Dr Clayton Hickey

Supervisor Details

Contact Details

Dr Clayton HickeyLink opens in a new window

School of Psychology, University of BirminghamLink opens in a new window

Scientific Background

Research Interests

My research investigates how the brain selects and prioritises information in complex visual environments. I am particularly interested in visual attention, eye movements, and how competition between object representations is resolved in the human brain. Our work combines behavioural experiments with EEG, eye tracking, MRI and MEG, alongside computational modelling and artificial intelligence approaches. Current projects examine how representations in AI vision models can help explain human perception and attention, as well as how attention is shaped by learning, reward and motivation

Research Groups

Motivated Cognition LabLink opens in a new window

Supervision Style

In three words or phrases: Collaborative, supportive, rigorous.

Provision of Training

Training will combine direct input from supervisors with support from experienced postdocs and PhD students, alongside relevant training provided by the University and CHBH. I expect students to become progressively more independent in choosing, developing and troubleshooting appropriate methods as the PhD progresses.

Progression Monitoring and Management

We will review progress regularly in supervision meetings, setting clear short- and longer-term goals while adapting these as the project develops.

Communication

I aim to maintain open and regular communication, with routine in-person supervision meetings and attendance at in-person lab meetings.

Meetings

PhD Students can expect scheduled meetings with me at least once per fortnight. These meetings will be a mixture of face-to-face and via video chat or telephone. I am usually contactable for an instant response 1 or 2 days per week.

Work Patterns

Certain tasks in my lab need to occur at set times, and students need to be able to commit to a rota/timetable shared with other members of the team.

Notice Period for Feedback

I need at least 2 weeks' notice to provide feedback on written work of up to 5,000 words.

MIBTP Project Details

Primary supervisor for:

Using artificial intelligence to reveal the computational principles of neural competition

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).

Using artificial intelligence to reveal the computational principles of neural competition

Secondary Supervisor(s): Dr Wieske van Zoest

University of Registration: University of Birmingham

BBSRC Research Themes:

Understanding the Rules of Life (Neuroscience and Behaviour)

Project Outline

Information in the brain is represented through distributed patterns of activity across populations of neurons. Simultaneously active representations can overlap and compete, a basic principle of neural information processing whose relationship to representational structure remains poorly understood.

The visual system provides a powerful model for investigating this principle and establishing new links between artificial and biological intelligence. This project will test whether distributed representations in cutting-edge artificial intelligence systems predict competition in the human brain. Building on divisive normalisation, similarity and relative representational strength will be combined to quantify the directional ‘representational pressure’ that competing objects exert on one another.

Initial studies will determine how AI-derived representational pressure relates to biological mechanisms that resolve competition. Controlled visual-search experiments will combine EEG, eye tracking and behaviour to test whether pressure predicts target enhancement and distractor suppression (N2pc and Pd), the timing and direction of gaze, and behavioural performance.

The project will then ask whether these principles scale to natural vision. Natural-scene experiments will test whether AI-derived representational structure predicts which objects attract attention, when they are selected, and how observers explore scenes containing multiple competitors. Eye tracking will trace this competition over successive fixations, linking model predictions to the evolving priorities of biological vision.

The project tests a fundamental hypothesis: that the structure of distributed population representations determines competition between them, and that this computational principle can be captured in artificial intelligence systems to predict neural processing and behaviour.