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

The received view of dynamical explanation is that dynamical cognitive science seeks to provide covering law explanations of cognitive phenomena. By analyzing three prominent examples of dynamicist research, I show that the received view is misleading: some dynamical explanations are mechanistic explanations, and in this way resemble computational and connectionist explanations. Interestingly, these dynamical explanations invoke the mathematical framework of dynamical systems theory to describe mechanisms far more complex and distributed than the ones typically considered by philosophers. Therefore, contemporary dynamicist (...) research reveals the need for a more sophisticated account of mechanistic explanation. (shrink)

Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial ...

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.

The overall goal of this target article is to demonstrate a mechanism for an embodied cognition. The particular vehicle is a much-studied, but still widely debated phenomenon seen in 7–12 month-old-infants. In Piaget's classic “A-not-B error,” infants who have successfully uncovered a toy at location “A” continue to reach to that location even after they watch the toy hidden in a nearby location “B.” Here, we question the traditional explanations of the error as an indicator of infants' concepts of objects (...) or other static mental structures. Instead, we demonstrate that the A-not-B error and its previously puzzling contextual variations can be understood by the coupled dynamics of the ordinary processes of goal-directed actions: looking, planning, reaching, and remembering. We offer a formal dynamic theory and model based on cognitive embodiment that both simulates the known A-not-B effects and offers novel predictions that match new experimental results. The demonstration supports an embodied view by casting the mental events involved in perception, planning, deciding, and remembering in the same analogic dynamic language as that used to describe bodily movement, so that they may be continuously meshed. We maintain that this mesh is a pre-eminently cognitive act of “knowing” not only in infancy but also in everyday activities throughout the life span. Key Words: cognitive development; dynamical systems theory; embodied cognition; infant development; motor control; motor planning; perception and action. (shrink)

In this paper, I outline two strands of evidence for the conclusion that the dynamical approach to cognitive science both seeks and provides covering law explanations. Two of the most successful dynamical models—Kelso’s model of rhythmic finger movement and Thelen et al.’s model of infant perseverative reaching—can be seen to provide explanations which conform to the famous explanatory scheme first put forward by Hempel and Oppenheim. In addition, many prominent advocates of the dynamical approach also express the provision of this (...) kind of explanation as a goal of dynamical cognitive science. I conclude by briefly outlining two consequences. First, dynamical cognitive science’s explanatory style may strengthen its links to the so-called “situated” approach to cognition, but, secondly, it may also undermine the widespread intuition that dynamics is related to emergentism in the philosophy of mind. (shrink)

This paper concerns what Jerry Fodor calls a 'metaphysical mystery': How can there by macroregularities that are realized by wildly heterogeneous lower level mechanisms? But the answer to this question is not as mysterious as many, including Jaegwon Kim, Ned Block, and Jerry Fodor might think. The multiple realizability of the properties of the special sciences such as psychology is best understood as a kind of universality, where 'universality' is used in the technical sense one finds in the physics literature. (...) It is argued that the same explanatory strategy used by physicists to provide understanding of universal behavior in physics can be used to explain how special science properties can be heterogeneously multiply realized. (shrink)

John Beatty (1995) and Alexander Rosenberg (1994) have argued against the claim that there are laws in biology. Beatty's main reason is that evolution is a process full of contingency, but he also takes the existence of relative significance controversies in biology and the popularity of pluralistic approaches to a variety of evolutionary questions to be evidence for biology's lawlessness. Rosenberg's main argument appeals to the idea that biological properties supervene on large numbers of physical properties, but he also develops (...) case studies of biological controversies to defend his thesis that biology is best understood as an instrumental discipline. The present paper assesses their arguments. (shrink)

To explain the phenomena in the world of our experience, to answer the question “why?” rather than only the question “what?”, is one of the foremost objectives of all rational inquiry; and especially, scientific research in its various branches strives to go beyond a mere description of its subject matter by providing an explanation of the phenomena it investigates. While there is rather general agreement about this chief objective of science, there exists considerable difference of opinion as to the function (...) and the essential characteristics of scientific explanation. In the present essay, an attempt will be made to shed some light on these issues by means of an elementary survey of the basic pattern of scientific explanation and a subsequent more rigorous analysis of the concept of law and of the logical structure of explanatory arguments. (shrink)

We provide a taxonomy of the two most important debates in the philosophy of the cognitive and neural sciences. The first debate is over methodological individualism: is the object of the cognitive and neural sciences the brain, the whole animal, or the animal--environment system? The second is over explanatory style: should explanation in cognitive and neural science be reductionist-mechanistic, inter-level mechanistic, or dynamical? After setting out the debates, we discuss the ways in which they are interconnected. Finally, we make some (...) recommendations that we hope will help philosophers interested in the cognitive and neural sciences to avoid dead ends. (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.

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)

In treating cognition as problem solving, Andy Clark suggests, we may often abstract too far from the very body and world in which our brains evolved to guide...

Advocates of dynamical systems theory (DST) sometimes employ revolutionary rhetoric. In an attempt to clarify how DST models differ from others in cognitive science, I focus on two issues raised by DST: the role for representations in mental models and the conception of explanation invoked. Two features of representations are their role in standing-in for features external to the system and their format. DST advocates sometimes claim to have repudiated the need for stand-ins in DST models, but I argue that (...) they are mistaken. Nonetheless, DST does offer new ideas as to the format of representations employed in cognitive systems. With respect to explanation, I argue that some DST models are better seen as conforming to the covering-law conception of explanation than to the mechanistic conception of explanation implicit in most cognitive science research. But even here, I argue, DST models are a valuable complement to more mechanistic cognitive explanations. (shrink)

Arguments in favor of anti-representationalism in cognitive science often suffer from a lack of attention to detail. The purpose of this paper is to fill in the gaps in these arguments, and in so doing show that at least one form of anti- representationalism is potentially viable. After giving a teleological definition of representation and applying it to a few models that have inspired anti- representationalist claims, I argue that anti-representationalism must be divided into two distinct theses, one ontological, one (...) epistemological. Given the assumptions that define the debate, I give reason to think that the ontological thesis is false. I then argue that the epistemological thesis might, in the end, turn out to be true, despite a potentially serious difficulty. Along the way, there will be a brief detour to discuss a controversy from early twentieth century physics. (shrink)

This chapter explores the possibility of weakening the criteria for causal explanation in Making Things Happen to yield various forms of non-causal explanation. These include the following: retaining the idea that explanations must answer what if things had been different questions but dropping the requirement the answers to such questions must take the form of claims about what would happen under interventions. Retaining the w- question requirement but allowing generalizations that hold for mathematical or conceptual reasons to figure in explanations. (...) Dropping the w-question requirement to accommodate the role of information about irrelevance in explanation. (shrink)

Completeness is an important but misunderstood norm of explanation. It has recently been argued that mechanistic accounts of scientific explanation are committed to the thesis that models are complete only if they describe everything about a mechanism and, as a corollary, that incomplete models are always improved by adding more details. If so, mechanistic accounts are at odds with the obvious and important role of abstraction in scientific modelling. We respond to this characterization of the mechanist’s views about abstraction and (...) articulate norms of completeness for mechanistic explanations that have no such unwanted implications. _1_ Introduction _2_ A Balancing Act: When Do Details Matter? _3_ The Norms of Causal Explanation _4_ The Norms of Constitutive Explanation _5_ Salmon-Completeness _6_ From More Details to More Relevant Details _7_ Non-explanatory Virtues of Abstraction _8_ From Explanatory Models to Explanatory Knowledge _9_ Mechanistic Completeness Reconsidered _10_ Conclusion. (shrink)

We sketch the mechanistic approach to levels, contrast it with other senses of “level,” and explore some of its metaphysical implications. This perspective allows us to articulate what it means for things to be at different levels, to distinguish mechanistic levels from realization relations, and to describe the structure of multilevel explanations, the evidence by which they are evaluated, and the scientific unity that results from them. This approach is not intended to solve all metaphysical problems surrounding physicalism. Yet it (...) provides a framework for thinking about how the macroscopic phenomena of our world are or might be related to its most fundamental entities and activities. (shrink)

Machamer, Darden, and Craver argue that causal explanations explain effects by describing the operations of the mechanisms which produce them. One of this paper’s aims is to take advantage of neglected resources of Mechanism to rethink the traditional idea that actual or counterfactual natural regularities are essential to the distinction between causal and non-causal co-occurrences, and that generalizations describing natural regularities are essential components of causal explanations. I think that causal productivity and regularity are by no means the same thing, (...) and that the Regularists are mistaken about the roles generalizations play in causal explanation. Humean, logical empiricist, and other Regularist accounts of causal explanation have had the unfortunate effect of distracting philosophers’ from important non-explanatory scientific uses of laws and lesser generalizations which purport to describe natural regularities. My second aim is to characterize some of these uses, illustrating them with examples from neuroscientific research. (shrink)

Explanations in the life sciences frequently involve presenting a model of the mechanism taken to be responsible for a given phenomenon. Such explanations depart in numerous ways from nomological explanations commonly presented in philosophy of science. This paper focuses on three sorts of differences. First, scientists who develop mechanistic explanations are not limited to linguistic representations and logical inference; they frequently employ diagrams to characterize mechanisms and simulations to reason about them. Thus, the epistemic resources for presenting mechanistic explanations are (...) considerably richer than those suggested by a nomological framework. Second, the fact that mechanisms involve organized systems of component parts and operations provides direction to both the discovery and testing of mechanistic explanations. Finally, models of mechanisms are developed for specific exemplars and are not represented in terms of universally quantified statements. Generalization involves investigating both the similarity of new exemplars to those already studied and the variations between them. (shrink)

In this paper, I aim to provide access to the current debate on non-causal explanations in philosophy of science. I will first present examples of non-causal explanations in the sciences. Then, I will outline three alternative approaches to non-causal explanations – that is, causal reductionism, pluralism, and monism – and, corresponding to these three approaches, different strategies for distinguishing between causal and non-causal explanation. Finally, I will raise questions for future research on non-causal explanations.

This article discusses minimal model explanations, which we argue are distinct from various causal, mechanical, difference-making, and so on, strategies prominent in the philosophical literature. We contend that what accounts for the explanatory power of these models is not that they have certain features in common with real systems. Rather, the models are explanatory because of a story about why a class of systems will all display the same large-scale behavior because the details that distinguish them are irrelevant. This story (...) explains patterns across extremely diverse systems and shows how minimal models can be used to understand real systems. (shrink)

In a recent paper, Kaplan (Synthese 183:339–373, 2011) takes up the task of extending Craver’s (Explaining the brain, 2007) mechanistic account of explanation in neuroscience to the new territory of computational neuroscience. He presents the model to mechanism mapping (3M) criterion as a condition for a model’s explanatory adequacy. This mechanistic approach is intended to replace earlier accounts which posited a level of computational analysis conceived as distinct and autonomous from underlying mechanistic details. In this paper I discuss work in (...) computational neuroscience that creates difficulties for the mechanist project. Carandini and Heeger (Nat Rev Neurosci 13:51–62, 2012) propose that many neural response properties can be understood in terms of canonical neural computations. These are “standard computational modules that apply the same fundamental operations in a variety of contexts.” Importantly, these computations can have numerous biophysical realisations, and so straightforward examination of the mechanisms underlying these computations carries little explanatory weight. Through a comparison between this modelling approach and minimal models in other branches of science, I argue that computational neuroscience frequently employs a distinct explanatory style, namely, efficient coding explanation. Such explanations cannot be assimilated into the mechanistic framework but do bear interesting similarities with evolutionary and optimality explanations elsewhere in biology. (shrink)

A prominent approach to scientific explanation and modeling claims that for a model to provide an explanation it must accurately represent at least some of the actual causes in the event's causal history. In this paper, I argue that many optimality explanations present a serious challenge to this causal approach. I contend that many optimality models provide highly idealized equilibrium explanations that do not accurately represent the causes of their target system. Furthermore, in many contexts, it is in virtue of (...) their independence of causes that optimality models are able to provide a better explanation than competing causal models. Consequently, our account of explanation and modeling must expand beyond the causal approach. (shrink)

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It (...) also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists. (shrink)

We argue that dynamical and mathematical models in systems and cognitive neuro- science explain (rather than redescribe) a phenomenon only if there is a plausible mapping between elements in the model and elements in the mechanism for the phe- nomenon. We demonstrate how this model-to-mechanism-mapping constraint, when satisfied, endows a model with explanatory force with respect to the phenomenon to be explained. Several paradigmatic models including the Haken-Kelso-Bunz model of bimanual coordination and the difference-of-Gaussians model of visual receptive fields are (...) explored. (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.

Many accounts of scientific modelling assume that models can be decomposed into the contributions made by their accurate and inaccurate parts. These accounts then argue that the inaccurate parts of the model can be justified by distorting only what is irrelevant. In this paper, I argue that this decompositional strategy requires three assumptions that are not typically met by our best scientific models. In response, I propose an alternative view in which idealized models are characterized as holistically distorted representations that (...) are justified by allowing for the application of various modelling techniques. (shrink)

The renormalization method is specifically aimed at connecting theories describing physical processes at different length scales and thereby connecting different theories in the physical sciences.The renormalization method used today is the outgrowth of 150 years of scientific study of thermal physics and phase transitions. Different phases of matter show qualitatively different behaviors separated by abrupt phase transitions. These qualitative differences seem to be present in experimentally observed condensed-matter systems. However, the “extended singularity theorem” in statistical mechanics shows that sharp changes (...) can only occur in infinitely large systems. Abrupt changes from one phase to another are signaled by fluctuations that show correlation over infinitely long distances, and are measured by correlation functions that show algebraic decay as well as various kinds of singularities and infinities in thermodynamic derivatives and in measured system parameters.Renormalization methods were first developed in field theory to get around difficulties caused by apparent divergences at both small and large scales. However, no renormalization gives a fully satisfactory formulation of field theory.The renormalization group theory of phase transitions was put together by Kenneth G. Wilson in 1971 based upon ideas of scaling and universality developed earlier in the context of phase transitions and of couplings dependent upon spatial scale coming from field theory. Correlations among regions with fluctuations in their order underlie renormalization ideas. Wilson's theory is the first approach to phase transitions to agree with the extended singularity theorem.Some of the history of the study of these correlations and singularities is recounted, along with the history of renormalization and related concepts of scaling and universality. Applications, particularly to condensed-matter physics and particle physics, are summarized.This note is partially a summary of a talk given at the workshop “Part and Whole” in Leiden during the period March 22–26, 2010. (shrink)

There has been considerable debate in the literature about the relative merits of information processing versus dynamical approaches to understanding cognitive processes. In this article, we explore the relationship between these two styles of explanation using a model agent evolved to solve a relational categorization task. Specifically, we separately analyze the operation of this agent using the mathematical tools of information theory and dynamical systems theory. Information-theoretic analysis reveals how task-relevant information flows through the system to be combined into a (...) categorization decision. Dynamical analysis reveals the key geometrical and temporal interrelationships underlying the categorization decision. Finally, we propose a framework for directly relating these two different styles of explanation and discuss the possible implications of our analysis for some of the ongoing debates in cognitive science. (shrink)

In its broadest sense, "universality" is a technical term for something quite ordinary. It refers to the existence of patterns of behavior by physical systems that recur and repeat despite the fact that in some sense the situations in which these patterns recur and repeat are different. Rainbows, for example, always exhibit the same pattern of spacings and intensities of their bows despite the fact that the rain showers are different on each occasion. They are different because the shapes of (...) the drops, and their sizes can vary quite widely due to differences in temperature, wind direction, etc. There are different questions one might ask about such patterns. For instance, one might ask why the particular rainbow... (shrink)