Online research in philosophy

We provide an account of mechanistic representation and explanation that has several advantages over previous proposals. In our view, explaining mechanistically is not simply giving an explanation of a mechanism. Rather, an explanation is mechanistic because of particular relations that hold between a mechanical representation, or model, and the target of explanation. Under this interpretation, mechanistic explanation is possible even when the explanatory target is not a mechanism. We argue that taking this view is not only coherent and plausible, it gives a more sophisticated view of the relationship between mechanical models and their targets. This allows us to address some ambiguities within the mechanist framework, and delivers a more intuitive way to interpret scientists' use of the term "mechanism".

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

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda.

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.

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.

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.

Due to the wide array of phenomena that are of interest to them, psychologists offer highly diverse and heterogeneous types of explanations. Initially, this suggests that the question "What is psychological explanation?" has no single answer. To provide appreciation of this diversity, we begin by noting some of the more common types of explanations that psychologists provide, with particular focus on classical examples of explanations advanced in three different areas of psychology: psychophysics, physiological psychology, and information-processing psychology. To analyze what is involved in these types of explanations, we consider the ways in which law-like representations of regularities and representations of mechanisms factor in psychological explanations. This consideration directs us to certain fundamental questions, e.g., "To what extent are laws necessary for psychological explanations?" and "What do psychologists have in mind when they appeal to mechanisms in explanation?" In answering such questions, it appears that laws do play important roles in psychological explanations, although most explanations in psychology appeal to accounts of mechanisms. Consequently, we provide a unifying account of what psychological explanation is.

This paper argues that besides mechanistic explanations, there is a kind of explanation that relies upon “topological” properties of systems in order to derive the explanandum as a consequence, and which does not consider mechanisms or causal processes. I first investigate topological explanations in the case of ecological research on the stability of ecosystems. Then I contrast them with mechanistic explanations, thereby distinguishing the kind of realization they involve from the realization relations entailed by mechanistic explanations, and explain how both kinds of explanations may be articulated in practice. The second section, expanding on the case of ecological stability, considers the phenomenon of robustness at all levels of the biological hierarchy in order to show that topological explanations are indeed pervasive there. Reasons are suggested for this, in which “neutral network” explanations are singled out as a form of topological explanation that spans across many levels. Finally, I appeal to the distinction of explanatory regimes to cast light on a controversy in philosophy of biology, the issue of contingence in evolution, which is shown to essentially involve issues about realization.

This paper discusses various problems of explanations by mechanisms. Two positions are distinguished: the narrow position claims that only explanations by mechanisms are acceptable. It is argued that this position leads to an infinite regress because the discovery of a mechanism must entail the search for other mechanisms etc. Another paradoxical consequence of this postulate is that every successful explanation by mechanisms is unsatisfactory because it generates new ``black box'' explanations. The second – liberal – position that is advanced in this paper regards, besides explanations by mechanisms, also the discovery of bivariate correlations as a first step of an explanation by mechanisms as meaningful. It is further argued that there is no contradiction between causal analysis and the explanation by mechanisms. Instead, explanations by mechanisms always presuppose the analysis of causal structures (but not vice versa). The final point is that an explanation by mechanisms is not inconsistent with the Hempel-Oppenheim scheme of explanation.

Abstract While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turvey's contention that they constitute an alternative to mechanistic explanations. We review several problems dynamical models face as putative explanations when they are not grounded in mechanisms. Further, we argue that the opposition of dynamical models and mechanisms is a false one and that those dynamical models that characterize the operations of mechanisms overcome these problems. By briefly considering examples involving the generation of action potentials and circadian rhythms, we show how decomposing a mechanism and modeling its dynamics are complementary endeavors.