We contrast reactive and endogenously active perspectives on brain activity. Both have been pursued continuously in neurophysiology laboratories since the early 20thcentury, but the endogenous perspective has received relatively little attention until recently. One of the many successes of the reactive perspective was the identification, in the second half of the 20th century, of the distinctive contributions of different brain regions involved in visual processing. The recent prominence of the endogenous perspective is due to new findings of ongoing oscillatory activity (...) in the brain at a wide range of time scales, exploiting such techniques as single-cell recording, EEG, and fMRI. We recount some of the evidence pointing to ways in which this endogenous activity is relevant to cognition and behavior. Our major objective is to consider certain implications of the contrast between the reactive and endogenous perspectives. In particular, we relate these perspectives to two different characterizations of explanation in the new mechanistic philosophy of science. In a basic mechanistic explanation, the operations of a mechanism are characterized qualitatively and as functioning sequentially until a terminating condition is realized. In contrast, a dynamic mechanistic explanation allows for non-sequential organization and emphasizes quantitative modeling of the mechanisms's behavior. For example, with appropriate parameter values a set of differential equations can be used to demonstrate ongoing oscillations in a system organized with feedback loops. We conclude that the basic conception of mechanistic explanation is adequate for reactive accounts of brain activity, but dynamical accounts are required to explain sustained, endogenous activity. (shrink)
Mechanistic explanation is the dominant approach to explanation in the life sciences, but it has been challenged as incompatible with a conception of humans as agents whose capacity for self-direction endows them with freedom and dignity. We argue that the mechanical philosophy, properly construed, has sufficient resources to explain how such characteristics can arise in a material world. Biological mechanisms must be regarded as active, not only reactive, and as organized so as to maintain themselves far from thermodynamic equilibrium. Notions (...) from systems biology make key contributions, particularly Gánti’s chemoton, Ruiz-Mirazo and Moreno’s basic autonomy, and Barandiaran and Moreno’s adaptive autonomous agents. The reconstrual is then extended to mental life by conceiving of cognitive mechanisms as control components in inherently active systems, as illustrated in models offered by Randall Beer and Cees van Leeuwen. (shrink)
Although a reactive framework has long been dominant in cognitive science and neuroscience, an alternative framework emphasizing dynamics and endogenous activity has recently gained prominence. We review some of the evidence for endogenous activity and consider the implications not only for understanding cognition but also for accounts of explanation offered by philosophers of science. Our recent characterization of dynamic mechanistic explanation emphasizes the coordination of accounts of mechanisms that identify parts and operations with computational models of their activity. These can, (...) and should, be extended to incorporate attention to mechanisms that are not only active, but endogenously active. (shrink)
The mechanistic perspective has dominated biological disciplines such as biochemistry, physiology, cell and molecular biology, and neuroscience, especially during the 20th century. The primary strategy is reductionist: organisms are to be decomposed into component parts and operations at multiple levels. Researchers adopting this perspective have generated an enormous body of information about the mechanisms of life at scales ranging from the whole organism down to genetic and other molecular 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.
In the context of mechanistic explanation, reductionistic research pursues a decomposition of complex systems into their component parts and operations. Using research on circadian rhythms and memory consolidation as exemplars, we consider the gains to be made by finding genes and proteins that figure in mechanisms underlying behavioral phenomena. However, we also show that such research is insufficient to explain the initial phenomenon. Accordingly, researchers have increasingly recognized the need to consider higher-level organization and integration with other systems. This illustrates (...) a common need to complement reductionistic inquiry with investigations at higher levels and identifies a trajectory whereby cognitive science can embrace molecular neuroscience without surrendering its own contributions. (shrink)
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. (shrink)
Diagrams have distinctive characteristics that make them an effective medium for communicating research findings, but they are even more impressive as tools for scientific reasoning. Focusing on circadian rhythm research in biology to explore these roles, we examine diagrammatic formats that have been devised (a) to identify and illuminate circadian phenomena and (b) to develop and modify mechanistic explanations of these phenomena.
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. (shrink)
Mechanistic explanation is the dominant approach to explanation in the life sciences, but it has been challenged as incompatible with a conception of humans as agents whose capacity for self-direction endows them with freedom and dignity. We argue that the mechanical philosophy, properly construed, has sufficient resources to explain how such characteristics can arise in a material world. Biological mechanisms must be regarded as active, not only reactive, and as organized so as to maintain themselves far from thermodynamic equilibrium. Notions (...) from systems biology make key contributions, particularly Gánti’s chemoton, Ruiz-Mirazo and Moreno’s basic autonomy, and Barandiaran and Moreno’s adaptive autonomous agents. The reconstrual is then extended to mental life by conceiving of cognitive mechanisms as control components in inherently active systems, as illustrated in models offered by Randall Beer and Cees van Leeuwen. (shrink)
Cognitive science is, more than anything else, a pursuit of cognitive mechanisms. To make headway towards a mechanistic account of any particular cognitive phenomenon, a researcher must choose among the many architectures available to guide and constrain the account. It is thus fitting that this volume on contemporary debates in cognitive science includes two issues of architecture, each articulated in the 1980s but still unresolved:
• Just how modular is the mind? (section 1) – a debate initially pitting encapsulated (...) mechanisms (Fodorian modules that feed their ultimate outputs to a nonmodular central cognition) against highly interactive ones (e.g., connectionist networks that continuously feed streams of output to one another). • Does the mind process language-like representations according to formal rules? (this section) – a debate initially pitting symbolic architectures (such as Chomsky’s generative grammar or Fodor’s language of thought) against less language-like architectures (such as connectionist or dynamical ones).
Our project here is to consider the second issue within the broader context of where cognitive science has been and where it is headed. The notion that cognition in general—not just language processing—involves rules operating on language-like representations actually predates cognitive science. In traditional philosophy of mind, mental life is construed as involving propositional attitudes—that is, such attitudes towards propositions as believing, fearing, and desiring that they be true—and logical inferences from them. On this view, if a person desires that a proposition be true and believes that if she performs a certain action it will become true, she will make the inference and (absent any overriding consideration) perform the action. (shrink)
Both in biology and psychology there has been a tendency on the part of many investigators to focus solely on the mature organism and ignore development. There are many reasons for this, but an important one is that the explanatory framework often invoked in the life sciences for understanding a given phenomenon, according to which explanation consists in identifying the mechanism that produces that phenomenon, both makes it possible to side-step the development issue and to provide inadequate resources for actually (...) explaining development. When biologists and psychologists do take up the question of development, they find themselves confronted with two polarizing positions of nativism and empiricism. However, the mechanistic framework, insofar as it emphasizes organization and recognizes the potential for self-organization, does in fact provide the resources for an account of development which avoids the nativism-empiricism dichotomy. (shrink)
