In the context of referential signalling games played with realistic images, intelligent agents modelled as deep neural networks have previously been shown to develop successful token-based communication protocols to achieve a shared goal. This research draws inspiration from how human communication developed and first compares the processes involved in transmitting meaningful information between humans and machines. This thesis seeks to uncover how inter-agent communication about the visual world, emerging in a completely self-supervised way, can be modelled and its interpretability improved.

As such, advancing artificial intelligent agents capable of interaction through communication with each other and with humans has been a long-standing goal. The development of such intelligence is promoted by exploration of the environment and social interaction. These capabilities are essential in numerous tasks we carry out in our daily life. Supported by a sufficiently complex brain, we start to learn and develop these cognitive abilities and skills such as communication from the moment we are born. Our ability to perceive, represent and understand the surrounding visual world is one of the most fascinating, but equally intricate, parts of our nervous system.

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