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Model comparison, not model falsification

Published online by Cambridge University Press:  10 January 2019

Bradley C. Love*
Affiliation:
Experimental Psychology, University College London, London WC1H 0AP, United Kingdom. [email protected]://bradlove.org

Abstract

Systematically comparing models that vary across components can be more informative and explanatory than determining whether behaviour is optimal, however defined. The process of model comparison has a number of benefits, including the possibility of integrating seemingly disparate empirical findings, understanding individual and group differences, and drawing theoretical connections between model proposals.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2018 

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References

Jones, M. & Love, B. C. (2011) Bayesian fundamentalism or enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition. Behavioral and Brain Sciences 34(4):169–88. Available at: http://www.journals.cambridge.org/abstract_S0140525X10003134.Google Scholar
Love, B. C., Medin, D. L. & Gureckis, T. M. (2004) SUSTAIN: A network model of category learning. Psychological Review 111:309–32.Google Scholar
Mack, M. L., Preston, A. R. & Love, B. C. (2013) Decoding the brain's algorithm for categorization from its neural implementation. Current Biology 23:2023–27.Google Scholar