The notion of ‘accommodation’ may be understood as involving either explanation or metaphysical determination; it is also unclear how we are to understand ‘the …. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

Philosophers of science typically associate the causal-mechanical view of scientific explanation with the work of Railton and Salmon. In this paper I shall argue that the defects of this view arise from an inadequate analysis of the concept of mechanism. I contrast Salmon's account of mechanisms in terms of the causal nexus with my own account of mechanisms, in which mechanisms are viewed as complex systems. After describing these two concepts of mechanism, I show how the complex-systems approach avoids certain (...) objections to Salmon's account of causal-mechanical explanation. I conclude by discussing how mechanistic explanations can provide understanding by unification. (shrink)

In this paper I offer an analysis of causation based upon a theory of mechanisms-complex systems whose internal parts interact to produce a system's external behavior. I argue that all but the fundamental laws of physics can be explained by reference to mechanisms. Mechanisms provide an epistemologically unproblematic way to explain the necessity which is often taken to distinguish laws from other generalizations. This account of necessity leads to a theory of causation according to which events are causally related when (...) there is a mechanism that connects them. I present reasons why the lack of an account of fundamental physical causation does not undermine the mechanical account. (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)

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)

Jonathan Walkan challenges cognitive science's dominant model of mental representation and proposes a novel, well-devised alternative. The traditional view in the cognitive sciences uses a linguistic model of mental representation. That logic-based model of cognition informs and constrains both the classical tradition of artificial intelligence and modeling in the connectionist tradition. It falls short, however, when confronted by the frame problem---the lack of a principled way to determine which features of a representation must be updated when new information becomes available. (...) So far, proposed alternatives, including the imagistic model, have not resolved the problem. Waskan proposes the Intrinsic Cognitive Models hypothesis, according to which representational states can be concpetualized as the cognitive equivalent of scale models.Waskan argues further that the proposal that humans harbor and manipulate cognitive counterparts to scale models offers the only viable explanation for what most clearly differentiates humans from other creatures: the capacity to engage in truth-preserving manipulation of representations. The ICM hypothesis, he claims, can be distinguished from sentence-based accounts of truth preservation in a way that is fully compatible with what is known about the brain. (shrink)

Carl F. Craver investigates what we are doing when we use neuroscience to explain what's going on in the brain. When does an explanation succeed and when does it fail? Craver offers explicit standards for successful explanation of the workings of the brain, on the basis of a systematic view about what neuroscientific explanations are.

Contemporary philosophers of mind tend to assume that the world of nature can be reduced to basic physics. Yet there are features of the mind consciousness, intentionality, normativity that do not seem to be reducible to physics or neuroscience. This explanatory gap between mind and brain has thus been a major cause of concern in recent philosophy of mind. Reductionists hold that, despite all appearances, the mind can be reduced to the brain. Eliminativists hold that it cannot, and that this (...) implies that there is something illegitimate about the mentalistic vocabulary. Dualists hold that the mental is irreducible, and that this implies either a substance or a property dualism. Mysterian non-reductive physicalists hold that the mind is uniquely irreducible, perhaps due to some limitation of our self-understanding. In this book, Steven Horst argues that this whole conversation is based on assumptions left over from an outdated philosophy of science. While reductionism was part of the philosophical orthodoxy fifty years ago, it has been decisively rejected by philosophers of science over the past thirty years, and for good reason. True reductions are in fact exceedingly rare in the sciences, and the conviction that they were there to be found was an artifact of armchair assumptions of 17th century Rationalists and 20th century Logical Empiricists. The explanatory gaps between mind and brain are far from unique. In fact, in the sciences it is gaps all the way down.And if reductions are rare in even the physical sciences, there is little reason to expect them in the case of psychology. Horst argues that this calls for a complete re-thinking of the contemporary problematic in philosophy of mind. Reductionism, dualism, eliminativism and non-reductive materialism are each severely compromised by post-reductionist philosophy of science, and philosophy of mind is in need of a new paradigm. Horst suggests that such a paradigm might be found in Cognitive Pluralism: the view that human cognitive architecture constrains us to understand the world through a plurality of partial, idealized, and pragmatically-constrained models, each employing a particular representational system optimized for its own problem domain. Such an architecture can explain the disunities of knowledge, and is plausible on evolutionary grounds. (shrink)

Carl Craver investigates what we are doing when we sue neuroscience to explain what's going on in the brain.

Summary. The viability of the proposal that human cognition involves the utilization of nonsentential models is seriously undercut by the fact that no one has yet given a satisfactory account of how neurophysiological circuitry might realize representations of the right sort. Such an account is offered up here, the general idea behind which is that high-level models can be realized by lower—level computations and, in turn, by neural machinations. It is shown that this account can be usefully applied to deal (...) with problems in fields ranging from artificial intelligence to the philosophy of science. (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)

This paper explores some issues having to do with the perception of causation. It discusses the role that phenomena that that are associated with causal perception, such as Michottean launching interactions, play within philosophical accounts of causation and also speculates on their possible role in development.

This paper analyzes recent work in psychology on the nature of the representation of complex forms of knowledge with the goal of understanding how theories are represented. The analysis suggests that, as a psychological form of representation, theories are mental structures that include theoretical entities (usually nonobservable), relationships among the theoretical entities, and relationships of the theoretical entities to the phenomena of some domain. A theory explains the phenomena in its domain by providing a conceptual framework for the phenomena that (...) leads to a feeling of understanding in the reader/hearer. The explanatory conceptual framework goes beyond the original phenomena, integrates diverse aspects of the world, and shows how the original phenomena follow from the framework. This analysis is used to argue that mental models are the subclass of theories that use causal/mechanical explanatory frameworks. In addition, an argument is made for a new psychologism in the philosophy of science, in which the mental representation of scientific theories must be taken into account. (shrink)