GW4 BioMed3 MRC DLP PhD project: Anxiety-Related Attention Bias in Everyday Situations: Mechanisms and VR-Based Modification

About the project

About the Project This project is one of several in competition for funding from the GW4 BioMed3 MRC Doctoral Landscape Programme (DLP), which is offering up to 17 studentships for entry in September 2026. The partnership brings together the Universities of Bath, Bristol, Cardiff and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities. More information may be found on the DLP’s website . Please note that the application process may close early to either home or international candidates (or both) before the stated deadline if an unprecedented number of applications are received – check the DLP’s website for details and updates. Supervisory Team: Dr Alexandra Voinescu Dr Ali Khatibi Professor Edmund Keogh Professor Neil Vaughan The Project : Anxiety is associated with attentional biases, including heightened vigilance to threat, difficulty disengaging, and reduced flexibility in shifting focus. Cognitive models propose these biases are causally involved in anxiety, yet most evidence comes from simplified lab tasks that poorly reflect everyday situations like driving, where continuous, flexible attention is essential. This project examines how anxiety-related attentional mechanisms operate in realistic immersive environments, and whether attention bias modification (ABM) can be translated into virtual reality (VR) to target these mechanisms in everyday-like contexts. All core VR tasks (an immersive driving simulator and VR attention paradigms), plus EEG, eye-tracking and autonomic recording equipment, are already available in our laboratories. The central question: how do cognitive and clinical mechanisms of anxiety-related attention bias manifest in realistic settings, and can immersive VR-based ABM modify them to reduce anxiety and improve functioning? Four linked objectives, each mapped to a study, give the student clear ownership over design, analysis and intervention development. Objective 1 (Study 1) is a systematic review of anxiety and attention research in high-immersion or realistic settings (driving simulators, VR, complex real-world tasks). The student will design the search strategy, set eligibility criteria, extract and code data on attentional mechanisms (threat orienting, sustained attention, disengagement, inhibitory control) and clinical variables, and assess risk of bias. This establishes which mechanisms have been tested in realistic contexts, how they were operationalised, and where gaps remain, grounding the empirical studies that follow. Objective 2 (Study 2) experimentally characterises general attentional mechanisms linked to anxiety in immersive VR, using the driving simulator and a complementary VR attention task, without emphasising threat content. Driving scenarios will vary traffic density, multitasking demands and complexity, measuring hazard detection, reaction time, lane position and speed regulation; the VR attention task will assess sustained attention, vigilance and inhibitory control. Trait/state anxiety self-reports, EEG, eye-tracking and autonomic measures (e.g., heart-rate variability) will index attentional engagement, scanning and arousal, linking anxiety to broad cognitive and physiological mechanisms under everyday-like load. Objective 3 (Study 3) targets threat-specific attention bias and its functional relevance. Driving and VR scenarios will embed threat-relevant stimuli (threatening pedestrians, near-miss hazards, negative social cues) in realistic scenes. Eye-tracking will quantify initial orienting, maintained attention, and disengagement from threat versus neutral events, testing whether higher anxiety predicts biased gaze and altered scanning, and how these relate to missed hazards, slower responses and errors. EEG will capture temporal dynamics of threat processing. Healthy and elevated-anxiety participants will enable dimensional and group comparisons linking bias indices to symptom severity and everyday functioning. Objective 4 (Study 4) develops and evaluates an immersive VR-based ABM intervention targeting the mechanisms identified in Studies 2–3. An ABM paradigm embedded in the driving simulator or a related VR task will aim to strengthen flexible, goal-directed attention and reduce maladaptive threat-focused patterns (e.g., training attention toward task-relevant road information and away from irrelevant threat cues), drawing on established ABM principles while leveraging VR's ecological validity. Outcomes include changes in attention-bias indices, EEG markers, self-reported anxiety, and functional performance in the immersive environment. The student will have substantial scope to select the ABM approach and mechanisms to prioritise, refining the intervention using pilot data. Across the four studies, the student will gain experience in systematic reviewing, immersive VR task design, multimodal data collection (behavioural, self-report, EEG, eye-tracking, autonomic), advanced statistical/computational analysis, and recruitment of healthy and elevated-anxiety samples. The project combines genuine student ownership with existing VR and psychophysiological infrastructure to advance understanding and modification of anxiety-related attentional mechanisms in everyday-like situations. Requirements: Applicants must have obtained, or be about to obtain, a first or upper second-class UK honours degree, or the equivalent qualifications gained outside the UK, in an appropriate area of medical sciences, computing, mathematics or the physical sciences. Applicants with a lower second-class degree will only be considered if they have a grade of Merit or above in a master’s degree. Academic qualifications are considered alongside significant relevant non-academic experience. Non-UK applicants will be required to have met the English language entry requirements of the University of Bath. Enquiries and Applications: Informal enquiries are welcomed and should be directed to Alexandra Voinescu on email address av561@bath.ac.uk . Formal applications must be submitted direct to the GW4 BioMed3 DLP using their online application form . A list of all the projects and details on how to apply are available DLP’s website . You may apply for up to 2 projects and submit one application per candidate only. APPLICATIONS CLOSE AT 17:00 (GMT) ON 21 OCTOBER 2026. IMPORTANT: You do NOT need to apply to the University of Bath at this stage – only those applicants who are successful in obtaining an offer of funding from the DTP will be required to submit an application for an offer of study from Bath. Equality, Diversity and Inclusion: We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups. If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.

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