Nancy J. Nersessian

Scientific thinking is one of the most creative expressions of human cognition. This paper discusses my research contributions to the cognitive science of science. I have advanced the position that data on the cognitive practices of scientists drawn from extensive research into archival records of historical science or collected in extended ethnographic studies of contemporary science can provide valuable insight into the nature of scientific cognition and its relation to cognition in ordinary contexts. I focus on contributions of my research on analogy, model‐based reasoning, and conceptual change and on how scientists enhance their natural cognitive capacities by creating modeling environments that integrate cognitive, social, material, and cultural resources. I provide an outline of my trajectory from a physicist to a philosopher of science to a hybrid cognitive scientist in my quest to understand the nature of scientific thinking.

We lay groundwork for applying ethnographic methods in philosophy of science. We frame our analysis in terms of two tasks: (1) to identify the benefits of an ethnographic approach in philosophy of science and (2) 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.

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.

In this article, we provide a case study examining how integrative systems biologists build simulation models in the absence of a theoretical base. Lacking theoretical starting points, integrative systems biology researchers rely cognitively on the model-building process to disentangle and understand complex biochemical systems. They build simulations from the ground up in a nest-like fashion, by pulling together information and techniques from a variety of possible sources and experimenting with different structures in order to discover a stable, robust result. Finally, we analyze the alternative role and meaning theory has in systems biology expressed as canonical template theories like Biochemical Systems Theory.