Learning the Meaning of the Vervet Alarm Calls Using a Cognitive And Computational Model.
       This thesis explains how the infant vervet, Chlorocebus pygerthrus, learns the meaning of vervet alarm calls using the Learning Intelligent Distribution Agent's (LIDA) perceptual learning mechanism. We consider an approach of multiple meanings which correspond to a feeling-based meaning, an action-based meaning, and a referential meaning. The first part of simulations was performed to test the learning of the meaning of these alarm calls while the infant is attached physically to the mother. The second part of simulations was performed to study the infant's understanding of these alarm calls while the infant is detached physically from the mother. The results show that a LIDA- based agent is capable to learn such multiple meanings. The agent learned in sequence the feeling-based meaning, the action-based meaning, and the referential meaning. The LIDA agent achieved a good performance of understanding. This was verified by checking the correct escape action after hearing a specific alarm call.