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The cognitive neuroscience of creative innovation: using EEG, brain stimulation and computational modelling to understand creativity in music


Project Description

Applications are invited for a fully funded PhD studentship in Psychology at the School of Biological and Chemical Sciences (SBCS) at Queen Mary, University of London. The PhD project falls within the field of Cognitive Neuroscience and involves the combination of advanced neuroimaging (EEG), brain stimulation, and computational modelling in order to understand cognitive processes related to the perception of innovation and creativity. The student will be supervised by Dr Caroline Di Bernardi Luft ((http://www.sbcs.qmul.ac.uk/staff/carolinedibernadriluft.html)_and co-supervised by Dr Marcus Pearce (http://webprojects.eecs.qmul.ac.uk/marcusp/) .

BACKGROUND
Creative innovation involves producing artefacts that are novel and surprising but also culturally valuable, distinguishing them from errors which are novel but not valuable. While there may be a fine line between errors and innovations, the former disappear from a culture while the latter are reinforced and influence subsequent cultural innovation. Therefore, it is important to understand the difference between creative innovation and errors and how these are perceived and transmitted by individuals.

PROJECT AIM
The aim of the project is to investigate the neural signatures of errors and innovations and their relation to the probabilistic structure of music both in perception and production. A series of EEG, brain stimulation, and computational modelling studies will be conducted in order to understand the differences in how the brain processes novelty associated with errors and innovations. The PhD project will apply advanced neuroimaging and transcranial brain stimulation methods, combined with computational models of music, to understand the neural mechanisms behind this process.

The successful PhD candidate will be involved in planning/programming experiments, collecting and analysing data, and reporting the results for publication and writing a thesis. Candidates must have a Master degree in Psychology, Neuroscience, Cognitive Science, Computer Science, Biomedical Engineering or a closely related field. Applicants from outside of the UK are required to provide evidence of their English language ability (please see our website for details: https://www.qmul.ac.uk/sbcs/postgraduate/phd-programmes/entry-requirements/). Matlab programming is a requirement and the candidate must have some experience of EEG/MEG data analysis. Brain stimulation and computational modelling experience would be an advantage. This project requires motivation and a broad interest to improve skills in various fields (e.g. computational modelling, signal processing, data analysis, programming, etc.). A basic knowledge of and interest in music is also desirable.

For informal enquiries please email Dr Caroline Di B. Luft at . To apply, please follow the online process at Queen Mary website. Applicants are required to provide their CV, transcripts, 2 references and a research statement which addresses the following questions: 1) Why do you want to research on this topic? 2) Why do you want to pursue a PhD? 3) What skills and experience do you have that would make you a successful candidate for this PhD position?

Funding Notes

The studentship will cover tuition fees and provide an annual tax-free maintenance allowance for 3 years at the Research Council rate (£17,009 in 2019/20).

References

[1] Asch, S. Psych Monographs 70,1(1956).
[2] Koelsch, S.,et al. Brain 12(2006).
[3] Zioga,I.,Luft,CDB.& Bhattacharya, J. Brain Research 1650, 267-282 (2016).
[4] Pearce,M.T., et al.. NeuroImage 50, 302-313 (2010).
[5] Luft,CDB,Nolte,G. & Bhattacharya, J. Journal of neuroscience 33, 2029-2038 (2013).
[6] Pearce,M.T. & Wiggins, G. Topics in cognitive science 4, 625-652 (2012).
[7] Wiggins,G. A.,et al. Phil. Trans. R. Soc. B 370, 20140099 (2015).
[8] Lumaca,M.& Baggio, G. SCAN 11, 1970-1979 (2016).

How good is research at Queen Mary University of London in Biological Sciences?

FTE Category A staff submitted: 23.39

Research output data provided by the Research Excellence Framework (REF)

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