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The associative nature of human associative learning

Published online by Cambridge University Press:  23 April 2009

David R. Shanks
David R. Shanks
Affiliation:
Division of Psychology and Language Sciences, University College London, London WC1H 0AP, United Kingdom. d.shanks@ucl.ac.ukwww.ucl.ac.uk/psychlangsci
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Abstract

The extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which “cognitive” phenomena are emergent properties, is likely to offer a fuller account of human learning than the propositional framework Mitchell et al. propose.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 32 , Issue 2 , April 2009 , pp. 225 - 226
Copyright
Copyright © Cambridge University Press 2009

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References

Barsalou, L. W., Simmons, W. K., Barbey, A. K. & Wilson, C. D. (2003) Grounding conceptual knowledge in modality-specific systems. Trends in Cognitive Sciences 7:84–91.CrossRefGoogle ScholarPubMed
Cleeremans, A. (1993) Mechanisms of implicit learning. MIT Press.CrossRefGoogle Scholar
Endo, N. & Takeda, Y. (2004) Selective learning of spatial configuration and object identity in visual search. Perception and Psychophysics 66:293–302.CrossRefGoogle ScholarPubMed
Ghirlanda, S. (2005) Retrospective revaluation as simple associative learning. Journal of Experimental Psychology: Animal Behavior Processes 31:107–11.Google ScholarPubMed
Lagnado, D. A. & Shanks, D. R. (2002) Probability judgment in hierarchical learning: A conflict between predictiveness and coherence. Cognition 83:81–112.CrossRefGoogle ScholarPubMed
Macdonald, J. S. P. & Lavie, N. (2008) Load induced blindness. Journal of Experimental Psychology: Human Perception and Performance 34:1078–91.Google ScholarPubMed
Maia, T. V. & McClelland, J. L. (2004) A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa gambling task. Proceedings of the National Academy of Sciences USA 102:16075–80.CrossRefGoogle Scholar
Niedenthal, P. M. (2007) Embodying emotion. Science 316:1002–05.CrossRefGoogle ScholarPubMed
Nosofsky, R. M., Kruschke, J. K. & McKinley, S. C. (1992) Combining exemplar-based category representations and connectionist learning rules. Journal of Experimental Psychology: Learning, Memory, and Cognition 18:211–33.Google ScholarPubMed
Oaksford, M. & Chater, N. (2007) Bayesian rationality: The probabilistic approach to human reasoning. Oxford University Press.CrossRefGoogle Scholar
Perruchet, P. & Amorim, M. A. (1992) Conscious knowledge and changes in performance in sequence learning: Evidence against dissociation. Journal of Experimental Psychology: Learning, Memory, and Cognition 18:785–800.Google ScholarPubMed
Postman, L. (1947) The history and present status of the law of effect. Psychological Bulletin 44:489–563.CrossRefGoogle ScholarPubMed
Schmajuk, N. A. & Larrauri, J. A. (2008) Associative models can describe both causal learning and conditioning. Behavioral Processes 77:443–45.CrossRefGoogle ScholarPubMed
Shanks, D. R. (1985) Forward and backward blocking in human contingency judgement. Quarterly Journal of Experimental Psychology 37B:1–21.Google Scholar
Shanks, D. R. (1990) Connectionism and the learning of probabilistic concepts. Quarterly Journal of Experimental Psychology 42A:209–37.CrossRefGoogle Scholar
Shanks, D. R. & Pearson, S. M. (1987) A production system model of causality judgment. In: Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 210–20. Erlbaum.Google Scholar
Shanks, D. R., Wilkinson, L. & Channon, S. (2003) Relationship between priming and recognition in deterministic and probabilistic sequence learning. Journal of Experimental Psychology: Learning, Memory, and Cognition 29:248–61.Google ScholarPubMed
Smyth, A. C. & Shanks, D. R. (2008) Awareness in contextual cuing with extended and concurrent explicit tests. Memory and Cognition 36:403–15.CrossRefGoogle ScholarPubMed
Spence, K. W. (1950) Cognitive versus stimulus-response theories of learning. Psychological Review 57:159–72.CrossRefGoogle ScholarPubMed
Thorndike, E. L. (1931) Human learning. Century.CrossRefGoogle Scholar
Tunney, R. J. & Shanks, D. R. (2003) Subjective measures of awareness and implicit cognition. Memory and Cognition 31:1060–71.CrossRefGoogle ScholarPubMed
Van Hamme, L. J., & Wasserman, E. A. (1994) Cue competition in causality judgments: The role of nonpresentation of compound stimulus elements. Learning and Motivation 25:127–51.CrossRefGoogle Scholar