# Full Day Tutorial on Quantum Models of Cognition and Decision

- Jennifer Trueblood,
*Vanderbilt University, Nashville, Tennessee, USA*
- James Yearsley,
*Vanderbilt University, Nashville, Tennessee, USA*
- Peter Kvam,
*Michigan State University, East Lansing, MI, United States*
- Zheng Wang,
*The Ohio State University, Columbus, OH, U.S.*
- Jerome Busemeyer,
*Indiana University*

## Abstract

This tutorial is an exposition of a rapidly growing new
alternative approach to building computational models of cognition based on
quantum theory. The cognitive revolution that occurred in the 1960’s was
based on classical computational logic, and the connectionist/neural network
movements of the 1970’s were based on classical dynamical systems. These
classical assumptions remain at the heart of both cognitive architecture and
neural network theories, and they are so commonly and widely applied that we take
them for granted. Quantum theory provides a different approach to logic,
reasoning, probabilistic inference, and dynamical systems. For example, quantum
logic does not follow the distributive axiom of Boolean logic; quantum
probabilities do not obey the disjunctive axiom of Kolmogorov probability;
quantum reasoning does not obey the principle of monotonic reasoning. It turns
out that humans do not obey these restrictions either, which is why we consider a
quantum approach.

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