Artificial agents perceiving and processing time

Time perception is a fundamental component of intelligence that structures the way humans act in various contexts. As action evolves over time, timing is necessary to appreciate environmental contingencies, estimate relations between events and predict the effects of our actions at future moments. Despite the fundamental role of time in human cognition it remains largely unexplored in the field of artificial cognitive systems. The present work makes concrete steps towards making artificial systems aware that the notion of time as a unique entity that can be processed on its own right. To this end, we evolve artificial neural networks to perceive the flow of time and to be able to accomplish three different duration processing tasks. Subsequently we study the internal dynamics of neural networks to obtain insight on the representation and processing mechanisms of time. The self-organized neural network solutions exhibit important brain-like properties and suggests directions for extending existing theories in timing neuro-psychology.