A quantum probability account of order effects in inference

Area of Science:

• Cognitive Science
• Quantum Probability Theory
• Decision-Making Models

Background:

• Order effects significantly impact belief updating, posing challenges for classical and Bayesian inference.
• Existing belief-adjustment models offer ad hoc explanations for these order effects.

Purpose of the Study:

• To introduce and validate a novel quantum inference model for explaining order effects.
• To compare the quantum model's explanatory power against traditional belief-adjustment models.

Main Methods:

• Developed a quantum inference model based on axiomatic quantum probability.
• Applied the quantum model to data from medical diagnosis and jury decision-making tasks.
• Conducted new experiments, including one with extreme evidence, to differentiate models.

Main Results:

• The quantum inference model effectively explains order effects in various tasks.
• Both the quantum and adding models fit initial data well.
• The quantum model demonstrated superior performance with extreme evidence compared to the adding model.

Conclusions:

• The quantum inference model provides a more coherent and robust explanation for order effects than traditional models.
• Quantum probability offers a powerful framework for understanding cognitive processes like belief updating.