Cohort 2 (2020-2024)
Serena Dimitri
I started my journey with a BSc in Psychology at the University of Pavia where I learned to appreciate individual differences. Following, I undertook a joint Master degree course at the University of Pavia and University School of Advanced Studies where I specialized In Neuroscience with the growing will of understanding more about the brain. During my BSc and MSc, I took part in exchange programs at the University Complutense of Madrid, Trinity College of Dublin and the University of Plymouth where I dealt with different ways of doing research. I worked as a researcher on a 3-years-project, which culminated in my Master dissertation: “Neuroscience, Psychology and Computer Science: An Innovative Software on Aggressive Behaviour”. My research interests follow my academic path and my personality, I am mainly captivated by the exploration of how the brain and the individuals work in reaction to technology and how to shape technology to interact effectively with individuals. I am now a PhD student at the University of Glasgow, where I found the perfect harmony between my psychology background, my neuroscience studies and the world of AI and computer science. These three are for me: my knowledge, my specialization and my greatest interest.
The Project: Testing social predictive processing in virtual reality
Virtual reality (VR) is a powerful entertainment tool allowing highly immersive and richly contextual experiences. At the same time, it can be used to flexibly manipulate the 3D (virtual) environment allowing to tailor behavioural experiments systematically. VR is particularly useful for social interaction research, because the experimenter can manipulate rich and realistic social environments, and have participants behave naturally within them [RB18].
While immersed in VR, a participant builds an inner map of the virtual space and stores multiple expectations about the environment mechanisms i.e., where objects or rooms are and their interaction with them, but also about physical and emotional properties of virtual agents (e.g. theory of mind). Using this innovative and powerful technology, it is possible to manipulate both the virtual space and virtual agents within the virtual world, to test internal participants’ expectations and register their reactions to predictable and unpredictable scenarios.
The phenomenon of “change blindness” demonstrates the surprising difficulty observers have in noticing unpredictable changes to visual scenes[SR05]. When presented with two almost identical images, people can fail to notice small changes (e.g. in object colour) and even large changes (e.g. object disappearance). This process arises because the brain cannot attend to the entire wealth of environmental signals presented to our visual systems at any given moment, and instead use attentional networks to selectively process the most relevant features whilst ignoring others. Testing which environmental attributes drive the detection of changes can give useful insights on how humans use predictive processing in social contexts.
In this PhD the student will run behavioural and brain imaging experiments in which they will use VR to investigate how contextual information drives predictive expectations in relation to changes to the environment and agents within it. They will investigate if change detection is due to visual attention or to a social cognitive mechanism such as empathy. This will involve testing word recognition whilst taking the visuospatial perspective of the agents previously seen in the VR (e.g. [FKS18]). The student will examine if social contextual information originating in higher brain areas modulates the processing of visual information. In brain imaging literature, an effective method to study contextual feedback information is the occlusion paradigm [MPM19]. Cortical layer specific fMRI is possible with 7T brain imaging; the student will test how top-down signals during social cognition activate specific layers of cortex. This data would contribute to redefining current theories explaining the predictive nature of the human brain.
The student will also develop quantitative models in order to assess developed theories. In recent work [PMT19], model checking was proposed as a simple technology to test and develop brain models. Model checking [CHVB18] involves building a simple, finite state model, and defining temporal properties which specify behaviour of interest. These properties can then be automatically checked using exhaustive search. Model checking can replace the need to perform thousands of simulations to measure the effect of an intervention, or of a modification to the model.
References
[MPM19] Morgan, A. T., Petro, L. S., & Muckli, L. (2019). Scene representations conveyed by cortical feedback to early visual cortex can be described by line drawings. Journal of Neuroscience, 39(47), 9410-9423.
[SR05] Simons, D. J., & Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in cognitive sciences, 9(1), 16-20.
[RB18] de la Rosa, S., & Breidt, M. (2018). Virtual reality: A new track in psychological research. British Journal of Psychology, 109(3), 427-430.
[FKS18] Freundlieb, M., Kovács, Á. M., & Sebanz, N. (2018). Reading Your Mind While You Are Reading—Evidence for Spontaneous Visuospatial Perspective Taking During a Semantic Categorization Task. Psychological science, 29(4), 614-622.
[PMT19] Porr, B., Miller, A., & Trew, A. (2019). An investigation into serotonergic and environmental interventions against depression in a simulated delayed reward paradigm. Adaptive behaviour, (online version available).
[CHVB-8] Clarke, E. M., Henzinger, T. and Veith, H. & Bloem, R (2018). Handbook of model checking. Springer.