A comparison of quantum and multinomial processing tree models of the interference effect
We compare the qualitative predictions of an existing quantum model and a novel multinomial processing tree (MPT) model of the interference effect using parameter space partitioning (PSP). An interference effect occurs when categorizing a stimulus changes the marginal probability of a subsequent decision, leading to a violation of the law of total probability. The PSP analysis revealed that our MPT model can produce the same qualitative patterns as the quantum model. Further analysis, however, revealed that the models differ in several important ways. First, a larger volume of the MPT model's parameter space produces a smaller number of interference effects compared to the quantum model. Second, the distribution of volume across patterns is more diffuse for the MPT model, indicating it is more flexible than the quantum model. We discuss limitations and future directions.