Rules in the mist: Emerging probabilistic rules in uncertain categorization

In this study, we explored the development of rules in probabilistic category learning, focusing on how knowledge acquired with uncertain feedback conditions transfers to a categorization task with similarity judgments. Using the Probabilistic Categorization Task (PCT) across two experiments, we examined whether rule-based knowledge learned under probabilistic feedback could be applied in the subsequent transfer phase. In Experiment 1, participants learned a unidimensional categorization rule with feedback reliability set at 70 %, 80 %, and 90 %. The findings indicated a strong correlation between feedback reliability during training and transfer phase performance, particularly in the 80 % and 90 % conditions. Experiment 2 expanded this approach by introducing a more complex categorization rule (XNOR), requiring participants to integrate two features. Here, participants trained with 80 % and 90 % reliable feedback successfully applied the learned rules in a similarity judgment task, proportionally to feedback reliability. Altogether, we argue that these findings question dual-system theories positing category learning as a sequential or competitive process between implicit and explicit systems. Instead, our results support the idea that a single either explicit rule-based or implicit similarity-based systems can effectively adapt to probabilistic settings, either independently or in close interaction with each other.