Developing a model for location based route learning in a virtual world

A robot has been given a route learning task. It’s goal is decision making based on the recognition of situations. It features a behavioural model which includes object recognition based on the ventral stream, dual-process decision making and motor control. This model tries to follow the computational cognitive neuroscience (CCN) ideals. Implementation is done using a combination of neural networks and programming. PCA reveals that representations can emerge at different level of processing. Lesions study and PCA shows that location detection can be enhanced by combining vision and sonar. Results also show the benefits from using dual-processing decision making. This thesis ends with stating that combining CCN modelling with traditional research can provide a powerful tool in understanding cognition.