I begin with a representative example of a contemporary scientific activity, observations using the Hubble Space Telescope, and ask what approaches within the cognitive sciences seem most fruitful as aids in developing an overall account of this sort of scientific activity. After presenting the Hubble Space Telescope System and a recent result, I consider applying a standard computational paradigm to this system. I find difficulties in identifying an appropriate cognitive agent and in making a suitable place for the instrumentation that (...) constitutes such a large part of the whole system. I next consider applying the notion of distributed cognition as developed by Hutchins (1995), and then return to the question whether The Hubble System, understood as a distributed cognitive system, should be regarded as a computational system. I find a large computational component, but also an important part, the Hubble Telescope itself, that seems better characterized as a dynamic system than as a computational system. Moreover, the group of scientists interpreting the images produced by the system seem best thought of as a human/cultural system along the lines advocated by those developing a cognitive (Lakoff, 1987) or usage-based (Tomasello, 2003) approach to language acquisition and language use. I argue next that, while cognition may be theorized as distributed among both humans and instruments, there is no need to introduce into cognitive science a notion of distributed knowledge beyond simple collective knowledge. Even less is there any need to introduce notions of distributed mind or distributed consciousness. The result is that the agency involved in distributed cognitive systems remains simply human agency as ordinarily conceived. I conclude that distributed cognitive systems like The Hubble System are hybrid systems composed partly of dynamic physical systems, partly of computational systems, and partly of human cultural systems. (shrink)
Among philosophers, Don Campbell is best known for his naturalistic, evolutionary approach to epistemology. There can be no doubt, however, that he was a thoroughgoing naturalist in all matters, even though he seems to have had little interest in exploring naturalism as a general philosophical position. He was professionally more interested in the origins and workings of knowledge producing social systems. I am interested in these things too, but also naturalism in general. So my tribute to Don will be to (...) present some aspects of a generalized naturalism that I hope he would have liked. Campbell's naturalism, like his epistemology, would be evolutionary, but, like his realism, would be more. It would be hypothetical, a posit, and not a first principle. And it would be critical, subject to ongoing refinement in light of new experience. Thus my title. It is a title I fancy Don would have liked, perhaps one he himself would have used had he ever taken up naturalism as a general theme. It is much easier to give a negative characterization of naturalism than to provide a positive account of what it.. (shrink)
activity, particularly in experimental sciences, as involving the operation of distributed cognitive systems, since these are understood in the contemporary cognitive sciences. Introducing a notion of distributed cognition, however, invites consideration of whether, or in what way, related cognitive activities, such as knowing, might also be distributed. In this paper I will argue that one can usefully introduce a notion of distributed cognition without..
6 ABSTRACT. Scientiﬁc realism is a doctrine that was both in and out of fashion 7 several times during the twentieth century. I begin by noting three presuppositions of 8 a succinct characterization of scientiﬁc realism oﬀered initially by the foremost critic..
In this essay I argue that T. S. Kuhn, at least in his later works, can be regarded as a perspectival realist. This is a retrospective interpretation based mainly on the essays published posthumously under the title The Road Since Structure (Kuhn 2000). Among the strongest grounds for this interpretation is that Kuhn explicitly states that one must have a “lexicon” in place before raising questions about the truth or falsity of claims made using elements of the lexicon. This, in (...) a linguistic framework, can be understood as an affirmation of perspectival realism. The essay concludes with an examination of Donald Davidson’s famous paper, “On The Very Idea of a Conceptual Scheme,” arguing, along lines Kuhn himself suggested, that Davidson’s presentation is no threat to his notion of a conceptual scheme, or, I would add, a theoretical perspective. (shrink)
While agreeing that cognition in the sciences is usefully thought of as involving processes encompassing both humans and artifacts, I object to attributing cognitive states to extended systems. I argue that cognitive states, such as ?knowing?, should be confined to the human components of cognitive systems. My argument appeals to the large dimensions, both spatial and temporal, of many scientific cognitive systems, the existence of epistemic norms, and the need for agents in science.
At issue is the usefulness of a concept of distributed cognition for the philosophy of science. I have argued for the desirability of regarding scientific systems such as the Hubble Space Telescope as distributed cognitive systems. But I disagree with those who would ascribe cognitive states, such as knowledge, to such systems as a whole, and insist that cognitive states are ascribable only to the human components of such systems. Vaesen, appealing to a well-known ?parity principle,? insists that if there (...) is a distributed cognitive system, it must have cognitive states. Otherwise, we are left with only the cognitive states of individual humans who are then not part of a distributed cognitive system. I argue that Vaesen has misinterpreted the parity principle, which, in any case, I reject, and go on to argue for an understanding of scientific cognition as human centered even though not human bound. (shrink)
Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a (...) target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system. (shrink)
Hanson claims that moral responsibility should be distributed among both the humans and artifacts comprising complex wholes that produce morally relevant outcomes in the world. I argue that this claim is not sufficiently supported. In particular, adopting a consequentialist understanding of morality does not by itself support the view that the existence of a causally necessary object in such a complex whole is sufficient for assigning moral responsibility to that object. Moreover, there are good reasons, both evolutionary and contemporary, for (...) not adopting this stance. (shrink)
The usual question, “Are models fictions?” is replaced by the question, “Should scientific models be regarded as works of fiction?” This makes it clear that the issue is not one of definition but of interpretation. First one must distinguish between the ontology of scientific models and their function in the practice of science. Theoretical models and works of fiction are ontologically on a par, their both being creations of human imagination. It is their differing functions in practice that makes it (...) inappropriate to regard scientific models as works of fiction. Three reasons for thinking scientific models should be regarded as works of fiction are rejected. First, scientists themselves sometimes invoke the idea of fictions in their discussions of specific models. Second, many scientific models are physically impossible to realize in the real world. Third, regarding scientific models as works of fiction supports a general fictionalist understanding of scientific theories. It is concluded that promoting the general idea that scientific models are works of fiction unnecessarily supports attacks on the legitimacy of science itself. (shrink)
In earlier works, I have argued that it is useful to think of much scientific activity, particularly in experimental sciences, as involving the operation of distributed cognitive systems, as these are understood in the contemporary cognitive sciences. Introducing a notion of distributed cognition, however, invites consideration of whether, or in what way, related cognitive activities, such as knowing, might also be distributed. In this paper I will argue that one can usefully introduce a notion of distributed cognition without attributing other (...) cognitive attributes, such as knowing, let alone having a mind or being conscious, to distributed cognitive systems. I will first briefly introduce the cognitive science understanding of distributed cognition, partly so as to distinguish full-blown distributed cognition from mere collective cognition.1. (shrink)
Many people assume that the claims of scientists are objective truths. But historians, sociologists, and philosophers of science have long argued that scientific claims reflect the particular historical, cultural, and social context in which those claims were made. The nature of scientific knowledge is not absolute because it is influenced by the practice and perspective of human agents. Scientific Perspectivism argues that the acts of observing and theorizing are both perspectival, and this nature makes scientific knowledge contingent, as Thomas Kuhn (...) theorized forty years ago. Using the example of color vision in humans to illustrate how his theory of “perspectivism” works, Ronald N. Giere argues that colors do not actually exist in objects; rather, color is the result of an interaction between aspects of the world and the human visual system. Giere extends this argument into a general interpretation of human perception and, more controversially, to scientific observation, conjecturing that the output of scientific instruments is perspectival. Furthermore, complex scientific principles—such as Maxwell’s equations describing the behavior of both the electric and magnetic fields—make no claims about the world, but models based on those principles can be used to make claims about specific aspects of the world. Offering a solution to the most contentious debate in the philosophy of science over the past thirty years, Scientific Perspectivism will be of interest to anyone involved in the study of science. (shrink)
I begin by arguing that a consistent general naturalism must be understood in terms of methodological maxims rather than metaphysical doctrines. Some specific maxims are proposed. I then defend a generalized naturalism from the common objection that it is incapable of accounting for the normative aspects of human life, including those of scientific practice itself. Evolutionary naturalism, however, is criticized as being incapable of providing a sufficient explanation of categorical moral norms. Turning to the epistemological norms of science itself, particularly (...) those governing the empirical testing of specific models, I argue that these should be regarded as conditional rather than categorical and that, as such, can be given a naturalistic justification. The justification, however, is more cognitive than evolutionary. The historical development of science is found to be a better place for applying evolutionary ideas. After briefly considering the possibility of a naturalistic understanding of mathematics and logic, I turn to the problem of reconciling scientific realism with an evolutionary picture of scientific development. The solution, I suggest, is to understand scientific knowledge as being “perspectival” rather than absolutely objective. I first argue that scientific observation, whether by humans or instruments, is perspectival. This argument is extended to scientific theorizing which is regarded not as the formulation of universal laws of nature but as the construction of principles to be used in the construction of models to be applied to specific natural systems. The application of models, however, is argued to be not merely opportunistic but constrained by the methodological presumption that we live in a world with a definite causal structure even though we can understand it only from various perspectives. (shrink)
In previous publications I have argued that much scientific activity should be thought of as involving the operation of distributed cognitive systems. Since these contributions to the cognitive study of science appear in venues not necessarily frequented by philosophers of science, I begin with a brief introduction to the notion of a distributed cognitive system. I then describe what I take to be an exemplary case of a scientific distributed cognitive system, the Hubble Space Telescope (HST). I do not here (...) reargue the case for conceiving of systems like the HST as distributed cognitive systems. Rather, I examine a question that arises once one has adopted the perspective of distributed cognitive systems, namely, the role of agency in a distributed cognitive system. Here I argue, contrary to several advocates of distributed cognitive systems, that we should regard the human components of distributed cognitive systems as the only sources of agency within such systems. In particular, we should not extend notions of agency to such systems as a whole. (shrink)
Scientific realism is a doctrine that was both in and out of fashion several times during the twentieth century. I begin by noting three presuppositions of a succinct characterization of scientific realism offered initially by the foremost critic in the latter part of the century, Bas van Fraassen. The first presupposition is that there is a fundamental distinction to be made between what is “empirical” and what is “theoretical”. The second presupposition is that a genuine scientific realism is committed to (...) their being “a literally true story of what the world is like”. The third presupposition is that there are methods for justifying a belief in the empirical adequacy of a theory which do not also suffice to justify beliefs in its literal truth. Each of these presuppositions raises a number of problems, some of which are quite old and others rather newer. In each case, I briefly review some of the old problems and then elaborate the newer problems. (shrink)
Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)
From the perspective of cognitive science, it is illuminating to think of much contemporary scienti?c research as taking place in distributed cognitive systems. This is particularly true of large-scale experimental and observational systems such as the Hubble Telescope. Clark, Hutchins, Knorr-Cetina, and Latour insist or imply such a move requires expanding our notions of knowledge, mind, and even consciousness. Whether this is correct seems to me not a straightforward factual question. Rather, the issue seems to be how best to develop (...) a theoretical understanding of such systems appropriate to the study of science and technology. I argue that there is no need to attribute to such systems as a whole any form of cognitive agency. We can well understand the importance of such systems while restricting agency to the human components. The implication is that we think of these large-scale distributed cognitive systems not so much as uni?ed wholes, but as hybrid systems including both physical artifacts and ordinary humans. (shrink)
I contend that Janet Kourany's "A Philosophy of Science for the Twenty-First Century" contains three levels of projects: (1) a naturalistic project, (2) a critical project, and (3) a political project. The naturalistic project is already well established. The critical project is less valued and less established within the profession, but seems a worthy and achievable goal. The political project, I argue, takes one outside the professional pursuit of the philosophy of science. The critical project encompasses both the evaluation of (...) scientific research programs and of empirical conclusions. I contend that the former is widely acknowledged as legitimate while the latter is unacceptable. (shrink)
Among the many contested boundaries in science studies is that between the cognitive and the social. Here, we are concerned to question this boundary from a perspective within the cognitive sciences based on the notion of distributed cognition. We ﬁrst present two of many contemporary sources of the notion of distributed cognition, one from the study of artiﬁcial neural networks and one from cognitive anthropology. We then proceed to reinterpret two well-known essays by Bruno Latour, ‘Visualization and Cognition: Thinking with (...) Eyes and Hands’ and ‘Circulating Reference: Sampling the Soil in the Amazon Forest’. In both cases we ﬁnd the cognitive and the social merged in a system of distributed cognition without any appeal to agonistic encounters. For us, results do not come to be regarded as veridical because they are widely accepted; they come to be widely accepted because, in the context of an appropriate distributed cognitive system, their apparent veracity can be made evident to anyone with the capacity to understand the workings of the system. (shrink)
In Epistemic Cultures (1999), Karin Knorr Cetina argues that different scientific fields exhibit different epistemic cultures. She claims that in high energy physics (HEP) individual persons are displaced as epistemic subjects in favor of experiments themselves. In molecular biology (MB), by contrast, individual persons remain the primary epistemic subjects. Using Ed Hutchins' (1995) account of navigation aboard a traditional US Navy ship as a prototype, I argue that both HEP and MB exhibit forms of distributed cognition. That is, in both (...) fields cognition is distributed across individual persons and complex artifacts. The cognitive system producing the knowledge is heterogeneous. Nevertheless, in both fields we can reserve epistemic agency for the human components of these systems. We do not need to postulate new distributed cognitive agents, let alone ones exhibiting new forms of consciousness. (shrink)
What are models that they may be used to represent reality? Here is a first pass. Models are objects that can be used to represent reality by exhibiting a designated similarity to physical objects. To be more specific, I need to indicate the kinds of objects models may be and how they may exhibit a designated similarity to real objects. My prototype for a model is a standard road map. This is a physical object (usually made of paper) that I (...) would say represents a terrain in virtue of quite specific spatial similarities. I move on to scale models, such as Watson?s original physical model of DNA. Next I treat abstract models, which are abstract objects not to be confused with the linguistic entities that may be used to characterize them. Finally, I discuss theoretical models which I now regard as abstract models constructed according to the principles of an overarching theory. Serious use of the notion of similarity is often criticized on the ground that anything may be similar to anything else in some respect or other. It is also often claimed that there is no satisfactory general characterization of similarity. I exploit these facts by insisting that claims of similarity between models and real objects must be accompanied by (perhaps tacit) specifications of the respects and the degrees to which similarity is claimed. Such specifications cannot be intrinsic to either a model or a physical object, but must be supplied by those using the model according to their own interests. Thus, taking the relationship between models and physical systems to be one of similarity implies that nothing is intrinsically a model of anything. It is only by intention, or convention, that some object becomes a model of some physical things. For models, at least, the motto is: No representation without representers. Moreover, no general characterization of similarity is needed. It is enough that we can say what counts as sufficiently similar for specific respects. This we can certainly do. (shrink)
There is no best scientific model of anything; there are only models more or less good for different purposes. Thus, there is no general answer to the question of whether one should model biological behavior using computer simulations or robots. It all depends on what one wants to learn. This is not a question about models, but about scientific goals.
Debate over the nature of science has recently moved from the halls of academia into the public sphere, where it has taken shape as the "science wars." At issue is the question of whether scientific knowledge is objective and universal or socially mediated, whether scientific truths are independent of human values and beliefs. Ronald Giere is a philosopher of science who has been at the forefront of this debate from its inception, and Science without Laws offers a much-needed mediating perspective (...) on an increasingly volatile line of inquiry. Giere does not question the major findings of modern science: for example, that the universe is expanding or that inheritance is carried by DNA molecules with a double helical structure. But like many critics of modern science, he rejects the widespread notion of science--deriving ultimately from the Enlightenment--as a uniquely rational activity leading to the discovery of universal truths underlying all natural phenomena. In these highly readable essays, Giere argues that it is better to understand scientists as merely constructing more or less abstract models of limited aspects of the world. Such an understanding makes possible a resolution of the issues at stake in the science wars. The critics of science are seen to be correct in rejecting the Enlightenment idea of science, and its defenders are seen to be correct in insisting that science does produce genuine knowledge of the natural world. Giere is utterly persuasive in arguing that to criticize the Enlightenment ideal is not to criticize science itself, and that to defend science one need not defend the Enlightenment ideal. Science without Laws thus stakes out a middle ground in these debates by showing us how science can be better conceived in other ways. (shrink)
This short paper serves as an introduction to a debate between representatives of two fundamentally different points of view regarding the nature of scientific inference. Colin Howson and Peter Urbach represent a Bayesian point of view and Deborah Mayo represents a version of classical statistics called error statistics. The paper begins by reviewing earlier versions of the same two points of view due to Rudolf Carnap and Hans Reichenbach, respectively. After a few remarks about philosophical approaches to understanding scientific reasoning (...) between 1960 and 1980, I turn to substantive differences between the two approaches. (shrink)
This paper explores a new reason for preferring a model-theoretic approach to understanding the nature of scientific theories. Identifying the models in philosophers' model-theoretic accounts of theories with the concepts in cognitive scientists' accounts of categorization suggests a structure to families of models far richer than has commonly been assumed. Using classical mechanics as an example, it is argued that families of models may be "mapped" as an array with "horizontal" graded structures, multiply hierarchical "vertical" structures, and local "radial" structures. (...) These structures promise important implications for how scientific theories are learned and used in actual scientific practice. (shrink)
This address focuses on those of us engaged in viewing science, particularly philosophers and sociologists of science. I begin with a historical perspective on the philosophy of science, focusing on the historical contingencies which have shaped its development since the 1930s. I then turn my gaze to the more recent history of the sociology of science. For both disciplines I hold up to view the reflexive problem of the status of that discipline's claims from its own perspective. I conclude with (...) a realist vision of science which rejects asymmetric notions, such as rationality, in favor of a naturalistic, perspectival realism. (shrink)
Does recent work in the cognitive sciences have any implications for theories or methods employed within the philosophy of science itself? It does if one takes a naturalistic approach in which understanding the nature of representations or judgments of representational success in science requires reference to the cognitive capacities or activities of individual scientists. Here I comment on recent contributions from three areas of the cognitive sciences represented respectively by Paul Churchland's neurocomputational perspective, Nancy Nersessian's cognitive-historical approach, and Paul Thagard's (...) computational philosophy of science. The main general conclusion is that we need to replace traditional linguistic notions of representation in science. (shrink)
This paper provides a general defense of the idea that the cognitive sciences provide models that are useful for exploring issues that have traditionally occupied philosophers of science. Questions about the nature of theories, for example, are assimilated into studies of the nature of cognitive representations, while questions concerning the choice of theories fall under studies of human judgment and decision making. The implications of adopting "a cognitive approach" are explored, particularly the rejection of foundationist epistemologies which might provide a (...) philosophical justification of science. Instead I suggest a scientific foundation provided by evolutionary biology and the scientific goal of explaining science as a human phenomenon. (shrink)
In arguing a "role for history," Kuhn was proposing a naturalized philosophy of science. That, I argue, is the only viable approach to the philosophy of science. I begin by exhibiting the main general objections to a naturalistic approach. These objections, I suggest, are equally powerful against nonnaturalistic accounts. I review the failure of two nonnaturalistic approaches, methodological foundationism (Carnap, Reichenbach, and Popper) and metamethodology (Lakatos and Laudan). The correct response, I suggest, is to adopt an "evolutionary perspective." This perspective (...) is defended against one recent critic (Putnam). To argue the plausibility of a naturalistic approach, I next sketch a naturalistic account of theories and of theory choice. This account is then illustrated by the recent revolution in geology. In conclusion I return to Kuhn's question about the role of history in developing a naturalistic theory of science. (shrink)