Proceedings of the Annual Meeting of the Cognitive Science Society

Working Memory (WM) is a necessary component for models of human cognition and human-inspired robot cognitive architectures. Different theories explain how the limited capacity of WM should be maintained, including theories of forgetting through decay and interference. Yet, it is unclear how WM models informed by these theories might be used to inform robot cognition, and how they might shape robots' ability to engage in natural, situated, language-based interactions. To resolve this tension, in this work we consider entity-level, feature-based WM systems that can be integrated into robot cognitive architectures to reflect both decay- and interference-based dynamics. We demonstrate how different parameterizations of these WM strategies have fundamentally different error modes in different interaction contexts. We formulate rules that inform the selection of decay and interference parameters to be used in contexts with different factors that are important for language-based interaction.