Dr Lei Zhang

Supervisor Details

Contact Details

School of Psychology, University of Birmingham

Scientific Background

Research Interests

Learning and decision-making, flexible & adaptive (learning) behaviour, decision neuroscience/neuroeconomics, social learning, reinforcement learning, cognitive modelling, Bayesian methods, neuroimaging, fMRI, TMS, pain, aversive learning, computational psychiatry.

Scientific Inspiration

Peter Dayan inspired many of my research work, including the newly emerged field, Computational Psychiatry. Plus, I believe PhD students are likewise inspiring, because they are absolutely amazing in showing the highest level of curiosity about research.

Research Groups

Institute for Mental Health

Supervision Style

In three words or phrases: Supportive, collaborative, growing together.

Provision of Training

I provide individualized training with my own experience and perspectives. As the students getting more expertise and experience, I gradually retreat from my supervision and provide them the freedom to help with their independence.

Progression Monitoring and Management

Goals will be jointly discussed and set between the students and the supervisor, with the aim to produce high quality research and scientific productivity. Progress will be monitored in a relaxed manner once a week in face to face “catch-up” meetings. In addition to the regular meetings, I am available for advice and guidance to reach the goals.

Communication

Main contact between supervisor and students is conducted through email – this is to ensure all communications are well documented. The contact also includes brief weekly face to face “catch up” meetings, during which, students are encouraged to freely discuss various topic, such as research-related issues, technical problems, and career developments. More in-depth meetings for detailed discussion (e.g., experimental design, data analysis, practice talks) will be held during weekly lab meetings. The lab maintains a flat structure, and keeps a healthy social life (which is entirely voluntary): meeting for coffee, social events, sports, etc.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once a week

In year 1 of PhD study

Regular short meetings once a week of under 30 minutes + longer duration meetings whenever required

In year 2 of PhD study

Regular short meetings once a week of under 30 minutes + longer duration meetings whenever required

In year 3 of PhD study

Regular short meetings once a week of under 30 minutes + longer duration meetings whenever required

These meetings will be mainly face to face, and I am usually contactable for an instant response 3 or 4 days a week.

Working Patterns

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I usually need at least 2 weeks' notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Current Projects (2025-26)

Primary supervisor for:

Computational and brain-spinal mechanisms underlying the role of expectation in pain

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Computational and brain-spinal mechanisms underlying the role of expectation in pain

Secondary Supervisor(s): Dr Ali Khatibi, Dr Matthew Apps

University of Registration: University of Birmingham

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

Project Outline

Pain is an unpleasant and distressing condition, affecting more than 30% of the UK population. It costs more than £10 billion annually and has resulted in significant societal and economic challenges. Theories of pain neuroscience have been unable to characterise one of its most fundamental features: pain is subjective that is heavily modulated by expectations. Recent computational frameworks have been powerful in precisely quantifying the role of expectation in reward-related learning, but have rarely been extended to aversive experiences such as pain. Plus, only recently, simultaneous brain/cortical-spinal neuroimaging has enabled the investigation of both the “top–down” (i.e., from brain to spine) and the “bottom–up” (i.e., from spine cord to brain) pain processing. However, the exact computational AND brain-spinal mechanisms of pain perception remain largely unknown.

Objectives and methods

To close these gaps, our project will provide a systematic investigation into the neurocomputational mechanisms of how prior expectations (i.e., how much pain is expected) and noxious information (i.e., how much pain is received) are integrated during pain processing. We hypothesize that the variation in the degree of pain perception associated with the differences in the degree of Bayesian integration between prior expectations and noxious input, modulated by the source of how prior expectation is formed. Methods will include novel experimental paradigm development, latest non-invasive neuroimaging techniques (brain and spine fMRI; uni- and multi-variate analyses), together with state-of-the-art Hierarchical Bayesian Modelling. This project will be powered with best open science practices, such as preregistration, power analysis, and open data.

Training and outcomes

This project brings together supervisory expertise in social learning and decision-making and Bayesian modelling (Dr. Zhang), motivation and cognitive control (Prof. Apps), and brain-spine fMRI imaging (Dr. Khatibi). The student will be primarily embedded in the Adaptive Learning Psychology & Neuroscience (ALPN) Lab (head: Dr. Zhang), that addresses the fundamental question of the “adaptive social brain” by studying the cognitive, computational, and neurobiological basis of social learning and decision-making in healthy individuals, and in psychiatric disorders. Students will get training in methods from psychology, neuroimaging and computational science, allowing them to develop a wide range of skills. Students will be given the independence to address their own questions of interest under the realm of the project. Students will also have the opportunity to visit collaborators in Austria, Germany, and China.

Impact

This project employs rigorous approaches to address original research questions about the underlying neurocomputational mechanisms of Bayesian integration in pain, which moves beyond previous verbal models that lack formal quantification. This will thus help us gain a better understanding of how we process pain in oneself, and thus how we manage better to avoid harm to ourselves. All these will enable us to develop better individualized health care for people with pain and similar chronic conditions. Ultimately, results from this project will offer important translational implications for patient-centred pain treatment.

Interested candidates are strongly encouraged to get in contact (l.zhang.13 @bham.ac.uk) for informal discussion about projects before submitting an application.

Relevant papers

Cohen-Adad, J., et al (2021). Nature protocols. https://www.nature.com/articles/s41596-021-00588-0.

Zhang, L., & Gläscher, J. (2020). Science advances. https://www.science.org/doi/full/10.1126/sciadv.abb4159.

Zhang, L., et al., Soc. Cog. and Aff. Neuro. https://academic.oup.com/scan/article-abstract/15/6/695/5864690.

Zhao, Y., et al., (2021). eLife. https://elifesciences.org/articles/69994.

Previous Projects (2024-25)

Primary supervisor for:

Neurocomputational account of predictive processing during first-hand pain and empathy for pain

Co-supervisor on a project with Professor Suzanne Higgs.

Previous Projects (2023-24)

Primary supervisor for:

Unravelling adaptive and flexible learning behaviour under social dynamics in health and disease