This paper presents an implemented computational model of lexical development for the case of action verbs. A simulated agent is trained by an informant giving labels to the agent's actions (here hand motions] and the system learns to both label and carry out similar actions. Computationally, the system employs a novel form of active representation and is explicitly intended to be neurally plausible. The learning methodology is a version of Bayesian model merging (Omohundro, 1992). The verb learning model is placed in the broader context of the L0 project on embodied natural language and its acquisition.
