Authors
Rafael Sousa Silva and Tom Williams
Venue
Cognitive Systems Research
Publication Year
2026
Working memory (WM) is an important component of human cognition that shapes reasoning, planning, learning, language understanding, and language generation. Given its importance to humans, we believe that WM could also play a key role in robot cognition, especially for language-based processes. In
this paper, we take inspiration from human WM models to propose three different robot WM configurations, each of which differently distributes information across a robot cognitive architecture. In addition to defining these configurations, we propose a set of recommendations for parameterizing the forgetting mechanisms that manage these WM systems in order to optimize WM-facilitated referring expression generation, by promoting lexical entrainment while avoiding the communication of outdated information. Finally, we step through proof-sof-concept that demonstrate how our approach achieves these
aims while also improving space and time efficiency, and provides the foundation for future experimental and architectural research.
