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Propositional learning is a useful research heuristic but it is not a theoretical algorithm

Published online by Cambridge University Press:  23 April 2009

A. G. Baker ,
Irina Baetu  and
Robin A. Murphy
A. G. Baker
Affiliation:
Department of Psychology, McGill University, Montréal, Québec, H3A 1B1, [email protected]://www.psych.mcgill.ca/faculty/[email protected]
Irina Baetu
Affiliation:
Department of Psychology, McGill University, Montréal, Québec, H3A 1B1, [email protected]://www.psych.mcgill.ca/faculty/[email protected]
Robin A. Murphy
Affiliation:
Cognitive Perceptual and Brain Sciences Unit, Division of Psychology and Language Sciences, University College London, London, WC1E 6BT, United Kingdom. [email protected]
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Abstract

Mitchell et al.'s claim, that their propositional theory is a single-process theory, is illusory because they relegate some learning to a secondary memory process. This renders the single-process theory untestable. The propositional account is not a process theory of learning, but rather, a heuristic that has led to interesting research.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 2 , April 2009 , pp. 199 - 200
Copyright
Copyright © Cambridge University Press 2009

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References

Anderson, J. R. & Lebiere, C. J. (1998) Hybrid modeling of cognition: Review of the atomic components of thought. Erlbaum.Google Scholar
Baetu, I. & Baker, A. G. (in press) Human judgments of positive and negative causal chains. Journal of Experimental Psychology: Animal Behavior Processes.Google Scholar
Baeyens, F., Eelen, P. & Van den Bergh, O. (1990a) Contingency awareness in evaluative conditioning: A case for unaware affective-evaluative learning. Cognition and Emotion 4:3–18.CrossRefGoogle Scholar
Baker, A. G. & Mackintosh, N. J. (1979) Pre-exposure to the CS alone, US alone, or CS and US uncorrelated: Latent inhibition, blocking by context or learned irrelevance? Learning and Motivation 10:278–94.CrossRefGoogle Scholar
Baker, A. G., Murphy, R. A. & Mehta, R. (2003) Learned irrelevance and retrospective correlation learning. Quarterly Journal of Experimental Psychology 56:90–101.CrossRefGoogle ScholarPubMed
Bouton, M. E. (2004) Context and behavioral processes in extinction. Learning and Memory 11:485–94.CrossRefGoogle ScholarPubMed
Brewer, W. F. (1974) There is no convincing evidence for operant or classical conditioning in adult humans. In: Cognition and the symbolic processes, ed. Weimer, W. B. & Palermo, D. S., pp. 1–42. Erlbaum.Google Scholar
Cheng, P. W. (1997) From covariation to causation: A causal power theory. Psychological Review 104:367–405.CrossRefGoogle Scholar
De Houwer, J. & Beckers, T. (2003) Secondary task difficulty modulates forward blocking in human contingency learning. Quarterly Journal of Experimental Psychology 56B:345–57.CrossRefGoogle Scholar
Diez-Chamizo, V., Sterio, D. & Mackintosh, N. J. (1985) Blocking and overshadowing between intra-maze and extra-maze cues: A test of the independence of locale and guidance learning. The Quarterly Journal of Experimental Psychology 37B:235–53.CrossRefGoogle Scholar
Marr, D. (1982) Vision: A computational investigation into the human representation and processing of visual information. Freeman.Google Scholar
McCulloch, W. S. & Pitts, W. H. (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5:115–33.CrossRefGoogle Scholar
Murphy, R. A. & Baker, A. G. (2004) A role for CS-US contingency in Pavlovian conditioning. Journal of Experimental Psychology: Animal Behavior Processes 30:229–39.Google ScholarPubMed
Pylyshyn, Z. W. (1973) What the mind's eye tells the mind's brain: A critique of mental imagery. Psychological Bulletin 80:1–24.CrossRefGoogle Scholar
Seligman, M. E. P. (1970) On the generality of the laws of learning. Psychological Review 77:406–18.CrossRefGoogle Scholar
Shultz, T. R. (2003) Computational developmental psychology. MIT Press.Google Scholar
Shultz, T. R., Mareschal, D. & Schmidt, W. C. (1994) Modeling cognitive development on balance scale phenomena. Machine Learning 16:57–86.CrossRefGoogle Scholar
Swartzentruber, D. & Rescorla, M. E. (1994) Modulation of trained and extinguished stimuli by facilitators and inhibitors. Animal Learning and Behavior 22:309–16.CrossRefGoogle Scholar
Tolman, E. C. (1948) Cognitive maps in rats and men. Psychological Review 55:189–208.CrossRefGoogle ScholarPubMed
Waldmann, M. R. & Walker, J. M. (2005) Competence and performance in causal learning. Learning and Behavior 33:211–29.CrossRefGoogle ScholarPubMed
Wasserman, E. A., Elek, S. M., Chatlosh, D. L. & Baker, A. G. (1993) Rating causal relations: Role of probability in judgments of response-outcome contingency. Journal of Experimental Psychology: Learning, Memory, and Cognition 19:174–88.Google Scholar