Nancy Nersessian

This chapter focuses on a novel class of models used in frontier research in the bioengineering sciences – in vitro simulation models – that provide the basis for biological experimentation. These bioengineered models are hybrid constructions, composed of living tissues or cells and engineered materials. Specifically, it discusses the processes through which in vitro models were built, experimented with, and justified in a tissue engineering lab. It examines processes of design, construction, experimentation, evaluation, and redesign of in vitro simulation models, in general, as instances of building the source analogy (as distinguished from retrieving an analogy), which figures prominently in creative frontier scientific research. Building the analogical source is a bootstrapping process, which furthers the articulation, as well as the solution of the problem.

A cognitive ethnography of how bioengineering scientists create innovative modeling methods. In this first full-scale, long-term cognitive ethnography by a philosopher of science, Nancy J. Nersessian offers an account of how scientists at the interdisciplinary frontiers of bioengineering create novel problem-solving methods. Bioengineering scientists model complex dynamical biological systems using concepts, methods, materials, and other resources drawn primarily from engineering. They aim to understand these systems sufficiently to control or intervene in them. What Nersessian examines here is how cutting-edge bioengineering scientists integrate the cognitive, social, material, and cultural dimensions of practice. Her findings and conclusions have broad implications for researchers in philosophy, science studies, cognitive science, and interdisciplinary studies, as well as scientists, educators, policy makers, and funding agencies. In studying the epistemic practices of scientists, Nersessian pushes the boundaries of the philosophy of science and cognitive science into areas not ventured before. She recounts a decades-long, wide-ranging, and richly detailed investigation of the innovative interdisciplinary modeling practices of bioengineering researchers in four university laboratories. She argues and demonstrates that the methods of cognitive ethnography and qualitative data analysis, placed in the framework of distributed cognition, provide the tools for a philosophical analysis of how scientific discoveries arise from complex systems in which the cognitive, social, material, and cultural dimensions of problem-solving are integrated into the epistemic practices of scientists. Specifically, she looks at how interdisciplinary environments shape problem-solving. Although Nersessian’s case material is drawn from the bioengineering sciences, her analytic framework and methodological approach are directly applicable to scientific research in a broader, more general sense, as well.

How do vague notions about how one might understand certain physical phenomena get transformed into scientific concepts such as “field”, “quark”, and “gene”? Philosophers of as disparate views as Reichenbach and Feyerabend have held that the process through which scientific concepts emerge is not a reasoned process. In a manner completely mysterious and unanalyzable, scientific concepts emerge fully grown, like Athena from the head of Zeus. However, when one examines actual cases of concept formation in science, a different picture can be painted: Scientific concepts do not pop out of heads, but are constructed in response to specific problems by utilizing methods appropriate to the solution of the problem. Thus, as with other aspects of the scientific enterprise, concept formation is a problem-solving process that involves the application of systematic procedures in the attempt to solve specific problems. It is a reasoned process.

Much of the attention of philosophy of science, history of science, and psychology in the twentieth century has focused on the nature of conceptual change. Conceptual change in science has occupied pride of place in these disciplines, as either the subject of inquiry or the source of ideas about the nature of conceptual change in other domains. There have been numerous conceptual changes in the history of science, some more radical than others. One of the most radical was the chemical revolution. In the seventeenth century, chemists believed that the processes of combustion and calcination involved the absorption or release of a substance called phlogiston. On this theory, when an ore is heated with charcoal, it absorbs phlogiston to produce a metal; when a metal is burned, it releases phlogiston and leaves behind a residue, or calx. The concept of phlogiston derived from a quite complex Aristotelian/medieval structure that included three concepts central to chemical theory: sulphur, the principle of inflammability; mercury, the principle of fluidity; and salt, the principle of inertness. All material substances were believed to contain these three principles in the form of earths. The phlogiston theory held that in combustion, the sulphurous earth (phlogiston) returns to the substance from which it escaped during some earlier burning process in its history, and that in calcination the process is reversed. However, chemists also knew that a calx is heavier than the metal from which it was derived. So, the theory implies that phlogiston has a negative weight, or a positive lightness. This did not present a problem, though, because it was compatible with the Aristotelian elements of fire and air (the others being earth and water), which were not attracted towards the center of the earth. The development of the oxygen theory of combustion and calcination by Lavoisier in the late eighteenth century has been called the chemical revolution because it required replacing the whole conceptual structure with, for example, different concepts of substance and element and new concepts of oxygen and caloric. In the new system, it was no longer possible to believe in the existence of substances with negative weight. According to the oxygen theory, oxygen gas is released in combustion and absorbed in calcination. Thus calx is metal (substance) plus oxygen, rather than metal minus phlogiston. The concept of phlogiston was eliminated from the chemical lexicon. The reconceptualization of chemical phenomena that took place in the chemical revolution made possible the atomic theory of matter, which, as we know, posits quite different constituents of material substances from the principles central to the earlier conceptual structure. Just what constitutes conceptual change, how it relates to theory change, and how it relates to changes in belief continues to be a subject of much debate. Clearly, though, as the preceding example demonstrates, the three are significantly interrelated.

We describe our efforts to address theoretical opportunities and methodological challenges that arose in the context of our ethnographic investigation of research labs in four different fields of bioengineering science. The multiyear study compared the common and specific features of four sites of interdisciplinary practice and aimed to analyze personal and collective goals, problem formulations, methods, technologies, and social organization within each lab. In the second phase of the study we sought to inform curriculum development for biomedical engineering from the ethnographic study, with the broader goal of positioning students to engage in the kind of thinking scientists actually do. Evaluating thinking in action posed considerable challenges, and required us to develop an approach to assessing the extent to which students enrolled in the new course were “thinking like a biomedical engineer.” Our principal point of emphasis for this chapter is that honest confrontation of the challenges of ethnographic study of science practice must lead to caution, yet that ethnographic study of science enables innovative adaptations of methods and can yield vital insights into psychological dimensions of science, including interdisciplinary science.

Thomas Kuhn's The Structure of Scientific Revolutions was published at the beginning of what has come to be known as “the cognitive revolution.” With hindsight one can construct significant parallels between the problems of knowledge, perception, and learning with which Kuhn and cognitive scientists were grappling and between the accounts developed by each. However, by and large Kuhn never utilized the research in cognitive science—especially in cognitive psychology—that we believe would have furthered his own paradigm. This is puzzling since he did not have the traditional philosophical aversion to “psychologizing” and in fact drew on insights from psychology to support the most radical claims in Structure, such as the “Gestalt switch” nature of conceptual change. Indeed, the research program outlined there seems intrinsically historical, philosophical, and psychological and Kuhn's work has had considerable influence on research in cognitive science.

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: to identify the benefits of an ethnographic approach in philosophy of science and to structure an ethnographic approach for philosophical investigation best adapted to provide information relevant to philosophical interests and epistemic values. To this end, we advocate for a purpose-guided form of cognitive ethnography that mediates between the explanatory and normative interests of philosophy of science, while maintaining openness and independence when framing such an investigation to achieve robust unbiased results.

The book examines the emerging approach of using qualitative methods, such as interviews and field observations, in the philosophy of science. Qualitative methods are gaining popularity among philosophers of science as more and more scholars are resorting to empirical work in their study of scientific practices. At the same time, the results produced through empirical work are quite different from those gained through the kind of introspective conceptual analysis more typical of philosophy. This volume explores the benefits and challenges of an empirical philosophy of science and addresses questions such as: What do philosophers gain from empirical work? How can empirical research help to develop philosophical concepts? How do we integrate philosophical frameworks and empirical research? What constraints do we accept when choosing an empirical approach? What constraints does a pronounced theoretical focus impose on empirical work? Nine experts discuss their thoughts and empirical results in the chapters of this book with the aim of providing readers with an answer to these questions.

In 1877 Louis Paul Cailletet in France and Raoul Pictet in Switzerland liquefied oxygen in the form of a mist. The liquefaction of the first of the so-called permanent gases heralded the birth of low-temperature research and is often described in the literature as having started a ‘race’ for attaining progressively lower temperatures. In fact, between 1877 and 1908, when helium, the last of the permanent gases, was liquefied, there were many priority disputes—something quite characteristic of the emergence of a new research field. This paper examines Cailletet’s path to the liquefaction of oxygen, as well as a debate between him and the Polish physicist Zygmunt Wróblewski over the latter’s contribution to the liquefaction of gases

The paper frames interdisciplinary research as creating complex, distributed cognitive-cultural systems. It introduces and elaborates on the method of cognitive ethnography as a primary means for investigating interdisciplinary cognitive and learning practices in situ. The analysis draws from findings of nearly 20 years of investigating such practices in research laboratories in pioneering bioengineering sciences. It examines goals and challenges of two quite different kinds of integrative problem-solving practices: biomedical engineering (hybridization) and integrative systems biology (collaborative interdependence). Practical lessons for facilitating research and learning in these specific fields are discussed and a preliminary set of interdisciplinary epistemic virtues are proposed as candidates for cultivation in interdisciplinary practices of these kinds more widely.

Computational systems biologists create and manipulate computational models of biological systems, but they do not always have straightforward epistemic access to the content and behavioural profile of such models because of their length, coding idiosyncrasies, and formal complexity. This creates difficulties both for modellers in their research groups and for their bioscience collaborators who rely on these models. In this paper we introduce a new kind of visualization that was developed to address just this sort of epistemic opacity. The visualization is unusual in that it depicts the dynamics and structure of a computer model instead of that model’s target system, and because it is generated algorithmically. Using considerations from epistemology and aesthetics, we explore how this new kind of visualization increases scientific understanding of the content and function of computer models in systems biology to reduce epistemic opacity.

We have been studying cognition and learning in research laboratories in the field of biomedical engineering. Through our combining of ethnography and cognitive-historical analysis in studying these settings we have been led to understand these labs as comprising evolving distributed cognitive systems and as furnishing agentive learning environments. For this paper we develop the theme of 'models-in-action,' a variant of what Knorr Cetina has called 'knowledge-in-action.' Among the epistemically most salient objects in these labs are so called "model systems," which are designs that blend engineering with the study and use of biological systems for purposes of simulative model-based reasoning. We portray the prevalent design-orientation in this engineering specialty and how the prevailing activity of cell-culturing in these labs transitions into a design activity for the bio-medical engineers, leading them to work with 'wet' devices. We discuss how devices, 'wet' and 'dry,' situate model-based understandings and how they participate in model systems in these labs. Models tend to come in clusters or configurations, and the model systems in these labs are epistemically salient junctures of interlocking models. Model systems in these labs evolve thereby consolidating what we want to call a 'fabric of interlocking models,' which functions as point of stability and departure in these labs. We convey a taste of such a 'fabric' for a tissue-engineering lab. We conjecture that through this 'fabric' extended developments in technology and methodology have a 'situated' presence in the workings of these labs.

Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations.

Building computational models of engineered exemplars, or prototypes, is a common practice in the bioengineering sciences. Computational models in this domain are often built in a patchwork fashion, drawing on data and bits of theory from many different domains, and in tandem with actual physical models, as the key objective is to engineer these prototypes of natural phenomena. Interestingly, such patchy model building, often combined with visualizations, whose format is open to a wide range of choice, leads to the discovery of new concepts and control structures. Two key questions are raised by this practice: how could discoveries arise from building external representations for which there is wide latitude in choice of the components from which they are built, and thus can be considered significantly arbitrary, and how could such discoveries allow engineering a real-world prototype system. To examine these questions, we present two case studies of discoveries that emerged from the building of such computational models in the bioengineering sciences. We then develop a process model that accounts for the discovery and transfer problems raised by both these cases, focusing on the process of building the model. Specifically, to account for the discovery problem, we propose that the process of building such models gradually leads to a close coupling between the modeler’s internal processes and the external dynamic model. To account for the transfer problem, we propose that the process of building the model leads to the creation of an enactive model that is generic, which closely enacts, and thus reveals, the way the system-level behavior of the engineered prototype emerges in time through the interaction of its parts. This enactive replication process leads to the model and the prototype forming a new class, which allows concepts and control structures developed for the computational model to be transfered to the real-world prototype. We argue that this account requires rethinking correspondence in engineering sciences as a plastic and enactive relation. A closer focus on the process of building models is required to develop a general account of this emerging approach to discovery.

Confronting any science studies or learning sciences researcher in the 21st century is the reality of interdisciplinary science. New hybrid fields1 collaboratively build new concepts, combine models from two or more disciplines and forge inter-reliant relationships among specialists with different skill sets to solve new problems. This paper emerges from our recognition that inescapable psychological factors, including identity dynamics, must be described and analyzed in order to better understand the social and cognitive practices specific to interdisciplinary science. In analysis of the foundations and opportunities for an...