Nancy Nersessian

Visual analogy is believed to be important in human problem solving. Yet, there are few computational models of visual analogy. In this paper, we present a preliminary computational model of visual analogy in problem solving. The model is instantiated in a computer program, called Galatea, which uses a language for representing and transferring visual information called Privlan. We describe how the computational model can account for a small slice of a cognitive-historical analysis of Maxwell’s reasoning about electromagnetism.

Designing, building, and experimenting with physical simulation models are central problem‐solving practices in the engineering sciences. Model‐based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher–artifact system. It draws on and further develops the framework of “distributed cognition” to interpret data collected in ethnographic and cognitive‐historical studies of two biomedical engineering research laboratories, and articulates the notion of distributed model‐based cognition to answer the question posed in the title.

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.

We historically and conceptually situate distributed cognition by drawing attention to important similarities in assumptions and methods with those of American ?functional psychology? as it emerged in contrast and complement to controlled laboratory study of the structural components and primitive ?elements? of consciousness. Functional psychology foregrounded the adaptive features of cognitive processes in environments, and adopted as a unit of analysis the overall situation of organism and environment. A methodological implication of this emphasis was, to the extent possible, the study of cognitive and other processes in the natural (real world) contexts in which they occur. We therefore emphasize commonalities and differences between functional psychology and D-Cog. One purpose of the comparison is to consider the extent to which criticisms directed at functional psychology are relevant to D-Cog. We also examine the relation between functional psychology and philosophical pragmatism and conclude that D-Cog's conceptual framework would be strengthened through more explicit adoption of philosophical pragmatism, consistent with the eventual trajectory of functional psychology

The paper argues that the practice of thought experintenting enables scientists to follow through the implications of a way of representing nature by simulating an exemplary or representative situation that is feasible within that representation. What distinguishes thought experimenting from logical argument and other forms of propositional reasoning is that reasoning by means of a thought experiment involves constructing and simulating a mental model of a representative situation. Although thought experimenting is a creative part of scientific practice, it is a highly refined extension of a mundane form of reasoning. It is not a mystery why scientific thought experiments are a reliable source of empirical insights. Thought experimenting uses and manipulates representations that derive from real-world experiences and our conceptualizations of them.

Concept formation in science is a reasoned process, commensurate with ordinary problem-solving processes. An account of how analogical reasoning and reasoning from imagistic representations generate new scientific concepts is presented. The account derives from case studies of concept formation in science and from computational theories of analogical problem solving in cognitive science. Concept formation by analogy is seen to be a process of increasing abstraction from existing conceptual structures.

Giere's assessment is that the cognitive sciences, especially cognitive psychology, have much to offer the philosophy of science as it attempts to develop theories of the growth, development, and change of scientific knowledge as human activities. Margolis produces a model of scientific change by drawing from recent work in the cognitive sciences and attempts to show how this model explains salient cases of conceptual change. While agreeing with Giere's assessment, I argue that Margolis provides the wrong model both for scientific change and for how the interaction between cognitive science and philosophy of science should proceed.

Thought experiments have played a prominent role in numerous cases of conceptual change in science. I propose that research in cognitive psychology into the role of mental modeling in narrative comprehension can illuminate how and why thought experiments work. In thought experimenting a scientist constructs and manipulates a mental simulation of the experimental situation. During this process, she makes use of inferencing mechanisms, existing representations, and general world knowledge to make realistic transformations from one possible physical state to the next. The simulation reveals the impossibility of integrating multiple constraints drawn from existing representations and the world and pinpoints the locus of the required conceptual reform.

This paper presents an analysis of emotional and affectively toned discourse in biomedical engineering researchers’ accounts of their problem solving practices. Drawing from our interviews with scientists in two laboratories, we examine three classes of expression: explicit, figurative and metaphorical, and attributions of emotion to objects and artifacts important to laboratory practice. We consider the overall function of expressions in the particular problem solving contexts described. We argue that affective processes are engaged in problem solving, not as simply tacked onto reasoning but as integral to it. The examples we present illustrate the close relation of emotion to problem solving and experimentation; they also implicate social and cultural dimensions of emotion expression. The analysis underscores a need to consider emotional expression to be intimately and importantly tied to the cognitive achievements and social negotiations of laboratory practices

The claims of the epistemological 'anarchists' have their roots in the orthodox tradition as well as in the Popperian. In particular they follow from the work of Quine. Meaning variance and incommensurability follow directly from the holistic conception of meaning in his 'network' view. Quine's efforts to evade this conclusion fail. His attempt to develop a theory-neutral notion of observation sentence is shown (1) to be inconsistent with his previous claims since it involves the tacit acceptance of the 'dogma of reductionism', (2) to involve the acceptance of a questionable 'third dogma', i.e. the stimulus-response theory of behaviorist psychology, and (3) ultimately to miss the point of the anarchists. In Quinian terms, in order for observation sentences to perform the function of intertheoretical 'arbiters', what is needed is 'similarity of assent', which is not a meaningful notion on an internetwork basis.

Cases where analogy has played a significant role in the formation of a new scientific concept are well-documented. Yet, how is it that genuinely new representations can be constructed from existing representations? It is argued that the process of ‘generic modeling’ enables abstraction of features common to both the domain of the source of the analogy and of the target phenomena. The analysis focuses on James Clerk Maxwell's construction of the electromagnetic field concept. The mathematical representation Maxwell constructed turned out to be a system of abstract laws that when applied to electromagnetic systems yield laws of a dynamical system that will not map back onto the mechanicals domains used in their construction

There is substantial evidence that traditional instructional methods have not been successful in helping students to restructure their commonsense conceptions and learn the conceptual structures of scientific theories. This paper argues that the nature of the changes and the kinds of reasoning required in a major conceptual restructuring of a representation of a domain are fundamentally the same in the discovery and in the learning processes. Understanding conceptual change as it occurs in science and in learning science will require the development of a common cognitive model of conceptual change. The historical construction of an inertial representation of motion is examined and the potential instructional implications of the case are explored.

In his article, "Is Essentialism Unscientific?" (1988), Jarrett Leplin claims that I do not have sufficient grounds for rejecting the customary "philosophical method of discovery" that allows for the direct transfer of theories developed in the philosophy of language to science. While admitting that all attempts at transfer thus far have failed, he still maintains that method is sound. I argue that the wholesale failure of these attempts is reason enough to suspect the method and to try to devise one more suitable to fathoming how "meaning", "reference", and "meaning change" are to be understood for scientific theories. The method I have proposed in Nersessian (1984b), and subsequent work, demands that we learn how to incorporate the actual practices of meaning construction in science into our analyses. Leplin distorts my analysis and, thus, fails to understand the insights that study provides

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.

How do novel scientific concepts arise? In Creating Scientific Concepts, Nancy Nersessian seeks to answer this central but virtually unasked question in the problem of conceptual change. She argues that the popular image of novel concepts and profound insight bursting forth in a blinding flash of inspiration is mistaken. Instead, novel concepts are shown to arise out of the interplay of three factors: an attempt to solve specific problems; the use of conceptual, analytical, and material resources provided by the cognitive-social-cultural context of the problem; and dynamic processes of reasoning that extend ordinary cognition. Focusing on the third factor, Nersessian draws on cognitive science research and historical accounts of scientific practices to show how scientific and ordinary cognition lie on a continuum, and how problem-solving practices in one illuminate practices in the other.