Social cognition, communication complexity and creativity in birdsong communication systems

Investigate how improvisational creativity plays into the complex of constraints introduced by social network complexity and cognitive abilities in birdsong communication systems.


Cognitive neuroscience, Computational modelling, Creative arts

The relationship between social complexity and communicative complexity is currently a ‘hot’ research topic in both animals and humans. However, the way in which communicative complexity is affected by the combination of constraints introduced by the social learning environment and cognitive abilities is not well understood. Song birds participate in complex communication networks using mainly acoustic signals, and must learn to recognise and understand the meaning of the communication signals of conspecifics and how to use them to mediate their social interactions. Acquiring these abilities is essential for an adept social networker, but the mechanism by which this is achieved is not fully understood. In addition, during the acquisition of these abilities birds must both improvise and copy other songs. The optimal level of improvisational innovation (creativity) versus imitation will vary depending upon both social and biological constraints; this process has parallels with the development of traditional songs in humans.

In this project you will investigate the way in which songbirds actively explore and regulate their social environment using communication signals, how the early improvisational and imitative ’babbling’ of young birds develops into adult song, and how they can learn effective signalling strategies. This will be achieved by developing computational models, and designing a sound installation based on different song development strategies where the song properties are an emergent property of the system. You will explore how differences in cognitive ability, opportunity to learn, and complexity of the social system combine to produce a particular signalling system and level of improvisation. The predictions and solutions that arise from these computational models will be compared against the literature available on the wide variety signalling systems found in song birds in nature, against results from field studies being conducted in a related study during the project and, time permitting, to the cultural evolution of songs in human communities.


Essential skills: Research experience in computational modelling, learning theory and a strong interest in communication networks. An understanding of signal, or information complexity would also be useful 

Desirable skills: An understanding of evolutionary biology and animal cognition, an interest in installation/sonic art

Qualifications: Honours first degree (minimum upper second) or equivalent in Computer Science, Applied Mathematics, Computational Neuroscience, or related fields. Candidates who also have a Master’s degree in these fields are especially desirable.