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Microgenetic methods are used to analyze moment-to-moment processes of learning, reasoning, and problem-solving. Microgenetic methods are useful when studying learning that does not occur in a straight line from lesser to greater understanding, but rather occurs through a learning trajectory that includes iterative and unpredictable paths and sometimes even setbacks or failure. Microgenetic methods are also useful in studying learning that is mediated by tools and artifacts in the learning environment, and what role those artifacts play in the developing learning trajectory. These methods are time-consuming and it’s not practical to conduct studies with large sample sizes or that occur over very long periods of time; rather, the focus is on developing a deep and thorough understanding of a specific learning environment and then to generalize those findings to a broader range of contexts. Microgenetic methods are particularly well-suited to five types of research questions: questions about the variability or stability of strategies; events that precipitate or initiate change; co-occurring events and processes; trajectories or paths of change; and the rate of change.
The purpose of this chapter is to examine the process of knowledge change in the domains of science, religion, and magic. We examine knowledge change in institutions, in expert adults, in nonexpert adults, and in children. Throughout the chapter, we consider (a) similarities and differences in the knowledge change process across the three domains; and (b) similarities and differences in the knowledge change process across institutions, expert adults, nonexpert adults, and children.
Our focus is on how people modify their knowledge in response to anomalous data. Anomalous data are observations of real-world events, or reports of such observations, that contradict current beliefs. For instance, for a child who believes that God always answers prayers, an unanswered prayer would be anomalous data. We have chosen to examine the role of anomalous data in knowledge change because in many instances of fundamental knowledge change, encounters with anomalous data are at the core of the knowledge change process (Kuhn, 1962; Piaget, 1985). We think that anomalous data play a significant role in knowledge change in all three domains and at all levels of expertise.
At the outset, it is important to be clear about how we are defining the domains and the levels of expertise. Scientific knowledge consists of conceptions about the physical and biological world. Religious knowledge includes conceptions of a transcendent reality such as conceptions of God, salvation, heaven and hell, enlightenment, and Buddhahood.
This paper presents a cognitive account of the process of evaluating scientific data. Our account assumes that when individuals evaluate data, they construct a mental model of a data-interpretation package, in which the data and theoretical interpretations of the data are integrated. We propose that individuals attempt to discount data by seeking alternative explanations for events within the mental model; data-interpretation packages are accepted when the individual cannot find alternative accounts for these events. Our analysis indicates that there are many levels at which data-interpretation packages can be accepted or denied.
The purpose of this paper is to provide a systematic account of the role of anomaly in theory change in science. We adopt a naturalist approach to the philosophy of science (e.g., Giere 1985; Maffie 1990) and support our proposal with evidence from the history of science and from the psychology of science.
Previous discussions of the topic of anomaly in theory change have rarely used evidence from psychology. Probably this has been due to the relatively immature state of research in the psychology of science (cf. Gorman forthcoming; Gholson, Shadish, Neimeyer, and Houts 1989). However there is now enough research on the topic of belief change (cf. Chinn and Brewer 1993b) to provide a foundation for theory development in the area of responses to anomaly. Much of the research in this area uses data from undergraduates and children and not scientists (see Dunbar forthcoming, for an exception).
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