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Behavioural and brain mechanisms of social learning and motivation

Primary Supervisor: Dr Patricia Lockwood, School of Psychology

Secondary supervisor: Professor Ole Jensen, University of Birmingham and Dr Joe Galea, University of Birmingham

Collaborators: Professor Matthew Rushworth, University of Oxford, Professor Masud Husain, University of Oxford, Professor Christian Ruff, University of Zurich

PhD project title: Behavioural and brain mechanisms of social learning and motivation

University of Registration:University of Birmingham

Project outline:

Background

Humans are highly social creatures, spending much of their lives thinking about and making decisions that affect other people. In parallel, social isolation is as damaging to health as excessive alcohol consumption and smoking, yet we know surprisingly little about the mechanisms that drive people to engage with others (Lockwood et al., 2020).

Recent advances in cognitive neuroscience have allowed the combination of measures of behaviour, computational models of decision-making, neuroimaging and self-report which can get us closer to understanding why there are differences in social behaviour between people, and the fundamental mechanisms (Ruff & Fehr, 2014; Wittmann, Lockwood & Rushworth, 2018; Lockwood & Klein-Flugge, 2020). 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 learning – how we learn which of our actions help and avoid harming others – and social motivation – how willing we are to engage with other people in the first place.

First, we will collect big-data samples online from computerised tasks measuring social learning and motivation, combined with computational modelling. This will allow us to probe what drives individual differences between people in social learning and motivation and how the two processes relate to one another.

Next, we will use functional magnetic resonance imaging (fMRI) and advanced computational modelling approaches, with multivariate analyses, to examine the neural basis of social learning and motivation. fMRI provides excellent spatial resolution of brain data. We will also use Magnetoencephalography (MEG) based advanced computational modelling and multivariate analyses of social learning and motivation. MEG provides excellent temporal resolution of brain data. These imaging approaches will allow us to uncover the mechanisms in the brain that track how we learn and decide in social situations and the neural representations that underpin social learning and motivation.

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 social development across the lifespan and, ultimately, potential interventions to reduce antisocial behaviour.

References

  1. Lockwood, P. L.  Apps, M. A. J., Chang, S. W. (2020). Is there a ‘social’ brain? Implementations and algorithms. Trends in Cognitive Sciences.
  2. Lockwood, P. L., Apps, M. A. J., Valton, V., Viding, E. & Roiser, J. P. (2020). Neurocomputational mechanisms of prosocial learning and links to empathy. Proc. Natl. Acad. Sci.
  3. Lockwood, P. L., Klein-Flugge, Abdurahman, & Crockett (2020). Model-free decision making is prioritized when learning to avoid harming others. Proc. Natl. Acad. Sci.
  4. Lockwood, P. L. et al. (2017). Prosocial apathy for helping others when effort is required. Nature Human Behaviour.
  5. Lockwood, P. L. et al. (2018). Neural mechanisms for learning self and other ownership. Nature Communications.
  6. Ruff, C., & Fehr, E. (2014). The neurobiology of rewards and values in social decision making. Nature Reviews Neuroscience.

BBSRC Strategic Research Priority: Understanding the Rules of Life: Neuroscience and behaviour

      Techniques that will be undertaken during the project:

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

      Contact: Dr Patricia Lockwood, University of Birmingham