New MRC Funded Collaborative Project
Prof Darren Cosker will launch a new collaborative project funded by the Medical Research Council (MRC), working alongside Dr Isabelle Mareschal (Principal Investigator), of Queen Mary University and Prof Essi Viding of University College London. The project will investigate individual differences in facial emotion perception and their association with psychiatric risk indicators.
“This is a fantastic opportunity to not only develop a state of the art system for creating virtual humans but to apply it to important problems in psychology. This type of multidisciplinary project is exactly what CAMERA was established to tackle, and we look forward to working with the UCL and Queen Mary University teams.” Prof Cosker
We use facial expressions to communicate our emotions but there are large differences in how people react to these expressions. At its most severe, failures to respond appropriately to emotional expressions are associated with many clinical conditions. For example, people with high anxiety often report that what other people perceive as neutral expressions appear threatening to them. Critically, the reason why these expressions look different to certain individuals remains unknown. Without this knowledge, it is very difficult to develop targeted and effective therapies for people suffering, for example, from anxiety or other disorders. The aim of this project is to understand why some people are unable to correctly respond to, or identify, particular emotional expressions, and how this relates to indicators of psychiatric risk.
The team have recently created a unique toolkit that allows us to measure how emotional expressions look to different people. They will use the toolkit to allow individuals to create the facial expressions that they associate with particular emotions. These individualised emotional expressions will allow investigators to understand the basis of individual differences in emotional expression perception and how they are associated with psychiatric risk indicators. The research will establish if these evolved expressions improve current clinical and pre-clinical tests of “emotional processing” using standard, pre-validated tasks. The result of this work will be an improved understanding of how emotional expressions are interpreted in typical and atypical populations, new tools for better characterization of individuals at risk of psychiatric disorders, and therefore the potential for more precise diagnosis and treatment of these disorders.
Objective 1: To refine genetic algorithm techniques to enable precise measurement of individual differences in internalised facial emotion expressions.
Objective 2: To determine how individual differences in emotional expression perception relate to individual differences in psychiatric risk indicators (anxiety and psychopathy).
Objective 3: To generate normative datasets that allow comparison of responses to individualized emotional facial configurations and to the standard emotional stimuli used on different types of behavioural tasks that rely on emotional faces.