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Cognitive architectures combine formal and heuristic approaches

Published online by Cambridge University Press:  14 May 2013

Cleotilde Gonzalez  and
Christian Lebiere
Cleotilde Gonzalez
Affiliation:
Social and Decision Sciences Department, Dynamic Decision Making Laboratory, Carnegie Mellon University, Pittsburgh, PA 15213. coty@cmu.eduhttp://www.cmu.edu/ddmlab/
Christian Lebiere
Affiliation:
Psychology Department, Carnegie Mellon University, Pittsburgh, PA 15213. cl@cmu.eduhttp://www.cmu.edu/ddmlab/
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Abstract

Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g., heuristics) to address effects that motivate QP. They provide simple and natural explanations of these phenomena based on general cognitive processes such as memory retrieval, similarity-based partial matching, and associative learning.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 285 - 286
Copyright
Copyright © Cambridge University Press 2013 

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References

Anderson, J. R. (2007) Using brain imaging to guide the development of a cognitive architecture. In: Integrated models of cognitive systems, ed. Gray, W. D., pp. 49–62. Oxford University Press.Google Scholar
Anderson, J. R. & Lebiere, C. (1998) The atomic components of thought. Lawrence Erlbaum Associates.Google Scholar
Anderson, J. R. & Lebiere, C. (2003) The Newell Test for a theory of cognition. Behavioral and Brain Sciences 26:587–640.CrossRefGoogle ScholarPubMed
Gonzalez, C. (2013). The boundaries of Instance-Based Learning Theory for explaining decisions from experience. pp. 73–98. In Decision making: Neural and behavioural approaches. Vol. 202. ed. Pammi, V. S. C. & Srinivasan, N., Progress in Brain Research. Elsevier. ISBN 978-0-444-62604-2.Google Scholar
Gonzalez, C. & Dutt, V. (2011) Instance-based learning: Integrating decisions from experience in sampling and repeated choice paradigms. Psychological Review 118(4):523–51.Google Scholar
Lejarraga, T., Dutt, V. & Gonzalez, C. (2012) Instance-based learning: A general model of repeated binary choice. Journal of Behavioral Decision Making 25(2):143–53.Google Scholar
Marewski, J. N. & Mehlhorn, K. (2011) Using the ACT-R architecture to specify 39 quantitative process models of decision making. Judgment and Decision Making 6(6):439–519.Google Scholar
Marewski, J. N. & Schooler, L. J. (2011) Cognitive niches: An ecological model of strategy selection. Psychological Review 118(3):393–437.Google Scholar
Newell, A. (1990) Unified theories of cognition. Harvard University Press.Google Scholar
Schooler, L. J. & Hertwig, R. (2005) How forgetting aids heuristic inference. Psychological Review 112(3):610–28.Google Scholar