Project Outline

Background

Humans are highly social creatures, spending much of their lives thinking about and making decisions that affect other people. However, whilst the capacity to successfully engage in social interactions is critical, we still lack a clear characterisation of the mechanisms of social decision-making and its brain and behavioural basis (Lockwood et al., 2020).

Recent advances in cognitive neuroscience have allowed us to combine measures of behaviour, computational models of decision-making, neuroimaging, deep brain stimulation, and self-report which can get us closer to understanding why there are differences in social decision-making between people, and the fundamental mechanisms (Ruff & Fehr, 2014; Lockwood et al., 2020, TiCS). Moreover, such models can bridge levels of explanation from neuroscience to psychology.

Objectives and Methods

The proposed project will use these novel approaches to examine the behavioural and neural basis of social decision-making and ultimately to disrupt them using non-invasive deep brain stimulation. First we will collect big-data samples online from novel computerised tasks measuring social decision-making, combined with computational modelling. Next there will be an opportunity to use functional magnetic resonance imaging (fMRI) to uncover the neural basis. Finally, we hope to use recent advances in focused ultrasound stimulation to disrupt the neural basis.

Training and Outcomes

Students will receive advanced training in methods from psychology, cognitive neuroscience, neuroimaging, and computational modelling. The findings will have important implications for healthy development across the lifespan and potential interventions to reduce antisocial behaviour.

References

Cutler, J., Wittmann, M. K., Abdurahman, A., Hargitai, L., Drew, D., Husain, M. & Lockwood., P. L. (2021). Ageing disrupts reinforcement learning whilst learning to help others is preserved. Nature Communications.

Konovalov, A., Hill, C., Daunizeau, J., & Ruff, C. C. (2021). Dissecting functional contributions of the social brain to strategic behavior. Neuron, 109(20), 3323-3337.

Lockwood, P. L.  Apps, M. A. J., Chang, S. W. (2020). Is there a ‘social’ brain? Implementations and algorithms. Trends in Cognitive Sciences.

Lockwood, P. L., Apps, M. A. J., Valton, V., Viding, E. & Roiser, J. P. (2016). Neurocomputational mechanisms of prosocial learning and links to empathy. Proc. Natl. Acad. Sci.

Lockwood, P. L., Klein-Flugge, M. C., Abdurahman, A, & Crockett, M. J. (2020). Model-free decision making is prioritized when learning to avoid harming others. Proc. Natl. Acad. Sci.

Lockwood, P. L. et al. (2017). Prosocial apathy for helping others when effort is required. Nature Human Behaviour.

Lockwood, P. L. et al. (2018). Neural mechanisms for learning self and other ownership. Nature Communications.

Ruff, C., & Fehr, E. (2014). The neurobiology of rewards and values in social decision making. Nature Reviews Neuroscience

Techniques

• Advanced techniques in computational modelling (model fitting, model simulation, model generation)
• Analysis of brain imaging data (functional MRI, structural MRI, connectivity analyses)
• Programming of behavioural tasks (Matlab, Presentation)
• Advanced statistical analysis (Matlab, R)
• Data collection with clinical populations, children, adolescents and older adults.
• Additional opportunities for learning of cutting-edge cognitive neuroscience techniques with collaborators at the University of Birmingham, University of Oxford and University of Zurich, amongst others.