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Cognitive architectures combine formal and heuristic approaches

  • Cleotilde Gonzalez (a1) and Christian Lebiere (a2)
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.

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COPYRIGHT: © Cambridge University Press 2013 
References
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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.
Gonzalez, C. & Dutt, V. (2011) Instance-based learning: Integrating decisions from experience in sampling and repeated choice paradigms. Psychological Review 118(4):523–51.
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.
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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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