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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
A learning algorithm for Boltzmann machines
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
Cited by
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817
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
Self-citationJones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533
Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014) · Cognition · doi:10.1016/j.cognition.2013.12.010 · Cited by 240
Human-like systematic generalization through a meta-learning neural network
Lake, B. M., & Baroni, M. (2023) · Nature · doi:10.1038/s41586-023-06668-3 · Cited by 230
Infants are rational constructivist learners
Xu, F., & Kushnir, T. (2013) · Current Directions in Psychological Science · doi:10.1177/0963721412469396 · Cited by 119
Toward the neural implementation of structure learning
Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.014 · Cited by 106
Self-citationRadulescu, A., Shin, Y. S., & Niv, Y. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092920-120559 · Cited by 63
Learning a commonsense moral theory
Kleiman-Weiner, M., Saxe, R., & Tenenbaum, J. B. (2017) · Cognition · doi:10.1016/j.cognition.2017.03.005 · Cited by 53
Self-citationHuman biases limit cumulative innovation
Thompson, B., & Griffiths, T. L. (2021) · Proceedings of the Royal Society B: Biological Sciences · doi:10.1098/rspb.2020.2752 · Cited by 40
Bayesian cognitive science, predictive brains, and the nativism debate
Colombo, M. (2018) · Synthese · doi:10.1007/s11229-017-1427-7 · Cited by 15
Peterson, S., Chavira, J., Garcia Arango, J. A., Seamans, D., Cimino, E. D., & Keiflin, R. (2024) · Current Biology · doi:10.1016/j.cub.2024.10.049 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
