Characterising Cognitive Biases Elicited by Unreliable Information Using Reinforcement Learning

About the Project

Supervisors:

• Dr Rani Moran;
• Prof. Tali Sharot (UCL & MIT)

Studentship Funding:

• Name: SBBS Studentship
• Funder: School of Biological and Behavioural Sciences (SBBS) at QMUL
• Application Deadline: 18/05/2026
• Expected Start Date: Sep 2026

Project Overview

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

Misinformation is one of today’s most pressing challenges, linked to public health risks, political extremism, and the spread of conspiracy theories. To address this problem, we need to understand why unreliable information can be so persuasive and how it shapes the way people form and update their beliefs. This PhD will investigate how people learn from unreliable information, asking: which parts of belief formation follow rational principles, and which are distorted by cognitive biases?

The project combines behavioural experiments (mainly online studies) with computational modelling using Reinforcement Learning (RL) — a framework widely used in psychology and neuroscience to study learning and decision-making. RL tasks will be used to explore how unreliable information alters learning and choices.

This work builds on ongoing research by Dr Rani Moran [1-2], which has shown that unreliable information can lead to learning biases — such as failing to ignore unreliable sources, jumping to conclusions, and seeking out confirming evidence.

Key research questions include:

• What cognitive biases make people vulnerable to unreliable information?
• How do people distinguish between reliable and unreliable information?
• How can we design interventions to improve belief accuracy?
• How do people revise beliefs after discovering a trusted source was false?

This project offers a unique opportunity to study a pressing issue through the lens of cognitive psychology and computational modelling. You will gain strong skills in experimental design, data analysis, programming, and academic writing, and have opportunities to collaborate with researchers at UCL.

Keywords:

Misinformation; Decision Making; Cognitive Biases; Reinforcement Learning; Computational Modelling

Research Environment

You would be based in the Centre for Brain and Behaviour within the School of Biological and Behavioural Sciences and the department of Psychology at Queen Mary. The School of Biological and Behavioural Sciences is one of the UK’s elite research centres, according to the 2021 Research Excellence Framework (REF). We offer a multi-disciplinary research environment and have PhD students working on projects in the biological and psychological sciences. Our students have access to a variety of research facilities supported by experienced staff, as well as a range of student support services.

Dr Moran’s Lab studies the cognitive mechanisms that support decision-making, memory, and learning, focusing on how these processes adapt flexibly to different task demands. Our research combines computational modelling with behavioural experiments, both online and in the lab. We aim to understand how learning and decision-making are shaped by unreliable information, how people balance exploration and exploitation, and how they use mental models to guide better choices. Our broader goal is to use these insights to design interventions that strengthen learning, reasoning, and decision-making in everyday life.

Prof. Tali Sharot (UCL & MIT) will be the secondary supervisor on this project and there are additional opportunities to collaborate with researchers from UCL e.g., from the MPC UCL Centre for Computational Psychiatry and Ageing Research.

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 and necessary.

Knowledge and prior experience with online data collection would be advantageous but are not required.

Find out more about our entry requirements here.

Applicants from outside of the UK are required to provide evidence of their English language ability. Details can be found on our English Language requirements page.

Funding

The studentship is funded by the Queen Mary University of London. It will cover home tuition fees, and provide an annual tax-free maintenance allowance for 3 years (£22,618 in 2026/27).

To qualify for Home Fees, this typically means the candidate will be unrestricted in how long they can remain in the UK.

International students will need to cover the difference in fees between the home and overseas basic rate from external sources. Further details can be found on our PhD Tuition Fees page.

Funding and eligibility queries can be sent to the sbbs-pgadmissions@qmul.ac.uk

How to Apply

Formal applications must be submitted through our online form by the stated deadline for consideration.

Applicants are required to submit the following documents:

• Your CV
• Personal Statement including:
  • Previous experience relevant to the project
  • Your motivations for pursuing this position
  • Your career aspirations
  • Any further information you think is relevant to the application
• Research Proposal
• References
• Copies of academic transcripts and degree certificates

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

Please contact Rani Moran at r.moran@qmul.ac.uk prior to applying to express your intention to apply, discuss research questions and/or with any informal enquires.

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

Apply Online

The School of Biological and Behavioural Sciences is committed to promoting diversity in science; we have been awarded an Athena Swan Silver Award. We positively welcome applications from underrepresented groups.