Nancy J. Nersessian, Creating Scientific Concepts, Cambridge, MA: MIT Press, 2008.

For more than two decades now Nancy Nersessian has been working at the intersection of philosophy of science and the cognitive sciences, investigating how scientists actually think at work. Creating Scientific Concepts provides a general introduction to her approach, covering, on the one hand, historical research on the ways of thinking of 19th century physicists (namely, James Clerk Maxwell) and, on the other hand, an analysis of the cognitive foundations of scientific modelling. Nersessian’s essay is more than introduction though. Her goal is to explain conceptual innovation in science. More precisely, to articulate an analytic framework to account for the “specific modelling practices that historical records implicate in problem solving leading to conceptual innovation, specifically, analogical modelling, visual modelling and thought-experimenting” (p. 13). Let me spell this out.

Nersessian adopts an empiricist view of concepts, drawing mainly on the work of Lawrence Barsalou (Chapter 4). According to this latter, concepts would be perceptual symbols, neural correlates of sensorimotor experience (p. 124). These symbols constitute analogical representations and the sensioriomotor processes in which they originate are re-enacted whenever we use them to think. In our concepts about physical systems, the analogy captures the constraints we discern in the phenomena (e.g., causal structures). These constraints are then preserved in our mental simulations, even if we change other properties and relations of our physical concepts in order to solve whatever problem we are dealing with. The possession of a concept implies thus the skill “for constructing a potentially infinite number of simulations” according to our needs and goals (p. 126). Nersessian also argues for a coupling between external and internal representations: mental simulation often needs real-world resources “outside” our heads. However, she acknowledges that the nature of the cognitive mechanisms at the interface of this coupling is still to be articulated. Images ―diagrams, for instance― provide representational tools to extend our mental simulations.

Nersessian analyzes the role of analogies, images and thought-experimental narratives in our modelling practices (chapter 5). She departs from the standard assumption that these are separate resources, treating them as a continuum of tools for model based reasoning. Instead of direct inferences (mappings from sources to target domains), Nersessian studies how analogies, images and narratives contribute to the creation of intermediary models, with their own sets of constraints, that can be gradually elaborated in a series of representations that finally reaches real world phenomena. The assessment of these intermediary models according to the way they preserve and extend the relevant structural constraints (drawing here on Dedre Gentner’s ideas) provides the epistemic warrant for model based reasoning. Satisfactory models should exemplify features relevant to the epistemic goals of the problem solver (p. 157).

All these claims are illustrated with two case studies of scientific problem solving. In chapter 2 Nersessian revisits her analyses of the historical record of models constructed by James Clerk Maxwell leading to the field equations for electromagnetic phenomena. In chapter 3, she studies an experiment conducted by John Clement in which an expert is asked to solve a physics problem recording every step on his way to the solution. Both cases show at different scales how the cycles of construction, simulation, evaluation and adaptation of models with the aforementioned resources finally yield an original solution. Those interested in bringing together history and philosophy of science will surely appreciate Nersessian’s naturalist approach. Her cognitive appraisal of Maxwell’s models allows her to interpret as positive steps on his way to success what were previously considered misguided attempts at getting there. The analogy between Maxwell’s written records and Clement’s in vivo experiment is equally refreshing. Not every philosopher will enjoy this book though. Nersessian’s approach takes sides and her combination of concept empiricism plus embodied cognition is not precisely mainstream. She is honest enough to admit the limitations of her approach: the view she presents is far from consensual at most points and certainly needs further elaboration. However, the research program she presents is articulated enough to deserve serious consideration. I am not particularly happy though with the purported output of this book: the account Nersessian presents of scientific creativity appears as rather a poor corollary of the previous analysis: if we are able to construct “a potentially infinite number of simulations” many of them will be innovative, but how do we distinguish those of scientific value?

The answer depends of course on the goals and needs of the scientific community. But even if Nersessian wants her account to be social, little is said about how the community chooses the relevant innovations. We are left with the impression that the choice of constraints is always epistemic, but our goals and needs often impose non-epistemic constraints (David Bloor famously argued that scientific concepts are closed according to these latter) and we may legitimately wonder how do they contribute to the success of a model. Part of the plausibility of Nersessian’s analysis depends indeed on her choice of purely epistemic success stories as illustrations. But her argument made me think instead of an already old controversy on failed analogies between physics and economics sparked by Philip Mirowski’s book More Heat than Light in 1989. The equations never proved to be as empirically successful in the target domain (the analysis of the supply and demand) as they had originally been in physics. And yet they were acknowledged as an innovative analogy, perhaps because they observed several constraints considered relevant by some schools of thought in economics. Perhaps for Nersessian the explanation of innovative success and error is symmetrical (in Bloor’s sense), but I would have expected a more explicit discussion of this problem in this book. I can only recommend it nonetheless.

{December 2010}

{Metapsychology online review}