Hostname: page-component-89b8bd64d-r6c6k Total loading time: 0 Render date: 2026-05-09T14:17:37.809Z Has data issue: false hasContentIssue false
  • English
  • Français

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.edu http://www.cmu.edu/ddmlab/
Christian Lebiere
Affiliation:
Psychology Department, Carnegie Mellon University, Pittsburgh, PA 15213. cl@cmu.edu http://www.cmu.edu/ddmlab/
Get access Rights & Permissions [Opens in a new window]

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.

Information

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

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.)

Article purchase

Temporarily unavailable

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