The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Learning overhypotheses with hierarchical Bayesian models
Open accessAbstract
Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses – overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.
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Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
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Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Concepts, Kinds, and Cognitive Development
Keil, F. C. (1989) · doi:10.7551/mitpress/2065.001.0001 · Cited by 1,886
Theory-based Bayesian models of inductive learning and reasoning
Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.05.009 · Cited by 880 · cited 1×
“The hierarchical Bayesian approach is familiar to statisticians (Good, 1980), but is just beginning to be explored as a framework for modeling human learning and reasoning ( Tenenbaum, Griffiths & Kemp, 2006; Lee, 2006).”
Self-citationComments on Part I: Psychological essentialism
Medin, D., & Ortony, A. (1989) · Similarity and Analogical Reasoning · doi:10.1017/cbo9780511529863.009 · Cited by 674
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Self-citationHow to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Self-citationAction understanding as inverse planning
Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009) · Cognition · doi:10.1016/j.cognition.2009.07.005 · Cited by 920
Self-citationComparing families of dynamic causal models
Penny, W. D., Stephan, K. E., Daunizeau, J., Rosa, M. J., Friston, K. J., Schofield, T. M., & Leff, A. P. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000709 · Cited by 688
Probabilistic models of cognition: exploring representations and inductive biases
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.05.004 · Cited by 568
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Metadata
- Authors
- Kemp, C., Perfors, A., & Tenenbaum, J. B.
- Venue
- Developmental Science
- Publisher
- Wiley
- Published
- 2007-04-17
- Volume
- 10
- Issue
- 3
- Pages
- 307-321
- ISSN
- 1363-755X (ISSN-L)
- 1467-7687
- OpenAlex
- W2048269309
- Cited by
- 416
- References
- 5
- FWCI
- 20.86
- Type
- journal article
- Language
- en
- Open access
- green
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