Online research in philosophy

The phenomenon of religious belief has been much discussed in philosophy of religion. However, a priori argumentation alone cannot establish what religious belief is like as a psychological attitude. Recent advances in the cognitive science of religion have paved the way for a new, naturalized philosophy of religion. Taking into account the relevant results and hypotheses presented within these disciplines, it is possible to develop a more empirically informed philosophy of religious belief. Instead of asking whether believing is rational, it is here asked how religious belief is cognitively possible. Combining Boyer's evolutionary account of religion with Sperber's and Cosmides and Tooby's theory of metarepresentation, we get the sort of conceptual toolkit needed to specify those cognitive mechanisms and operations that make religious belief possible. Religious belief is shown to require a unique combination of these mechanisms and operations.

The phenomenon of religious belief has been much discussed in philosophy of religion. However, a priori argumentation alone cannot establish what religious belief is like as a psychological attitude. Recent advances in the cognitive science of religion have paved the way for a new, naturalized philosophy of religion. Taking into account the relevant results and hypotheses presented within these disciplines, it is possible to develop a more empirically informed philosophy of religious belief. Instead of asking whether believing is rational, it is here asked how religious belief is cognitively possible. Combining Boyer's evolutionary account of religion with Sperber's and Cosmides and Tooby's theory of metarepresentation, we get the sort of conceptual toolkit needed to specify those cognitive mechanisms and operations that make religious belief possible. Religious belief is shown to require a unique combination of these mechanisms and operations.

Reductionist inquiry, which involves decomposing a mechanism into its parts and operations, is only one of the tasks of mechanistic research. A second task (which may be undertaken largely simultaneously) is recomposing it—conceptually reassembling the parts and operations into an organized arrangement that constitutes the mechanism. Other tasks include determining how multiple operations are orchestrated in real time, and investigating how the mechanism interacts with the environment in which it is situated.

1. The Naturalistic Turn in Philosophy of Science 2. The Framework of Mechanistic Explanation: Parts, Operations, and Organization 3. Representing and Reasoning About Mechanisms 4. Mental Mechanisms: Mechanisms that Process Information 5. Discovering Mental Mechanisms 6 . Summary.

Explaining the complex dynamics exhibited in many biological mechanisms requires extending the recent philosophical treatment of mechanisms that emphasizes sequences of operations. To understand how nonsequentially organized mechanisms will behave, scientists often advance what we call dynamic mechanistic explanations. These begin with a decomposition of the mechanism into component parts and operations, using a variety of laboratory-based strategies. Crucially, the mechanism is then recomposed by means of computational models in which variables or terms in differential equations correspond to properties of its parts and operations. We provide two illustrations drawn from research on circadian rhythms. Once biologists identified some of the components of the molecular mechanism thought to be responsible for circadian rhythms, computational models were used to determine whether the proposed mechanisms could generate sustained oscillations. Modeling has become even more important as researchers have recognized that the oscillations generated in individual neurons are synchronized within networks; we describe models being employed to assess how different possible network architectures could produce the observed synchronized activity.

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.

In many domains of biology, explanation takes the form of characterizing the mechanism responsible for a particular phenomenon in a specific biological system. How are such explanations generalized? One important strategy assumes conservation of mechanisms through evolutionary descent. But conservation is seldom complete. In the case discussed, the central mechanism for circadian rhythms in animals was first identified in Drosophila and then extended to mammals. Scientists' working assumption that the clock mechanisms would be conserved both yielded important generalizations and served as a heuristic for discovery, especially when significant differences between the insect and mammalian mechanism were identified. †To contact the author, please write to: Department of Philosophy and Interdisciplinary Programs in Science Studies and Cognitive Science, 0119, University of California, San Diego, La Jolla, CA 92093‐0119; e‐mail: bill@mechanism.ucsd.edu.

Two widely accepted assumptions within cognitive science are that (1) the goal is to understand the mechanisms responsible for cognitive performances and (2) computational modeling is a major tool for understanding these mechanisms. The particular approaches to computational modeling adopted in cognitive science, moreover, have significantly affected the way in which cognitive mechanisms are understood. Unable to employ some of the more common methods for conducting research on mechanisms, cognitive scientists’ guiding ideas about mechanism have developed in conjunction with their styles of modeling. In particular, mental operations often are conceptualized as comparable to the processes employed in classical symbolic AI or neural network models. These models, in turn, have been interpreted by some as themselves intelligent systems since they employ the same type of operations as does the mind. For this paper, what is significant about these approaches to modeling is that they are constructed specifically to account for behavior and are evaluated by how well they do so—not by independent evidence that they describe actual operations in mental mechanisms.

The project of referring to localized cognitive operations in the brain has a long history and many impressive successes. It is a core element in the practice of giving mechanistic explanations of mental abilities. But it has also been challenged by prominent critics. One of the critics’ claims is that brain regions are not specialized for specific cognitive operations and any science that refers to them is misguided. Most recently this claim has been advanced by theorists promoting a dynamical-systems perspective on cognition. There are, however, two ways to view the dynamical-systems perspective. The first is as a competitor to the mechanist perspective, rejecting altogether the conception of the brain as a mechanism or set of mechanisms underlying mental phenomena and thereby rejecting any reference to localized cognitive operations. The second is as a corrective to an overly simplistic conception of a mechanism and as complementary to a more adequate understanding of how mechanisms function. In this chapter I defend the later perspective. On this perspective, the traditional project of referring to localized mental operations in the brain is still important, but both the cognitive operations and brain regions in which they are localized must be conceived in the context of a dynamically active system.

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.