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Enlightenment grows from fundamentals

Published online by Cambridge University Press:  25 August 2011

Daniel Joseph Navarro  and
Amy Francesca Perfors
Daniel Joseph Navarro
Affiliation:
School of Psychology, University of Adelaide, Adelaide, SA 5005, Australia. [email protected]@adelaide.edu.auhttp://www.psychology.adelaide.edu.au/personalpages/staff/danielnavarro/http://www.psychology.adelaide.edu.au/personalpages/staff/amyperfors/
Amy Francesca Perfors
Affiliation:
School of Psychology, University of Adelaide, Adelaide, SA 5005, Australia. [email protected]@adelaide.edu.auhttp://www.psychology.adelaide.edu.au/personalpages/staff/danielnavarro/http://www.psychology.adelaide.edu.au/personalpages/staff/amyperfors/
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Abstract

Jones & Love (J&L) contend that the Bayesian approach should integrate process constraints with abstract computational analysis. We agree, but argue that the fundamentalist/enlightened dichotomy is a false one: Enlightened research is deeply intertwined with – and to a large extent is impossible without – the basic, fundamental work upon which it is based.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 34 , Issue 4 , August 2011 , pp. 207 - 208
Copyright
Copyright © Cambridge University Press 2011

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References

Colunga, E. & Smith, L. (2005) From the lexicon to expectations about kinds: A role for associative learning. Psychological Review 112(2):347–82.Google Scholar
Kemp, C., Perfors, A. & Tenenbaum, J. B. (2007) Learning overhypotheses with hierarchical Bayesian models. Developmental Science 10:307–21.Google Scholar
Klayman, J. & Ha, Y.-W. (1987) Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review 94:211–28.Google Scholar
Navarro, D. J., Dry, M. J. & Lee, M. D. (in press) Sampling assumptions in inductive generalization. Cognitive Science.Google Scholar
Navarro, D. J. & Perfors, A. F. (2009) Learning time-varying categories. In: Proceedings of the 31st Annual Conference of the Cognitive Science Society, Austin, TX, ed. Taatgen, N., van Rijn, H., Schomaker, L. & Nerbonne, J., pp. 419–24. Cognitive Science Society.Google Scholar
Navarro, D. J. & Perfors, A. F. (2011) Hypothesis generation, sparse categories and the positive test strategy. Psychological Review 118:120–34.Google Scholar
Oaksford, M. & Chater, N. (1994) A rational analysis of the selection task as optimal data selection. Psychological Review 101:608–31.Google Scholar
Perfors, A. (2011) Memory limitations alone do not lead to over-regularization: An experimental and computational investigation. In: Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Boston, MA, ed. Carlson, L., Hoelscher, C. & Shipley, T. F., pp. 3274–79. Cognitive Science Society.Google Scholar
Perfors, A. & Tenenbaum, J. (2009) Learning to learn categories. In: Proceedings of the 31st Annual Conference of the Cognitive Science Society, ed. Taatgen, N., van Rijn, H., Schomaker, L. & Nerbonne, J., pp. 136–41. Cognitive Science Society.Google Scholar
Perfors, A., Tenenbaum, J. & Wonnacott, E. (2010) Variability, negative evidence, and the acquisition of verb argument constructions. Journal of Child Language 37:607–42.Google Scholar
Sakamoto, Y., Jones, M. & Love, B. C. (2008) Putting the psychology back into psychological models: Mechanistic versus rational approaches. Memory and Cognition 36(6):1057–65.CrossRefGoogle ScholarPubMed
Sanborn, A. N., Griffiths, T. L. & Navarro, D. J. (2010a) Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review 117:1144–67.Google Scholar
Shepard, R. N. (1987) Toward a universal law of generalization for psychological science. Science 237:1317–23.Google Scholar
Tenenbaum, J. B. & Griffiths, T. L. (2001) Generalization, similarity, and Bayesian inference. Behavioral and Brain Sciences 24(4):629–40.Google Scholar
Wason, P. (1960) On the failure to eliminate hypotheses in a conceptual task. Quarterly Journal of Experimental Psychology 12:129–40.Google Scholar
Xu, F. & Tenenbaum, J. B. (2007a) Sensitivity to sampling in Bayesian word learning. Developmental Science 10(3):288–97.Google Scholar