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Quantum modeling of common sense

Published online by Cambridge University Press:  14 May 2013

Hamid R. Noori  and
Rainer Spanagel
Hamid R. Noori
Affiliation:
Institute of Psychopharmacology, Central Institute for Mental Health, Medical Faculty Mannheim, University of Heidelberg, J 5, 68159 Mannheim, Germany. [email protected]@zi-mannheim.dehttp://www.zi-mannheim.de/psychopharmacology.html
Rainer Spanagel
Affiliation:
Institute of Psychopharmacology, Central Institute for Mental Health, Medical Faculty Mannheim, University of Heidelberg, J 5, 68159 Mannheim, Germany. [email protected]@zi-mannheim.dehttp://www.zi-mannheim.de/psychopharmacology.html
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Abstract

Quantum theory is a powerful framework for probabilistic modeling of cognition. Strong empirical evidence suggests the context- and order-dependent representation of human judgment and decision-making processes, which falls beyond the scope of classical Bayesian probability theories. However, considering behavior as the output of underlying neurobiological processes, a fundamental question remains unanswered: Is cognition a probabilistic process at all?

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 302
Copyright
Copyright © Cambridge University Press 2013 

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References

Aerts, D. & Aerts, S. (1995) Applications of quantum statistics in psychological studies of decision processes. Foundations of Science 1:85–97.Google Scholar
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A. & Tenenbaum, J. B. (2010) Probabilistic models of cognition: Exploring representations and inductive biases. Trends in Cognitive Sciences 14:357–64.Google Scholar
Knowlton, B. J., Morrison, R. G., Hummel, J. E. & Holyoak, K. J. (2012) A neurocomputational model for relational reasoning. Trends in Cognitive Sciences 16:373–81.Google Scholar
Maia, T. V. & Frank, M. J. (2011) From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience 14:154–62.Google Scholar
Morrison, J. H. & Baxter, M. G. (2012) The aging cortical synapse: Hallmarks and implications for cognitive decline. Nature Reviews Neuroscience 13:240–50.Google Scholar
Noori, H. R. & Jäger, W. (2010) Neurochemical oscillations in the basal ganglia. Bulletin of Mathematical Biology 72:133–47.Google Scholar
Noori, H. R., Spanagel, R. & Hansson, A. C. (2012) Neurocircuitry for modeling drug effects. Addiction Biology 17:827–64.Google Scholar
Shin, L. M. & Liberzon, I. (2010) The neurocircuitry of fear, stress, and anxiety disorders. Neuropsychopharmacology 35:169–91.CrossRefGoogle ScholarPubMed