Comparing Historical and Intuitive Explanations of Motion: Does "Naïve Physics" Have a Structure

Are students' explanations of motion generated by an underlying structure? We address this question by exploring striking parallels between intuitive explanations and those offered by medieval scholastics. Using the historical record, it is possible to reconstructan inferential structure that generates medieval explanations. W e posit a parallel structure for intuitive explanations

Why/How to Study Scientific Thinking

Scientific research is a highly complex and creative domain of human activity. In addition to its intrinsic value, understanding scientific thinking provides insight into the creative potential of human psychological capacities, as they are imbedded in rich social, material, and cultural environments. I discuss findings from my own investigations using two forms of qualitative research suited to studying scientific thinking as situated in context: cognitive-historical and cognitive-ethnographic

Conceptual diagrams: representing ideas in design

Studies in cognition In many studies of well-defined problems, diagrammatic representations illustrate either causal or temporal relationships between parts of entities and phenomena that the diagram represents. In architecture, diagrams are used to represent causal relationships, such as with orientation diagrams, or temporal relationships, such as with circulation diagrams. There is, however, another kind of diagram that is used to represent the main idea or the core of a design. We call these diagrams conceptual diagrams. They differ, potentially, from other diagrammatic representations studied thus far in that they represent an abstract conceptualization of a potential problem solution. Diagrams in other fields can be interpreted as conceptual diagrams as well, such as a diagram that shows the electron orbiting around a nucleus in atomic physics, or the supply-demand diagram in economics. In the domain of scientific discovery, Nersessian Conceptual diagrams are abstract representations that embed the core of a conceptualization of a problem solution. They are concise, yet powerful aids in problem solving in that they provide high-level commitments constraining solutions. In architecture, they embed the core of a design solution encapsulating its generic characteristics and constraints and conveying the form of possible specific solutions. That they are not detailed prevents early commitment to a specific design solution and, thus, they facilitate exploratory reasoning. At the same time they are not ambiguous in the way sketches are in that they fix meaning and define a set of related solutions. This latter is important because design problems are ill-defined in that either the initial state, the goal state, or the operators--or all of them--require further specification. With ill-defined problems there exists a set of potential goal states instead of one goal state. One way that architects delimit the range of alternatives is by analogy. Conceptual diagrams function in a way similar to analogies in that they provide constraints that restrict the set of specified goal states. We propose salient characteristics of conceptual diagrams that are significant fo

Modeling complexity: cognitive constraints and computational model-building in integrative systems biology

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

Psychology of science: Implicit and explicit processes

I. The Acting Person as Unit of Analysis In any effort to establish the parameters of a psychology of science we face preliminary questions that are not empirical in a strict sense, beginning with how to demarcate the core concepts in play (science, psychology). A related question is one we take up in this paper: What is the appropriate unit of analysis for a psychology of science? This question relates to but can be distinguished from that which concerns the appropriate unit of measuremen