The term conceptual simulation refers to a type of everyday reasoning strategy commonly called “what if” reasoning. It has been suggested in a number of contexts that this type of reasoning plays an important role in scientific discovery; however, little direct evidence exists to support this claim. This article proposes that conceptual simulation is likely to be used in situations of informational uncertainty, and may be used to help scientists resolve that uncertainty. We conducted two studies to investigate the relationship (...) between conceptual simulation and informational uncertainty. Study 1 was an in vivo study of expert scientists; the results suggest that scientists do use conceptual simulation in situations of informational uncertainty, and that they use conceptual simulation to make inferences from their data using the analogical reasoning process of alignment by similarity detection. Study 2 experimentally manipulated experts' level of uncertainty and provides further support for the hypothesis that conceptual simulation is more likely to be used in situations of informational uncertainty. Finally, we discuss the relationship between conceptual simulation and other types of reasoning using qualitative mental models. (shrink)

A productive way to think about imagistic mental models of physical systems is as though they were sources of quasi‐empirical evidence. People depict or imagine events at those points in time when they would experiment with the world if possible. Moreover, just as they would do when observing the world, people induce patterns of behavior from the results depicted in their imaginations. These resulting patterns of behavior can then be cast into symbolic rules to simplify thinking about future problems and (...) to reveal higher order relationships. Using simple gear problems, three experiments explored the occasions of use for, and the inductive transitions between, depictive models and number‐based rules. The first two experiments used the convergent evidence of problem‐solving latencies, hand motions, referential language and error data to document the initial use of a model, the induction of rules from the modeling results, and the fallback to a model when a rule fails. The third experiment explored the intermediate representations that facilitate the induction of rules from depictive models. The strengths and weaknesses of depictive modeling and more analytic systems of reasoning are delineated to motivate the reasons for these transitions. (shrink)

We have developed a process model that learns in multiple ways while finding faults in a simple control panel device. The model predicts human participants' learning through its own learning. The model's performance was systematically compared to human learning data, including the time course and specific sequence of learned behaviors. These comparisons show that the model accounts very well for measures such as problem‐solving strategy, the relative difficulty of faults, and average fault‐finding time. More important, because the model learns and (...) transfers its learning across problems, it also accounts for the faster problem‐solving times due to learning when examined across participants, across faults, and across the series of 20 trials on an individual participant basis. The model shows how learning while problem solving can lead to more recognition‐based performance, and helps explain how the shape of the learning curve can arise through learning and be modified by differential transfer. Overall, the quality of the correspondence appears to have arisen from procedural, declarative, and episodic learning all taking place within individual problem‐solving episodes. (shrink)

We explore the extent to which Anderson's (1987) theory of knowledge compilation can account for the relationship between instructions and practice in learning to use a simple device. Bibby and Payne (1993) reported experimental support for knowledge compilation in this domain. This article replicates the finding of a performance cross‐over between instruction type and task type that disappears with practice on the tasks. The research is extended by using verbal protocols to model the strategies of novice and more experienced individuals. (...) Production system models of these strategies suggest that knowledge compilation only provides an adequate account of practice for one of two Instruction groups. To model the strategy shifts for the second group, we employ a “procedure modification heuristic” (after Neches, 1987), which relies on access to a declarative model of the configuration of the device. This suggests that instructionally derived declarative knowledge may have a subtle ongoing effect on the changes in procedural knowledge with practice. (shrink)

Scientific models represent aspects of the empirical world. I explore to what extent this representational relationship, given the specific properties of models, can be analysed in terms of propositions to which truth or falsity can be attributed. For example, models frequently entail false propositions despite the fact that they are intended to say something "truthful" about phenomena. I argue that the representational relationship is constituted by model users "agreeing" on the function of a model, on the fit with data and (...) on the aspects of a phenomenon that are modelled. Model users weigh the propositions entailed by a model and from this decide which of these propositions are crucial to the acceptance and continued use of the model. Thus, models represent phenomena when certain propositions they entail are true, but this alone does not exhaust the representational relationship. Therefore, the constraints that produce the choice of the relevant propositions in a model must also be examined and their analysis contributes to understanding the relationship between models and phenomena. (shrink)

The function of groups as information processors is increasingly being recognised in a number of theories of group cognition. A theme of many of these is an emphasis on sharing cognition. This paper extends current conceptualisations of groups by critiquing the focus on shared cognition and emphasising the distribution of cognition in groups. In particular, it develops an account of the distribution of one cognitive construct, mental models. Mental models have been chosen as a focus because they are used in (...) a number of theories of high level cognition from different areas of research such as cognitive science and human factors and so the implication of this development is wide reaching. This paper reviews the unconnected literatures on distributed cognition and mental models and integrates them in order to extend the theory of mental models to distributed cognitive systems such as groups. The distributed cognition literature is reviewed and the importance of considering the group as single cognitive system is adopted. A range of mental model theories are reviewed leading to the conclusion that they all have, in some form, the central feature of a mapping onto the cognitive system. Combining these two ideas, it is proposed that the model can be a mapping onto the whole group, if the information is distributed appropriately and the connections between parts of the model maintained through communication. This cognitive construct is referred to as a distributed mental model. Implications and applications of this theory are discussed. (shrink)