Validating instance-based learning mechanisms outside of ACT-R

Instance-based learning theory (IBLT) has explained human decision-making in several decision tasks. IBLT works by retrieving past experiences (i.e., instances) using a subset of cognitive mechanisms from a popular cognitive architecture, ACT-R. Until recently, most IBLT models were built within the ACT-R architecture. However, due to an integrated view of cognition and ACT-R's complexity, it is difficult to distinguish between the specific contributions of ACT-R mechanisms used in IBLT from all the other mechanisms existent in ACT-R. Also, models built within the ACT-R architecture are often difficult to explain, communicate, and reuse in other systems. This research validates the main mechanisms of IBLT when used within ACT-R and when used in isolation, outside of ACT-R. Our results show that an IBLT model performs equally well in capturing human behavior within and outside of ACT-R, demonstrating the independence of these mechanisms from any complex interaction with other mechanisms in ACT-R. We discuss the implications of our results for a modular view of cognition.