| Qualification Type: | PhD |
|---|---|
| Location: | Lancashire |
| Funding for: | UK Students, EU Students, International Students |
| Funding amount: | £21,805 |
| Hours: | Full Time |
| Placed On: | 12th March 2026 |
|---|---|
| Closes: | 13th April 2026 |
| Reference: | RS/26/02 NWSSDTP CASE |
Applications are invited for an ESRC-funded North West Social Sciences Doctoral Training Partnership (NWSSDTP) PhD studentship (with potential to apply for 1+3 award) at the University of Central Lancashire. The studentship is for up to 3.5 years full time (tuition fees and annual stipend at the 2026/27 UKRI rate (£21,805)). UK and EU/International applicants may apply. The studentship includes access to the NWSSDTP’s Research Training Support Grant (RTSG) which supports research-related expenses including conference attendance, specialist training courses, and the purchase of books and other essential resources.
The successful applicant will commence in October 2026.
Project Description
Effective training is critical in high-stakes environments such as aviation, where personnel make rapid decisions under volatile, uncertain, complex, and ambiguous conditions (Baran & Woznyj, 2020). Ineffective training in such contexts carries risks for both individuals and organisations. Well-designed training can substantially reduce decision-making errors and improve outcomes. Findings highlight that training is not simply about skill acquisition but also about facilitating “adaptive expertise”, whereby trainees can apply skills flexibly to novel or unexpected situations (Klein, 2017; Spiro & Jehng, 2012).
The key research aim is to understand how to support the acquisition of adaptive expertise in aviation training. It will draw upon conceptual ideas captured within Ackerman and Thompson’s (2017) “metareasoning framework” to inform an investigation of the metacognitive monitoring and control processes underpinning complex decision-making during training simulations, and clarify the design of intervention approaches. According to the metareasoning framework, people’s detection of points of peak uncertainty during task-based processing can trigger shifts from intuitive to analytic reasoning, with this strategic change potentially engendering enhanced decision outcomes (Ackerman & Thompson’s, 2017). The challenge is that trainees can struggle to monitor and identify their own levels of uncertainty, missing opportunities to shift strategies in ways that can benefit processing.
This research will address this latter challenge by developing reliable behavioural markers of uncertainty during decision-making training, which can then be used to enhance trainees’ metacognitive awareness of peak uncertainty to benefit downstream outcomes. Previous research indicates that fluctuating states of metacognitive uncertainty can be tracked in think-aloud protocols (Fleming & Lau, 2014), with uncertainty being reliably signalled through people’s use of hedge words (“maybe”, “perhaps”), which often precede strategy change (Christensen & Ball, 2016). Despite evidence for the validity of verbal protocol analysis (VPA; Bannert & Mengelkamp, 2008), it has not yet been applied systematically to detect episodes of peak uncertainty in aviation training, nor has it been used to inform methods to bolster trainees’ metacognitive awareness of uncertainty during decision-making.
Candidates should have a UK BSc Honours degree at 2:1 level or above in Psychology (or equivalent qualification) or have completed a Masters level qualification in a relevant area (e.g., cognitive psychology, cyberpsychology and/or human factors). EU and International applicants require an English Language level of UKVI IELTS 6.5 with no sub-score less than 6.0 (or acceptable equivalent qualification).
Further information
Informal enquiries to Dr Beth H. Richardson (bhrichardson@lancashire.ac.uk )
For full details:
Apply here: online application system.
Quote reference NWSSDTP CASE Studentship Psychology on your application.
Expected Start Date: October 2026
Type / Role:
Subject Area(s):
Location(s):