Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences (Kaplan and Bechtel; Kaplan and Craver). We argue that mechanistic explanation is defined by localization and decomposition. We argue further that systems neuroscience contains explanations that violate both localization and decomposition. We conclude that the mechanistic model of explanation needs to either stretch to now include explanations wherein localization or decomposition fail or acknowledge that there are counterexamples to mechanistic explanation (...) in the biological sciences. (shrink)

The interpretation of functional imaging experiments is complicated by the pluripotency of brain regions. As there is a many-to-one mapping between cognitive functions and their neural substrates, region-based analyses of imaging data provide only weak support for cognitive theories. Price and Friston argue that we need a ‘cognitive ontology’ that abstractly categorizes the function of regions. I argue that abstract characterizations are unlikely to be cognitively interesting. I argue instead that we should attribute functions to regions in a context-sensitive manner. (...) I review recent meta-analyses that approach fMRI data in this light and argue that they have revisionary potential. (shrink)

Accounts of mechanistic explanation have emphasized the importance of looking down—decomposing a mechanism into its parts and operations. Using research on visual processing as an exemplar, I illustrate how productive such research has been. But once multiple components of a mechanism have been identified, researchers also need to figure out how it is organized—they must look around and determine how to recompose the mechanism. Although researchers often begin by trying to recompose the mechanism in terms of sequential operations, they frequently (...) find that the components of a mechanism interact in complex ways involving positive and negative feedback and that the organization often exhibits highly interactive local networks linked by a few long-range connections (small-worlds organization) and power law distributions of connections. The mechanisms are themselves active systems that are perturbed by inputs but not set in motion by them. Researchers also need to look up —situate a mechanism in its context, which may be a larger mechanism that modulates its behavior. When looking down is combined with looking around and up, mechanistic research results in an integrated, multi-level perspective. (shrink)

Many empirically minded philosophers have used neuroscientific data to argue against the multiple realization of cognitive functions in existing biological organisms. I argue that neuroscientists themselves have proposed a biologically based concept of multiple realization as an alternative to interpreting empirical findings in terms of one‐to‐one structure‐function mappings. I introduce this concept and its associated research framework and also how some of the main neuroscience‐based arguments against multiple realization go wrong. *Received October 2009; revised December 2009. †To contact the author, (...) please write to: Department of Philosophy, 260 English‐Philosophy Building, University of Iowa, Iowa City, IA 52242; e‐mail: carrie‐[email protected]. (shrink)

Abstract: The massive redeployment hypothesis (MRH) is a theory about the functional topography of the human brain, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components-originally developed to serve some specific purpose-were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs. If the (...) evolution of cognition was indeed driven by such exaptation, then we should be able to make some specific empirical predictions regarding the resulting functional topography of the brain. This essay discusses three such predictions, and some of the evidence supporting them. Then, using this account as a background, the essay considers the implications of these findings for an account of the functional integration of cognitive operations. For instance, MRH suggests that in order to determine the functional role of a given brain area it is necessary to consider its participation across multiple task categories, and not just focus on one, as has been the typical practice in cognitive neuroscience. This change of methodology will motivate (even perhaps necessitate) the development of a new, domain-neutral vocabulary for characterizing the contribution of individual brain areas to larger functional complexes, and direct particular attention to the question of how these various area roles are integrated and coordinated to result in the observed cognitive effect. Finally, the details of the mix of cognitive functions a given area supports should tell us something interesting not just about the likely computational role of that area, but about the nature of and relations between the cognitive functions themselves. For instance, growing evidence of the role of “motor” areas like M1, SMA and PMC in language processing, and of “language” areas like Broca’s area in motor control, offers the possibility for significantly reconceptualizing the nature both of language and of motor control. (shrink)

A spate of recent anti-localizationist publications have re-ignited the old debate about the localization of function. Many of the recent attacks on localization, however, are directed at what I will argue to be a narrow and outmoded view of localization, and thus have little conceptual or empirical impact. What I hope to present here is an analysis of functional localization that more adequately reflects the sophistication and complexity of its use in neuroscientific research, both historically and recently. Proceeding first by (...) way of contrast, I examine theanti-localizationist positions of holism andequipotentiationism. Then, I present a four-fold analysis of localization according to physical scope, physical kind, functional scope, and functional kind. Next, I turn to a discussion of the heuristic value oflocalization in deciphering structure-functionrelationships. Finally, I hope to show that the overall view of functional localization that emerges from these considerations constitutes a much more elusive target than its critics assume. It serves to mitigate, and insome instances even defeat, some forms ofanti-localizationist criticisms. (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)

The first part of this paper deals with the relations between mechanistic explanation and reduction. It is argued that there is no insuperable conflict between the two, but that the mechanistic framework adds requirements that are not acknowledged in the model of property reduction. The second part concerns the relations between organization and environmental factors. Internal organization may be so tightly linked to external context that both have to be considered together.

Several articles have recently appeared arguing that there really are no viable alternatives to mechanistic explanation in the biological sciences. This claim is meant to hold both in principle and in practice. The basic claim is that any explanation of a particular feature of a biological system, including dynamical explanations, must ultimately be grounded in mechanistic explanation. There are several variations on this theme, some stronger and some weaker. In order to avoid equivocation and miscommunication, in section 1 we will (...) argue that mechanistic explanation is defined by localization and decomposition. In section 2 we will argue that systems neuroscience contains explanations that violate both localization and decomposition on any non-trivial construal of these concepts. Therefore, in section 3 we conclude the mechanistic model of explanation either needs to stretch to now include explanations wherein localization or decomposition fail, or acknowledge that there are counter-examples to mechanistic explanation in the biological sciences. We will also consider consequences and possible replies on the part of the mechanist in section 3. (shrink)

An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (...) (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design. (shrink)