The Xzistor Mathematical Model of Mind is a cognitive architecture that uses a functional model of emotions, based on biological homeostatic and allostatic control loops, to explain how an artificial agent can systematically learn to navigate to a reward source. The model also explains how subtasks required for the agent to access reward sources can be learnt through reinforcement learning. Simple virtual and physical agent implementations of the model have demonstrated how agents successfully learn to navigate to reward sources from anywhere in their environments. These implementations also showed how agents become motivated to perform subtasks to gain access to the reward sources. This paper describes how this demonstrated learning ability in agents, provided by the Xzistor brain model, could be used as a theoretical basis for implementing a human-like language learning skill in agents. This goes beyond Large Language Model approaches by incorporating computational equivalents of many human brain functions, including sensing, recognition, inference, and emotions. The study concludes that this cognitive architecture could provide a proof-of-concept implementation in agents of the principles of verbal behavior identified by B.F. Skinner (Skinner, 1957). A multi-stage project is proposed to demonstrate how an artificial Xzistor agent could systematically develop basic language skills using artificial emotions and reinforcement learning and, over time, refine this skill towards improved syntax and grammar use. The paper includes sections on the mathematical principles underpinning the Xzistor brain model and how it could potentially unify behaviorist and structuralist language theories.