Characterising Cognitive Biases Elicited by Misinformation Using Reinforcement Learning

• Supervisors: Dr Rani Moran
• Studentship Funding:
  • Name: SBBS Studentship
  • Funder: School of Biological and Behavioural Sciences (SBBS) at QMUL
• Application Deadline: 23:59pm on 30th April 2025
• Expected Start Date: 15th Sept 2025 (Sept 2025 Entry)

Download this document for further details, eligibility criteria and how to apply. [PDF 138KB]

Project Overview

Applications are open for a 3-year funded PhD Studentship in the School of Biological and Behavioural Sciences (SBBS) at Queen Mary University of London.

Misinformation is a major threat to society, leading to issues like public health risks, political extremism, violence, and the spread of conspiracy theories (e.g., [1-5]). To address these dangers, we need to understand why misinformation is so appealing and find ways to reduce its harmful effects. However, we still lack a deep understanding of how disinformation affects how individuals form and update their beliefs (i.e., learn).

This project aims to explore how misinformation influences learning, focusing on which aspects align with rational principles and which are distorted by cognitive biases [6]. We believe that misinformation spreads by exploiting these cognitive biases. The project builds on ongoing research by Dr. Moran [10], which has identified cognitive biases triggered by misinformation, such as the failure to ignore unreliable information, confirmation bias (the tendency to seek out information that supports pre-existing beliefs [11]), and the tendency to draw conclusions too quickly.

Key research questions include (but are not limited to):

•  What cognitive biases does misinformation trigger?
• What interventions can reduce these biases and improve decision-making?
• How do people learn to distinguish between reliable and unreliable information?
• Can individuals maintain accurate beliefs in the face of misinformation?
• How do people revise their beliefs when they discover that previously trusted information was false?

As a PhD candidate, you will primarily use behavioural studies (especially online data collection) and computational modelling to investigate these questions. You will develop varied skills for example: research design, computational modelling, sophisticated statistical analysis, academic writing, teamwork, programming, and data collection.

Research Environment

Dr Moran's lab studies the cognitive mechanisms supporting decision-making, memory and learning with a focus on how these flexibly adapt to varying tasks demands. Research in the lab uses computational modelling of cognitive processes (particularly reinforcement-learning) and online and lab-based behavioural studies to understand how our learning and decisions are affected by disinformation, how we balance exploration and exploitation and how we use sophisticated mental models to improve our choices. Our vision is that a better understanding of these processes will allow us to develop interventions promoting their effective usage.

Find out more about the School of Biological and Behavioural Sciences on our website. 

Entry Requirements & Criteria

We are looking for outstanding candidates to have or expecting to receive a first-class honours degree in an area relevant to the project such as Psychology, Cognitive Sciences, Neuroscience, Biology, Economics, Mathematics, Statistics, Computer Sciences or Engineering. A Master’s degree is desirable, but not essential. 

Candidates must also have some experience conducting research. Knowledge and prior experience with computer coding, computational modelling, statistical testing, academic writing and behavioural studies are essential. Knowledge and prior experience with online data collection would be advantageous but are not required. Find out more about our entry requirements here.

Funding

The studentship is funded by Queen Mary University of London (QMUL). It will cover home tuition fees, and provide an annual tax-free maintenance allowance for 3 years at the UKRI rate (£21,237 in 2024/25). 

Please find out more about funding and eligibility via:Moran_QMUL Studentship Details [PDF 138KB] 

Any further queries can be sent to sbbs-pgadmissions@qmul.ac.uk 

How to Apply 

Find out more about our application process on our SBBS website.

Admissions-related queries can be sent to sbbs-pgadmissions@qmul.ac.uk.

Further details can be downloaded here: Moran_QMUL Studentship Details [PDF 138KB]

Apply Online

References

1. Global Risks Report 2024. World Economic Forum https://www.weforum.org/publications/global-risks-report-2024/.
2. Carrieri, V., Madio, L. & Principe, F. Vaccine hesitancy and (fake) news: Quasi-experimental evidence from Italy. Health Econ. 28, 1377–1382 (2019).
3. Rocha, Y. M. et al. The impact of fake news on social media and its influence on health during the COVID-19 pandemic: a systematic review. J. Public Health 31, 1007–1016 (2023).
4. Piazza, J. A. Fake news: the effects of social media disinformation on domestic terrorism. Dyn. Asymmetric Confl. 15, 55–77 (2022).
5. Roy, S., Singh, A. K. & Kamruzzaman. Sociological perspectives of social media, rumors, and attacks on minorities: Evidence from Bangladesh. Front. Sociol. 8, 1067726 (2023).
6. Gilovich, T., Griffin, D., & Kahneman, D. (Eds.). (2002). Heuristics and Biases: The Psychology of Intuitive Judgment. Cambridge University Press.
7. Farrell, S., & Lewandowsky, S. (2018). Computational Modeling of Cognition and Behavior. Cambridge University Press.
8. Sun, R. (2008). The Cambridge Handbook of Computational Psychology. Cambridge University Press.
9. Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
10. Vidal-Perez, J., Doaln, R., Moran, R., Disinformation elicits learning biases. https://www.researchsquare.com/article/rs-4468218/v1
11. Rollwage, M., Loosen, A., Hauser, T.U., Moran, R., Dolan, R. J., Fleming, S. M. Confidence drives a neural confirmation bias. (2020). Nature Communications. 11(1), 2634.