Abstract
The problem of how people process novel and unexpected information—deep learning (Ohlsson in Deep learning: how the mind overrides experience. Cambridge University Press, New York, 2011)—is central to several fields of research, including creativity, belief revision, and conceptual change. Researchers have not converged on a single theory for conceptual change, nor has any one theory been decisively falsified. One contributing reason is the difficulty of collecting informative data in this field. We propose that the commonly used methodologies of historical analysis, classroom interventions, and developmental studies, although indispensible, can be supplemented with studies of laboratory models of conceptual change. We introduce re-categorization, an experimental paradigm in which learners transition from one definition of a categorical concept to another, incompatible definition of the same concept, a simple form of conceptual change. We describe a re-categorization experiment, report some descriptive findings pertaining to the effects of category complexity, the temporal unfolding of learning, and the nature of the learner’s final knowledge state. We end with a brief discussion of ways in which the re-categorization model can be improved.
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Notes
For convenience, we will use the term “definition” to refer to the mental representation of the meaning of a concept. We do not mean to imply that such a representation consists of a list of necessary and sufficient conditions for category membership.
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Acknowledgments
The work reported in this article was supported, in part, by Award N00014-09-1-0125 from the Office of Naval Research (ONR), US Navy, to the first author. No endorsement should be inferred. We thank Bettina Chow for assistance in the development of the experimental stimuli and Justin Oesterreich for programming the first re-categorization experiment in the E-Prime laboratory software.
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Ohlsson, S., Cosejo, D.G. What Can Be Learned From a Laboratory Model of Conceptual Change? Descriptive Findings and Methodological Issues. Sci & Educ 23, 1485–1504 (2014). https://doi.org/10.1007/s11191-013-9658-6
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DOI: https://doi.org/10.1007/s11191-013-9658-6