Enabling Human-like Language-Capable Robots ThroughWorking Memory Modeling

Working Memory (WM) is a central component of cognition. It has direct impact not only on core cognitive processes, such as learning, comprehension, and reasoning, but also language-related processes, such as natural language understanding and referring expression generation. Thus, for robots to achieve human-like natural language capabilities, we argue that their cognitive models should include an accurate WM representation that plays a similarly central role. Our research investigates how different W Mmodels from cognitive psychology affect robots' natural language capabilities. Specifically, we explore the limited capacity nature of WM and how different information forgetting strategies, namely decay and interference, impact the human-likeness of utterances formulated by robots.