The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The role of content and abstract information in analogical transfer.
Abstract
Analogical transfer in problem solving is one example of analogical cognition, which also includes schemata mediate transfer (K. J. Holyoak, 1984a, 1985) or that problem solving by means of analogy is accomplished through application of the formal or deep structural characteristics of one problem to another (D. Centner, 1983, 1989). More recently, exemplar-based accounts (D. L. Medin&B. H. Ross, 1989; B. H. Ross, 1987) have emphasized problem content and exemplar-specific details in the various stages of transfer. The present article reviews research on analogical transfer and analyzes the theoretical models in light of this evidence. An adequate theory of analogical transfer must account not only for the use of schematic knowledge but also for the importance of surface informa-tion in all stages of transfer (Reeves & Weisberg, 1993a). As such, it will be a hybrid of the various models presented, with exemplar-based models such as that of B. H. Ross as a base. In many domains, the goal of learning and of teaching is to inculcate knowledge of abstract scientific or mathematical rules (Nisbett, Fong, Lehman, & Cheng, 1987). This learning is typi-cally preceded by a stage in which specific problems, rather than
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Citations
Cites
Schema induction and analogical transfer
Gick, M. L., & Holyoak, K. J. (1983) · Cognitive Psychology · doi:10.1016/0010-0285(83)90002-6 · Cited by 3,010
Duncker, K. (1945) · Psychological Monographs · doi:10.1037/h0093599 · Cited by 2,938
Gick, M. L., & Holyoak, K. J. (1980) · Cognitive Psychology · doi:10.1016/0010-0285(80)90013-4 · Cited by 2,552
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989) · Cognitive Science · doi:10.1207/s15516709cog1302_1 · Cited by 1,714
Cheng, P. W., & Holyoak, K. J. (1985) · Cognitive Psychology · doi:10.1016/0010-0285(85)90014-3 · Cited by 1,463
Effects of schema acquisition and rule automation on mathematical problem-solving transfer.
Cooper, G., & Sweller, J. (1987) · Journal of Educational Psychology · doi:10.1037/0022-0663.79.4.347 · Cited by 688
The Roles of Similarity in Transfer: Separating Retrievability From Inferential Soundness
Gentner, D., Rattermann, M. J., & Forbus, K. D. (1993) · Cognitive Psychology · doi:10.1006/cogp.1993.1013 · Cited by 640
The role of analogy in transfer between similar problem states
Reed, S. K., Ernst, G. W., & Banerji, R. (1974) · Cognitive Psychology · doi:10.1016/0010-0285(74)90020-6 · Cited by 400
Development of Analogical Problem-Solving Skill
Holyoak, K. J., Junn, E. N., & Billman, D. O. (1984) · Child Development · doi:10.2307/1129778 · Cited by 284
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“Economic man” in cross-cultural perspective: behavioral experiments in 15 small-scale societies
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., McElreath, R., Alvard, M., Barr, A., Ensminger, J., et al. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000142 · Cited by 1,898
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Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998) · Contemporary Educational Psychology · doi:10.1006/ceps.1997.0959 · Cited by 374
The road to excellence: deliberate practice and the development of expertise.
Ward, P., Hodges, N. J., Starkes, J. L., & Williams, M. A. (2007) · High Ability Studies · doi:10.1080/13598130701709715 · Cited by 353
Metadata
- Authors
- Reeves, L., & Weisberg, R. W.
- Venue
- Psychological Bulletin
- Published
- 1994-05
- Volume
- 115
- Issue
- 3
- Pages
- 381-400
- ISSN
- 0033-2909 (ISSN-L)
- 1939-1455
- OpenAlex
- W2004554833
- Cited by
- 296
- References
- 9
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- closed
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