Neural and computational principles underlying social vs non-social decision making
Supervisor: Dr Marios Philiastides (firstname.lastname@example.org)
Description: Most strategic decisions occur under considerable uncertainty. For example, when investing in the stock market, a trader may use purely probabilistic models to estimate risk in the market’s fluctuations. In contrast, when negotiating a deal in person, the trader’s risk assessment may rely instead on how trustworthy the other party appears. In standard utility models, the rules governing such decisions are the same, regardless of the source of uncertainty (e.g. human vs on-line platform). However, recent advances in social neuroscience suggest that separate brain networks might distinctly process probabilistic and social information, possibly leading to different outcomes.
To date, there is no unified framework for integrating social and non-social sources of decision uncertainty as previous studies looked at these factors in isolation. This shortcoming is mainly due to the interdisciplinary nature of the endeavour, which requires major methodological developments in experimental design and brain analytics.
Here, we will combine two popular brain imaging techniques (EEG-fMRI), with novel experimental design and computational modelling to obtain information on when, where and how the brain processes social and non-social information during decision-making. We will investigate two different phases of the decision process: (1) the choice-phase, where decision alternatives are evaluated and compared to guide action and (2) the outcome-phase, where expected reward and risk signals are computed to update future expectations. We will model the integration of social and non-social forms of uncertainty at each stage and characterise the computational principles of the relevant neural systems. In doing so, we will place new, neurobiologically-derived, constraints on decision-theoretic models of information integration. The marriage of social and non-social forms of uncertainty into a comprehensive theory of decision-making promises to significantly improve our understanding of important real-life events, ranging from policy making and risk management to informing individual decisions on health behaviours and savings strategies.
The studentship will be based at the Institute of Neuroscience and Psychology at the University of Glasgow under the supervision of Dr. Marios G. Philiastdes (http://mphiliastides.org). The project will take place at the Centre of Cognitive Neuroimaging (CCNi) a research-dedicated facility within the INP. The CCNi represents a state-of-the-art interdisciplinary effort to advance the understanding of the complex relationship between the brain, cognition and behavior using multimodal brain imaging. It brings together researchers with interests in cognitive neuroscience, functional neuroimaging, neuropsychology, computational modelling and advanced analysis methods development. The CCNi provides an exceptional environment for brain imaging research and it has the added advantage that the imaging facilities are used exclusively for cognitive neuroscience research within the INP, with dedicated support from world-leading radio physicists and frequency engineers. The center houses state-of-the-art 3T and 7T Siemens MR scanners, a magneto-encephalography (MEG) system, several EEG and TMS systems (including MR-compatible devices), eye-movement recorders and 4D model capture, all designed for non-invasive multi-modal brain imaging.
Funding notes: The scholarship is available as a +3 or a 1+3 programme depending on prior research training. This will be assessed as part of the recruitment process. The programme will commence in October 2019. It includes
- an annual maintenance grant at the RCUK rate (2018/19 rate £14,777 full-time)
- fees at the standard Home rate
- students can also draw on a pooled Research Training Support Grant, usually up to a maximum of £750 per year
The 1+3 scholarship is for candidates with a first degree in social sciences or a related area but no Masters-level training. For these candidates, the studentship would also provide fees and stipend for the additional year to complete the MRes in MSc Research Methods in Psychological Sciences or MSc Brain Sciences as preparation for undertaking the PhD.
Eligibility: Applicants must meet the following eligibility criteria:
- A good first degree (at least 2:1), preferably with a social science component
- strong interest in and familiarity with decision neuroscience, reinforcement learning and behavioural economics
- Strong interest in programming and data analysis
- Excellent writing skills, good team and communication skills and familiarity and affinity for open science practices
For the +3 option:
- A good MSc degree in Cognitive Sciences, Neuroscience, Behavioural Economics or related discipline
- Have a good grounding in experimental research methods, excellent statistical and programming skills
- Experience with brain imaging techniques (e.g. EEG, fMRI) would be a plus
How to Apply: All applicants should complete the Supervisor Led Awards Eligibility Checker prior to submitting an application.
- Applicants register on GradHub and fill out EO data (this is a requirement of the application process)
- Applicants complete and upload the prescribed list of required documentation to include:
- Application form
- Academic transcripts
- Any other additional questions as specified by the supervising team - this should be uploaded in a standalone document with a naming convention as follows: *name/supervisor/institution/competition/date*
- Applicants submit application through GradHub: https://gradhub.sgsss.ac.uk
Applications will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by mid-April 2019.
All scholarship awards are subject to candidates successfully securing admission to a PhD programme within the University of Glasgow. Successful scholarship applicants will be invited to apply for admission to the relevant PhD programme after they are selected for funding.
Deadline: 5 April 2019
Start Date: 1 October 2019