Postgraduate Research

The Department of Psychology at Lancaster University is a member of the Northwest Social Sciences Doctoral Training Partnership (NWSSDTP), along with the Universities of Manchester, Liverpool and Keele. The NWSSDTP offers studentships as 1+3 (Master's + 3 years PhD funding), +3 (3 years PhD funding) and as CASE awards (1+3, or +3, where proposals are supported by a non-academic partner).

For more information, visit the NWSSDTP website.

The award is for up to 3.5 years full-time (or part-time equivalent) for UK applicants and provides full tuition fees, a generous stipend and access to a grant towards research training support. The stipend is non-taxable and paid in regular instalment. The level of the stipend for the academic year 2023 to 2024 is to be confirmed, but will be a minimum of £17,668 per year.

How to apply:

Select 1 of the 4 projects outlined below. Informal enquiries about a specific project are encouraged and should be directed to the project’s primary supervisor (listed first in description below) before application. Applicants do not need to write a full research proposal, but should instead include a personal statement (no more than 750 words) of how their research interests, skills, experience and career plans are a good fit for the selected project. Applicants should make sure to address how they meet both the general (see below) as well as the specific abilities/skills listed for each project (see under the project outline) in this statement. Applicants will be assessed on the basis of their academic ability, skills and research experience in general, as well as their suitability for the specific project they are applying to.

Attributes for all projects:

Essential:

• Applicants should have (or will soon receive) a bachelor’s degree (or equivalent) in psychology or a cognate discipline relevant to their preferred project.

• Applicants should be able to critically engage with the literature, have a good understanding of (psychological) research methods and are passionate about doing research.

Desirable:

• To have completed a relevant master’s degree or have other relevant (research) experience.

To apply you must carry out both of the following steps before the deadline of Thursday 15^th June, 5 pm:

1. Follow this link: How to Apply and apply online to Lancaster University, uploading the required documents (see under ‘What to include’), but with your personal statement instead of a full research proposal. In your application, please state that you are applying for the EPSRC-funded PhD studentship in the Department of Psychology. The application should clearly identify which project you are applying for and the associated supervisor(s).
2. Email the Department of Psychology Postgraduate office (postgraduate.psychology@lancaster.ac.uk) indicating your interest in the studentship and attaching the same personal statement that you have also uploaded on the online system under step 1.

General enquiries about the studentship should be directed to the Department of Psychology Postgraduate office (postgraduate.psychology@lancaster.ac.uk). Informal enquiries about a specific project, should be directed to the project’s primary supervisor (listed first in description below) before application.

Interviews are planned for the week commencing 3^rd July 2023, but will be scheduled nearer the time. Interviews will take place online.

Using conversational agents to assess language development in remote and rural communities in South Africa

Dr Katie Alcock

The overall goal of this PhD is to develop an accessible, sustainable language assessment tool leveraging a conversational agent (CA), for integration into primary healthcare. The tool will gather norms for early language development in under-resourced settings using a conversational interface, ultimately improving childhood quality of life.

The project will adapt Communicative Development Inventories (CDIs) into an online tool for independent use by semi-literate South African families. Standard CDIs ask caregivers to read and mark off language items their 8- to 30-month-olds know. CDIs have been developed for at least 7 South African languages, but remain difficult to use by families with low literacy levels.

To adapt CDIs into an accessible digital tool, video, audio prompts, and visualisations for a mobile platform will be developed and validated, allowing families to report on their child’s communication in their language. We cannot use a purely web-based interface or an app limited by updates or mobile OS: Many families’ internet signal is insufficient for browser use; and older phones make app compatibility problematic. CAs can engage with parents at their own pace by using a trusted platform (e.g. WhatsApp), with a back end supported by colleagues at Stanford. This also enables interaction with a large number of remote, rural families efficiently.

Families’ anonymised data will be collated on a central database hosted by the South African Centre for Digital Language Resources, supported by Stellenbosch University colleagues. The project will generate norms for children’s acquisition of these languages, for use by healthcare professionals.

Additional attributes essential and/or desirable for this specific project:

Essential

• Experience with and knowledge of inferential statistics as well as of a suitable statistics program (R or SPSS).
• Experience in child language research.

Desirable

• Experience of the region and of its languages.
• Experience of survey-based research and of constructing good surveys and assessment materials, including Item Response Theory for question selection.
• Experience of research in an under-resourced country setting.
• Experience with Conversational Agents or other relevant programming skills.

Effect of availability of risky solar geoengineering on climate mitigation: Moral hazard or moral catalyst?

Dr Mark Hurlstone

The goal of the 1992 Framework Convention on Climate Change is to prevent dangerous anthropogenic interference with the climate system. However, since then, international cooperation to reduce emissions has faltered and atmospheric concentrations have continued to rise. As the window begins to close on the opportunity to meet the Paris Agreement goal of pursuing efforts to limit global warming to 1.5°C there has been much talk about the use of solar geoengineering to limit climate change. This would involve injecting sulphate aerosols into the stratosphere to scatter sunlight, which could reduce temperature in the lower atmosphere rapidly. However, solar geoengineering is incredibly risky with known side effects that would include stratospheric ozone depletion. Moreover, it does not address the root cause of climate change, which can only be tackled by reducing emissions. The need to reduce emissions thus remains, and opponents of solar geoengineering have argued that it presents a “moral hazard” to climate mitigation—knowledge of the availability of a quick-fix solution to cooling the climate may reduce the incentive to cut emissions. However, others contend that the mere presence of a credible solar geoengineering threat may serve as a “moral catalyst” to climate mitigation—knowledge of the risks of solar geoengineering may increase the incentive to cut emissions. Using experimental surveys and economic games, the goal of this project will be to adjudicate between these two positions by establishing how knowledge of the existence of a credible albeit risky solar geoengineering response impacts climate mitigation intentions and behaviours.

Additional attributes essential and/or desirable for this specific project:

Essential

• Keen interest in conducting research on the behavioural science of climate change adaptation and mitigation
• Experience with and knowledge of experimental research design
• Experience with and knowledge of inferential statistics as well as of a suitable statistics program (e.g., JASP, R, or SPSS)

Desirable

• Experience of survey-based research and theories of psychological measurement (e.g., Classical Test Theory, Item Response Theory)
• Interest and enthusiasm for research in hearing/neuroscience/ageing
• Willingness to learn new technology
• Excellent communication skills
• Excellent skills in data analysis and statistics

Evaluating hearing aid acclimatisation in older adults through wearable EEG

Dr Helen Nuttall, Dr Kate Slade (Lancaster Medical School), and Professor Chris Plack

Hearing loss affects over 70% of older adults aged 70+ and is often regarded as an unavoidable part of healthy ageing. Recognised as the largest potentially modifiable risk factor for cognitive decline and dementia, it is crucial that individuals seek hearing interventions to modify the risk of hearing loss to brain health. Adult hearing loss is estimated to cost the UK economy £30 billion per annum, and hidden costs include decreases in communication efficiency leading to increased frustration and effort, which in turn impacts social interaction, loneliness, and mental health.

The main clinical intervention for age-related hearing loss is a hearing aid. However, evaluation of hearing aids is dependent on subjective responses, which are sensitive to factors such as cognitive status, language background, and educational attainment, representing significant health inequality. We require novel, objective, technological solutions to establish how the brain is responding to sound after receiving a hearing aid. In this project, we will work on developing ways of measuring auditory brain activity to investigate hearing aid acclimatisation in older adults. Specifically, we will use cEEGrid flex-printed sensor arrays to collect data, which enable unobtrusive multi-channel EEG acquisition from around the ear. These arrays are portable and can be worn during activities of daily living. Using cEEGrid technology, we will acquire data during everyday, real-life auditory experiences, to provide objective insight into auditory function. This will provide ecologically valid data on how older adults’ brains are adapting to hearing aids over time, to improve evaluation of hearing aid acclimatisation.

Additional attributes essential and/or desirable for this specific project:

Essential

• Interest and enthusiasm for research in hearing/neuroscience/ageing
• Willingness to learn new technology
• Excellent communication skills
• Excellent skills in data analysis and statistics

Desirable

• Experience in testing participants
• Experience working with older adults

Addressing the bias-variance dilemma in deep learning networks

Professor Gert Westermann and Dr Hossein Rahmani (School for Computing and Communications)

Artificial (and natural) learning systems face a trade-off between bias and variance (Geman et al, 1992): the learner can be biased to learn only certain outcomes, which reduces the need for large amounts of training data but requires a priori knowledge of possible solutions to a problem or risks making the correct solution not learnable. As bias is lowered to make the learner more general, variance increases, requiring more training data to learn the solution. Current AI deep learning systems have opted for low bias, using general architectures that require vast amounts of training data. Biological learning systems, in contrast, involve two mechanisms that reduce variance and allow for successful learning from sparse data. First, children’s brains develop in experience-dependent ways, adding structure as learning progresses (Quartz & Sejnowski, 1997). It has been shown that in artificial neural networks, such gradual structural adaptation can keep variance low while relaxing bias in problem-specific ways (Quartz, 1993). Second, biological learners are curious and have been argued to actively sample information that provides maximal learning progress given their current state (Gottlieb et al, 2013), and such active information sampling leads to learning as good as in an a priori optimally structured environment (Twomey & Westermann, 2018). In this PhD project, we aim to integrate these biological mechanisms into deep learning systems to develop artificial systems that learn intelligently from smaller amounts of data than conventional systems, improving on current approaches while also providing new insights into children’s cognitive development.

Additional attributes essential and/or desirable for this specific project:

Essential

• Programming proficiency: The candidate should have a strong programming background, with proficiency in at least one programming language such as Python, C++, or Java.
• Interest in psychological and brain development

Desirable

• Experience with machine learning, and deep neural networks in particular
• Experience with machine learning toolboxes such as PyTorch and TensorFlow

Application Deadline

10th March 2023

Interviews

w/c 20th March 2023. Interviews will be scheduled nearer the time and will be online.

Please note this opportunity is for UK applicants only.

Humans and machines are increasingly entwined in complex socio-technical systems. These systems often involve different groups of people and different kinds of technology. In order for these systems to function effectively (and exhibit resilience), there has to be trust and cooperation amongst not only the humans, but also between the humans and the machines. Shared social identity is known to facilitate cooperation and trust between humans. In this PhD we will explore the potential for social identity processes to facilitate cooperation and trust with machines.

The project will ask questions like, ‘Under what conditions can humans share identity with machines?’ or ‘Can machines learn to recognise social identity in humans?’. The project will be based in the Psychology Department at Lancaster University and is part of a larger multidisciplinary UKRI project to improve the socio-technical resilience and trustworthiness of autonomous systems.

Supervisors

Mark Levine mark.levine@lancaster.ac.uk

Anastasia Kordoni a.kordoni@lancaster.ac.uk

About the Project

Autonomous systems are an increasing part of the infrastructure of daily life. As the range and importance of such systems increases and the underlying technology rapidly develops, the UKRI has funded a range of investigatory research “nodes” to address the trustworthiness of autonomous systems

As part of this large, interdisciplinary project, the Psychology Department at Lancaster University is exploring the resilience of emergency response systems for disasters and emergencies that include both humans and autonomous systems. We examine the interaction of ordinary citizens, emergency services (police, ambulance, fire service) and different types of technology (drones, robots) in emergency response.

Social Identity processes are now recognised as being key to understanding how ordinary people respond to emergencies. At the same time, social identity processes are increasing recognised as being key to the way emergency services operate in emergencies. This PhD project will explore the potential contribution of social identity ideas for how the relationship between humans and technologies can be supported to make for more resilient emergency response.

• You can see more about the UKRI Trustworthy Autonomous Systems project
• You can see more about the Resilience node (which is where this project is located)
• You can read more about the Social Processes group in the Psychology Department

Applicants should have (or will soon receive) a minimum of a strong undergraduate degree in psychology or related discipline. Students who already have a Master's qualification are also encouraged to apply. In all cases, we are looking for academically excellent students, who are passionate about doing research.

The award is for a 3.5 year training programme (October 2023 start date). This includes full tuition fees and a generous stipend of approximately £17,688 per year - as well as a research support and training budget of circa £1500 per year.

How to Apply

Applications should submit a research proposal based on the above project outline and focusing on the methods mentioned above, while making the project their own and explaining in more detail how they would carry out this research. (no more than 1000 words)

Applicants should also submit a personal statement highlighting your suitability for the project and PhD research more generally that clearly demonstrates your passion for the research project and makes clear how your research interests, skills, experience, and career plans are a good fit for the proposed project. Applicants will be assessed based on their academic credentials, skills, knowledge, experience, and enthusiasm for the project. (no more than 2 pages).

In all cases, informal enquiries should be directed to the project’s primary Supervisor (mark.levine@lancaster.ac.uk) before application.

Closing date for applications: March 1st 2023

The Department is offering one fully-funded PhD Faculty of Science and Technology (FST) Teaching Scholarship commencing October 2023.

We are looking for academically excellent students, who are passionate about doing research and have an exciting project they want to pursue.

Faculty of Science and Technology Scholarship (all areas of psychology)

The award is for 3.5 years and provides UK-level tuition fees, a generous stipend of a minimum of £17,668 and access to a grant towards research training support. Non-UK applicants must fund the difference between UK and non-UK fees if successful.

We welcome applications from students in all areas of supervisory expertise. Before sending your application, you must contact individual staff members to discuss your specific interests and to develop a research proposal. Applications that bring important and innovative ideas to match and complement the research agenda of current staff are more likely to be successful.

As a department, we particularly encourage applications to work with early career staff. This year these include: Dr Amy Atkinson, Dr Marina Bazhydai, Dr Jaime Benjamin, Dr Mark Hurlstone, Dr Richard Philpot, Dr Heather Shaw, Dr Hannah Stewart, and Dr L-J Stokes. Applications to work with these members of staff are weighted preferentially during the evaluation of applications.

Informal enquiries should be directed to members of staff prior to application, using the contact details on their departmental web page: http://www.lancaster.ac.uk/psychology/about-us/people/

Successful applicants will be asked to provide (reasonable) support for teaching activities in the Department, with training support provided by the University. This would normally involve leading first or second-year seminar groups, or assisting in laboratory classes.

By the deadline of March 1st, 2023, applicants must have completed an application to the Department via the University Portal, and include the following information:

• Clearly state that you wish to apply for the Faculty of Science and Technology (FST) Scholarship;
• Identify appropriate supervisor(s).
• Include a research proposal of up to 1500 words.
• Supporting documentation (including a personal statement, degree certificates, references etc.). For more details see under ‘How to apply’.

After the deadline, applications will be reviewed and shortlisted candidates will be invited for interview (online, via Teams), before award decisions are made. Applicants who are unsuccessful for the scholarship will nonetheless be considered in terms of an offer of registration to the Department (on a self-funded basis or where alternative funding can be identified).

The expectation is that applicants will have completed, at the start of their award, an MSc degree in Psychology or a related discipline, although in exceptional circumstances, students with other relevant experience may not have completed Master's level studies.

You should apply online here https://www.lancaster.ac.uk/study/postgraduate/applying-for-postgraduate-study/

For further information concerning postgraduate study more generally, please contact the Lancaster University Postgraduate admissions team at fst-pg-admissions@lancaster.ac.uk