Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T20:41:55.643Z Has data issue: false hasContentIssue false
  • English
  • Français

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]
Get access Rights & Permissions [Opens in a new window]

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

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

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