A Negation Quantum Decision Model to Predict the Interference Effect in
  Categorization
  Categorization is a significant task in decision-making, which is a key part
of human behavior. An interference effect is caused by categorization in some
cases, which breaks the total probability principle. A negation quantum model
(NQ model) is developed in this article to predict the interference. Taking the
advantage of negation to bring more information in the distribution from a
different perspective, the proposed model is a combination of the negation of a
probability distribution and the quantum decision model. Information of the
phase contained in quantum probability and the special calculation method to it
can easily represented the interference effect. The results of the proposed NQ
model is closely to the real experiment data and has less error than the
existed models.
