William Bechtel

Multiple Realizability Revisited: Linking Cognitive and Neural States

The contention that psychological states are multiply realizable in different substrates has been used to support the contention that neuroscience is not likely to be very useful in guiding an understanding how cognition works. But in the context of scientific research, how seriously should we really take this threat of multiple realizability? By examining how brain areas are identified in neuroscience (where the approach is comparative and employs functional criteria), we show that the skepticism about neurosciences role in understanding cognition is misguided and that the apparent success of multiple realizability is based on methodological error. With respect to the former point, we develop and analyze a representative case in which the organization of the brain provides crucial information about the organization of the cognitive system. Then we show that the initial plausibility of claims to multiple realizability rest on (a) mismatching a broad-grained criterion (to show sameness of psychological states) with a fine-grained criterion (to differentiate brain states), and (b) a failure to attend to the purposes for which taxonomies of brain and psychological states are developed.

Integrating Scientific Disciplines

The Nature of Scientific Integration.- I: The Coming Together of Biochemistry.- Intermediary Metabolism in the Early Twentieth Century.- Biochemistry: A Cross-Disciplinary Endeavor That Discovered A Distinctive Domain.- Editors Commentary.- II: Dobzhanskys Contribution to the Evolutionary Synthesis.- Relations Among Fields in the Evolutionary Synthesis.- The Synthesis and the Synthetic Theory.- Editors Commentary.- III: Incorporating Developmental Biology into The Evolutionary Synthesis.- Can Embryologists Contribute to an Understandin gof Evolutionary Mechanisms?.- A Framework to Think About Evolving Genetic Regulatory Systems.- Developmental Constraints, Generative Entrenchment, and the Innate-Acquired Distinction.- On Integrating the Study of Evolution and of Development.- Editors Commentary.- IV: Extending Cognitive Science.- The Evolution of Communicative Capacities.- Language, Thought, and Communication.- Editors Commentary.- V: Infusing Cognitive Approaches into Animal Ethology.- Behavior Implies Cognition.- Intelligence: From Genes to Genius in the Quest for Control.- Cognitive Explanations and Cognitive Ethology.- Editors Commentary.

Representations and Cognitive Explanations: Assessing the Dynamicist's Challenge in Cognitive Science

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.

Explanatory Pluralism and Heuristic Identity Theory

Explanatory pluralism holds that the sorts of comprehensive theoretical and ontological economies that microreductionists and New Wave reductionists envision and antireductionists fear offer misleading views of both scientific practice and scientific progress. Both advocates and foes of employing reductionist strategies at the interface of psychology and neuroscience have overplayed the alleged economies that interlevel connections (including identities) justify while overlooking their fundamental role in promoting scientific research. A brief review of research on visual processing provides support for the explanatory pluralists general model of cross-scientific relations and discloses the valuable heuristic role hypothetical identities play in cross-scientific research. That model also supplies grounds for hesitation about the correlation objection to the psychophysical identity theory and complaints about an explanatory gap in physicalist accounts of consciousness. These takes on psycho-neural connections miss both the sorts of considerations that motivate hypothetical identities in science and their fundamental contribution to progressive research. Thus, their focus on the contributions of research at multiple levels of analysis does not bar explanatory pluralists from considering heuristic identity theory (HIT). Arguably, it encourages it.

Mechanism and Biological Explanation

This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts (entities) and operations (activities) with research using computational models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations.

Abstraction and the Organization of Mechanisms

Proponents of mechanistic explanation all acknowledge the importance of organization. But they have also tended to emphasize specificity with respect to parts and operations in mechanisms. We argue that in understanding one important mode of organization—patterns of causal connectivity—a successful explanatory strategy abstracts from the specifics of the mechanism and invokes tools such as those of graph theory to explain how mechanisms with a particular mode of connectivity will behave. We discuss the connection between organization, abstraction, and mechanistic explanation and illustrate our claims by looking at an example from recent research on so-called network motifs.

Levels of description and explanation in cognitive science

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level.

Mechanisms and psychological explanation

Publisher Summary This chapter outlines the history of machine-based approaches to psychology. The work on mechanistic explanation that is having a large impact on the philosophy of science is described in the chapter. The research on motivation and reward exemplifies the common explanatory practice in psychology — decomposing a composite, hierarchically organized system into its component parts and operations and then constructing models that abet scientific understanding of the way they might be organized so as to comprise the mechanisms activity. Mechanistic approaches reconfigure a number of issues in the philosophy of psychology beyond that of explanation. Two issues are considered in this chapter: the question of reductionism and the question of scientific discoveries. The mechanistic explanation is partially reductionistic in the sense that it appeals to lower-level parts and their operations in explaining why a mechanism behaves as it does; but mechanistic explanation is not reductionistic in the sense of deriving higher-level theories from lower-level ones nor in the sense of supplanting the explanations of causal processes at higher levels, where the mechanism as a whole engages other entities in its environment.

Dynamical Models: An Alternative or Complement to Mechanistic Explanations?

While agreeing that dynamical models play a major role in cognitive science, we reject Stepp, Chemero, and Turveys 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.

Realism, instrumentalism, and the Intentional Stance*

One of Dennetts principal arguments for an instrumentalistic construal of intentional attributions (e.g., attributions of belief, etc.) is that such attributions are environment relative. I argue that one can and should adopt a realist perspective toward such attributions, but accommodate their environmental relativity by treating intentional properties as relational properties. By doing so one acquires a useful perspective on experimental cognitive psychology; in particular, one can overcome the temptation to treat ecological accounts and information processing accounts as incompatible alternatives and come to see them as mutually supportive. Treating intentional properties as relational may be counter-intuitive, but I provide examples of how other sciences have had to treat what seem to be intrinsic properties as relational.