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Neurocomputational Mechanisms of Cooperative Behaviour

Principal Supervisor: Dr Arkady Konovalov

Secondary Supervisor(s): Dr Patricia Lockwood

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024


Project Outline

Background

Cooperation is essential for human society. Typically, we understand cooperative behaviour as a phenomenon where individuals incur some personal cost to achieve a benefit for the group. Understanding why people engage in cooperation, or why they behave selfishly instead, and how we can foster cooperation is essential for pressing challenges of the 21st century including climate change, infectious disease, and aging populations. Despite the progress made by social science, we still know very little about the psychological and neural mechanisms underlying cooperative social decisions. The project will focus on the motives underlying cooperation and neurocognitive mechanisms that govern these motives, using methods and insights across several fields of research including behavioural economics, cognitive psychology, and social neuroscience.

Objectives

The project will bridge economic, psychological, and neuroscientific methods to employ a comprehensive approach to the study of cooperative behaviour that will include building neurocomputational models that will link specific underlying mechanisms (including altruism, risk attitude, and learning) to differential activity in regions of the human brain. From the last two decades of fMRI research, we know that social interactions often involve activations in the so-called “social brain” network. The project will move beyond the state of the art from studying simple neural correlates to a model-based, quantitative framework. This framework presents social choices as results of computations that depend on the inputs from the environment (for instance, other people’s actions or personal outcomes), individual preferences (regarding risk or inequality), and learning mechanisms. This approach will involve building new computational model of cooperation that relies on neurobiological mechanisms that can be specifically tested using stimulation methods.

Methods

The proposed project includes a combination of methods of behavioural economics (big data analysis, social dilemmas, and preference functions), models of cognitive and decision psychology (drift-diffusion and learning models), and methods of neuroscience (such as fMRI, eye-tracking, and brain stimulation). The proposal will utilize the facilities of the Centre for Human Brain Health at the University of Birmingham, including the MRI scanner and the brain stimulation lab. Combining computational modelling with neural recordings, the project will identify specific brain areas that are linked to specific computations necessary for cooperation. These candidate regions will be used as targets in a series of brain stimulation studies that will demonstrate the causal role of these regions in cooperative decisions. These studies will show that cooperative behaviour can be changed via enhancing or disrupting neural activity in these target areas that underlies specific parts of the decision process. The combination of behavioural and neuroimaging methods will allow us to link social choice characterizations, neural mechanisms, mental health issues, individual traits (personality, psychopathy, autism spectrum disorder), and cooperative behaviour that can impact real-world outcomes.

References

Konovalov, A., Hu, J., & Ruff, C. C. (2018). Neurocomputational approaches to social behavior. Current Opinion in Psychology, 24, 41-47.

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.

Hu, J., Konovalov, A., & Ruff, C. C. (2023). A unified neural account of contextual and individual differences in altruism. Elife, 12, e80667.

Techniques

  • Behavioral experiments
  • Statistical analysis including Bayesian and non-parametric methods
  • Computational modelling
  • Eye-tracking
  • fMRI
  • High-performance computing
  • Transcranial magnetic stimulation