Using diverging predictions from classical and quantum models to dissociate between categorization systems

Quantum probability theory has successfully provided accurate descriptions of behavior in the areas of judgment and decision making, and here we apply the same principles to two category learning tasks, one task using information-integration categories and the other using rule-based categories. Since information-integration categories lack verbalizable descriptions, unlike rule-based ones, we assert that an information-integration categorization decision results from an intuitive probabilistic reasoning system characterized by quantum probability theory, whereas a rule-based categorization decision results from a logical, rational probabilistic reasoning system characterized classical probability theory. In our experiment, participants learn to categorize simple, visual stimuli as members of either category S or category K during an acquisition phase, and then rate the likelihood on a scale of 0 to 5 that a stimulus belongs to one category and subsequently perform the same likelihood rating for the other category during a transfer phase. Following the principle of complementarity in quantum theory, we expect the category likelihood ratings to exhibit order effects in the information-integration task, but not in the rule-based task. In the information-integration task, we found definitive order effects in the likelihood ratings. But, in the rule-based task, we found that the order effects in the likelihood ratings are not significant.