Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-05T09:53:18.948Z Has data issue: false hasContentIssue false

The computational nature of associative learning

Published online by Cambridge University Press:  23 April 2009

N. A. Schmajuk
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC 27516. [email protected]@duke.edu
G. M. Kutlu
Affiliation:
Department of Psychology and Neuroscience, Duke University, Durham, NC 27516. [email protected]@duke.edu

Abstract

An attentional-associative model (Schmajuk et al. 1996), previously evaluated against multiple sets of classical conditioning data, is applied to causal learning. In agreement with Mitchell et al.'s suggestion, according to the model associative learning can be a conscious, controlled process. However, whereas our model correctly predicts blocking following or preceding subadditive training, the propositional approach cannot account for those results.

Type
Open Peer Commentary
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

Beckers, T., De Houwer, J., Pineno, O., & Miller, R. R. (2005) Outcome additivity and outcome maximality influence cue competition in human causal learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 31:238–49.Google ScholarPubMed
De Houwer, J., & Beckers, T. (2002) Higher-order retrospective revaluation in human causal learning. The Quarterly Journal of Experimental Psychology 55B:137–51.CrossRefGoogle Scholar
Gray, J. A., Buhusi, C. V. & Schmajuk, N. A. (1997) The transition from automatic to controlled processing. Neural Networks 10:1257–68.CrossRefGoogle Scholar
Larrauri, J. A. & Schmajuk, N. A. (2008) Attentional, associative, and configural mechanisms in extinction. Psychological Review 115:640–76.CrossRefGoogle ScholarPubMed
Lubow, R. E. & Moore, A. U. (1959) Latent inhibition: The effect of non-reinforced preexposure to the conditional stimulus. Journal of Comparative and Physiological Psychology 52:415–19.CrossRefGoogle Scholar
Mitchell, C. J., Lovibond, P. F. & Condoleon, M. (2005) Evidence for deductive reasoning in blocking of causal judgments. Learning and Motivation 36:7787.CrossRefGoogle Scholar
Pearce, J. M. & Hall, G. (1980) A model for Pavlovian learning: Variations in the effectiveness of conditioned but not of unconditioned stimuli. Psychological Review 87:532–52.CrossRefGoogle Scholar
Rescorla, R. A. & Wagner, A. R. (1972) A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and non-reinforcement. In: Classical conditioning: Current theory and research, vol. 2, ed. Black, A. H. & Prokasy, W. F., pp. 6499. Appleton-Century-Crofts.Google Scholar
Schmajuk, N. A., Lam, Y. & Gray, J. A. (1996) Latent inhibition: A neural network approach. Journal of Experimental Psychology: Animal Behavior Processes 22:321–49.Google ScholarPubMed
Schmajuk, N. A. & Larrauri, J. A. (2006) Experimental challenges to theories of classical conditioning: Application of an attentional model of storage and retrieval. Journal of Experimental Psychology: Animal Behavior Processes 32:120.Google ScholarPubMed
Schneider, W. & Shiffrin, R. M. (1977) Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review 84:166.CrossRefGoogle Scholar
Young, M. E. & Wasserman, E. A. (2002) Limited attention and cue order consistency affect predictive learning: A test of similarity measures. Journal of Experimental Psychology: Learning, Memory and Cognition 28:484–96.Google ScholarPubMed