The question of fake news and choice manipulation has emerged as a topical issue in recent years, following reports of inappropriate political influence and the rise of conspiracy theories through social media. An important component of this issue is how people use the degree of trustworthiness or reliability of the information presented to them to make decisions. Importantly, despite clear mathematical models of how reliability of information should inform choice (Tenenbaum et al., 2006) and the wide use of reliability indicators to inform consumer behaviour (Hoffart et al., 2019), there remains a lack of understanding on how humans use explicit cues about reliability of information to make choices.
This project will capitalize on ongoing research by Dr Charles on how people use explicit indicators of information reliability (Jiwa et al., n.d.) and introspect being influenced in their decisions (Kummen et al., 2023). It will use computational models to pinpoint how people encode and combine the reliability of the information they receive, using Bayesian inference as a benchmark of optimality. Electro-encephalography (EEG) will be used to uncover the neural markers of information reliability encoding. Finally, it will explore how implicit beliefs about trustworthiness can influence decision making, probing with ecologically valid tasks, such as social media thread, how misinformation influences behaviour. This PhD studentship will produce fundamental and applied research on cognitive neuroscience, neuroeconomics, behaviour change and social psychology.
Dr Charles' lab studies the cognitive processes allowing humans to introspect and evaluate their own thoughts and action. Research in the lab uses neuroimaging (fRMI & EEG), online and lab-based behavioural studies to understand the cognitive processes underlying decision-making and metacognition, with a particular focus on confidence, freedom of choice and action awareness.
Find out more about the School of Biological and Behavioural Sciences on our website.
We are looking for candidates to have or expecting to receive a first or upper-second class honours degree and a Master’s degree in an area relevant to the project such as Psychology, Cognitive Sciences and Neuroscience. Candidates with a degree in Biology, Economics, Mathematics, Statistics, Computer Sciences or Engineering are also encouraged to apply. A masters degree is desirable, but not essential.
Prior experience with behavioural and EEG data collection, computational modelling, data analysis, statistics, coding and academic writing are desirable.
You must meet the IELTS requirements for your course and upload evidence before CSC’s application deadline, ideally by 1st March 2025. You are therefore strongly advised to sit an approved English Language test as soon as possible, where your IELTS test must still be valid when you enrol for the programme.
Please find further details on our English Language requirements page.
Formal applications must be submitted through our online form by 29th January 2025 for consideration. Please identify yourself as a ‘CSC Scholar’ in the funding section of the application.
Applicants are required to submit the following documents:
Find out more about our application process on our SBBS website.
Informal enquiries about the project can be sent to Dr Lucie Charles AT l.charles@qmul.ac.uk Admissions-related queries can be sent to sbbs-pgadmissions@qmul.ac.uk
Shortlisted applicants will be invited for a formal interview by the supervisor. If you are successful in your QMUL application, then you will be issued an QMUL Offer Letter, conditional on securing a CSC scholarship along with academic conditions still required to meet our entry requirements.
Once applicants have obtained their QMUL Offer Letter, they should then apply to CSC for the scholarship with the support of the supervisor.
For further information, please go to the QMUL China Scholarship Council webpage.
Apply Online
Hoffart, J. C., Olschewski, S., & Rieskamp, J. (2019). Reaching for the star ratings: A Bayesian-inspired account of how people use consumer ratings. Journal of Economic Psychology, 72, 99–116. https://doi.org/10.1016/j.joep.2019.02.008 Jiwa, M., Yu, Y., Boonyaratvej, J., Ciston, A., Haggard, P., Charles, L., & Bode, S. (n.d.). Exposure to misleading and unreliable information reduces active information-seeking. https://doi.org/10.31234/OSF.IO/4ZKXW Kummen, Å., Haggard, P., Williams, G., & Charles, L. (2023). Mistaking opposition for autonomy: psychophysical studies on detecting choice bias. Proceedings of the Royal Society B: Biological Sciences, 290(1996). https://doi.org/10.1098/rspb.2022.1785 Tenenbaum, J. B., Griffiths, T. L., & Kemp, C. (2006). Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences, 10(7), 309–318. https://doi.org/10.1016/j.tics.2006.05.009