Nancy Nersessian

The origins of the ‘ incommensurability problem’ and its central aspect, the ‘ meaning variance thesis’ are traced to the successive collapse of several distinctions maintained by the standard empiricist account of meaning in scientific theories. The crucial distinction is that between a conceptual structure and a theory. The ‘thesis’ and the ‘problem’ follow from critiques of this distinction by Duhem, Quine and Feyerabend. It is maintained that, rather than revealing the ‘problem’, the arguments leading to it simply show the inadequacy of the reductionist theory of meaning. The genuine remaining problem is that of the development of a new theory of meaning in science

This paper examines the nature of model-based reasoning in the interplay between theory and experiment in the context of biomedical engineering research laboratories, where problem solving involves using physical models. These "model systems" are sites of experimentation where in vitro models are used to screen, control, and simulate specific aspects of in vivo phenomena. As with all models, simulation devices are idealized representations, but they are also systems themselves, possessing engineering constraints. Drawing on research in contemporary cognitive science that construes cognition as occurring in a complex distributed system comprising people and artifacts, I argue that reasoning with model systems is a constraint satisfaction process involving co-construction, manipulation, and revision of mental and physical models.

Interdisciplinary collaboration figures centrally in frontier research in many fields. Participants in inter-disciplinary projects face problems they would not encounter within their own disciplines. Among those are problems of mutual understanding, of finding a language to communicate both within projects and with the scientific community and society at large, and of needing to master concepts and methods of different disciplines. We think that a concentrated research and development effort is necessary to analyze, on the one hand, cognitive conditions of successful understanding, communication, and interaction and, on the other, to develop specific tools and methods that support and facilitate inter-disciplinarity both in practice and in educational projects that prepare future generations of professionals within and outside of academia. Those tools need to be developed and their cognitive efficiency measured

The importation of computational methods into biology is generating novel methodological strategies for managing complexity which philosophers are only just starting to explore and elaborate. This paper aims to enrich our understanding of methodology in integrative systems biology, which is developing novel epistemic and cognitive strategies for managing complex problem-solving tasks. We illustrate this through developing a case study of a bimodal researcher from our ethnographic investigation of two systems biology research labs. The researcher constructed models of metabolic and cell-signaling pathways by conducting her own wet-lab experimentation while building simulation models. We show how this coupling of experiment and simulation enabled her to build and validate her models and also triangulate and localize errors and uncertainties in them. This method can be contrasted with the unimodal modeling strategy in systems biology which relies more on mathematical or algorithmic methods to reduce complexity. We discuss the relative affordances and limitations of these strategies, which represent distinct opinions in the field about how to handle the investigation of complex biological systems.