Neurocomputational mechanisms of self-referencing in social anxiety at University of Birmingham on FindAPhD.com

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Dr Lei Zhang, Dr P Lockwood, Dr Mark Haslegrove No more applications being accepted Competition Funded PhD Project (Students Worldwide)

Birmingham United Kingdom Data Analysis Neurology Neuroscience Psychology Statistics

About the Project

Industry partner: Dr. Mona Garvert

Social anxiety is one of the most common, debilitating, and costly mental-health conditions. Individuals with social anxiety may suffer from a self-referencing bias (i.e., a strong bias to focus more on themselves when in social situations). This bias has been linked to impaired learning. However, little is known aboutthe mechanisms behind why those with social anxiety have a self-referencing bias, what happens in the brain, or how we can change it through interventions. This interdisciplinary project will combine expertise from cognitive neuroscience (University of Birmingham, University of Nottingham) and mobile-app-based interventions (Industry partner: Alena, AYA Technologies London). The successful student will have the opportunity to conduct a large-scale online study, a lab-based fMRI study, and an intervention study. We will measure behavioural responses, develop computational models of learning, and link them to brain-imaging data. The student will benefit from research visits and an internship at Alena, that provides neuroscience-based assessments and implements evidencebased therapy. The student will therefore develop a range of cutting-edge cognitive neuroscience skills and industrial experience with a highly supportive team of scientists across University of Birmingham and University of Nottingham.

HOW TO APPLY

Interested candidates should first contact the main supervisor (Dr. Lei Zhang, [Email Address Removed]) with their CV and a short statement of research intentions (ie. cover letter) with the subject "MRC AIM studentship FirstNAME LastName", before applying through the schemes, by 9th January 2023.

Only a small number of the most competitive applicants be supported and put forward to submit an application through the online application process.

* Applicants who have a research-oriented background in cognitive neuroscience and or computational psychiatry are desirable.

Informal enquiries should be directed to Dr Lei Zhang

To apply, please download the application form and complete all documentation available at https://more.bham.ac.uk/mrc-aim/phd-opportunities/.

The official deadline for submitting applications is 09:00 GMT Monday 16 January 2023. Please ensure that your application is submitted with all required documentation as incomplete applications will not be considered.

Funding Notes

The studentship is funded by the MRC AIM Doctoral Training Partnership scheme. The funding covers tuition fees and tax-free stipend at the standard UKRI rate, travel and conference budget, etc. Call open to both UK and International students. (Note that international students will have to pay for their own visas and a healthcare surcharge which is approximately £2500).
See also:
https://more.bham.ac.uk/mrc-aim/

References

Crawley*, D., Zhang*, L., Jones, E.J.H., et al. (2020). Modeling flexible behavior in childhood to adulthood shows age-dependent learning mechanisms and less optimal learning in autism in each age group F. Ramus (ed). PLOS Biology, 18, e3000908
Everaert, J., Bronstein, M. V., Castro, A. A., Cannon, T. D., & Joormann, J. (2020). When negative interpretations persist, positive emotions don't! Inflexible negative interpretations encourage depression and social anxiety by dampening positive emotions. Behaviour Research and Therapy, 124, 103510.
Haselgrove, M. (2016). Learning: A Very Short Introduction (Vol. 481). Oxford University Press.
Liebowitz, M.R. (1987). Social Phobia. Modern Problems of Pharmacopsychiatry, 22, 141–73
Lockwood, P.L., Apps, M.A.J., Chang, S.W.C. (2020). Is There a ‘Social’ Brain? Implementations and Algorithms. Trends in Cognitive Sciences, 24, 802–13
Lockwood, P.L., Apps, M.A.J., Valton, V., et al. (2016). Neurocomputational mechanisms of prosocial learning and links to empathy. Proceedings of the National Academy of Sciences, 113, 9763–68
Norton, A. R., & Abbott, M. J. (2016). Self-focused cognition in social anxiety: A review of the theoretical and empirical literature. Behaviour Change, 33, 44-64.
Stein, M.B., Stein, D.J. (2008). Social anxiety disorder. The Lancet, 371, 1115–25
Zhang, L. (2022). Examining mental disorders with computational neuroscience. Nature Reviews Psychology. In press
Zhang, L., Lengersdorff, L., Mikus, N., et al. (2020). Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices. Social Cognitive and Affective Neuroscience, 15, 695–707
Zhang, L., & Gläscher, J. (2020). A brain network supporting social influences in human decision-making. Science advances, 6(34), eabb4159.

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