Below is an overview of young, talented grant laureates who (partly) work at the IAS.
Sacha Epskamp will work on intelligent apps to aid clinical therapists. This project will lay the foundations of an intelligent adaptive administration system, capable of quickly diagnosing people on psychological symptoms as well as efficiently monitoring people over time. Underlying the system, a statistical network model will be updated as new responses come in. These network models can be used to gain insight in the complexity of human behavior and to shape treatment of individual patients.
Casper Hesp studies the emergence of social life in the context of neurocognitive evolution, building on principles that combine formal information-theoretic concepts with dynamical systems theory. To further current neuroscientific accounts of social cognition, he plans to construct a computational ecology containing multiple organisms to simulate their co-evolution towards competitive and cooperative interactions.
Recent research on modelling complex interactions between symptoms of mental illness provides striking implications for psychotherapy. Yet, when it comes to the implementation of these newly developed techniques, there is still a big gap with clinical practice. Julian Burger aims to bridge this gap by working on tools which do not require a technical background from clinicians. The project aims to advance personalised network modelling for psychotherapy through targeting different barriers in diagnostics, the conceptualisation of a case, and the selection of patient-tailored interventions.
Symptom network analysis has been argued to facilitate treatment of psychopathology. However, while treatment targets processes taking place within the individual, most network structures in the literature are estimated from between-subjects’ data. This has been argued to be fallacious because within-person network processes can only be estimated using within-person data. Ria Hoekstra’s aim is to investigate this issue by developing, testing and applying a toolbox to detect heterogeneity, i.e., qualitative and quantitative differences between individual networks and population networks.