Prof W Penny
No more applications being accepted
Competition Funded PhD Project (Students Worldwide)
About the Project
Classic strategic games defined in game theory and neuroeconomics allow for mathematical and neuroscientific studies of the tensions, trade-offs and computational bases of decisions as to whether to cooperate or compete. In most of these games, however, agents have a very limited set of actions to choose from. In more realistic settings, people often have a very large number of ways of interacting with one another – even if people would like to cooperate it may not be clear how to. In this project we will develop agent models that embody Representation Learning methods from the field of machine learning. These agents reduce the large product space of actions to a lower-dimensional latent space. We hypothesise that it is the use of this latent space that allows people to efficiently learn how to best interact with one another across multiple related contexts. This hypothesis will be tested using computational models and behavioural experiments. If the project goes well we will also use brain imaging experiments to find out which neural circuits support the computations underlying these behaviours.
For more information on the supervisor for this project, please go here: https://people.uea.ac.uk/w_penny
This is a PhD programme.
The start date of the project is October 2020.
The mode of study is full-time/part-time. The studentship length is 3 years for full-time students and 6 years for part-time students.
Funding Notes
This PhD project is in a School of Psychology competition for funded studentships. These studentships are funded for 3 years and comprise of home/EU tuition fees and an annual stipend of £15,009.
Entry requirements:
Acceptable first degree in Psychology, Computer Science, Economics, Engineering or other relevant discipline.
The standard minimum entry requirement is 2:1.
References
M Nowak and R Highfield (2012) Supercooperators: Altruism, Evolution and Why We Need Each Other to Succeed. Simon and Schuster.
C Ruff and E Fehr (2014) The Neurobiology of Rewards and Values in Social Decision Making. Nature Reviews Neuroscience 15, 549-562.
Y Bengio et al. (2014) Representation Learning: A Review and New Perspectives. International Conference on Learning Representations.
N Menghi and W Penny (2019) Configural Learning depends on Task Complexity and Temporal Structure. Conference on Computational Cognitive Science, Berlin.
Devaine et al. (2014) Theory of Mind: Did evolution fool us ? PLoS One, 9(2), e87619.
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