Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-19T03:41:37.209Z Has data issue: false hasContentIssue false

Theories or fragments?

Published online by Cambridge University Press:  10 November 2017

Nick Chater
Affiliation:
Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom. Nick.Chater@wbs.ac.ukhttp://www.wbs.ac.uk/about/person/nick-chater/
Mike Oaksford
Affiliation:
Department of Psychological Sciences, Birkbeck, University of London, London WC1E 7HX, United Kingdom. m.oaksford@bbk.ac.ukhttp://www.bbk.ac.uk/psychology/our-staff/mike-oaksford

Abstract

Lake et al. argue persuasively that modelling human-like intelligence requires flexible, compositional representations in order to embody world knowledge. But human knowledge is too sparse and self-contradictory to be embedded in “intuitive theories.” We argue, instead, that knowledge is grounded in exemplar-based learning and generalization, combined with high flexible generalization, a viewpoint compatible both with non-parametric Bayesian modelling and with sub-symbolic methods such as neural networks.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Blackburn, S. (1984) Spreading the word: Groundings in the philosophy of language. Oxford University Press.Google Scholar
Christiansen, M. H. & Chater, N. (2016) Creating language: Integrating evolution, acquisition, and processing. MIT Press.Google Scholar
Goldberg, A. E. (1995) Constructions: A construction grammar approach to argument structure. University of Chicago Press.Google Scholar
Gombrich, E. (1960) Art and illusion. Pantheon Books.Google Scholar
Hoffman, D. D. (2000) Visual intelligence: How we create what we see. W. W. Norton.Google Scholar
Hofstadter, D. R. (2001) Epilogue: Analogy as the core of cognition. In: The analogical mind: perspectives from cognitive science, ed. Gentner, D., Holyoak, K. J. & Kokinov, B. N., pp. 499538. MIT Press.Google Scholar
Irvine, A. D. & Deutsch, H. (2016) Russell's paradox. In: The Stanford encyclopedia of philosophy (Winter 2016 Edition), ed. Zalta, E. N.. Available at: https://plato.stanford.edu/archives/win2016/entries/russell-paradox.Google Scholar
Kolodner, J. (1993) Case-based reasoning. Morgan Kaufmann.Google Scholar
Logan, G. D. (1988) Toward an instance theory of automatization. Psychological Review 95(4):492527.Google Scholar
Medin, D. L. & Schaffer, M. M. (1978) Context theory of classification learning. Psychological Review 85(3):207–38.Google Scholar
Nisbett, R. E. & Ross, L. (1980) Human inference: Strategies and shortcomings of social judgment. Prentice-Hall. ISBN 0-13-445073-6.Google Scholar
Oaksford, M. & Chater, N. (1991) Against logicist cognitive science. Mind and Language 6(1):138.CrossRefGoogle Scholar
Rock, I. (1983) The logic of perception. MIT Press.Google Scholar
Rozenblit, L. & Keil, F. (2002) The misunderstood limits of folk science: An illusion of explanatory depth. Cognitive Science 26(5):521–62.CrossRefGoogle ScholarPubMed