A Negation Quantum Decision Model to Predict the Interference Effect in Categorization

TL;DR

This paper introduces a negation quantum model that predicts interference effects in categorization tasks, effectively aligning with experimental data and outperforming existing models in accuracy.

Contribution

The paper develops a novel negation quantum model combining negation of probability distributions with quantum decision principles to predict interference effects.

Findings

Model closely matches experimental data

Achieves lower error than existing models

Effectively captures interference in categorization

Abstract

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.