There is a growing interest in alternative explanations to the dual-system account of how people learn category structures varying in their optimal decision bounds (unidimensional and information-integration structures). Recognition memory performance and hippocampal activation patterns in these tasks are two interesting findings, which have not been formally explained. Here, we carry out a formal simulation with SUSTAIN (Love, Medin, & Gureckis, 2004), an adaptive model of category learning, which had great success in accounting for recognition memory performance and fMRI activity patterns. We show, for the first time, that a formal single-system model of category learning can accommodate recognition performance after learning and is consistent with fMRI data obtained while participants learned these structures.

Considerable behavioral evidence has been cited in support ofthe COVIS dual-system model of category learning (Ashby &Valentin, 2016). The validity of the inferences drawn fromthese data critically depend on the accurate identification ofparticipants’ categorization strategies. In the COVIS literature,participants’ strategies are identified using a model-based anal-ysis inspired by General Recognition Theory (Maddox, 1999).Here, we examine the accuracy of this analysis in a model-recovery simulation. We find that participants can appear tobe using implicit, procedural strategies when their responseswere actually generated by explicit rule-based strategies. Theimplications of this for the COVIS literature are discussed.