Prof. Dr. W. Verwey
My current projects involve basic research on movement sequence acquisition in younger and older people, determining the neurophysiological foundations of the associated cognitive processes, and modelling these cognitive and neurophysiological processes in the neural network architecture of our iCub robot. In addition, I am involved in research into the Human Factor aspects of Intelligent Transport Systems.
Process innovation in crisis management to enhance performance
Prof. Dr. J. M. C. Schraagen
Developing new ways of supporting judgment and decision making processes in policy teams dealing with crisis management situations, for instance by training and process support. This project draws heavily on Macrocognitive processes and functions such as sensemaking and flexecution.
Human Enhancement – Adaptive Automation
Prof. Dr. J. M. C. Schraagen
Development of adaptive automation solutions to flexibly support and enhance human performance in automotive and marine transport.
Personnel concepts future submarine naval forces
Prof. Dr. J. M. C. Schraagen
Development of sustainable personnel concepts for the replacement of the Walrus submarine class.
Seahorse
Prof. Dr. J. M. C. Schraagen
This project aims to seek feasible and effective transfer of the successful safety concepts, such as resilience engineering and other tools adopted from the aeronautical industry to marine transport.
Prof. Dr. R. van der Lubbe
Recently, the influence of time pressure was examined together with Prof. J.-P. Fox in the Eriksen Flanker task by combining the hierarchical drift diffusion model with the contingent negative variation, a measure that can be derived from the EEG. The idea is to extend this project by using additional measures derived from the EEG, and expand this approach to various other cognitive tasks.
Three other research projects (in cooperation with Dr. M Schmettow) focused on the possibility to establish the mental state of individuals by using the EEG, thereby attempting to predict performance in two vigilance tasks, but also in a gaming context. Another research project focused on differences in brain activity when people are either or not telling the truth (in cooperation with Dr. Noordzij). Research that is carried out in Poland focuses on motor imagery (together with Dr. A. Pzrekoracka-Krawczyk and MSc. J. Sobierajewicz), metacontrast masking (together with MSc. I. Szumska), and individual differences (together with Dr. M. Fajkowska and Dr. Wytykowska-Kaczorek). In addition, there is an ongoing cooperation on the relevance of motor imagery for children with cerebral palsy (together with Dr. M. Jongsma from the Radboud University in Nijmegen).
Dr. M. Schmettow
Modern surgical technologies such as laparoscopy and robotic surgery are generally beneficial in terms of risks, patient comfort and hospital stay. On the downside, these techniques reduce the intuitive interaction with organs and tissue for the surgeon (e.g., unfamiliar angle-of-view, lack of tactile feedback and reversed manipulation). Recent research suggests that dedicated training, as well as individual differences in sensorimotor abilities, play a crucial role in mastering these minimally invasive surgery (MIS) techniques. Lengthy training programs result in huge training costs and a waste of resources, with the special concern that, presumably, 5-10% of all surgeons are completely unable to acquire these techniques (“dyslaparoscopia”).
Technological advances in combination with reduced training time in the operating room call for new training and selection methods. Predicting whether a surgeon will be able to master minimally invasive techniques is therefore highly desirable. Unfortunately, the predictive power of common experimental tests is much too low to be of practical relevance.
In a joint project with Marleen Groenier (Technical Medicine), we are currently investigating (1) alternative theoretical approaches to understand the underlying cognitive abilities, (2) new tests that mimic procedures in minimally invasive surgery (for example, tying a knot) and (3) the dynamics of the training trajectory by use of non-linear growth models.