Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-28T02:38:34.047Z Has data issue: false hasContentIssue false
  • English
  • Français

A causal framework for integrating learning and reasoning

Published online by Cambridge University Press:  23 April 2009

David A. Lagnado
David A. Lagnado
Affiliation:
Department of Cognitive, Perceptual, and Brain Sciences, University College London, London WC1E 6BT, United [email protected]://www.psychol.ucl.ac.uk/people/profiles/lagnado_david.htm
Get access Rights & Permissions [Opens in a new window]

Abstract

Can the phenomena of associative learning be replaced wholesale by a propositional reasoning system? Mitchell et al. make a strong case against an automatic, unconscious, and encapsulated associative system. However, their propositional account fails to distinguish inferences based on actions from those based on observation. Causal Bayes networks remedy this shortcoming, and also provide an overarching framework for both learning and reasoning. On this account, causal representations are primary, but associative learning processes are not excluded a priori.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 2 , April 2009 , pp. 211 - 212
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

Burns, P. & McCormack, T. (under review) Temporal information and children's and adults' causal inferences. Thinking and Reasoning.Google Scholar
De Houwer, J. (2009) The propositional approach to associative learning as an alternative for association formation models. Learning and Behavior 37:1–20.CrossRefGoogle ScholarPubMed
Glymour, C. (2007) Statistical jokes and social effects: Intervention and invariance in causal relations. In: Causal learning: Psychology, philosophy, and computation, ed. Gopnik, A. & Schulz, L., pp. 294–300. Oxford University Press.CrossRefGoogle Scholar
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T. & Danks, D. (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111:1–32.CrossRefGoogle ScholarPubMed
Griffiths, T. L. & Tenenbaum, J. B. (2005) Structure and strength in causal induction. Cognitive Psychology 51:354–84.CrossRefGoogle ScholarPubMed
Lagnado, D. A. & Sloman, S. A. (2004) The advantage of timely intervention. Journal of Experimental Psychology: Learning, Memory, and Cognition 30:856–76.Google ScholarPubMed
Lagnado, D. A. & Sloman, S. A. (2006) Time as a guide to cause. Journal of Experimental Psychology: Learning, Memory, and Cognition 32:451–60.Google ScholarPubMed
Lagnado, D. A., Waldmann, M. R., Hagmayer, Y. & Sloman, S. A. (2007) Beyond covariation: Cues to causal structure. In: Causal learning: Psychology, philosophy, and computation, ed. Gopnik, A. & Schulz, L., pp. 154–72. Oxford University Press.CrossRefGoogle Scholar
Lovibond, P. F. & Shanks, D. R. (2002) The role of awareness in Pavlovian conditioning: Empirical evidence and theoretical implications. Journal of Experimental Psychology: Animal Behavior Processes 28:3–26.Google ScholarPubMed
Pearl, J. (2000) Causality: Models, reasoning, and inference. Cambridge University Press.Google Scholar
Pearl, J. & Russell, S. (2001) Bayesian networks. In: Handbook of brain theory and neural networks, ed. Arbib, M.. MIT Press.Google Scholar
Sloman, S. A. & Lagnado, D. A. (2005) Do we “do”? Cognitive Science 29:5–39.CrossRefGoogle Scholar
Spirtes, P., Glymour, C. & Schienes, R. (1993) Causation, prediction and search. Springer-Verlag.CrossRefGoogle Scholar
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J. & Blum, B. (2003) Inferring causal networks from observations and interventions. Cognitive Science 27:453–89.CrossRefGoogle Scholar
Waldmann, M. R. & Hagmayer, Y. (2005) Seeing versus doing: Two modes of accessing causal knowledge. Journal of Experimental Psychology: Learning, Memory, and Cognition 31:216–27.Google ScholarPubMed